Bioactivity of the ubiquitous tire preservative 6PPD and degradant, 6PPD-quinone in fish- and mammalian-based assays

6PPD-quinone (N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone), a transformation product of the antiozonant 6PPD (N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine) is a likely causative agent of coho salmon (Oncorhynchus kisutch) pre-spawn mortality. Stormwater runoff transports 6PPD-quinone into freshwater streams, rapidly leading to neurobehavioral, respiratory distress, and rapid mortality in laboratory-exposed coho salmon, but causing no mortality in many laboratory-tested species. Given this identified hazard, and potential for environmental exposure, we evaluated a set of U.S. Environmental Protection Agency's high-throughput assays for their capability to detect the large potency difference between 6PPD and 6PPD-quinone observed in coho salmon and screen for bioactivities of concern. Assays included transcriptomics in larval fathead minnow (FHM), developmental and behavioral toxicity in larval zebrafish, phenotypic profiling in a rainbow trout gill cell line, acute and developmental neurotoxicity in mammalian cells, and reporter transcription factor activity in HepG2 cells. 6PPD was more consistently bioactive across assays, with distinct activity in the developmental neurotoxicity assay (mean 50th centile activity concentration = 0.91 µM). Although 6PPD-quinone was less potent in FHM and zebrafish, and displayed minimal neurotoxic activity in mammalian cells, it was highly potent in altering organelle morphology in RTgill-W1 cells (phenotype-altering concentration = 0.024 µM compared with 0.96 µM for 6PPD). Although in vitro sensitivity of RTgill-W1 cells may not be as sensitive as intact Coho salmon, the assay may be a promising approach to test chemicals for 6PPD-quinone-like activities. The other assays each identified unique bioactivities of 6PPD, with neurobehavioral and developmental neurotoxicity being most affected, indicating a need for further assessment of this chemical. Our results demonstrate that the common tire additive, 6PPD, is bioactive in a broader set of assays than the environmental transformation product 6PPD-quinone and that it may be a developmental neurotoxicant in mammals, whereas 6PPD-quinone was much more potent than 6PPD in altering the intracellular phenotype of rainbow trout gill cells. Application of the set of high-throughput and high-content bioassays to test the bioactivity of this emerging pollutant has provided data to inform both ecological and human health assessments.

Cell Painting: a decade of discovery and innovation in cellular imaging

Modern quantitative image analysis techniques have enabled high-throughput, high-content imaging experiments. Image-based profiling leverages the rich information in images to identify similarities or differences among biological samples, rather than measuring a few features, as in high-content screening. Here, we review a decade of advancements and applications of Cell Painting, a microscopy-based cell-labeling assay aiming to capture a cell's state, introduced in 2013 to optimize and standardize image-based profiling. Cell Painting's ability to capture cellular responses to various perturbations has expanded owing to improvements in the protocol, adaptations for different perturbations, and enhanced methodologies for feature extraction, quality control, and batch-effect correction. Cell Painting is a versatile tool that has been used in various applications, alone or with other -omics data, to decipher the mechanism of action of a compound, its toxicity profile, and other biological effects. Future advances will likely involve computational and experimental techniques, new publicly available datasets, and integration with other high-content data types.

LIN28-Targeting Chromenopyrazoles and Tetrahydroquinolines Induced Cellular Morphological Changes and Showed High Biosimilarity with BRD PROTACs

The probing of small molecules with heterocyclic scaffolds covering unexplored chemical space and the evaluation of their biological relevance are essential parts of forward chemical genetics approaches and for the development of potential small-molecule therapeutics. In this study, we profiled sets of chromenopyrazoles (CMPs) and tetrahydroquinolines (THQs), originally developed to target the protein-RNA interaction of LIN28-let-7, in a cell painting assay (CPA) measuring cellular morphological changes. Selected LIN28-inactive CMPs and THQs induced cellular morphological changes to different extents. The most CPA-active CMPs 2 and 3 exhibited high bio-similarity with the LCH and BET clusters, while the most CPA-active THQs 13 and 20 indicated a mechanism of action beyond the currently established biosimilarity clusters. Overall, this work demonstrated that CPA is useful in revealing "hidden" biological targets and mechanisms of action for biologically inactive small molecules, which are CMPs and THQs targeting the RNA-binding protein LIN28 in this case, evaluated in target-based strategies. When compared with annotated reference compounds, CMP 3 exhibited a high biosimilarity with the dual BRD7/9 degrading PROTAC VZ185, suggesting that CPA could potentially function as a new phenotypic approach to identify degrader molecules.

SPACe: an open-source, single-cell analysis of Cell Painting data

Phenotypic profiling by high throughput microscopy, including Cell Painting, has become a leading tool for screening large sets of perturbations in cellular models. To efficiently analyze this big data, available open-source software requires computational resources usually not available to most laboratories. In addition, the cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused. We introduce SPACe (Swift Phenotypic Analysis of Cells), an open-source platform for analysis of single-cell image-based morphological profiles produced by Cell Painting. We highlight several advantages of SPACe, including processing speed, accuracy in mechanism of action recognition, reproducibility across biological replicates, applicability to multiple models, sensitivity to variable cell-to-cell responses, and biological interpretability to explain image-based features. We illustrate SPACe in a defined screening campaign of cell metabolism small-molecule inhibitors tested in seven cell lines to highlight the importance of analyzing perturbations across models.

