Arabidopsis transcription factor TCP13 promotes shade avoidance syndrome-like responses by directly targeting a subset of shade-responsive gene promoters.

TCP13 belongs to a subgroup of TCP transcription factors implicated in the shade avoidance syndrome (SAS), but its exact role remains unclear. Here, we show that TCP13 promotes the SAS-like response by enhancing hypocotyl elongation and suppressing flavonoid biosynthesis as a part of the incoherent feed-forward loop in light signaling. Shade is known to promote the SAS by activating PHYTOCHROME-INTERACTING FACTOR (PIF)-auxin signaling in plants, but we found no evidence in a transcriptome analysis that TCP13 activates PIF-auxin signaling. Instead, TCP13 mimics shade by activating the expression of a subset of shade-inducible and cell elongation-promoting SAUR genes including SAUR19, by direct targeting of their promoters. We also found that TCP13 and PIF4, a molecular proxy for shade, repress the expression of flavonoid biosynthetic genes by directly targeting both shared and distinct sets of biosynthetic gene promoters. Together, our results indicate that TCP13 promotes the SAS-like response by directly targeting a subset of shade-responsive genes without activating the PIF-auxin signaling pathway.

Dark-Field Microscopic Study of Cellular Uptake of Carbon Nanodots: Nuclear Penetrability.

Carbon nanodots are fascinating candidates for the field of biomedicine, in applications such as bioimaging and drug delivery. However, the nuclear penetrability and process are rarely studied and lack understanding, which limits their applications for drug carriers, single-molecule detection and live cell imaging. In this study, we attempt to examine the uptake of CNDs in cells with a focus on the potential nuclear penetrability using enhanced dark-field microscopy (EDFM) associated with hyperspectral imaging (HSI) to quantitatively determine the light scattering signals of CNDs in the cells. The effects of both CND incubation time and concentration are investigated, and plausible nuclear penetration involving the nuclear pore complex (NPC) is discussed. The experimental results and an analytical model demonstrate that the CNDs' uptake proceeds by a concentration-dependent three-stage behavior and saturates at a CND incubation concentration larger than 750 µg/mL, with a half-saturated concentration of 479 µg/mL. These findings would potentially help the development of CNDs' utilization in drug carriers, live cell imaging and other biomedical applications.

Machine Learning Models for Predicting Liver Toxicity.

Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial to reduce costs and the potential for drug failure. However, current in vivo animal toxicity testing is very expensive and time consuming. As an alternative approach, various machine learning models have been developed to predict potential liver toxicity in humans. This chapter reviews current advances in the development and application of machine learning models for prediction of potential liver toxicity in humans and discusses possible improvements to liver toxicity prediction.

Machine Learning Models for Predicting Cytotoxicity of Nanomaterials.

The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.

Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials.

Nanomaterials have drawn increasing attention due to their tunable and enhanced physicochemical and biological performance compared to their conventional bulk materials. Owing to the rapid expansion of the nano-industry, large amounts of data regarding the synthesis, physicochemical properties, and bioactivities of nanomaterials have been generated. These data are a great asset to the scientific community. However, the data are on diverse aspects of nanomaterials and in different sources and formats. To help utilize these data, various databases on specific information of nanomaterials such as physicochemical characterization, biomedicine, and nano-safety have been developed and made available online. Understanding the structure, function, and available data in these databases is needed for scientists to select appropriate databases and retrieve specific information for research on nanomaterials. However, to our knowledge, there is no study to systematically compare these databases to facilitate their utilization in the field of nanomaterials. Therefore, we reviewed and compared eight widely used databases of nanomaterials, aiming to provide the nanoscience community with valuable information about the specific content and function of these databases. We also discuss the pros and cons of these databases, thus enabling more efficient and convenient utilization.

Informing selection of drugs for COVID-19 treatment through adverse events analysis.

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic and there is an urgent need for safe and effective drugs for COVID-19 treatment. Since developing a new drug is time consuming, many approved or investigational drugs have been repurposed for COVID-19 treatment in clinical trials. Therefore, selection of safe drugs for COVID-19 patients is vital for combating this pandemic. Our goal was to evaluate the safety concerns of drugs by analyzing adverse events reported in post-market surveillance. We collected 296 drugs that have been evaluated in clinical trials for COVID-19 and identified 28,597,464 associated adverse events at the system organ classes (SOCs) level in the FDA adverse events report systems (FAERS). We calculated Z-scores of SOCs that statistically quantify the relative frequency of adverse events of drugs in FAERS to quantitatively measure safety concerns for the drugs. Analyzing the Z-scores revealed that these drugs are associated with different significantly frequent adverse events. Our results suggest that this safety concern metric may serve as a tool to inform selection of drugs with favorable safety profiles for COVID-19 patients in clinical practices. Caution is advised when administering drugs with high Z-scores to patients who are vulnerable to associated adverse events.

