Nanoparticles as smart treatment-delivery systems in plants: assessment of different techniques of microscopy for their visualization in plant tissues

BacKGROUND AND AIMS: The great potential of using nanodevices as delivery systems to specific targets in living organisms was first explored for medical uses. In plants, the same principles can be applied for a broad range of uses, in particular to tackle infections. Nanoparticles tagged to agrochemicals or other substances could reduce the damage to other plant tissues and the amount of chemicals released into the environment. To explore the benefits of applying nanotechnology to agriculture, the first stage is to work out the correct penetration and transport of the nanoparticles into plants. This research is aimed (a) to put forward a number of tools for the detection and analysis of core-shell magnetic nanoparticles introduced into plants and (b) to assess the use of such magnetic nanoparticles for their concentration in selected plant tissues by magnetic field gradients. METHODS: Cucurbita pepo plants were cultivated in vitro and treated with carbon-coated Fe nanoparticles. Different microscopy techniques were used for the detection and analysis of these magnetic nanoparticles, ranging from conventional light microscopy to confocal and electron microscopy. KEY RESULTS: Penetration and translocation of magnetic nanoparticles in whole living plants and into plant cells were determined. The magnetic character allowed nanoparticles to be positioned in the desired plant tissue by applying a magnetic field gradient there; also the graphitic shell made good visualization possible using different microscopy techniques. CONCLUSIONS: The results open a wide range of possibilities for using magnetic nanoparticles in general plant research and agronomy. The nanoparticles can be charged with different substances, introduced within the plants and, if necessary, concentrated into localized areas by using magnets. Also simple or more complex microscopical techniques can be used in localization studies.     

Interaction between Shaoyao–Gancao–Tang and a laxative with respect to alteration of paeoniflorin metabolism by intestinal bacteria in rats

Shaoyao-Gancao-Tang (SGT), a traditional Chinese herbal medicine (Kampo formulation) containing Shaoyao (Paeoniae Radix) and Gancao (Glycyrrhizae Radix), is co-administered with laxative sodium picosulfate as a premedication for relieving the pain accompanying colonoscopy. Paeoniflorin (PF), an active glycoside of SGT, is metabolized into the antispasmodic agent paeonimetabolin-I (PM-I) by intestinal bacteria after oral administration. The objective of the present study was to investigate whether the co-administered laxative (sodium picosulfate) influences the metabolism of PF to PM-I by intestinal bacteria. We found that the PF-metabolizing activity of intestinal bacteria in rat feces was significantly reduced to approximately 34% of initial levels by a single sodium picosulfate pretreatment and took approximately 6 days to recover. Repeated administration of SGT after the sodium picosulfate pretreatment significantly shortened the recovery period to around 2 days. Similar results were also observed for plasma PM-I concentration. Since PM-I has muscle relaxant activity, the present results suggest that repetitive administration of SGT after sodium picosulfate pretreatment might be useful to relieve the pain associated with colonoscopy.     

Advanced biomaterials and their potential applications in the treatment of periodontal disease

Periodontal disease is considered as a widespread infectious disease and the most common cause of tooth loss in adults. Attempts for developing periodontal disease treatment strategies, including drug delivery and regeneration approaches, provide a useful experimental model for the evaluation of future periodontal therapies. Recently, emerging advanced biomaterials including hydrogels, films, micro/nanofibers and particles, hold great potential to be utilized as cell/drug carriers for local drug delivery and biomimetic scaffolds for future regeneration therapies. In this review, first, we describe the pathogenesis of periodontal disease, including plaque formation, immune response and inflammatory reactions caused by bacteria. Second, periodontal therapy and an overview of current biomaterials in periodontal regenerative medicine have been discussed. Third, the roles of state-of-the-art biomaterials, including hydrogels, films, micro/nanofibers and micro/nanoparticles, developed for periodontal disease treatment and periodontal tissue regeneration, and their fabrication methods, have been presented. Finally, biological properties, including biocompatibility, biodegradability and immunogenicity of the biomaterials, together with their current applications strategies are given. Conclusive remarks and future perspectives for such advanced biomaterials are discussed.     

sars: an R package for fitting,evaluating and comparing species–area relationship models

