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Nanomaterials are widely used in various fields, and ongoing research is focused on developing safe and sustainable nanomaterials. Using zebrafish as a model organism for studying the potentially toxic effects of nanomaterials highlights the importance of developing safe and sustainable nanomaterials. Studies conducted on nanomaterials and their toxicity and potential risks to human and environmental health are vital in biomedical sciences. In the present review, we discuss the potential toxicity of nanomaterials (inorganic and organic) and exposure risks based on size, shape, and concentration. The review further explores various types of nanomaterials and their impacts on zebrafish at different levels, indicating that exposure to nanomaterials can lead to developmental defects, changes in gene expressions, and various toxicities. The review also covers the importance of considering natural organic matter and chorion membranes in standardized nanotoxicity testing. While some nanomaterials are biologically compatible, metal and semiconductor nanomaterials that enter the water environment can increase toxicity to aquatic creatures and can potentially accumulate in the human body. Further investigations are necessary to assess the safety of nanomaterials and their impacts on the environment and human health.

Advances in high-throughput microscopy have enabled the rapid acquisition of large numbers of high-content microscopy images. Whether by deep learning or classical algorithms, image analysis pipelines then produce single-cell features. To process these single-cells for downstream applications, we present Pycytominer, a user-friendly, open-source python package that implements the bioinformatics steps, known as "image-based profiling". We demonstrate Pycytominer's usefulness in a machine learning project to predict nuisance compounds that cause undesirable cell injuries., Competing Interests: Disclosure of competing interests The authors declare they have no competing interests relevant to this work.

The identification of genetic and chemical perturbations with similar impacts on cell morphology can elucidate compounds' mechanisms of action or novel regulators of genetic pathways. Research on methods for identifying such similarities has lagged due to a lack of carefully designed and well-annotated image sets of cells treated with chemical and genetic perturbations. Here we create such a Resource dataset, CPJUMP1, in which each perturbed gene's product is a known target of at least two chemical compounds in the dataset. We systematically explore the directionality of correlations among perturbations that target the same protein encoded by a given gene, and we find that identifying matches between chemical and genetic perturbations is a challenging task. Our dataset and baseline analyses provide a benchmark for evaluating methods that measure perturbation similarities and impact, and more generally, learn effective representations of cellular state from microscopy images. Such advancements would accelerate the applications of image-based profiling of cellular states, such as uncovering drug mode of action or probing functional genomics., (© 2024. The Author(s).)

Mechanistic studies of Geobacillus stearothermophilus tryptophanyl-tRNA synthetase (TrpRS) afford an unusually detailed description-the escapement mechanism-for the distinct steps coupling catalysis to domain motion, efficiently converting the free energy of ATP hydrolysis into biologically useful alternative forms of information and work. Further elucidation of the escapement mechanism requires understanding thermodynamic linkages between domain configuration and conformational stability. To that end, we compare experimental thermal melting of fully liganded and apo TrpRS with a computational simulation of the melting of its fully liganded form. The simulation also provides important structural cameos at successively higher temperatures, enabling more confident interpretation. Experimental and simulated melting both proceed through a succession of three transitions at successively higher temperature. The low-temperature transition occurs at approximately the growth temperature of the organism and so may be functionally relevant but remains too subtle to characterize structurally. Structural metrics from the simulation imply that the two higher-temperature transitions entail forming a molten globular state followed by unfolding of secondary structures. Ligands that stabilize the enzyme in a pre-transition (PreTS) state compress the temperature range over which these transitions occur and sharpen the transitions to the molten globule and fully denatured states, while broadening the low-temperature transition. The experimental enthalpy changes provide a key parameter necessary to convert changes in melting temperature of combinatorial mutants into mutationally induced conformational free energy changes. The TrpRS urzyme, an excerpted model representing an early ancestral form, containing virtually the entire catalytic apparatus, remains largely intact at the highest simulated temperatures., Competing Interests: The authors have no conflicts to disclose., (© 2023 Author(s).)

In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations on the basis of their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells and predicting assay outcomes by using machine learning, among many others. Here, we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, Golgi apparatus, plasma membrane, endoplasmic reticulum and mitochondria. The original protocol was updated in 2016 on the basis of several years' experience running it at two sites, after optimizing it by visual stain quality. Here, we describe the work of the Joint Undertaking for Morphological Profiling Cell Painting Consortium, to improve upon the assay via quantitative optimization by measuring the assay's ability to detect morphological phenotypes and group similar perturbations together. The assay gives very robust outputs despite various changes to the protocol, and two vendors' dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1-2 weeks for typically sized batches of ≤20 plates; feature extraction and data analysis take an additional 1-2 weeks.This protocol is an update to Nat. Protoc. 11, 1757-1774 (2016): https://doi.org/10.1038/nprot.2016.105., (© 2023. Springer Nature Limited.)

Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb human A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOAs) and gene targets, we find that the two assays not only provide a partially shared but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Single-mode equivalent space-time representations of the acoustic wave propagating in a Biot poroelastic medium have previously been found only for asymptotic cases: In the low frequency regime, where the viscous skin depth is greater than the characteristic pore size, the time domain equivalent is represented with integer order temporal and spatial loss terms, whereas in the high frequency regime, it is represented with fractional order temporal and spatial loss terms. In the current work, a time domain representation in terms of a partial differential equation is proposed for all three wave solutions of the Biot model across all frequencies, and it is derived from the material response function of the Biot poroelastic medium with suitable approximations for the compressional modes and the dynamic permeability. The dynamic permeability in the time domain is represented by a fractional pseudo-differential operator. Optimal correction factors are introduced into the wave equation to compensate for the discrepancies in the compressional wave dispersion and attenuation. Additionally, the method for incorporating the squirt flow mechanism into the wave equation via the Extended Biot poroviscoelastic model is described. The proposed wave equation has a physical basis and satisfies the passivity criterion.

Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug's activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.

In cheminformatics, compound-target binding profiles has been a main source of data for research. For data repositories that only provide positive profiles, a popular assumption is that unreported profiles are all negative. In this paper, we caution the audience not to take this assumption for granted, and present empirical evidence of its ineffectiveness from a machine learning perspective. Our examination is based on a setting where binding profiles are used as features to train predictive models; we show (1) prediction performance degrades when the assumption fails and (2) explicit recovery of unreported profiles improves prediction performance. In particular, we propose a framework that jointly recovers profiles and learns predictive model, and show it achieves further performance improvement. The presented study not only suggests applying matrix recovery methods to recover unreported profiles, but also initiates a new missing feature problem which we called Learning with Positive and Unknown Features.

The biphasic extended Biot poroviscoelastic model takes into account the squirt flow in grain-grain contacts and introduces the bulk and shear relaxation modes associated with it. This model has been criticized for its empirical approach, but here the constitutive equations and the time domain wave equations of the model are derived. This also makes it possible to find single phase viscoelastic equivalents for all three wave solutions of the extended Biot model. Particularly, the viscoelastic equivalents for shear wave propagation can be obtained with considerably fewer parameters than the original model. These equivalents are linear viscoelastic models with springs and dampers for the low frequencies and contain half-order spring-pots for high frequencies. For high frequencies, the non-physicality of the shear relaxation mode is highlighted. The relaxation modes of the extended Biot model are interpreted in the framework of multiple relaxation mechanisms showing that the P- and S-wave modes of the model are not much more complex than that for seawater. The model's near linear frequency dependent attenuation in the intermediate frequency range is the result of weighting each relaxation mechanism appropriately.

PATH algorithms for identifying conformational transition states provide computational parameters-time to the transition state, conformational free energy differences, and transition state activation energies-for comparison to experimental data and can be carried out sufficiently rapidly to use in the "high throughput" mode. These advantages are especially useful for interpreting results from combinatorial mutagenesis experiments. This report updates the previously published algorithm with enhancements that improve correlations between PATH convergence parameters derived from virtual variant structures generated by RosettaBackrub and previously published kinetic data for a complete, four-way combinatorial mutagenesis of a conformational switch in Tryptophanyl-tRNA synthetase.

We measured and cross-validated the energetics of networks in Bacillus stearothermophilus Tryptophanyl-tRNA synthetase (TrpRS) using both multi-mutant and modular thermodynamic cycles. Multi-dimensional combinatorial mutagenesis showed that four side chains from this "molecular switch" move coordinately with the active-site Mg 2+ ion as the active site preorganizes to stabilize the transition state for amino acid activation. A modular thermodynamic cycle consisting of full-length TrpRS, its Urzyme, and the Urzyme plus each of the two domains deleted in the Urzyme gives similar energetics. These dynamic linkages, although unlikely to stabilize the transition-state directly, consign the active-site preorganization to domain motion, assuring coupled vectorial behavior.

PATH rapidly computes a path and a transition state between crystal structures by minimizing the Onsager-Machlup action. It requires input parameters whose range of values can generate different transition-state structures that cannot be uniquely compared with those generated by other methods. We outline modifications to estimate these input parameters to circumvent these difficulties and validate the PATH transition states by showing consistency between transition-states derived by different algorithms for unrelated protein systems. Although functional protein conformational change trajectories are to a degree stochastic, they nonetheless pass through a well-defined transition state whose detailed structural properties can rapidly be identified using PATH.

Aminoacyl-tRNA synthetases (aaRS) catalyze both chemical steps that translate the universal genetic code. Rodin and Ohno offered an explanation for the existence of two aaRS classes, observing that codons for the most highly conserved Class I active-site residues are anticodons for corresponding Class II active-site residues. They proposed that the two classes arose simultaneously, by translation of opposite strands from the same gene. We have characterized wild-type 46-residue peptides containing ATP-binding sites of Class I and II synthetases and those coded by a gene designed by Rosetta to encode the corresponding peptides on opposite strands. Catalysis by WT and designed peptides is saturable, and the designed peptides are sensitive to active-site residue mutation. All have comparable apparent second-order rate constants 2.9-7.0E-3 M(-1) s(-1) or ∼750,000-1,300,000 times the uncatalyzed rate. The activities of the two complementary peptides demonstrate that the unique information in a gene can have two functional interpretations, one from each complementary strand. The peptides contain phylogenetic signatures of longer, more sophisticated catalysts we call Urzymes and are short enough to bridge the gap between them and simpler uncoded peptides. Thus, they directly substantiate the sense/antisense coding ancestry of Class I and II aaRS. Furthermore, designed 46-mers achieve similar catalytic proficiency to wild-type 46-mers by significant increases in both kcat and Km values, supporting suggestions that the earliest peptide catalysts activated ATP for biosynthetic purposes., (© 2015 by The American Society for Biochemistry and Molecular Biology, Inc.)