Identification of Epidemiological Traits by Analysis of SARS-CoV-2 Sequences.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the ongoing global COVID-19 pandemic that began in late December 2019. The rapid spread of SARS-CoV-2 is primarily due to person-to-person transmission. To understand the epidemiological traits of SARS-CoV-2 transmission, we conducted phylogenetic analysis on genome sequences from >54K SARS-CoV-2 cases obtained from two public databases. Hierarchical clustering analysis on geographic patterns in the resulting phylogenetic trees revealed a co-expansion tendency of the virus among neighboring countries with diverse sources and transmission routes for SARS-CoV-2. Pairwise sequence similarity analysis demonstrated that SARS-CoV-2 is transmitted locally and evolves during transmission. However, no significant differences were seen among SARS-CoV-2 genomes grouped by host age or sex. Here, our identified epidemiological traits provide information to better prevent transmission of SARS-CoV-2 and to facilitate the development of effective vaccines and therapeutics against the virus.

Carbon Nanodots Derived from Urea and Citric Acid in Living Cells: Cellular Uptake and Antioxidation Effect.

Carbon nanodots (CNDs), reported as polyatomic carbon domains surrounded by amorphous carbon frames, have drawn extensive attention due to their easy-to-synthesis, outstanding electronic properties, and superior biocompatibility. However, substantial assessments regarding their biological performance are still needed, considering the complex nature of this type of relatively new nanoparticles. In this report, CNDs derived from urea and citric acid (U-CNDs) are investigated in the treatment of two cell lines, EA.hy926 and A549 cells, to examine the biocompatibility, cellular uptake, and antioxidation effect. The intracellular uptake study suggests an energy-dependent transport process into the cells mainly involving macropinocytosis and lipid raft-mediated endocytosis pathways. Moreover, the U-CNDs mostly target the mitochondria and present strong antioxidative effects by scavenging reactive oxygen species (ROS) in cells. Overall the findings in this report manifest that the U-CNDs could serve as a bioimaging reagent and antioxidant causing little deleteriousness in the respects of viability, plasma membrane integrity, and mitochondrial activity in both cell lines, and demonstrate some efficacy for inhibiting the metabolic activities of A549 cancer cells at higher concentration.

Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). As of October 21, 2020, more than 41.4 million confirmed cases and 1.1 million deaths have been reported. Thus, it is immensely important to develop drugs and vaccines to combat COVID-19. The spike protein present on the outer surface of the virion plays a major role in viral infection by binding to receptor proteins present on the outer membrane of host cells, triggering membrane fusion and internalization, which enables release of viral ssRNA into the host cell. Understanding the interactions between the SARS-CoV-2 trimeric spike protein and its host cell receptor protein, angiotensin converting enzyme 2 (ACE2), is important for developing drugs and vaccines to prevent and treat COVID-19. Several crystal structures of partial and mutant SARS-CoV-2 spike proteins have been reported; however, an atomistic structure of the wild-type SARS-CoV-2 trimeric spike protein complexed with ACE2 is not yet available. Therefore, in our study, homology modeling was used to build the trimeric form of the spike protein complexed with human ACE2, followed by all-atom molecular dynamics simulations to elucidate interactions at the interface between the spike protein and ACE2. Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) and in silico alanine scanning were employed to characterize the interacting residues at the interface. Twenty interacting residues in the spike protein were identified that are likely to be responsible for tightly binding to ACE2, of which five residues (Val445, Thr478, Gly485, Phe490, and Ser494) were not reported in the crystal structure of the truncated spike protein receptor binding domain (RBD) complexed with ACE2. These data indicate that the interactions between ACE2 and the tertiary structure of the full-length spike protein trimer are different from those between ACE2 and the truncated monomer of the spike protein RBD. These findings could facilitate the development of drugs and vaccines to prevent SARS-CoV-2 infection and combat COVID-19.

Tuning the Functional Groups on Carbon Nanodots and Antioxidant Studies.

Carbon nanodots (CNDs) have shown good antioxidant capabilities by scavenging oxidant free radicals such as diphenyl-1-picrylhydrazyl radical (DPPH•) and reactive oxygen species. While some studies suggest that the antioxidation activities associate to the proton donor role of surface active groups like carboxyl groups (⁻COOH), it is unclear how exactly the extent of oxidant scavenging potential and its related mechanisms are influenced by functional groups on CNDs' surfaces. In this work, carboxyl and the amino functional groups on CNDs' surfaces are modified to investigate the individual influence of intermolecular interactions with DPPH• free radical by UV-Vis spectroscopy and electrochemistry. The results suggest that both the carboxyl and the amino groups contribute to the antioxidation activity of CNDs through either a direct or indirect hydrogen atom transfer reaction with DPPH•.