Profiling Cellular Morphological Changes Induced by Dual-Targeting PROTACs of Aurora Kinase and RNA-Binding Protein YTHDF2

Proteolysis targeting chimeras (PROTACs) are new chemical modalities that degrade proteins of interest, including established kinase targets and emerging RNA-binding proteins (RBPs). Whereas diverse sets of biochemical, biophysical and cellular assays are available for the evaluation and optimizations of PROTACs in understanding the involved ubiquitin-proteasome-mediated degradation mechanism and the structure-degradation relationship, a phenotypic method profiling the cellular morphological changes is rarely used. In this study, first, we reported the only examples of PROTACs degrading the mRNA-binding protein YTHDF2 via screening of multikinase PROTACs. Second, we reported the profiling of cellular morphological changes of the dual kinase- and RBP-targeting PROTACs using the unbiased cell painting assay (CPA). The CPA analysis revealed the high biosimilarity with the established aurora kinase cluster and annotated aurora kinase inhibitors, which reflected the association between YTHDF2 and the aurora kinase signaling network. Broadly, the results demonstrated that the cell painting assay can be a straightforward and powerful approach to evaluate PROTACs. Complementary to the existing biochemical, biophysical and cellular assays, CPA provided a new perspective in characterizing PROTACs at the cellular morphology.

Detection of a Mitochondrial Fragmentation and Integrated Stress Response Using the Cell Painting Assay

Mitochondria are cellular powerhouses and are crucial for cell function. However, they are vulnerable to internal and external perturbagens that may impair mitochondrial function and eventually lead to cell death. In particular, small molecules may impact mitochondrial function, and therefore, their influence on mitochondrial homeostasis is at best assessed early on in the characterization of biologically active small molecules and drug discovery. We demonstrate that unbiased morphological profiling by means of the cell painting assay (CPA) can detect mitochondrial stress coupled with the induction of an integrated stress response. This activity is common for compounds addressing different targets, is not shared by direct inhibitors of the electron transport chain, and enables prediction of mitochondrial stress induction for small molecules that are profiled using CPA.

Illuminating Dark Chemical Matter Using the Cell Painting Assay

Screening for small-molecule modulators of disease-relevant targets and phenotypes is the first step on the way to new drugs. Large compound libraries have been synthesized by academia and, particularly, pharmaceutical companies to meet the need for novel chemical entities that are as diverse as possible. Screening of these compound libraries revealed a portion of small molecules that is inactive in more than 100 different assays and was therefore termed "dark chemical matter" (DCM). Deorphanization of DCM promises to yield very selective compounds as they are expected to have less off-target effects. We employed morphological profiling using the Cell Painting assay to detect bioactive DCM. Within the DCM collection, we identified bioactive compounds and confirmed several modulators of microtubules, DNA synthesis, and pyrimidine biosynthesis. Profiling approaches are, therefore, powerful tools to probe compound collections for bioactivity in an unbiased manner and are particularly suitable for deorphanization of DCM.

SPACe (Swift Phenotypic Analysis of Cells): an open-source, single cell analysis of Cell Painting data

Phenotypic profiling by high throughput microscopy has become one of the leading tools for screening large sets of perturbations in cellular models. Of the numerous methods used over the years, the flexible and economical Cell Painting (CP) assay has been central in the field, allowing for large screening campaigns leading to a vast number of data-rich images. Currently, to analyze data of this scale, available open-source software ( i.e. , CellProfiler) requires computational resources that are not available to most laboratories worldwide. In addition, the image-embedded cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused. Here we introduce SPACe ( S wift P henotypic A nalysis of Ce lls), an open source, Python-based platform for the analysis of single cell image-based morphological profiles produced by CP experiments. SPACe can process a typical dataset approximately ten times faster than CellProfiler on common desktop computers without loss in mechanism of action (MOA) recognition accuracy. It also computes directional distribution-based distances (Earth Mover's Distance - EMD) of morphological features for quality control and hit calling. We highlight several advantages of SPACe analysis on CP assays, including reproducibility across multiple biological replicates, easy applicability to multiple (∼20) cell lines, sensitivity to variable cell-to-cell responses, and biological interpretability to explain image-based features. We ultimately illustrate the advantages of SPACe in a screening campaign of cell metabolism small molecule inhibitors which we performed in seven cell lines to highlight the importance of testing perturbations across models.

High-content analysis identified synergistic drug interactions between INK128, an mTOR inhibitor, and HDAC inhibitors in a non-small cell lung cancer cell line

BACKGROUND

The development of drug resistance is a major cause of cancer therapy failures. To inhibit drug resistance, multiple drugs are often treated together as a combinatorial therapy. In particular, synergistic drug combinations, which kill cancer cells at a lower concentration, guarantee a better prognosis and fewer side effects in cancer patients. Many studies have sought out synergistic combinations by small-scale function-based targeted growth assays or large-scale nontargeted growth assays, but their discoveries are always challenging due to technical problems such as a large number of possible test combinations.

METHODS

To address this issue, we carried out a medium-scale optical drug synergy screening in a non-small cell lung cancer cell line and further investigated individual drug interactions in combination drug responses by high-content image analysis. Optical high-content analysis of cellular responses has recently attracted much interest in the field of drug discovery, functional genomics, and toxicology. Here, we adopted a similar approach to study combinatorial drug responses.

RESULTS

By examining all possible combinations of 12 drug compounds in 6 different drug classes, such as mTOR inhibitors, HDAC inhibitors, HSP90 inhibitors, MT inhibitors, DNA inhibitors, and proteasome inhibitors, we successfully identified synergism between INK128, an mTOR inhibitor, and HDAC inhibitors, which has also been reported elsewhere. Our high-content analysis further showed that HDAC inhibitors, HSP90 inhibitors, and proteasome inhibitors played a dominant role in combinatorial drug responses when they were mixed with MT inhibitors, DNA inhibitors, or mTOR inhibitors, suggesting that recessive drugs could be less prioritized as components of multidrug cocktails.

CONCLUSIONS

In conclusion, our optical drug screening platform efficiently identified synergistic drug combinations in a non-small cell lung cancer cell line, and our high-content analysis further revealed how individual drugs in the drug mix interact with each other to generate combinatorial drug response.

