Improving the Accuracy of Bulk Fitness Assays by Correcting Barcode Processing Biases.

Measuring the fitnesses of genetic variants is a fundamental objective in evolutionary biology. A standard approach for measuring microbial fitnesses in bulk involves labeling a library of genetic variants with unique sequence barcodes, competing the labeled strains in batch culture, and using deep sequencing to track changes in the barcode abundances over time. However, idiosyncratic properties of barcodes can induce nonuniform amplification or uneven sequencing coverage that causes some barcodes to be over- or under-represented in samples. This systematic bias can result in erroneous read count trajectories and misestimates of fitness. Here, we develop a computational method, named REBAR (Removing the Effects of Bias through Analysis of Residuals), for inferring the effects of barcode processing bias by leveraging the structure of systematic deviations in the data. We illustrate this approach by applying it to two independent data sets, and demonstrate that this method estimates and corrects for bias more accurately than standard proxies, such as GC-based corrections. REBAR mitigates bias and improves fitness estimates in high-throughput assays without introducing additional complexity to the experimental protocols, with potential applications in a range of experimental evolution and mutation screening contexts.

Advancing Cardiovascular Drug Screening Using Human Pluripotent Stem Cell-Derived Cardiomyocytes.

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) have emerged as a promising tool for studying cardiac physiology and drug responses. However, their use is largely limited by an immature phenotype and lack of high-throughput analytical methodology. In this study, we developed a high-throughput testing platform utilizing hPSC-CMs to assess the cardiotoxicity and effectiveness of drugs. Following an optimized differentiation and maturation protocol, hPSC-CMs exhibited mature CM morphology, phenotype, and functionality, making them suitable for drug testing applications. We monitored intracellular calcium dynamics using calcium imaging techniques to measure spontaneous calcium oscillations in hPSC-CMs in the presence or absence of test compounds. For the cardiotoxicity test, hPSC-CMs were treated with various compounds, and calcium flux was measured to evaluate their effects on calcium dynamics. We found that cardiotoxic drugs withdrawn due to adverse drug reactions, including encainide, mibefradil, and cetirizine, exhibited toxicity in hPSC-CMs but not in HEK293-hERG cells. Additionally, in the effectiveness test, hPSC-CMs were exposed to ATX-II, a sodium current inducer for mimicking long QT syndrome type 3, followed by exposure to test compounds. The observed changes in calcium dynamics following drug exposure demonstrated the utility of hPSC-CMs as a versatile model system for assessing both cardiotoxicity and drug efficacy. Overall, our findings highlight the potential of hPSC-CMs in advancing drug discovery and development, which offer a physiologically relevant platform for the preclinical screening of novel therapeutics.

Enantioselective High-Throughput Assay Showcased for the Identification of (R)- as well as (S)-Selective Unspecific Peroxygenases for C-H Oxidation.

Identifying (bio)catalysts displaying high enantio-/stereoselectivity is a fundamental prerequisite for the advancement of asymmetric catalysis. Herein, a high-throughput, stereoselective screening assay is reported that gives information on enantioselectivity, stereopreference and activity as showcased for peroxygenase-catalyzed hydroxylation. The assay is based on spectrophotometric analysis of the simultaneous formation of NAD(P)H from the alcohol dehydrogenase catalyzed enantioselective oxidation of the sec-alcohol product formed in the peroxygenase reaction. The assay was applied to investigate a library comprising 44 unspecific peroxygenases (UPOs) containing 25 UPOs not reported yet. Thereby, previously non-described wild-type UPOs displaying (S)- as well as (R)-stereoselectivity for the hydroxylation of representative model substrates were identified, reaching up to 98 % ee for the (R)- and 94 % ee for the (S)-enantiomer. Homology models with concomitant docking studies indicated the structural reason for the observed complementary stereopreference.

Assigning Functions of Unknown Enzymes by High-Throughput Enzyme Characterization.

The discovery of new enzymes is strongly enabled by the implementation of high-throughput screening methods to detect enzymatic activity in single organisms or clone expression libraries, or to benchmark their performances against known prototypes. In this chapter, a number of methods, applicable at high-throughput scale, are described that allow the screening and characterization of enzymes relevant to biotechnology, particularly, ester-hydrolases (esterases, lipases, phospholipases, and polyester hydrolases).

High-throughput proteomics and in vitro functional characterization of the 26 medically most important elapids and vipers from sub-Saharan Africa.

