Mass Cytometry and Single-Cell RNA-seq Profiling of the Heterogeneity in Human Peripheral Blood Mononuclear Cells Interacting with Silver Nanoparticles.

Understanding the interactions between nanoparticles (NPs) and human immune cells is necessary for justifying their utilization in consumer products and biomedical applications. However, conventional assays may be insufficient in describing the complexity and heterogeneity of cell-NP interactions. Herein, mass cytometry and single-cell RNA-sequencing (scRNA-seq) are complementarily used to investigate the heterogeneous interactions between silver nanoparticles (AgNPs) and primary immune cells. Mass cytometry reveals the heterogeneous biodistribution of the positively charged polyethylenimine-coated AgNPs in various cell types and finds that monocytes and B cells have higher association with the AgNPs than other populations. scRNA-seq data of these two cell types demonstrate that each type has distinct responses to AgNP treatment: NRF2-mediated oxidative stress is confined to B cells, whereas monocytes show Fcγ-mediated phagocytosis. Besides the between-population heterogeneity, analysis of single-cell dose-response relationships further reveals within-population diversity for the B cells and naïve CD4+ T cells. Distinct subsets having different levels of cellular responses with respect to their cellular AgNP doses are found. This study demonstrates that the complementary use of mass cytometry and scRNA-seq is helpful for gaining in-depth knowledge on the heterogeneous interactions between immune cells and NPs and can be incorporated into future toxicity assessments of nanomaterials.

Identification of Ca-rich dense granules in human platelets using scanning transmission X-ray microscopy.

Whole-mount (WM) platelet preparation followed by transmission electron microscopy (TEM) observation is the standard method currently used to assess dense granule (DG) deficiency (DGD). However, due to the electron-density-based contrast mechanism in TEM, other granules such as α-granules might cause false DG detection. Here, scanning transmission X-ray microscopy (STXM) was used to identify DGs and minimize false DG detection of human platelets. STXM image stacks of human platelets were collected at the calcium (Ca) L2,3 absorption edge and then converted to optical density maps. Ca distribution maps, obtained by subtracting the optical density maps at the pre-edge region from those at the post-edge region, were used to identify DGs based on the Ca richness. DGs were successfully detected using this STXM method without false detection, based on Ca maps for four human platelets. Spectral analysis of granules in human platelets confirmed that DGs contain a richer Ca content than other granules. The Ca distribution maps facilitated more effective DG identification than TEM which might falsely detect DGs. Correct identification of DGs would be important to assess the status of platelets and DG-related diseases. Therefore, this STXM method is proposed as a promising approach for better DG identification and diagnosis, as a complementary tool to the current WM TEM approach.

Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells.

Quantitative structure-activity relationship (QSAR) models for nanomaterials (nano-QSAR) were developed to predict the cytotoxicity of 20 different types of multiwalled carbon nanotubes (MWCNTs) to human lung cells by using quasi-SMILES. The optimal descriptors, recorded as quasi-SMILES, were encoded to represent the physicochemical properties and experimental conditions for the MWCNTs from 276 data records collected from previously published studies. The quasi-SMILES used to build the optimal descriptors were (i) diameter, (ii) length, (iii) surface area, (iv) in vitro toxicity assay, (v) cell line, (vi) exposure time, and (vii) dose. The model calculations were performed by using the Monte Carlo method and computed with CORAL software ( www.insilico.eu/coral ). The quasi-SMILES-based nano-QSAR model provided satisfactory statistical results ( R2 for internal validation data sets: 0.60-0.80; R2pred for external validation data sets: 0.81-0.88). The model showed potential for use in the estimation of human lung cell viability after exposure to MWCNTs with the following properties: diameter, 12-74 nm; length, 0.19-20.25 µm; surface area, 11.3-380.0 m2/g; and dose, 0-200 ppm.

G-749, a novel FLT3 kinase inhibitor, can overcome drug resistance for the treatment of acute myeloid leukemia.

Aberrant activations of Fms-like tyrosine receptor kinase (FLT) 3 are implicated in the pathogenesis of 20% to 30% of patients with acute myeloid leukemia (AML). G-749 is a novel FLT3 inhibitor that showed potent and sustained inhibition of the FLT3 wild type and mutants including FLT3-ITD, FLT3-D835Y, FLT3-ITD/N676D, and FLT3-ITD/F691L in cellular assays. G-749 retained its inhibitory potency in various drug-resistance milieus such as patient plasma, FLT3 ligand surge, and stromal protection. Furthermore, it displayed potent antileukemic activity in bone marrow blasts from AML patients regardless of FLT3 mutation status, including those with little or only minor responses to AC220 or PKC412. Oral administration of G-749 yielded complete tumor regression and increased life span in animal models. Thus, G-749 appears to be a promising next-generation drug candidate for the treatment of relapsed and refractory AML patients with various FLT3-ITD/FLT3-TKD mutants and further shows the ability to overcome drug resistance.

Highly potent and selective pyrazolylpyrimidines as Syk kinase inhibitors.

