Near-Infrared Fluorescent Carbon Nanotube Sensors for the Plant Hormone Family Gibberellins.

Gibberellins (GAs) are a class of phytohormones, important for plant growth, and very difficult to distinguish because of their similarity in chemical structures. Herein, we develop the first nanosensors for GAs by designing and engineering polymer-wrapped single-walled carbon nanotubes (SWNTs) with unique corona phases that selectively bind to bioactive GAs, GA3 and GA4, triggering near-infrared (NIR) fluorescence intensity changes. Using a new coupled Raman/NIR fluorimeter that enables self-referencing of nanosensor NIR fluorescence with its Raman G-band, we demonstrated detection of cellular GA in Arabidopsis, lettuce, and basil roots. The nanosensors reported increased endogenous GA levels in transgenic Arabidopsis mutants that overexpress GA and in emerging lateral roots. Our approach allows rapid spatiotemporal detection of GA across species. The reversible sensor captured the decreasing GA levels in salt-treated lettuce roots, which correlated remarkably with fresh weight changes. This work demonstrates the potential for nanosensors to solve longstanding problems in plant biotechnology.

Basic principles of hydrogel-based tissue transformation technologies and their applications.

Emerging tissue transformation technologies provide an unprecedented opportunity to investigate system-level molecular and anatomical features in situ. Hydrogel-based methods engineer physicochemical tissue properties to render intact organs optically transparent and size and shape adjustable while preserving biomolecules at their physiological locations. When combined with advanced molecular tools, labeling, and imaging techniques, tissue transformation enables three-dimensional (3D) mapping of molecules, cells, and their interrelationships at increasing speeds and resolutions. In this review, we discuss the basic engineering principles of tissue transformation and labeling techniques as well as their broad applications, current challenges, and future potential.

Occurrence and Molecular Identification of Microcotyle sebastis Isolated from Fish Farms of the Korean Rockfish, Sebastes schlegelii.

Microcotyle sebastis is a gill monogenean ectoparasite that causes serious problems in the mariculture of the Korean rockfish, Sebastes schlegelii. In this study, we isolated the parasite from fish farms along the coasts of Tongyeong, South Korea in 2016, and characterized its infection, morphology and molecular phylogeny. The prevalence of M. sebastis infection during the study period ranged from 46.7% to 96.7%, and the mean intensity was 2.3 to 31.4 ind./fish, indicating that the fish was constantly exposed to parasitic infections throughout the year. Morphological observations under light and scanning electron microscopes of the M. sebastis isolates in this study showed the typical characteristics of the anterior prohaptor and posterior opisthaptor of monogenean parasites. In phylogenetic trees reconstructed using the nuclear 28S ribosomal RNA gene and the mitochondrial cytochrome c oxidase I gene (cox1), they consistently clustered together with their congeneric species, and showed the closest phylogenetic relationships to M. caudata and M. kasago in the cox1 tree.

Erratum: Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate.

[This corrects the article DOI: 10.1016/j.omtm.2019.09.008.].

The proton electrochemical gradient induces a kinetic asymmetry in the symport cycle of LacY.

LacY catalyzes accumulation of galactosides against a concentration gradient by coupling galactoside and H+ transport (i.e., symport). While alternating access of sugar- and H+-binding sites to either side of the membrane is driven by binding and dissociation of sugar, the electrochemical H+ gradient ([Formula: see text]) functions kinetically by decreasing the Km for influx 50- to 100-fold with no change in Kd The affinity of protonated LacY for sugar has an apparent pK (pKapp) of ∼10.5, due specifically to the pKa of Glu325, a residue that plays an irreplaceable role in coupling. In this study, rates of lactose/H+ efflux were measured from pH 5.0 to 9.0 in the absence or presence of a membrane potential (ΔΨ, interior positive), and the effect of the imposed ΔΨ on the kinetics of efflux was also studied in right-side-out membrane vesicles. The findings reveal that [Formula: see text] induces an asymmetry in the transport cycle based on the following observations: 1) the efflux rate of WT LacY exhibits a pKapp of ∼7.2 that is unaffected by the imposed ΔΨ; 2) ΔΨ increases the rate of efflux at all tested pH values, but enhancement is almost 2 orders of magnitude less than observed for influx; 3) mutant Glu325 - Ala does little or no efflux in the absence or presence of ΔΨ, and ambient pH has no effect; and 4) the effect of ΔΨ (interior positive) on the Km for efflux is almost insignificant relative to the 50- to 100-fold decrease in the Km for influx driven by ΔΨ (interior negative).

Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate.

The spontaneous conversion of asparagine residues to aspartic acid or iso-aspartic acid, via deamidation, is a major pathway of protein degradation and is often seriously disruptive to biological systems. Deamidation has been shown to negatively affect both in vitro stability and in vivo biological function of diverse classes of proteins. During protein therapeutics development, deamidation liabilities that are overlooked necessitate expensive and time-consuming remediation strategies, sometimes leading to termination of the project. In this paper, we apply machine learning to a large (n = 776) liquid chromatography-tandem mass spectrometry (LC-MS/MS) dataset of monoclonal antibody peptides to create computational models for the post-translational modification asparagine deamidation, using the random decision forest method. We show that our categorical model predicts antibody deamidation with nearly 5% increased accuracy and 0.2 MCC over the best currently available models. Surprisingly, our model also paces or outperforms advanced and conventional models on an independent non-antibody dataset. In addition to deamidation probability, we are able to accurately predict deamidation rate (R2 = 0.963 and Q2 = 0.822), a capability with no peer in current models. This method should enable significant improvement in protein candidate selection, especially in biopharmaceutical development, and can be applied with similar accuracy to enzymes, monoclonal antibodies, next-generation formats, vaccine component antigens, and gene therapy vectors such as adeno-associated virus.

Ultrathin, transferred layers of thermally grown silicon dioxide as biofluid barriers for biointegrated flexible electronic systems.

Materials that can serve as long-lived barriers to biofluids are essential to the development of any type of chronic electronic implant. Devices such as cardiac pacemakers and cochlear implants use bulk metal or ceramic packages as hermetic enclosures for the electronics. Emerging classes of flexible, biointegrated electronic systems demand similar levels of isolation from biofluids but with thin, compliant films that can simultaneously serve as biointerfaces for sensing and/or actuation while in contact with the soft, curved, and moving surfaces of target organs. This paper introduces a solution to this materials challenge that combines (i) ultrathin, pristine layers of silicon dioxide (SiO2) thermally grown on device-grade silicon wafers, and (ii) processing schemes that allow integration of these materials onto flexible electronic platforms. Accelerated lifetime tests suggest robust barrier characteristics on timescales that approach 70 y, in layers that are sufficiently thin (less than 1 µm) to avoid significant compromises in mechanical flexibility or in electrical interface fidelity. Detailed studies of temperature- and thickness-dependent electrical and physical properties reveal the key characteristics. Molecular simulations highlight essential aspects of the chemistry that governs interactions between the SiO2 and surrounding water. Examples of use with passive and active components in high-performance flexible electronic devices suggest broad utility in advanced chronic implants.

Dynamic heterogeneity in crossover spin facilitated model of supercooled liquid and fractional Stokes-Einstein relation.

Kinetically constrained models have gained much interest as models that assign the origins of interesting dynamic properties of supercooled liquids to dynamical facilitation mechanisms that have been revealed in many experiments and numerical simulations. In this work, we investigate the dynamic heterogeneity in the fragile-to-strong liquid via Monte Carlo method using the model that linearly interpolates between the strong liquid-like behavior and the fragile liquid-like behavior by an asymmetry parameter b. When the asymmetry parameter is sufficiently small, smooth fragile-to-strong transition is observed both in the relaxation time and the diffusion constant. Using these physical quantities, we investigate fractional Stokes-Einstein relations observed in this model. When b is fixed, the system shows constant power law exponent under the temperature change, and the exponent has the value between that of the Frederickson-Andersen model and the East model. Furthermore, we investigate the dynamic length scale of our systems and also find the crossover relation between the relaxation time. We ascribe the competition between energetically favored symmetric relaxation mechanism and entropically favored asymmetric relaxation mechanism to the fragile-to-strong crossover behavior.