Assessing biologic/toxicologic effects of extractables from plastic contact materials for advanced therapy manufacturing using cell painting assay and cytotoxicity screening

Plastic components are essential in the pharmaceutical industry, encompassing container closure systems, laboratory handling equipment, and single-use systems. As part of their material qualification process, studies on interactions between plastic contact materials and process solutions or drug products are conducted. The assessment of single-use systems includes their potential impact on patient safety, product quality, and process performance. This is particularly crucial in cell and gene therapy applications since interactions with the plastic contact material may result in an adverse effect on the isolated therapeutic human cells. We utilized the cell painting assay (CPA), a non-targeted method, for profiling the morphological characteristics of U2OS human osteosarcoma cells in contact with chemicals related to plastic contact materials. Specifically, we conducted a comprehensive analysis of 45 common plastic extractables, and two extracts from single-use systems. Results of the CPA are compared with a standard cytotoxicity assay, an osteogenesis differentiation assay, and in silico toxicity predictions. The findings of this feasibility study demonstrate that the device extracts and most of the tested compounds do not evoke any measurable biological changes on the cells (induction  ≤ 5%) among the 579 cell features measured at concentrations  ≤ 50 µM. CPA can serve as an important assay to reveal unique information not accessible through quantitative structure-activity relationship analysis and vice versa. The results highlight the need for a combination of in vitro and in silico methods in a comprehensive assessment of single-use equipment utilized in advanced therapy medicinal products manufacturing.

Discovery of Molecular Glue Degraders via Isogenic Morphological Profiling

Molecular glue degraders (MGDs) are small molecules that degrade proteins of interest via the ubiquitin-proteasome system. While MGDs were historically discovered serendipitously, approaches for MGD discovery now include cell-viability-based drug screens or data mining of public transcriptomics and drug response datasets. These approaches, however, have target spaces restricted to the essential proteins. Here we develop a high-throughput workflow for MGD discovery that also reaches the nonessential proteome. This workflow begins with the rapid synthesis of a compound library by sulfur(VI) fluoride exchange chemistry coupled to a morphological profiling assay in isogenic cell lines that vary in levels of the E3 ligase CRBN. By comparing the morphological changes induced by compound treatment across the isogenic cell lines, we were able to identify FL2-14 as a CRBN-dependent MGD targeting the nonessential protein GSPT2. We envision that this workflow would contribute to the discovery and characterization of MGDs that target a wider range of proteins.

Morphological subprofile analysis for bioactivity annotation of small molecules

Fast prediction of the mode of action (MoA) for bioactive compounds would immensely foster bioactivity annotation in compound collections and may early on reveal off-targets in chemical biology research and drug discovery. Morphological profiling, e.g., using the Cell Painting assay, offers a fast, unbiased assessment of compound activity on various targets in one experiment. However, due to incomplete bioactivity annotation and unknown activities of reference compounds, prediction of bioactivity is not straightforward. Here we introduce the concept of subprofile analysis to map the MoA for both, reference and unexplored compounds. We defined MoA clusters and extracted cluster subprofiles that contain only a subset of morphological features. Subprofile analysis allows for the assignment of compounds to, currently, twelve targets or MoA. This approach enables rapid bioactivity annotation of compounds and will be extended to further clusters in the future.

Application of Cell Painting for chemical hazard evaluation in support of screening-level chemical assessments

'Cell Painting' is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens. HTPP is a high-throughput and cost-effective bioactivity screening method that detects effects associated with many different molecular mechanisms in an untargeted manner, enabling rapid in vitro hazard assessment for thousands of chemicals. Here, 1201 chemicals from the ToxCast library were screened in concentration-response up to ∼100 μM in human U-2 OS cells using HTPP. A phenotype altering concentration (PAC) was estimated for chemicals active in the tested range. PACs tended to be higher than lower bound potency values estimated from a broad collection of targeted high-throughput assays, but lower than the threshold for cytotoxicity. In vitro to in vivo extrapolation (IVIVE) was used to estimate administered equivalent doses (AEDs) based on PACs for comparison to human exposure predictions. AEDs for 18/412 chemicals overlapped with predicted human exposures. Phenotypic profile information was also leveraged to identify putative mechanisms of action and group chemicals. Of 58 known nuclear receptor modulators, only glucocorticoids and retinoids produced characteristic profiles; and both receptor types are expressed in U-2 OS cells. Thirteen chemicals with profile similarity to glucocorticoids were tested in a secondary screen and one chemical, pyrene, was confirmed by an orthogonal gene expression assay as a novel putative GR modulating chemical. Most active chemicals demonstrated profiles not associated with a known mechanism-of-action. However, many structurally related chemicals produced similar profiles, with exceptions such as diniconazole, whose profile differed from other active conazoles. Overall, the present study demonstrates how HTPP can be applied in screening-level chemical assessments through a series of examples and brief case studies.

Identification of Biologically Diverse Tetrahydronaphthalen-2-ols through the Synthesis and Phenotypic Profiling of Chemically Diverse, Estradiol-Inspired Compounds

Combining natural product fragments to design new scaffolds with unprecedented bioactivity is a powerful strategy for the discovery of tool compounds and potential therapeutics. However, the choice of fragments to couple and the biological screens to employ remain open questions in the field. By choosing a primary fragment containing the A/B ring system of estradiol and fusing it to nine different secondary fragments, we were able to identify compounds that modulated four different phenotypes: inhibition of autophagy and osteoblast differentiation, as well as potassium channel and tubulin modulation. The latter two were uncovered by using unbiased morphological profiling with a cell-painting assay. The number of hits and variety in bioactivity discovered validates the use of recombining natural product fragments coupled to phenotypic screening for the rapid identification of biologically diverse compounds.