A series of pyrazolylpyrimidine scaffold based Syk inhibitors were synthesized and evaluated for their biological activities and selectivity. Lead optimization efforts provided compounds with potent Syk inhibition in both enzymatic and TNF-α release assay.

Implementation of Complementary Model using Optimal Combination of Hematological Parameters for Sepsis Screening in Patients with Fever.

The early detection and timely treatment are the most important factors for improving the outcome of patients with sepsis. Sepsis-related clinical score, such as SIRS, SOFA and LODS, were defined to identify patients with suspected infection and to predict severity and mortality. A few hematological parameters associated with organ dysfunction and infection were included in the score although various clinical pathology parameters (hematology, serum chemistry and plasma coagulation) in blood sample have been found to be associated with outcome in patients with sepsis. The investigation of the parameters facilitates the implementation of a complementary model for screening sepsis to existing sepsis clinical criteria and other laboratory signs. In this study, statistical analysis on the multiple clinical pathology parameters obtained from two groups, patients with sepsis and patients with fever, was performed and the complementary model was elaborated by stepwise parameter selection and machine learning. The complementary model showed statistically better performance (AUC 0.86 vs. 0.74-0.51) than models built up with specific hematology parameters involved in each existing sepsis-related clinical score. Our study presents the complementary model based on the optimal combination of hematological parameters for sepsis screening in patients with fever.

Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects.

The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms' responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.

Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data.

Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment.

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.

NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment.

Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.

Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials.

A quasi-QSAR model was developed to predict the cell viability of human lung (BEAS-2B) and skin (HaCaT) cells exposed to 21 types of metal oxide nanomaterials. A wide range of toxicity datasets obtained from the S2NANO (www.s2nano.org) database was used. The data of descriptors representing the physicochemical properties and experimental conditions were coded to quasi-SMILES. In particular, hierarchical cluster analysis (HCA) and min-max normalization method were respectively used in assigning alphanumeric codes for numerical descriptors (e.g., core size, hydrodynamic size, surface charge, and dose) and then quasi-QSAR model performances for both methods were compared. The quasi-QSAR models were developed using CORAL software (www.insilico.eu/coral). Quasi-QSAR model built using quasi-SMILES generated by means of HCA showed better performance than the min-max normalization method. The model showed satisfactory statistical results (Radj2 for the training dataset: 0.71-0.73; Radj2 for the calibration dataset: 0.74-0.82; and Radj2 for the validation dataset: 0.70-0.76).

Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-based Screening Approaches.

Development of nanotoxicity prediction models is becoming increasingly important in the risk assessment of engineered nanomaterials. However, it has significant obstacles caused by the wide heterogeneities of published literature in terms of data completeness and quality. Here, we performed a meta-analysis of 216 published articles on oxide nanoparticles using 14 attributes of physicochemical, toxicological and quantum-mechanical properties. Particularly, to improve completeness and quality of the extracted dataset, we adapted two preprocessing approaches: data gap-filling and physicochemical property based scoring. Performances of nano-SAR classification models revealed that the dataset with the highest score value resulted in the best predictivity with compromise in its applicability domain. The combination of physicochemical and toxicological attributes was proved to be more relevant to toxicity classification than quantum-mechanical attributes. Overall, by adapting these two preprocessing methods, we demonstrated that meta-analysis of nanotoxicity literatures could provide an effective alternative for the risk assessment of engineered nanomaterials.

Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources.

A generalized toxicity classification model for 7 different oxide nanomaterials is presented in this study. A data set extracted from multiple literature sources and screened by physicochemical property based quality scores were used for model development. Moreover, a few more preprocessing techniques, such as synthetic minority over-sampling technique, were applied to address the imbalanced class problem in the data set. Then, classification models using four different algorithms, such as generalized linear model, support vector machine, random forest, and neural network, were developed and their performances were compared to find the best performing preprocessing methods as well as algorithms. The neural network model built using the balanced data set was identified as the model with best predictive performance, while applicability domain was defined using k-nearest neighbours algorithm. The analysis of relative attribute importance for the built neural network model identified dose, formation enthalpy, exposure time, and hydrodynamic size as the four most important attributes. As the presented model can predict the toxicity of the nanomaterials in consideration of various experimental conditions, it has the advantage of having a broader and more general applicability domain than the existing quantitative structure-activity relationship model.

Correction: Crystal Structure of Pim1 Kinase in Complex with a Pyrido[4,3-D]Pyrimidine Derivative Suggests a Unique Binding Mode.

[This corrects the article DOI: 10.1371/journal.pone.0070358.].

Crystal Structures of Spleen Tyrosine Kinase in Complex with Two Novel 4-Aminopyrido[4,3-d] Pyrimidine Derivative Inhibitors.