Venomous snakes are important parts of the ecosystem, and their behavior and evolution have been shaped by their surrounding environments over the eons. This is reflected in their venoms, which are typically highly adapted for their biological niche, including their diet and defense mechanisms for deterring predators. Sub-Saharan Africa is rich in venomous snake species, of which many are dangerous to humans due to the high toxicity of their venoms and their ability to effectively deliver large amounts of venom into their victims via their bite. In this study, the venoms of 26 of sub-Saharan Africa's medically most relevant elapid and viper species were subjected to parallelized toxicovenomics analysis. The analysis included venom proteomics and in vitro functional characterization of whole venom toxicities, enabling a robust comparison of venom profiles between species. The data presented here corroborate previous studies and provide biochemical details for the clinical manifestations observed in envenomings by the 26 snake species. Moreover, two new venom proteomes (Naja anchietae and Echis leucogaster) are presented here for the first time. Combined, the presented data can help shine light on snake venom evolutionary trends and possibly be used to further improve or develop novel antivenoms.

Use of computational toxicology tools to predict in vivo endpoints associated with Mode of Action and the endocannabinoid system: A case study with chlorpyrifos, chlorpyrifos-oxon and Δ9Tetrahydrocannabinol.

Currently, there is a lack of knowledge about the effects of co-exposures of cannabis, contaminated with pesticides like chlorpyrifos (CPF) and the toxic metabolite CPF-oxon (CPFO). CPF/CPFO residues, and Δ9Tetrahydrocannabinol (Δ9THC), the main component in cannabis, are known to disrupt the endocannabinoid system (eCBS) resulting in neurodevelopmental defects. Although there are in vivo data characterizing CPF/CPFO and Δ9THC, there are mechanistic data gaps and deficiencies. In this study, an investigation of open access CompTox tools and ToxCast/Tox21 data was performed to determine targets relating to the modes of action (MOA) for these compounds and, given the available biological targets, predict points of departure (POD). The main findings were as follows: 1) In vivo PODs for each chemical were from open literature, 2) Concordance between ToxCast/Tox21 assay targets and known targets in the metabolic and eCBS pathways was evaluated, 3) Human Equivalent Administered Dose (EADHuman) PODs showed the High throughput toxicokinetic (HTTK) 3 compartment model (3COMP) was more predictive of in vivo PODs than the PBTK model for CPF, CPFO and Δ9THC, 4) Age-adjusted 3COMP HTTK-Pop EADHuman, with CPF and CPFO ToxCast/Tox21 AC50 values as inputs were predictive for ages 0-4 when but not Δ9THC compared to in vivo PODs. 5) Age-related refinements for CPF/CPFO were primarily from ToxCast/Tox21 active hit-calls for nuclear receptors, CYP2B6 and AChE inhibition (CPFO only) associated with the metabolic pathway. Only one assay target (arylhydrocarbon hydroxylase receptor) was common between CPF/CPFO and Δ9THC. While computational refinements may select some sensitive events involved in the metabolic pathways; this is highly dependent on the cytotoxicity limits, availability of metabolic activity in the ToxCast/Tox21 assays and reliability of assay performance. Some uncertainties and data gaps for Δ9THC might be addressed with assays specific to the eCBS. For CPF, assays with appropriate metabolic activation could better represent the toxic pathway.

Performance Characteristics of High-Throughput Serologic Assays for Severe Acute Respiratory Syndrome Coronavirus 2 with Food and Drug Administration Emergency Use Authorization: A Review.

This review provides a broad summary of the performance characteristics of high-throughput severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serologic assays with Food and Drug Administration Emergency Use Authorization, which are commonly found in central clinical laboratories. In addition, this review discusses the current roles of serologic testing for SARS-CoV-2 and provides a perspective for the future.

Highly Accurate Filters to Flag Frequent Hitters in AlphaScreen Assays by Suggesting their Mechanism.

AlphaScreen is one of the most widely used assay technologies in drug discovery due to its versatility, dynamic range and sensitivity. However, a presence of false positives and frequent hitters contributes to difficulties with an interpretation of measured HTS data. Although filters do exist to identify frequent hitters for AlphaScreen, they are frequently based on privileged scaffolds. The development of such filters is time consuming and requires deep domain knowledge. Recently, machine learning and artificial intelligence methods are emerging as important tools to advance drug discovery and chemoinformatics, including their application to identification of frequent hitters in screening assays. However, the relative performance and complementarity of the Machine Learning and scaffold-based techniques has not yet been comprehensively compared. In this study, we analysed filters based on the privileged scaffolds with filters built using machine learning. Our results demonstrate that machine-learning methods provide more accurate filters for identification of frequent hitters in AlphaScreen assays than scaffold-based methods and can be easily redeveloped once new data are measured. We present highly accurate models to identify frequent hitters in AlphaScreen assays.