A statistical framework for high-content phenotypic profiling using cellular feature distributions

High-content screening (HCS) uses microscopy images to generate phenotypic profiles of cell morphological data in high-dimensional feature space. While HCS provides detailed cytological information at single-cell resolution, these complex datasets are usually aggregated into summary statistics that do not leverage patterns of biological variability within cell populations. Here we present a broad-spectrum HCS analysis system that measures image-based cell features from 10 cellular compartments across multiple assay panels. We introduce quality control measures and statistical strategies to streamline and harmonize the data analysis workflow, including positional and plate effect detection, biological replicates analysis and feature reduction. We also demonstrate that the Wasserstein distance metric is superior over other measures to detect differences between cell feature distributions. With this workflow, we define per-dose phenotypic fingerprints for 65 mechanistically diverse compounds, provide phenotypic path visualizations for each compound and classify compounds into different activity groups.

Identification of Dihydroorotate Dehydrogenase Inhibitors Using the Cell Painting Assay

Profiling approaches have been increasingly employed for the characterization of disease-relevant phenotypes or compound perturbation as they provide a broad, unbiased view on impaired cellular states. We report that morphological profiling using the cell painting assay (CPA) can detect modulators of de novo pyrimidine biosynthesis and of dihydroorotate dehydrogenase (DHODH) in particular. The CPA can differentiate between impairment of pyrimidine and folate metabolism, which both affect cellular nucleotide pools. The identified morphological signature is shared by inhibitors of DHODH and the functionally tightly coupled complex III of the mitochondrial respiratory chain as well as by UMP synthase, which is downstream of DHODH. The CPA appears to be particularly suited for the detection of DHODH inhibitors at the site of their action in cells. As DHODH is a validated therapeutic target, the CPA will enable unbiased identification of DHODH inhibitors and inhibitors of de novo pyrimidine biosynthesis for biological research and drug discovery.

Virtual screening for small-molecule pathway regulators by image-profile matching

Identifying the chemical regulators of biological pathways is a time-consuming bottleneck in developing therapeutics and research compounds. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to each disease. Here, our uncustomized, virtual, profile-based screening approach instead identifies compounds that match to pathways based on the phenotypic information in public cell image data, created using the Cell Painting assay. Our straightforward correlation-based computational strategy retrospectively uncovered the expected, known small-molecule regulators for 32% of positive-control gene queries. In prospective, discovery mode, we efficiently identified new compounds related to three query genes and validated them in subsequent gene-relevant assays, including compounds that phenocopy or pheno-oppose YAP1 overexpression and kill a Yap1-dependent sarcoma cell line. This image-profile-based approach could replace many customized labor- and resource-intensive screens and accelerate the discovery of biologically and therapeutically useful compounds.

Morphological profiling by means of the Cell Painting assay enables identification of tubulin-targeting compounds

In phenotypic compound discovery, conclusive identification of cellular targets and mode of action are often impaired by off-target binding. In particular, microtubules are frequently targeted in cellular assays. However, in vitro tubulin binding assays do not correctly reflect the cellular context, and conclusive high-throughput phenotypic assays monitoring tubulin binding are scarce, such that tubulin binding is rarely identified. We report that morphological profiling using the Cell Painting assay (CPA) can efficiently detect tubulin modulators in compound collections with a high throughput, including annotated reference compounds and unannotated compound classes with unrelated chemotypes and scaffolds. Small-molecule tubulin binders share similar CPA fingerprints, which enables prediction and experimental validation of microtubule-binding activity. Our findings suggest that CPA or a related morphological profiling approach will be an invaluable addition to small-molecule discovery programs in chemical biology and medicinal chemistry, enabling early identification of one of the most frequently observed off-target activities.

Marine dissolved organic matter: a vast and unexplored molecular space

Abstract

Marine dissolved organic matter (DOM) comprises a vast and unexplored molecular space. Most of it resided in the oceans for thousands of years. It is among the most diverse molecular mixtures known, consisting of millions of individual compounds. More than 1 Eg of this material exists on the planet. As such, it comprises a formidable source of natural products promising significant potential for new biotechnological purposes. Great emphasis has been placed on understanding the role of DOM in biogeochemical cycles and climate attenuation, its lifespan, interaction with microorganisms, as well as its molecular composition. Yet, probing DOM bioactivities is in its infancy, largely because it is technically challenging due to the chemical complexity of the material. It is of considerable interest to develop technologies capable to better discern DOM bioactivities. Modern screening technologies are opening new avenues allowing accelerated identification of bioactivities for small molecules from natural products. These methods diminish a priori the need for laborious chemical fractionation. We examine here the application of untargeted metabolomics and multiplexed high-throughput molecular-phenotypic screening techniques that are providing first insights on previously undetectable DOM bioactivities.

Key points

• Marine DOM is a vast, unexplored biotechnological resource.

• Untargeted bioscreening approaches are emerging for natural product screening.

• Perspectives for developing bioscreening platforms for marine DOM are discussed.

Combined morphological and proteome profiling reveals target-independent impairment of cholesterol homeostasis

Unbiased profiling approaches are powerful tools for small-molecule target or mode-of-action deconvolution as they generate a holistic view of the bioactivity space. This is particularly important for non-protein targets that are difficult to identify with commonly applied target identification methods. Thereby, unbiased profiling can enable identification of novel bioactivity even for annotated compounds. We report the identification of a large bioactivity cluster comprised of numerous well-characterized drugs with different primary targets using a combination of the morphological Cell Painting Assay and proteome profiling. Cluster members alter cholesterol homeostasis and localization due to their physicochemical properties that lead to protonation and accumulation in lysosomes, an increase in lysosomal pH, and a disturbed cholesterol homeostasis. The identified cluster enables identification of modulators of cholesterol homeostasis and links regulation of genes or proteins involved in cholesterol synthesis or trafficking to physicochemical properties rather than to nominal targets.