The species–area relationship (SAR) constitutes one of the most general ecological patterns globally. A number of different SAR models have been proposed. Recent work has shown that no single model universally provides the best fit to empirical SAR datasets: multiple models may be of practical and theoretical interest. However, there are no software packages available that a) allow users to fit the full range of published SAR models, or b) provide functions to undertake a range of additional SAR‐related analyses. To address these needs, we have developed the R package ‘sars’ that provides a wide variety of SAR‐related functionality. The package provides functions to: a) fit 20 SAR models using non‐linear and linear regression, b) calculate multi‐model averaged curves using various information criteria, and c) generate confidence intervals using bootstrapping. Plotting functions allow users to depict and scrutinize the fits of individual models and multi‐model averaged curves. The package also provides additional SAR functionality, including functions to fit, plot and evaluate the random placement model using a species–sites abundance matrix, and to fit the general dynamic model of oceanic island biogeography. The ‘sars’ R package will aid future SAR research by providing a comprehensive set of simple to use tools that enable in‐depth exploration of SARs and SAR‐related patterns. The package has been designed to allow other researchers to add new functions and models in the future and thus the package represents a resource for future SAR work that can be built on and expanded by workers in the field.     

GGIP: Structure and sequence‐based GPCR–GPCR interaction pair predictor

G Protein‐Coupled Receptors (GPCRs) are important pharmaceutical targets. More than 30% of currently marketed pharmaceutical medicines target GPCRs. Numerous studies have reported that GPCRs function not only as monomers but also as homo‐ or hetero‐dimers or higher‐order molecular complexes. Many GPCRs exert a wide variety of molecular functions by forming specific combinations of GPCR subtypes. In addition, some GPCRs are reportedly associated with diseases. GPCR oligomerization is now recognized as an important event in various biological phenomena, and many researchers are investigating this subject. We have developed a support vector machine (SVM)‐based method to predict interacting pairs for GPCR oligomerization, by integrating the structure and sequence information of GPCRs. The performance of our method was evaluated by the Receiver Operating Characteristic (ROC) curve. The corresponding area under the curve was 0.938. As far as we know, this is the only prediction method for interacting pairs among GPCRs. Our method could accelerate the analyses of these interactions, and contribute to the elucidation of the global structures of the GPCR networks in membranes. Proteins 2016; 84:1224–1233. © 2016 Wiley Periodicals, Inc.     

Experimental and computational tools useful for (re)construction of dynamic kinase–substrate networks

The explosion of site‐ and context‐specific in vivo phosphorylation events presents a potentially rich source of biological knowledge and calls for novel data analysis and modeling paradigms. Perhaps the most immediate challenge is delineating detected phosphorylation sites to their effector kinases. This is important for (re)constructing transient kinase–substrate interaction networks that are essential for mechanistic understanding of cellular behaviors and therapeutic intervention, but has largely eluded high‐throughput protein‐interaction studies due to their transient nature and strong dependencies on cellular context. Here, we surveyed some of the computational approaches developed to dissect phosphorylation data detected in systematic proteomic experiments and reviewed some experimental and computational approaches used to map phosphorylation sites to their effector kinases in efforts aimed at reconstructing biological signaling networks.     

Interface Engineering of MXene Composite Separator for High‐Performance Li–Se and Na–Se Batteries

Selenium (Se), due to its high electronic conductivity and high energy density, has recently attracted considerable interest as a cathode material for rechargeable Li/Na batteries. However, the poor cycling stability originating from the severe shuttle effect of polyselenides hinders their practical applications. Herein, highly stable Li/Na–Se batteries are developed using ultrathin (≈270 nm, loading of 0.09 mg cm^?2) cetrimonium bromide (CTAB)/carbon nanotube (CNT)/Ti₃C₂T_x MXene hybrid modified polypropylene (PP) (CCNT/MXene/PP) separators. The hybrid separator can immobilize the polyselenides via enhanced Lewis acid–base interactions between CTAB/MXene and polyselenides, which is demonstrated by theoretical calculations and X‐ray photoelectron spectroscopy. The incorporation of CNT helps to improve the electrolyte infiltration and facilitate the ionic transport. In situ permeation experiments are conducted for the first time to visually study the behavior of polyselenides, revealing the prohibited shuttle effect and protected Li anode from corrosion with CCNT/MXene/PP separators. As a result, the Li–Se batteries with CCNT/MXene/PP separators deliver an outstanding cycling performance over 500 cycles at 1C with an extremely low capacity decay of 0.05% per cycle. Moreover, the hybrid separators also perform well in Na–Se batteries. This study develops a preferable separator–electrolyte interface and the concept can be applied in other conversion‐type battery systems.     