A Transient Metabolic State in Melanoma Persister Cells Mediated by Chemotherapeutic Treatments.

Persistence is a transient state that poses an important health concern in cancer therapy. The mechanisms associated with persister phenotypes are highly diverse and complex, and many aspects of persister cell physiology remain to be explored. We applied a melanoma cell line and panel of chemotherapeutic agents to show that melanoma persister cells are not necessarily preexisting dormant cells; in fact, they may be induced by cancer chemotherapeutics. Our metabolomics analysis and phenotype microarray assays further demonstrated a transient upregulation in Krebs cycle metabolism in persister cells. We also verified that targeting electron transport chain activity can significantly reduce melanoma persister levels. The reported metabolic remodeling feature seems to be a conserved characteristic of melanoma persistence, as it has been observed in various melanoma persister subpopulations derived from a diverse range of chemotherapeutics. Elucidating a global metabolic mechanism that contributes to persister survival and reversible switching will ultimately foster the development of novel cancer therapeutic strategies.

Quantitative benzimidazole resistance and fitness effects of parasitic nematode beta-tubulin alleles.

Infections by parasitic nematodes inflict a huge burden on the health of humans and livestock throughout the world. Anthelmintic drugs are the first line of defense against these infections. Unfortunately, resistance to these drugs is rampant and continues to spread. To improve treatment strategies, we must understand the genetics and molecular mechanisms that underlie resistance. Studies of the fungus Aspergillus nidulans and the free-living nematode Caenorhabditis elegans discovered that a beta-tubulin gene is mutated in benzimidazole (BZ) resistant strains. In parasitic nematode populations, three beta-tubulin alleles, F167Y, E198A, and F200Y, have long been correlated with resistance. Additionally, improvements in sequencing technologies have identified new alleles - E198V, E198L, E198K, E198I, and E198Stop - also correlated with BZ resistance. However, none of these alleles have been proven to cause resistance. To empirically demonstrate this point, we independently introduced the F167Y, E198A, and F200Y alleles as well as two of the newly identified alleles, E198V and E198L, into the BZ susceptible C. elegans N2 genetic background using the CRISPR-Cas9 system. These genome-edited strains were exposed to both albendazole and fenbendazole to quantitatively measure animal responses to BZs. We used a range of concentrations for each BZ compound to define response curves and found that all five of the alleles conferred resistance to BZ compounds equal to a loss of the entire beta-tubulin gene. These results prove that the parasite beta-tubulin alleles cause resistance. The E198V allele is found at low frequencies along with the E198L allele in natural parasite populations, suggesting that it could affect fitness. We performed competitive fitness assays and demonstrated that the E198V allele reduces animal health, supporting the hypothesis that this allele might be less fit in field populations. Overall, we present a powerful platform to quantitatively assess anthelmintic resistance and effects of specific resistance alleles on organismal fitness in the presence or absence of the drug.

Biomedical applications of yeasts - a patent view, part two: era of humanized yeasts and expanded applications.

INTRODUCTION: Yeast humanization, ranging from a simple point mutation to substitution of yeast gene(s) or even a complete pathway by human counterparts has enormously expanded yeast biomedical applications. AREAS COVERED: General and patent-oriented insights into the application of native and humanized yeasts for production of human glycoproteins (gps) and antibodies (Abs), toxicity/mutagenicity assays, treatments of gastrointestinal (GI) disorders and potential drug delivery as a probiotic (with emphasis on Saccharomyces bulardii) and studies on human diseases/cancers and screening effective drugs. EXPERT OPINION: Humanized yeasts cover the classical advantageous features of a 'microbial eukaryote' together with advanced human cellular processes. These unique characteristics would permit their use in the production of functional and stable therapeutic gps and Abs in lower prices compared to mammalian (CHO) production-based systems. Availability of yeasts humanized for cytochrome P450 s will expand their application in metabolism-related chemical toxicity assays. Engineered S. bulardii for expression of human proteins might expand its application by synergistically combining the probiotic activity with the treatment of metabolic diseases such as phenylketonuria via GI-delivery. Yeast models of human diseases will facilitate rapid functional/phenotypic characterization of the disease-producing mutant genes and screening of the therapeutic compounds using yeast-based high-throughput research techniques (Yeast one/two hybrid systems) and viability assays.

DiSCount: computer vision for automated quantification of Striga seed germination.