Discovery of a σ1 receptor antagonist by combination of unbiased cell painting and thermal proteome profiling

Phenotypic screening for bioactive small molecules is typically combined with affinity-based chemical proteomics to uncover the respective molecular targets. However, such assays and the explored bioactivity are biased toward the monitored phenotype, and target identification often requires chemical derivatization of the hit compound. In contrast, unbiased cellular profiling approaches record hundreds of parameters upon compound perturbation to map bioactivity in a broader biological context and may link a profile to the molecular target or mode of action. Herein we report the discovery of the diaminopyrimidine DP68 as a Sigma 1 (σ1) receptor antagonist by combining morphological profiling using the Cell Painting assay and thermal proteome profiling. Our results highlight that integration of complementary profiling approaches may enable both detection of bioactivity and target identification for small molecules.

Developing Cyclic Peptomers as Broad-Spectrum Type III Secretion System Inhibitors in Gram-Negative Bacteria

Antibiotic-resistant bacteria are an emerging global health threat. New antimicrobials are urgently needed. The injectisome type III secretion system (T3SS), required by dozens of Gram-negative bacteria for virulence but largely absent from nonpathogenic bacteria, is an attractive antimicrobial target. We previously identified synthetic cyclic peptomers, inspired by the natural product phepropeptin D, that inhibit protein secretion through the Yersinia Ysc and Pseudomonas aeruginosa Psc T3SSs but do not inhibit bacterial growth. Here, we describe the identification of an isomer, 4EpDN, that is 2-fold more potent (50% inhibitory concentration [IC₅₀] of 4 μM) than its parental compound. Furthermore, 4EpDN inhibited the Yersinia Ysa and the Salmonella SPI-1 T3SSs, suggesting that this cyclic peptomer has broad efficacy against evolutionarily distant injectisome T3SSs. Indeed, 4EpDN strongly inhibited intracellular growth of Chlamydia trachomatis in HeLa cells, which requires the T3SS. 4EpDN did not inhibit the unrelated twin arginine translocation (Tat) system, nor did it impact T3SS gene transcription. Moreover, although the injectisome and flagellar T3SSs are evolutionarily and structurally related, the 4EpDN cyclic peptomer did not inhibit secretion of substrates through the Salmonella flagellar T3SS, indicating that cyclic peptomers broadly but specifically target the injectisome T3SS. 4EpDN reduced the number of T3SS needles detected on the surface of Yersinia pseudotuberculosis as detected by microscopy. Collectively, these data suggest that cyclic peptomers specifically inhibit the injectisome T3SS from a variety of Gram-negative bacteria, possibly by preventing complete T3SS assembly.

Morphological profiling of small molecules

Profiling approaches such as gene expression or proteome profiling generate small-molecule bioactivity profiles that describe a perturbed cellular state in a rather unbiased manner and have become indispensable tools in the evaluation of bioactive small molecules. Automated imaging and image analysis can record morphological alterations that are induced by small molecules through the detection of hundreds of morphological features in high-throughput experiments. Thus, morphological profiling has gained growing attention in academia and the pharmaceutical industry as it enables detection of bioactivity in compound collections in a broader biological context in the early stages of compound development and the drug-discovery process. Profiling may be used successfully to predict mode of action or targets of unexplored compounds and to uncover unanticipated activity for already characterized small molecules. Here, we review the reported approaches to morphological profiling and the kind of bioactivity that can be detected so far and, thus, predicted.

Image-based profiling for drug discovery: due for a machine-learning upgrade?

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.

Macrophage Cytological Profiling and Anti-Inflammatory Drug Discovery

Millions of people are affected by diseases and conditions related to the immune system. Unfortunately, our current supply of approved anti-inflammatory medicine is very limited and only treats a small fraction of inflammatory diseases. Nearly half of the drugs on the market today are natural products and natural product derivatives. The long-term objective of my research is to continue efforts toward the discovery of diverse chemical compounds and their mechanism of action (MOA) to inspire the next generation of novel therapeutics. This project approaches this objective by creating a robust platform for the in-depth phenotypic profiling of complex natural product samples with respect to their effect on pathways related to the innate immune response. This approach has the potential to elucidate the MOAs of novel natural products relevant to inflammation and accelerate the pace of drug discovery in this therapeutic area.

Prediction of modes of action of components of traditional medicinal preparations

Traditional medicine preparations are used to treat many ailments in multiple regions across the world. Despite their widespread use, the mode of action of these preparations and their constituents are not fully understood. Traditional methods of elucidating the modes of action of these natural products (NPs) can be expensive and time consuming e. g. biochemical methods, bioactivity guided fractionation, etc. In this review, we discuss some methods for the prediction of the modes of action of traditional medicine preparations, both in mixtures and as isolated NPs. These methods are useful to predict targets of NPs before they are experimentally validated. Case studies of the applications of these methods are also provided herein.

Image-Based Morphological Profiling Identifies a Lysosomotropic, Iron-Sequestering Autophagy Inhibitor

Chemical proteomics is widely applied in small-molecule target identification. However, in general it does not identify non-protein small-molecule targets, and thus, alternative methods for target identification are in high demand. We report the discovery of the autophagy inhibitor autoquin and the identification of its molecular mode of action using image-based morphological profiling in the cell painting assay. A compound-induced fingerprint representing changes in 579 cellular parameters revealed that autoquin accumulates in lysosomes and inhibits their fusion with autophagosomes. In addition, autoquin sequesters Fe²⁺ in lysosomes, resulting in an increase of lysosomal reactive oxygen species and ultimately cell death. Such a mechanism of action would have been challenging to unravel by current methods. This work demonstrates the potential of the cell painting assay to deconvolute modes of action of small molecules, warranting wider application in chemical biology.