CHO cell expression,long‐term stability,and primate pharmacokinetics and brain uptake of an IgG–paroxonase‐1 fusion protein

Paraoxonase (PON)‐1 is the most potent human organophosphatase known, but recombinant forms of human PON1 have been difficult to produce owing to poor secretion by host cells. In the present investigation, human PON1 is re‐engineered as an IgG–PON1 fusion protein. The 355 amino acid human PON1 is fused to the carboxyl terminus of the heavy chain of a chimeric monoclonal antibody (MAb) against the human insulin receptor (HIR), and this fusion protein is designated HIRMAb–PON1. The HIRMAb part of the fusion protein enables brain penetration of the PON1, which was considered important, because organophosphate toxicity causes death via a central nervous system site of action. A high producing line of stably transfected Chinese hamster ovary (CHO) cells secreting the HIRMAb–PON1 fusion protein in the absence of serum or lipid acceptors was cloned. The bioreactor generated fusion protein was purified to homogeneity with low impurities by protein A affinity chromatography and anion exchange chromatography. The HIRMAb–PON1 fusion protein was stable as a sterile liquid formulation stored at 4°C for at least 1 year. The plasma pharmacokinetics (PK) of the HIRMAb–PON1 fusion protein was evaluated in Rhesus monkeys, which is the first PK evaluation of a recombinant PON1 protein. The fusion protein was rapidly removed from blood, primarily by the liver. The blood–brain barrier permeation of the HIRMAb–PON1 fusion protein was high and comparable to other HIRMAb fusion proteins. Re‐engineering human PON1 as the HIRMAb fusion protein allows for production of a stable, field‐deployable formulation of the enzyme that is brain‐penetrating. Biotechnol. Bioeng. 2011; 108:186–196. © 2010 Wiley Periodicals, Inc.     

Using collections of structural models to predict changes of binding affinity caused by mutations in protein–protein interactions

Protein–protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state‐of‐the‐art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state‐of‐art methods.     

BSA–AuNPs@Tb–AMP metal–organic frameworks for ratiometric fluorescence detection of DPA and Hg2+

An easy and effective strategy for synthesizing a ratiometric fluorescent nanosensor has been demonstrated in this work. Novel fluorescent BSA–AuNPs@Tb–AMP (BSA, bovine serum albumin; AMP, adenosine 5′‐monophosphate; AuNPs, Au nanoparticles) metal–organic framework (MOF) nanostructures were synthesized by encapsulating BSA–AuNPs into Tb–AMP MOFs for the detection of 2,6‐pyridinedicarboxylic acid (DPA) and Hg²⁺. DPA could strongly co‐ordinate with Tb³⁺ to replace water molecules from the Tb³⁺ center and accordingly enhanced the fluorescence of Tb–AMP MOFs. The fluorescence of BSA–AuNPs at 405 nm remained constant. While the fluorescence of BSA–AuNPs at 635 nm was quenched after Hg²⁺ was added, the fluorescence of Tb–AMP MOFs remained constant. Accordingly, a ratiometric fluorescence nanosensor was constructed for detection of DPA and Hg²⁺. The ratiometric nanosensor exhibited good selectivity to DPA over other substances. The F₅₄₅/F₄₀₅ linearly increased with increase of DPA concentration in the range of 50 nM to 10 μM with a detection limit as low as 17.4 nM. F₆₃₅/F₄₀₅ increased linearly with increase of Hg²⁺ concentration ranging from 50 nM to 1 μM with a detection limit as low as 20.9 nM. Additionally, the nanosensor could be successfully applied for the determination of DPA and Hg²⁺ in running water.     