BACKGROUND: Plant parasitic weeds belonging to the genus Striga are a major threat for food production in Sub-Saharan Africa and Southeast Asia. The parasite's life cycle starts with the induction of seed germination by host plant-derived signals, followed by parasite attachment, infection, outgrowth, flowering, reproduction, seed set and dispersal. Given the small seed size of the parasite (< 200 µm), quantification of the impact of new control measures that interfere with seed germination relies on manual, labour-intensive counting of seed batches under the microscope. Hence, there is a need for high-throughput assays that allow for large-scale screening of compounds or microorganisms that adversely affect Striga seed germination. RESULTS: Here, we introduce DiSCount (Digital Striga Counter): a computer vision tool for automated quantification of total and germinated Striga seed numbers in standard glass fibre filter assays. We developed the software using a machine learning approach trained with a dataset of 98 manually annotated images. Then, we validated and tested the model against a total dataset of 188 manually counted images. The results showed that DiSCount has an average error of 3.38 percentage points per image compared to the manually counted dataset. Most importantly, DiSCount achieves a 100 to 3000-fold speed increase in image analysis when compared to manual analysis, with an inference time of approximately 3 s per image on a single CPU and 0.1 s on a GPU. CONCLUSIONS: DiSCount is accurate and efficient in quantifying total and germinated Striga seeds in a standardized germination assay. This automated computer vision tool enables for high-throughput, large-scale screening of chemical compound libraries and biological control agents of this devastating parasitic weed. The complete software and manual are hosted at https://gitlab.com/lodewijk-track32/discount_paper and the archived version is available at Zenodo with the DOI 10.5281/zenodo.3627138. The dataset used for testing is available at Zenodo with the DOI 10.5281/zenodo.3403956.

Intratumor Heterogeneity and Therapy Resistance: Contributions of Dormancy, Apoptosis Reversal (Anastasis) and Cell Fusion to Disease Recurrence.

A major challenge in treating cancer is posed by intratumor heterogeneity, with different sub-populations of cancer cells within the same tumor exhibiting therapy resistance through different biological processes. These include therapy-induced dormancy (durable proliferation arrest through, e.g., polyploidy, multinucleation, or senescence), apoptosis reversal (anastasis), and cell fusion. Unfortunately, such responses are often overlooked or misinterpreted as "death" in commonly used preclinical assays, including the in vitro colony-forming assay and multiwell plate "viability" or "cytotoxicity" assays. Although these assays predominantly determine the ability of a test agent to convert dangerous (proliferating) cancer cells to potentially even more dangerous (dormant) cancer cells, the results are often assumed to reflect loss of cancer cell viability (death). In this article we briefly discuss the dark sides of dormancy, apoptosis, and cell fusion in cancer therapy, and underscore the danger of relying on short-term preclinical assays that generate population-based data averaged over a large number of cells. Unveiling the molecular events that underlie intratumor heterogeneity together with more appropriate experimental design and data interpretation will hopefully lead to clinically relevant strategies for treating recurrent/metastatic disease, which remains a major global health issue despite extensive research over the past half century.

High-Throughput Quantification and Glycosylation Analysis of Antibodies Using Bead-Based Assays.

A novel version of bead -based assays with fluorescence detection enables the high-throughput analysis of antibodies and proteins. The protocols are carried out in special 384-well plates, require very few manual interventions, and are easy to automate. Here we describe how the technology can be used to determine antibody titers and screen for product glycosylation, a critical quality attribute, early in cell line and bioprocess development.

The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency.

The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA's Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.

Modeling and measuring extracellular matrix alterations in fibrosis: challenges and perspectives for antifibrotic drug discovery.

An imbalance of extracellular matrix (ECM) deposition and turnover is a hallmark of fibrotic pathologies as opposed to normal repair response to injury across several organs. Antifibrotic approaches to date have targeted multiple mechanisms and pathways involved in inflammation, angiogenesis, injury, wound repair, ECM biosynthesis, assembly, crosslinking and degradation. Many of these approaches have been unsuccessful which may in part be due to suboptimal models and the lack of validated functional ECM end points relevant to fibrosis. In addition, drug discovery and development for fibrotic diseases has been challenging due to the lack of translatability from in vivo models to the clinic. Targeting growth factor signaling pathways such as transforming growth factor beta (TGFß), platelet-derived growth factor (PDGF), and fibroblast growth factor (FGF) are possible in simple recombinant cell models and the approval of the tyrosine kinase inhibitor, nintedanib (Ofev) is testament to the approach. However, drug targets directly impacting ECM synthesis, assembly or degradation have proven clinically intractable to date. The reasons for a lack of progress are many and include; non-traditional drug targets, lack of suitable high throughput screening assays and translational models, incomplete understanding of the role of the target. Here, we review the role of ECM in fibrosis, the challenges of ECM-targeted antifibrotic approaches, progress in the development of functional and biomarker-related ECM assays and where new translational models of fibrotic ECM remodeling could support drug discovery for fibrotic diseases.