Advances in exploring the therapeutic potential of marine natural products

Marine natural products represent novel and diverse chemotypes that serve as templates for the discovery and development of therapeutic agents with distinct mechanisms of action. These genetically encoded compounds produced by an evolutionary optimized biosynthetic machinery are usually quite complex and can be difficult to recreate in the laboratory. The isolation from the source organism results in limited amount of material; however, the development of advanced NMR technologies and dereplication strategies has enabled the structure elucidation on small scale. In order to rigorously explore the therapeutic potential of marine natural products and advance them further, the biological characterization has to keep pace with the chemical characterization. The limited marine natural product supply has been a serious challenge for thorough investigation of the biological targets. Several marine drugs have reached the markets or are in clinical trials, where those challenges have been overcome, including through the development of scalable syntheses. However, the identification of mechanisms of action of marine natural products early in the discovery process is potentially game changing, since effectively linking marine natural products to potential therapeutic applications in turn triggers motivation to tackle challenging syntheses and solve the supply problem. An increasing number of sensitive technologies and methods have been developed in recent years, some of which have been successfully applied to marine natural products, increasing the value of these compounds with respect to their biomedical utility. In this review, we discuss advances in overcoming the bottlenecks in marine natural product research, emphasizing on the development and advances of diverse target identification technologies applicable for marine natural product research.

Machine learning and image-based profiling in drug discovery

The increase in imaging throughput, new analytical frameworks and high-performance computational resources open new avenues for data-rich phenotypic profiling of small molecules in drug discovery. Image-based profiling assays assessing single-cell phenotypes have been used to explore mechanisms of action, target efficacy and toxicity of small molecules. Technological advances to generate large data sets together with new machine learning approaches for the analysis of high-dimensional profiling data create opportunities to improve many steps in drug discovery. In this review, we will discuss how recent studies applied machine learning approaches in functional profiling workflows with a focus on chemical genetics. While their utility in image-based screening and profiling is predictably evident, examples of novel insights beyond the status quo based on the applications of machine learning approaches are just beginning to emerge. To enable discoveries, future studies also need to develop methodologies that lower the entry barriers to high-throughput profiling experiments by streamlining image-based profiling assays and providing applications for advanced learning technologies such as easy to deploy deep neural networks.

Nontargeted Metabolomics Reveals the Multilevel Response to Antibiotic Perturbations

Microbes have shown a remarkable ability in evading the killing actions of antimicrobial agents, such that treatment of bacterial infections represents once more an urgent global challenge. Understanding the initial bacterial response to antimicrobials may reveal intrinsic tolerance mechanisms to antibiotics and suggest alternative and less conventional therapeutic strategies. Here, we used mass spectrometry-based metabolomics to monitor the immediate metabolic response of Escherichia coli to a variety of antibiotic perturbations. We show that rapid metabolic changes can reflect drug mechanisms of action and reveal the active role of metabolism in mediating the first stress response to antimicrobials. We uncovered a role for ammonium imbalance in aggravating chloramphenicol toxicity and the essential function of deoxythymidine 5'-diphosphate (dTDP)-rhamnose synthesis for the immediate transcriptional upregulation of GyrA in response to quinolone antibiotics. Our results suggest bacterial metabolism as an attractive target to interfere with the early bacterial response to antibiotic treatments and reduce the probability for survival and eventual evolution of antibiotic resistance.

High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds

Rapidly spreading antibiotic resistance and the low discovery rate of new antimicrobial compounds demand more effective strategies for early drug discovery. One bottleneck in the drug discovery pipeline is the identification of the modes of action (MoAs) of new compounds. We have developed a rapid systematic metabolome profiling strategy to classify the MoAs of bioactive compounds. The method predicted MoA-specific metabolic responses in the nonpathogenic bacterium Mycobacterium smegmatis after treatment with 62 reference compounds with known MoAs and different metabolic and nonmetabolic targets. We then analyzed a library of 212 new antimycobacterial compounds with unknown MoAs from a drug discovery effort by the pharmaceutical company GlaxoSmithKline (GSK). More than 70% of these new compounds induced metabolic responses in M. smegmatis indicative of known MoAs, seven of which were experimentally validated. Only 8% (16) of the compounds appeared to target unconventional cellular processes, illustrating the difficulty in discovering new antibiotics with different MoAs among compounds used as monotherapies. For six of the GSK compounds with potentially new MoAs, the metabolome profiles suggested their ability to interfere with trehalose and lipid metabolism. This was supported by whole-genome sequencing of spontaneous drug-resistant mutants of the pathogen Mycobacterium tuberculosis and in vitro compound-proteome interaction analysis for one of these compounds. Our compendium of drug-metabolome profiles can be used to rapidly query the MoAs of uncharacterized antimicrobial compounds and should be a useful resource for the drug discovery community.

Mining prokaryotes for antimicrobial compounds: from diversity to function

The bacterial kingdom provides a major source of antimicrobials that can either be directly applied or used as scaffolds to further improve their functionality in the host. The rapidly increasing amount of bacterial genomic, metabolomic and transcriptomic data offers unique opportunities to apply a variety of approaches to mine for existing and novel antimicrobials. Here, we discuss several powerful mining approaches to identify novel molecules with antimicrobial activity across structurally diverse natural products, including ribosomally synthesized and posttranslationally modified peptides, nonribosomal peptides and polyketides. We not only discuss the direct mining of genomes based on identification of biosynthetic gene clusters, but also describe more advanced and integrative approaches in ecology-based mining, functionality-based mining and mode-of-action-based mining. These efforts are likely to accelerate the discovery and development of novel antimicrobial drugs.