The presence–absence matrix reloaded: the use and interpretation of range–diversity plots

Aim A great deal of information on distribution and diversity can be extracted from presence–absence matrices (PAMs), the basic analytical tool of many biogeographic studies. This paper presents numerical procedures that allow the analysis of such information by taking advantage of mathematical relationships within PAMs. In particular, we show how range–diversity (RD) plots summarize much of the information contained in the matrices by the simultaneous depiction of data on distribution and diversity. Innovation We use matrix algebra to extract and process data from PAMs. Information on the distribution of species and on species richness of sites is computed using the traditional R (by rows) and Q (by columns) procedures, as well as the new Rq (by rows, considering the structure of columns) and Qr (by columns, considering the structure by rows) methods. Matrix notation is particularly suitable for summarizing complex calculations using PAMs, and the associated algebra allows the implementation of efficient computational programs. We show how information on distribution and species richness can be depicted simultaneously in RD plots, allowing a direct examination of the relationship between those two aspects of diversity. We explore the properties of RD plots with a simple example, and use null models to show that while parameters of central tendency are not affected by randomization, the dispersion of points in RD plots does change, showing the significance of patterns of co‐occurrence of species and of similarity among sites. Main conclusion Species richness and range size are both valid measures of diversity that can be analysed simultaneously with RD plots. A full analysis of a system requires measures of central tendency and dispersion for both distribution and species richness.     

Gold nanoparticles–conjugated quercetin induces apoptosis via inhibition of EGFR/PI3K/Akt–mediated pathway in breast cancer cell lines (MCF‐7 and MDA‐MB‐231)

Epidermal growth factor plays a major role in breast cancer cell proliferation, survival, and metastasis. Quercetin, a bioactive flavonoid, is shown to exhibit anticarcinogenic effects against various cancers including breast cancer. Hence, the present study was designed to evaluate the effects of gold nanoparticles–conjugated quercetin (AuNPs‐Qu‐5) in MCF‐7 and MDA‐MB‐231 breast cancer cell lines. Borohydride reduced AuNPs were synthesized and conjugated with quercetin to yield AuNPs‐Qu‐5. Both were thoroughly characterized by several physicochemical techniques, and their cytotoxic effects were assessed by MTT assay. Apoptotic studies such as DAPI, AO/EtBr dual staining, and annexin V‐FITC staining were performed. AuNPs and AuNPs‐Qu‐5 were spherical with crystalline nature, and the size of particles range from 3.0 to 4.5 nm. AuNPs‐Qu‐5 exhibited lower IC₅₀ value compared to free Qu. There was a considerable increase in apoptotic population with increased nuclear condensation seen upon treatment with AuNPs‐Qu‐5. To delineate the molecular mechanism behind its apoptotic role, we analysed the proteins involved in apoptosis and epidermal growth factor receptor (EGFR)–mediated PI3K/Akt/GSK‐3β signalling by immunoblotting and immunocytochemistry. The pro‐apoptotic proteins (Bax, Caspase‐3) were found to be up regulated and anti‐apoptotic protein (Bcl‐2) was down regulated on treatment with AuNPs‐Qu‐5. Additionally, AuNPs‐Qu‐5 treatment inhibited the EGFR and its downstream signalling molecules PI3K/Akt/mTOR/GSK‐3β. In conclusion, administration of AuNPs‐Qu‐5 in breast cancer cell lines curtails cell proliferation through induction of apoptosis and also suppresses EGFR signalling. AuNPs‐Qu‐5 is more potent than free quercetin in causing cancer cell death, and hence, this could be a potential drug delivery system in breast cancer therapy.     

Threshold criteria for conversion of probability of species presence to either–or presence–absence

For many applications the continuous prediction afforded by species distribution modeling must be converted to a map of presence or absence, so a threshold probability indicative of species presence must be fixed. Because of the bias in probability outputs due to frequency of presences (prevalence), a fixed threshold value, such as 0.5, does not usually correspond to the threshold above which the species is more likely to be present. In this paper four threshold criteria are compared for a wide range of sample sizes and prevalences, modeling a virtual species in order to avoid the omnipresent error sources that the use of real species data implies. In general, sensitivity–specificity difference minimizer and sensitivity–specificity sum maximizer criteria produced the most accurate predictions. The widely-used 0.5 fixed threshold and Kappa-maximizer criteria are the worst ones in almost all situations. Nevertheless, whatever the criteria used, the threshold value chosen and the research goals that determined its choice must be stated.     