GAP43, a novel metastasis promoter in non-small cell lung cancer.

BACKGROUND: Brain metastasis is an extremely serious sequela with a dismal prognosis in non-small cell lung cancer (NSCLC). The present study aimed to identify novel biomarkers and potential therapeutic targets for brain metastases of NSCLC. METHODS: We performed high-throughput Luminex assays to profile the transcriptional levels of 36 genes in 70 operable NSCLC patients, among whom 37 developed brain metastases as the first relapse within 3 years after surgery. The Cox proportional hazards regression model was used to evaluate the association between genes and brain metastases. Wound healing assay and transwell assay was carried out to estimate the function of target gene in vitro. And left ventricular injection on nude mice was used to evaluate the effect of target gene in vivo. RESULTS: Growth-associated protein 43 (GAP43) was found to be related to brain metastasis. Multivariate Cox regression analysis showed that NSCLC patients with elevated GAP43 had a 3.29-fold increase in the risk for brain metastasis compared with those with low levels (95% confidence interval: 1.55-7.00; P = 0.002). Kaplan-Meier survival curves revealed that GAP43 was also associated with overall survival. Analysis of a cohort of 1926 NSCLC patients showed similar results: patients with high levels of GAP43 had worse progression-free and overall survival rates. Furthermore, in vitro experiments showed that GAP43 facilitated cell migration. Animal studies demonstrated that GAP43-silenced NSCLC cells were less likely to metastasize to the brain and bone than control cells. Immunofluorescence and F-actin/G-actin in vivo assays indicated that GAP43 knockdown triggered depolymerization of the F-actin cytoskeleton. Rho GTPase activation assays showed that Rac1 was deactivated after GAP43 was silenced. CONCLUSIONS: Our findings suggest that GAP43 is an independent predictor of NSCLC brain metastasis and that it may facilitate metastasis by regulating the Rac1/F-actin pathway.

Viability Assessment Following Anticancer Treatment Requires Single-Cell Visualization.

A subset of cells within solid tumors become highly enlarged and enter a state of dormancy (sustained proliferation arrest) in response to anticancer treatment. Although dormant cancer cells might be scored as "dead" in conventional preclinical assays, they remain viable, secrete growth-promoting factors, and can give rise to progeny with stem cell-like properties. Furthermore, cancer cells exhibiting features of apoptosis (e.g., caspase-3 activation) following genotoxic stress can undergo a reversal process called anastasis and survive. Consistent with these observations, single-cell analysis of adherent cultures (solid tumor-derived cell lines with differing p53 status) has demonstrated that virtually all cells-irrespective of their size and morphology-that remain adherent to the culture dish for a long time (weeks) after treatment with anticancer agents exhibit the ability to metabolize 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl- tetrazolium bromide (MTT). The purpose of this commentary is to briefly review these findings and discuss the significance of single-cell (versus population averaged) observation methods for assessment of cancer cell viability and metabolic activity.

Recent Advances and Trends in Microfluidic Platforms for C. elegans Biological Assays.

Microfluidics has proven to be a key tool in quantitative biological research. The C. elegans research community in particular has developed a variety of microfluidic platforms to investigate sensory systems, development, aging, and physiology of the nematode. Critical for the growth of this field, however, has been the implementation of concurrent advanced microscopy, hardware, and software technologies that enable the discovery of novel biology. In this review, we highlight recent innovations in microfluidic platforms used for assaying C. elegans and discuss the novel technological approaches and analytic strategies required for these systems. We conclude that platforms that provide analytical frameworks for assaying specific biological mechanisms and those that take full advantage of integrated technologies to extract high-value quantitative information from worm assays are most likely to move the field forward.

Structure-Guided Directed Evolution of Glycosidases: A Case Study in Engineering a Blood Group Antigen-Cleaving Enzyme.

Directed evolution is an incredibly powerful strategy for engineering enzyme function. Applying this approach to glycosidases offers enormous potential for the development of highly specialized tools in chemical glycobiology. Performing enzyme directed evolution requires the generation, by random mutagenesis, of mutant libraries from which large numbers of variant enzymes must be screened in high-throughput assays. A structure-guided "semirational" method for library creation allows researchers to target specific amino acid positions for mutagenesis, concentrating mutations where they might be most effective in order to produce mutant libraries of a manageable size, minimizing screening effort while maximizing the chances of finding improved mutants. Well-designed assays, which may use specially prepared substrates, enable efficient screening of these mutant libraries. This chapter will detail general methods in the structure-guided directed evolution of glycosidases, which have previously been employed in engineering a blood group antigen-cleaving enzyme.