Marine Mammal Microbiota Yields Novel Antibiotic with Potent Activity Against Clostridium difficile

The recent explosion of research on the microbiota has highlighted the important interplay between commensal microorganisms and the health of their cognate hosts. Metabolites isolated from commensal bacteria have been demonstrated to possess a range of antimicrobial activities, and it is widely believed that some of these metabolites modulate host behavior, affecting predisposition to disease and pathogen invasion. Our access to the local marine mammal stranding network and previous successes in mining the fish microbiota poised us to test the hypothesis that the marine mammal microbiota is a novel source of commensal bacteria-produced bioactive metabolites. Examination of intestinal contents from five marine mammals led to the identification of a Micromonospora strain with potent and selective activity against a panel of Gram-positive pathogens and no discernible human cytotoxicity. Compound isolation afforded a new complex glycosylated polyketide, phocoenamicin, with potent activity against the intestinal pathogen Clostridium difficile, an organism challenging to treat in hospital settings. Use of our activity-profiling platform, BioMAP, clustered this metabolite with other known ionophore antibiotics. Fluorescence imaging and flow cytometry confirmed that phocoenamicin is capable of shifting membrane potential without damaging membrane integrity. Thus, exploration of gut microbiota in hosts from diverse environments can serve as a powerful strategy for the discovery of novel antibiotics against human pathogens.

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.

Naphthablins B and C, Meroterpenoids Identified from the Marine Sediment-Derived Streptomyces sp. CP26-58 Using HeLa Cell-Based Cytological Profiling

HeLa cell-based cytological profiling (CP) was applied to an extract library of marine sediment-derived actinomycetes to discover new cytotoxic secondary metabolites. Among the hit strains, Streptomyces sp. CP26-58 was selected for further investigation to identify its cytotoxic metabolites. CP revealed that the known ionophore tetronasin (1) was responsible for the cytotoxic effect found in the extract. Furthermore, three naphthoquinone meroterpenoids, naphthablin A (2) and two new derivatives designated as naphthablins B (3) and C (4), were isolated from other cytotoxic fractions. The structures of the new compounds were elucidated based on analysis of their HRESIMS and comprehensive NMR data. The absolute configurations of the new compounds were deduced by simulating ECD spectra and calculating potential energies for the model compounds using density function theory (DFT) calculations. Compound 1 showed a significant cytotoxic effect against HeLa cells with an IC₅₀ value of 0.23 μM, and CP successfully clustered 1 with calcium ionophores.

Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites

Covering: 2000 to 2016The labor-intensive process of microbial natural product discovery is contingent upon identifying discrete secondary metabolites of interest within complex biological extracts, which contain inventories of all extractable small molecules produced by an organism or consortium. Historically, compound isolation prioritization has been driven by observed biological activity and/or relative metabolite abundance and followed by dereplication via accurate mass analysis. Decades of discovery using variants of these methods has generated the natural pharmacopeia but also contributes to recent high rediscovery rates. However, genomic sequencing reveals substantial untapped potential in previously mined organisms, and can provide useful prescience of potentially new secondary metabolites that ultimately enables isolation. Recently, advances in comparative metabolomics analyses have been coupled to secondary metabolic predictions to accelerate bioactivity and abundance-independent discovery work flows. In this review we will discuss the various analytical and computational techniques that enable MS-based metabolomic applications to natural product discovery and discuss the future prospects for comparative metabolomics in natural product discovery.

Meeting the Challenge: Using Cytological Profiling to Discover Chemical Probes from Traditional Chinese Medicines against Parkinson's Disease

Parkinson's disease (PD) is an incurable neurodegenerative disorder with a high prevalence rate worldwide. The fact that there are currently no proven disease-modifying treatments for PD underscores the urgency for a more comprehensive understanding of the underlying disease mechanism. Chemical probes have been proven to be powerful tools for studying biological processes. Traditional Chinese medicine (TCM) contains a huge reservoir of bioactive small molecules as potential chemical probes that may hold the key to unlocking the mystery of PD biology. The TCM-sourced chemical approach to PD biology can be advanced through the use of an emerging cytological profiling (CP) technique that allows unbiased characterization of small molecules and their cellular responses. This comprehensive technique, applied to chemical probe identification from TCM and used for studying the molecular mechanisms underlying PD, may inform future therapeutic target selection and provide a new perspective to PD drug discovery.

Applications in image-based profiling of perturbations

A dramatic shift has occurred in how biologists use microscopy images. Whether experiments are small-scale or high-throughput, automatically quantifying biological properties in images is now widespread. We see yet another revolution under way: a transition towards using automated image analysis to not only identify phenotypes a biologist specifically seeks to measure ('screening') but also as an unbiased and sensitive tool to capture a wide variety of subtle features of cell (or organism) state ('profiling'). Mapping similarities among samples using image-based (morphological) profiling has tremendous potential to transform drug discovery, functional genomics, and basic biological research. Applications include target identification, lead hopping, library enrichment, functionally annotating genes/alleles, and identifying small molecule modulators of gene activity and disease-specific phenotypes.

The Natural Product N-Palmitoyl-l-leucine Selectively Inhibits Late Assembly of Human Spliceosomes

The spliceosome is a dynamic complex of five structural RNAs and dozens of proteins, which assemble together to remove introns from nascent eukaryotic gene transcripts in a process called splicing. Small molecules that target different components of the spliceosome represent valuable research tools to investigate this complicated macromolecular machine. However, the current collection of spliceosome inhibitors is very limited. To expand the toolkit we used a high-throughput in vitro splicing assay to screen a collection of pre-fractions of natural compounds derived from marine bacteria for splicing inhibition. Further fractionation of initial hits generated individual peaks of splicing inhibitors that interfere with different stages of spliceosome assembly. With additional characterization of individual peaks, we identified N-palmitoyl-l-leucine as a new splicing inhibitor that blocks a late stage of spliceosome assembly. Structure-activity relationship analysis of the compound revealed that length of carbon chain is important for activity in splicing, as well as for effects on the cytological profile of cells in culture. Together these results demonstrate that our combination of in vitro splicing analysis with complex natural product libraries is a powerful strategy for identifying new small molecule tools with which to probe different aspects of spliceosome assembly and function.