Human–Cougar interactions in the wildland–urban interface of Colorado's front range

As human populations continue to expand across the world, the need to understand and manage wildlife populations within the wildland – urban interface is becoming commonplace. This is especially true for large carnivores as these species are not always tolerated by the public and can pose a risk to human safety. Unfortunately, information on wildlife species within the wildland – urban interface is sparse, and knowledge from wildland ecosystems does not always translate well to human‐dominated systems. Across western North America, cougars (Puma concolor) are routinely utilizing wildland – urban habitats while human use of these areas for homes and recreation is increasing. From 2007 to 2015, we studied cougar resource selection, human–cougar interaction, and cougar conflict management within the wildland – urban landscape of the northern Front Range in Colorado, USA. Resource selection of cougars within this landscape was typical of cougars in more remote settings but cougar interactions with humans tended to occur in locations cougars typically selected against, especially those in proximity to human structures. Within higher housing density areas, 83% of cougar use occurred at night, suggesting cougars generally avoided human activity by partitioning time. Only 24% of monitored cougars were reported for some type of conflict behavior but 39% of cougars sampled during feeding site investigations of GPS collar data were found to consume domestic prey items. Aversive conditioning was difficult to implement and generally ineffective for altering cougar behaviors but was thought to potentially have long‐term benefits of reinforcing fear of humans in cougars within human‐dominated areas experiencing little cougar hunting pressure. Cougars are able to exploit wildland – urban landscapes effectively, and conflict is relatively uncommon compared with the proportion of cougar use. Individual characteristics and behaviors of cougars within these areas are highly varied; therefore, conflict management is unique to each situation and should target individual behaviors. The ability of individual cougars to learn to exploit these environments with minimal human–cougar interactions suggests that maintaining older age structures, especially females, and providing a matrix of habitats, including large connected open‐space areas, would be beneficial to cougars and effectively reduce the potential for conflict.     

Ion–dipole interactions and their functions in proteins

Ion–dipole interactions in biological macromolecules are formed between atomic or molecular ions and neutral protein dipolar groups through either hydrogen bond or coordination. Since their discovery 30 years ago, these interactions have proven to be a frequent occurrence in protein structures, appearing in everything from transporters and ion channels to enzyme active sites to protein–protein interfaces. However, their significance and roles in protein functions are largely underappreciated. We performed PDB data mining to identify a sampling of proteins that possess these interactions. In this review, we will define the ion–dipole interaction and discuss several prominent examples of their functional roles in nature.     

Protein‐spanning water networks and implications for prediction of protein–protein interactions mediated through hydrophobic effects

Hydrophobic effects, often conflated with hydrophobic forces, are implicated as major determinants in biological association and self‐assembly processes. Protein–protein interactions involved in signaling pathways in living systems are a prime example where hydrophobic effects have profound implications. In the context of protein–protein interactions, a priori knowledge of relevant binding interfaces (i.e., clusters of residues involved directly with binding interactions) is difficult. In the case of hydrophobically mediated interactions, use of hydropathy‐based methods relying on single residue hydrophobicity properties are routinely and widely used to predict propensities for such residues to be present in hydrophobic interfaces. However, recent studies suggest that consideration of hydrophobicity for single residues on a protein surface require accounting of the local environment dictated by neighboring residues and local water. In this study, we use a method derived from percolation theory to evaluate spanning water networks in the first hydration shells of a series of small proteins. We use residue‐based water density and single‐linkage clustering methods to predict hydrophobic regions of proteins; these regions are putatively involved in binding interactions. We find that this simple method is able to predict with sufficient accuracy and coverage the binding interface residues of a series of proteins. The approach is competitive with automated servers. The results of this study highlight the importance of accounting of local environment in determining the hydrophobic nature of individual residues on protein surfaces. Proteins 2014; 82:3312–3326. © 2014 Wiley Periodicals, Inc.