We previously showed (Li, L., and Carter, C. W., Jr. (2013) J. Biol. Chem. 288, 34736-34745) that increased specificity for tryptophan versus tyrosine by contemporary Bacillus stearothermophilus tryptophanyl-tRNA synthetase (TrpRS) over that of TrpRS Urzyme results entirely from coupling between the anticodon-binding domain and an insertion into the Rossmann-fold known as Connecting Peptide 1. We show that this effect is closely related to a long range catalytic effect, in which side chain repacking in a region called the D1 Switch, accounts fully for the entire catalytic contribution of the catalytic Mg(2+) ion. We report intrinsic and higher order interaction effects on the specificity ratio, (kcat/Km)Trp/(kcat/Km)Tyr, of 15 combinatorial mutants from a previous study (Weinreb, V., Li, L., and Carter, C. W., Jr. (2012) Structure 20, 128-138) of the catalytic role of the D1 Switch. Unexpectedly, the same four-way interaction both activates catalytic assist by Mg(2+) ion and contributes -4.4 kcal/mol to the free energy of the specificity ratio. A minimum action path computed for the induced-fit and catalytic conformation changes shows that repacking of the four residues precedes a decrease in the volume of the tryptophan-binding pocket. We suggest that previous efforts to alter amino acid specificities of TrpRS and glutaminyl-tRNA synthetase (GlnRS) by mutagenesis without extensive, modular substitution failed because mutations were incompatible with interdomain motions required for catalysis.

We tested the idea that ancestral class I and II aminoacyl-tRNA synthetases arose on opposite strands of the same gene. We assembled excerpted 94-residue Urgenes for class I tryptophanyl-tRNA synthetase (TrpRS) and class II Histidyl-tRNA synthetase (HisRS) from a diverse group of species, by identifying and catenating three blocks coding for secondary structures that position the most highly conserved, active-site residues. The codon middle-base pairing frequency was 0.35 ± 0.0002 in all-by-all sense/antisense alignments for 211 TrpRS and 207 HisRS sequences, compared with frequencies between 0.22 ± 0.0009 and 0.27 ± 0.0005 for eight different representations of the null hypothesis. Clustering algorithms demonstrate further that profiles of middle-base pairing in the synthetase antisense alignments are correlated along the sequences from one species-pair to another, whereas this is not the case for similar operations on sets representing the null hypothesis. Most probable reconstructed sequences for ancestral nodes of maximum likelihood trees show that middle-base pairing frequency increases to approximately 0.42 ± 0.002 as bacterial trees approach their roots; ancestral nodes from trees including archaeal sequences show a less pronounced increase. Thus, contemporary and reconstructed sequences all validate important bioinformatic predictions based on descent from opposite strands of the same ancestral gene. They further provide novel evidence for the hypothesis that bacteria lie closer than archaea to the origin of translation. Moreover, the inverse polarity of genetic coding, together with a priori α-helix propensities suggest that in-frame coding on opposite strands leads to similar secondary structures with opposite polarity, as observed in TrpRS and HisRS crystal structures.

Design of a regulatable multistate protein is a challenge for protein engineering. Here we design a protein with a unique topology, called uniRapR, whose conformation is controlled by the binding of a small molecule. We confirm switching and control ability of uniRapR in silico, in vitro, and in vivo. As a proof of concept, uniRapR is used as an artificial regulatory domain to control activity of kinases. By activating Src kinase using uniRapR in single cells and whole organism, we observe two unique phenotypes consistent with its role in metastasis. Activation of Src kinase leads to rapid induction of protrusion with polarized spreading in HeLa cells, and morphological changes with loss of cell-cell contacts in the epidermal tissue of zebrafish. The rational creation of uniRapR exemplifies the strength of computational protein design, and offers a powerful means for targeted activation of many pathways to study signaling in living organisms.

Mammalian gastric lipases are stable and active under acidic conditions and also in the duodenal lumen. There has been considerable interest in acid stable lipases owing to their potential application in the treatment of pancreatic exocrine insufficiency. In order to gain insights into the domain movements of these enzymes, molecular dynamics simulations of human gastric lipase was performed at an acidic pH and under neutral conditions. For comparative studies, simulation of dog gastric lipase was also performed at an acidic pH. Analyses show, that in addition to the lid region, there is another region of high mobility in these lipases. The potential role of this novel region is discussed., (Copyright © 2010 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.)