Phenotype-Guided Natural Products Discovery Using Cytological Profiling

Phenotype-guided natural products discovery is emerging as a useful new discovery tool that addresses challenges in early, unbiased natural product biological annotation. These high-content approaches yield screening results that report directly on the impact of test compounds on cellular processes in target organisms and can be used to predict the modes of action of bioactive constituents from primary screening data. In this study we explored the use of our recently implemented cytological profiling platform for the isolation of compounds with a specific, predefined mode of action, namely, induction of mitotic arrest. Screening of a microbially derived extract library revealed six extracts whose cytological profiles clustered closely with those of known antimitotic agents from the pure compound training set. Subsequent examination of one of these extracts revealed the presence of two separate bioactive constituents, each of which possessed a unique cytological profile. The first, diketopiperazine XR334 (3), recapitulated the observed antimitotic phenotype of the original extract, demonstrating that cytological profiling can be used for the targeted isolation of compounds with specific modes of action. The second, nocapyrone L (6), possessed a cytological profile that clustered with known calcium channel modulators, in line with previous published activities for this compound class, indicating that cytological profiling is a flexible and powerful platform for the de novo characterization of compound modes of action.

Integration of high-content screening and untargeted metabolomics for comprehensive functional annotation of natural product libraries

Traditional natural products discovery using a combination of live/dead screening followed by iterative bioassay-guided fractionation affords no information about compound structure or mode of action until late in the discovery process. This leads to high rates of rediscovery and low probabilities of finding compounds with unique biological and/or chemical properties. By integrating image-based phenotypic screening in HeLa cells with high-resolution untargeted metabolomics analysis, we have developed a new platform, termed Compound Activity Mapping, that is capable of directly predicting the identities and modes of action of bioactive constituents for any complex natural product extract library. This new tool can be used to rapidly identify novel bioactive constituents and provide predictions of compound modes of action directly from primary screening data. This approach inverts the natural products discovery process from the existing "grind and find" model to a targeted, hypothesis-driven discovery model where the chemical features and biological function of bioactive metabolites are known early in the screening workflow, and lead compounds can be rationally selected based on biological and/or chemical novelty. We demonstrate the utility of the Compound Activity Mapping platform by combining 10,977 mass spectral features and 58,032 biological measurements from a library of 234 natural products extracts and integrating these two datasets to identify 13 clusters of fractions containing 11 known compound families and four new compounds. Using Compound Activity Mapping we discovered the quinocinnolinomycins, a new family of natural products with a unique carbon skeleton that cause endoplasmic reticulum stress.

Multiparametric Phenotypic Screening System for Profiling Bioactive Compounds Using Human Fetal Hippocampal Neural Stem/Progenitor Cells

Stem cell research has been progressing rapidly, contributing to regenerative biology and regenerative medicine. In this field, small-molecule compounds affecting stem cell proliferation/differentiation have been explored to understand stem cell biology and support regenerative medicine. In this study, we established a multiparametric screening system to detect bioactive compounds affecting the cell fate of human neural stem/progenitor cells (NSCs/NPCs), using human fetal hippocampal NSCs/NPCs, HIP-009 cells. We examined effects of 410 compounds, which were collected based on mechanisms of action (MOAs) and chemotypes, on HIP-009’s cell fate (self-renewal, neuronal and astrocytic differentiation) and morphology by automated multiparametric assays and profiled induced cellular phenotypes. We found that this screening classified compounds with the same MOAs into subgroups according to additional pharmacological effects (e.g., mammalian target of rapamycin complex 1 [mTORC1] inhibitors and mTORC1/mTORC2 dual inhibitors among mTOR inhibitors). Moreover, it identified compounds that have off-target effects under matrix analyses of MOAs and structure similarities (e.g., neurotropic effects of amitriptyline among tri- and tetracyclic compounds). Therefore, this automated, medium-throughput and multiparametric screening system is useful for finding compounds that affect the cell fate of human NSCs/NPCs for supporting regenerative medicine and to fingerprint compounds based on human stem cells’ multipotency, leading to understanding of stem cell biology.

Biosynthetic products from a nearshore-derived gram-negative bacterium enable reassessment of the kailuin depsipeptides

Sampling of California nearshore sediments resulted in the isolation of a Gram-negative bacterium, Photobacterium halotolerans, capable of producing unusual biosynthetic products. Liquid culture in artificial seawater-based media provided cyclic depsipeptides including four known compounds, kailuins B-E (2-5), and two new analogues, kailuins G and H (7 and 8). The structures of the new and known compounds were confirmed through extensive spectroscopic and Marfey's analyses. During the course of these studies, a correction was made to the previously reported double-bond geometry of kailuin D (4). Additionally, through the application of a combination of derivatization with Mosher's reagent and extensive (13)C NMR shift analysis, the previously unassigned chiral center at position C-3 of the β-acyloxy group of all compounds was determined. To evaluate bioactivity and structure-activity relationships, the kailuin core (13) and kailuin lactam (14) were prepared by chiral synthesis using an Fmoc solid-phase peptide strategy followed by solution-phase cyclization. All isolated compounds and synthetic cores were assayed for solid tumor cell cytotoxicity and showed only minimal activity, contrary to other published reports. Additional phenotypic screenings were done on 4 and 5, with little evidence of activity.

Borrelidin B: isolation, biological activity, and implications for nitrile biosynthesis

Borrelidin (1) is a nitrile-containing bacterially derived polyketide that is a potent inhibitor of bacterial and eukaryotic threonyl-tRNA synthetases. We now report the discovery of borrelidin B (2), a tetrahydro-borrelidin derivative containing an aminomethyl group in place of the nitrile functionality in borrelidin. The discovery of this new metabolite has implications for both the biosynthesis of the nitrile group and the bioactivity of the borrelidin compound class. Screening in the SToPS assay for tRNA synthetase inhibition revealed that the nitrile moiety is essential for activity, while profiling using our in-house image-based cytological profiling assay demonstrated that 2 retains biological activity by causing a mitotic stall, even in the absence of the nitrile motif.