Spleen tyrosine kinase (SYK) is a cytosolic non-receptor protein tyrosine kinase. Because SYK mediates key receptor signaling pathways involving the B cell receptor and Fc receptors, SYK is an attractive target for autoimmune disease and cancer treatments. To date, representative oral SYK inhibitors, including fostamatinib (R406 or R788), entospletinib (GS-9973), cerdulatinib (PRT062070), and TAK-659, have been assessed in clinical trials. Here, we report the crystal structures of SYK in complex with two newly developed inhibitors possessing 4-aminopyrido[4,3-D]pyrimidine moieties (SKI-G-618 and SKI-O-85). One SYK inhibitor (SKI-G-618) exhibited moderate inhibitory activity against SYK, whereas the other inhibitor (SKI-O-85) exhibited a low inhibitory profile against SYK. Binding mode analysis indicates that a highly potent SYK inhibitor might be developed by modifying and optimizing the functional groups that interact with Leu377, Gly378, and Val385 in the G-loop and the nearby region in SYK. In agreement with our structural analysis, one of our SYK inhibitor (SKI-G-618) shows strong inhibitory activities on the ß-hexosaminidase release and phosphorylation of SYK/Vav in RBL-2H3 cells. Taken together, our findings have important implications for the design of high affinity SYK inhibitors.

p-p Heterojunction of Nickel Oxide-Decorated Cobalt Oxide Nanorods for Enhanced Sensitivity and Selectivity toward Volatile Organic Compounds.

The utilization of p-p isotype heterojunctions is an effective strategy to enhance the gas sensing properties of metal-oxide semiconductors, but most previous studies focused on p-n heterojunctions owing to their simple mechanism of formation of depletion layers. However, a proper choice of isotype semiconductors with appropriate energy bands can also contribute to the enhancement of the gas sensing performance. Herein, we report nickel oxide (NiO)-decorated cobalt oxide (Co3O4) nanorods (NRs) fabricated using the multiple-step glancing angle deposition method. The effective decoration of NiO on the entire surface of Co3O4 NRs enabled the formation of numerous p-p heterojunctions, and they exhibited a 16.78 times higher gas response to 50 ppm of C6H6 at 350 °C compared to that of bare Co3O4 NRs with the calculated detection limit of approximately 13.91 ppb. Apart from the p-p heterojunctions, increased active sites owing to the changes in the orientation of the exposed lattice surface and the catalytic effects of NiO also contributed to the enhanced gas sensing properties. The advantages of p-p heterojunctions for gas sensing applications demonstrated in this work will provide a new perspective of heterostructured metal-oxide nanostructures for sensitive and selective gas sensing.

Crystal structures of spleen tyrosine kinase in complex with novel inhibitors: structural insights for design of anticancer drugs.

Spleen tyrosine kinase (SYK) is a cytosolic nonreceptor protein tyrosine kinase that mediates key signal transduction pathways following the activation of immune cell receptors. SYK regulates cellular events induced by the B-cell receptor and Fc receptors with high intrinsic activity. Furthermore, SYK has been regarded as an attractive target for the treatment of autoimmune diseases and cancers. Here, we report the crystal structures of SYK in complex with seven newly developed inhibitors (G206, G207, O178, O194, O259, O272, and O282) to provide structural insights into which substituents of the inhibitors and binding regions of SYK are essential for lead compound optimization. Our kinase inhibitors exhibited high inhibitory activities against SYK, with half-maximal inhibitory concentrations (IC50 ) of approximately 0.7-33 nm, but they showed dissimilar inhibitory activities against KDR, RET, JAK2, JAK3, and FLT3. Among the seven SYK inhibitors, O272 and O282 exhibited highly specific inhibitions against SYK, whereas O194 exhibited strong inhibition of both SYK and FLT3. Three inhibitors (G206, G207, and O178) more efficiently inhibited FLT3 while still substantially inhibiting SYK activity. The binding mode analysis suggested that a highly selective SYK inhibitor can be developed by optimizing the functional groups that facilitate direct interactions with Asn499. DATABASE: The atomic coordinates and structure factors for human SYK are in the Protein Data Bank under accession codes 4XG2 (inhibitor-free form), 4XG3 (G206), 4XG4 (G207), 5GHV (O178), 4XG6 (O194), 4XG7 (O259), 4XG8 (O272), and 4XG9 (O282).

Crystal structure of pim1 kinase in complex with a pyrido[4,3-d]pyrimidine derivative suggests a unique binding mode.

Human Pim1 kinase is a serine/threonine protein kinase that plays important biological roles in cell survival, apoptosis, proliferation, and differentiation. Moreover, Pim1 is up-regulated in various hematopoietic malignancies and solid tumors. Thus, Pim1 is an attractive target for cancer therapeutics, and there has been growing interest in developing small molecule inhibitors for Pim1. Here, we describe the crystal structure of Pim1 in complex with a newly developed pyrido[4,3-d]pyrimidine-derivative inhibitor (SKI-O-068). Our inhibitor exhibits a half maximum inhibitory concentration (IC50) of 123 (±14) nM and has an unusual binding mode in complex with Pim1 kinase. The interactions between SKI-O-068 and the Pim1 active site pocket residue are different from those of other scaffold inhibitor-bound structures. The binding mode analysis suggests that the SKI-O-068 inhibitor can be improved by introducing functional groups that facilitate direct interaction with Lys67, which aid in the design of an optimized inhibitor.