Plasmon-Exciton Coupling in Photosystem I Based Biohybrid Photoelectrochemical Cells.

The light-induced property of photosystem I (PSI) has been utilized to convert solar energy to electrical energy in photoelectrochemical cells. Here we provide new results on the relationship between surface plasmon generation (SPG) efficiency of nanoslits and the experimentally obtained photocurrent by immobilizing PSI on the gold nanoslit electrode surfaces regarding different nanoslit widths. The photocurrent increases with the increment of SPG efficiency. This finding can be attributed to the phenomenon of plasmon-exciton coupling effect on the PSI in the nanoslits. The enhancement of photocurrent generation is discussed on the basis of plasmonic light trapping and plasmon-induced resonance energy transfer.

Magnetic Field-Enhanced 4-Electron Pathway for Well-Aligned Co3 O4 /Electrospun Carbon Nanofibers in the Oxygen Reduction Reaction.

The sluggish reaction kinetics of the oxygen reduction reaction (ORR) has been the limiting factor for fuel energy utilization, hence it is desirable to develop high-performance electrocatalysts for a 4-electron pathway ORR. A constant low-current (50 µA) electrodeposition technique is used to realize the formation of a uniform Co3 O4 film on well-aligned electrospun carbon nanofibers (ECNFs) with a time-dependent growth mechanism. This material also exhibits a new finding of mT magnetic field-induced enhancement of the electron exchange number of the ORR at a glassy carbon electrode modified with the Co3 O4 /ECNFs catalyst. The magnetic susceptibility of the unpaired electrons in Co3 O4 improves the kinetics and efficiency of electron transfer reactions in the ORR, which shows a 3.92-electron pathway in the presence of a 1.32 mT magnetic field. This research presents a potential revolution of traditional electrocatalysis by simply applying an external magnetic field on metal oxides as a replacement for noble metals to reduce the risk of fuel-cell degradation and maximize the energy output.

Antioxidant Capacity of Nitrogen and Sulfur Codoped Carbon Nanodots.

Carbon nanodots (CNDs) have shown potential for antioxidative activity at the cellular level. Here we applied a facile hydrothermal method to prepare fluorescent nitrogen and sulfur (N,S-)codoped CNDs using α-lipoic acid, citric acid, and urea as precursor molecules. This work describes a comprehensive study for exploring their antioxidation activity using UV-vis absorption and electrochemistry measurements of 2,2-diphenyl-1-picrylhydrazyl radical (DPPH•), as well as a lucigenin chemiluminescence (lucigenin-CL) assay. The lucigenin-CL assay detects superoxide anion radicals, i.e., reactive oxygen species (ROS) produced through the xanthine/xanthine oxidase (XO) reaction. The electrochemically derived relationship between the unreacted nitrogen-centered DPPH• and CND concentrations agrees with that obtained from UV-vis measurements. A reaction pathway for the ROS antioxidative reaction of N,S-codoped CNDs is proposed. These findings should aid in the development of N,S-codoped CNDs for practical use in biomedical applications.

A fluorescence-electrochemical study of carbon nanodots (CNDs) in bio- and photoelectronic applications and energy gap investigation.

Carbon nanodots (CNDs) have attracted great attention due to their superior solubility, biocompatibility, tunable photoluminescence, and opto-electronic properties. This work describes a new fluorescence-based spectroelectrochemistry approach to simultaneously study the photoluminescence and wavelength dependent photocurrent of microwave synthesized CNDs. The fluorescence of CNDs shows selective quenching upon a reversible redox couple, ferricyanide/ferrocyanide, reaction during cyclic voltammetry. The CND modified gold slide electrode demonstrates wavelength dependent photocurrent generation during the fluorescence-electrochemical study, suggesting the potential application of CNDs in photoelectronics. UV-Vis absorption and electrochemistry are used to quantify the energy gap of the CNDs, and then to calibrate a Hückel model for CNDs' electronic energy levels. The Hückel (or tight binding) model treatment of an individual CND as a molecule combines the conjugated π states (C[double bond, length as m-dash]C) with the functional groups (C[double bond, length as m-dash]O, C-O, and COOH) associated with the surface electronic states. This experimental and theoretical investigation of CNDs provides a new perspective on the optoelectronic properties of CNDs and should aid in their development for practical use in biomedicine, chemical sensing, and photoelectric devices.