Biology subjects

O'Meara, M. J.

Publications and source records attributed to O'Meara, M. J..

At least 19 recordsLinked to original sources

bioRxiv2026

A High-Content Pseudotime Framework for Morphological Profiling of Dengue Virus Inhibitors from a Drug-Repurposing Library

Dengue virus (DENV) causes dengue fever, a globally prevalent, epidemic-prone disease with no FDA-approved antivirals. Additionally, the available vaccine for DENV can increase the risk of severe dengue fever for those who have never had a DENV infection due to antibody-dependent enhancement. Thus, there is an urgent need to identify DENV antivirals. Drug repurposing, high-content screening, and machine learning were leveraged to efficiently identify antivirals likely to inhibit DENV replication. Using high-content screening in a DENV infection model, we characterized the morphological patterns of NS4B and envelope (E) protein expression over time and developed a viral pseudotime model that predicts infection progression to aid in phenotypic interpretation of drug effects. We then developed a single cell infection classifier for antiviral efficacy and performed high-throughput drug screening of 960 compounds. We identified four concentration-dependent inhibitors of DENV with nanomolar-to-submicromolar potencies: esomeprazole, GW4064, pralatrexate, and LY411575. With our pseudotime machine learning model trained on data from the time-series assay, we scored the concentration-dependent inhibitors to map their mechanism of action onto time points in the viral life cycle. Our most notable results show that LY411575, a gamma secretase inhibitor, exhibited an IC of 72 nM and reduced percent infection to near the level of the mock infection control. Pseudotime inferences generated mechanistic predictions that were supported by time-of-addition experiments, indicating that LY411575 arrests DENV infection before the development of late-stage infection associated morphologies, while retaining activity when added after inoculation.

cell biology↗

bioRxiv2026

Assessing the Generalizability of Machine Learning and Physics Methods for DNA-Encoded Libraries

Predicting protein-ligand binding is a central challenge in computational drug discovery, and while machine learning (ML) and co-folding methods have advanced rapidly, their ability to generalize beyond training or parameterization regimes remains insufficiently understood. DNA-encoded libraries (DELs) enable ultra-large screening of billions of molecules simultaneously, providing a useful testbed for evaluating these approaches at scale. A recent NeurIPS competition revealed that even top performing ML models trained on DEL data failed at generalizing to out-of-distribution (OOD) chemical space. We investigated whether integrating structural modeling could bridge this generalization gap. We systematically assessed state-of-the-art ML, docking, and co-folding methods including Schrodinger Glide, Rosetta GALigandDock, and Boltz-2 with three biologically diverse protein targets screened against libraries containing multiple DEL synthesis formats. While ML excels in-distribution, OOD hit discrimination is dependent on both the target and ligand context, with no single method consistently dominating. These findings demonstrate that benchmark performance alone is insufficient to predict OOD performance, highlighting the need for system-dependent evaluation of binding prediction methods. We provide an open-source package for assessing protein-ligand prediction methods and analyzing high-throughput screening data: DEL-iver. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/719394v2_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@cd8a17org.highwire.dtl.DTLVardef@252d0aorg.highwire.dtl.DTLVardef@b020f0org.highwire.dtl.DTLVardef@1429e27_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2025

Phenotypic Screening Coupled with AI-Driven Target Deconvolution Identifies α-Terthienyl as a Dual DPP-IV/HSD17β13 Modulator with Efficacy in a Mouse Model of MASLD

BackgroundMetabolic dysfunction-associated steatotic liver disease (MASLD) is a highly prevalent condition characterized by fat build-up in the liver and ranges from benign steatosis to progression to metabolic dysfunction-associated steatohepatitis (MASH), fibrosis, cirrhosis and end-stage liver disease including hepatocellular carcinoma, representing a significant cause of chronic liver disease globally.1,2 Current treatment options are limited, primarily relying on lifestyle modifications, highlighting an urgent need for novel therapeutic strategies. MethodsA Cell Painting-style high-content screening phenotypic assay was employed using the PH5CH8 human hepatocyte cell line to identify small molecules capable of modulating induced hepatic steatosis. The Plex Research artificial intelligence (AI) platform was utilized for target deconvolution of the lead hit compound, -terthienyl. In vivo efficacy was assessed in a diet-induced obesity (DIO) C57BL/6J mouse model of MASLD. Biochemical assays and molecular docking simulations were performed to validate predicted target interactions. ResultsPhenotypic screening identified 15 chemical probes/drugs that elicit dose-responsive inhibition of steatosis, and 16 that exacerbate steatosis, which could contribute to worsening of the disease clinically. -terthienyl, a plant-derived natural product, was identified as a potent and non-toxic inhibitor of steatosis in PH5CH8 cells with an EC50 of 106 nM. In vivo, -terthienyl administration to diet-induced obesity (60% fat diet) mice significantly reduced hepatic steatosis histologically, improved glucose tolerance, and favorably modulated serum biomarkers including ALT and AST. AI-driven analysis predicted dipeptidyl peptidase 4 (DPP-IV) and 17-beta hydroxysteroid dehydrogenase 13 (HSD17{beta}13) as potential molecular targets of -terthienyl. Biochemical inhibition of DPP-IV was observed and an oxidized -terthienyl analog inhibited HSD17{beta}13. Molecular docking supported these predictions, indicating binding to DPP-IV and HSD17{beta}13. ConclusionThis study demonstrates the successful application of phenotypic screening integrated with AI-driven target deconvolution to identify compounds and drugs that ameliorate or exacerbate hepatic steatosis. -terthienyl was identified as a novel modulator of hepatic steatosis with in vivo efficacy in a MASLD model. Our findings suggest a dual-target mechanism involving DPP-IV and HSD17{beta}13, potentially engaged by the parent compound and its metabolite, respectively, offering a promising polypharmacological approach for MASLD treatment.

pharmacology and toxicology↗

bioRxiv2025

Deep homology and design of proteasome chaperone proteins in Candida auris

A central tenet of biology is that protein structure mediates the sequence-function relationship. Recently, there has been excitement about the promise of advances in protein structure modeling to generate hypotheses about sequence-structure-function relationships based on successes with controlled benchmarks. Here, we leverage structural similarity to identify rapidly evolving proteasome assembly chaperones and characterize their function in the emerging fungal pathogen Candida auris. Despite the large sequence divergence, we demonstrate conservation of structure and function across hundreds of millions of years of evolution, representing a case of rapid neutral evolution. Using the functional constraints on structure from these naturally evolved sequences, we prospectively designed de novo chaperones and demonstrate that these artificial proteins can rescue complex biological processes in the context of the whole cell.

evolutionary biology↗

bioRxiv2025

AMPK signaling is dysregulated in human tendinopathy and loss of AMPKα1 leads to cell, matrix and mechanical dysfunction in mouse Achilles tendon

Tendinopathy is a debilitating tendon disorder affecting millions of people, characterized by pain, swelling, and diminished biomechanical properties. While the precise mechanisms underlying tendon homeostasis remain unclear, metabolic regulation plays a critical role. In this study, we combine transcriptomic analysis of human tendinopathic samples with a conditional mouse model in which Prkaa1 (encoding AMPK1) is selectively deleted in tendon progenitors to elucidate the role of AMPK signaling in tendon homeostasis. RNA sequencing of diseased human tendons revealed downregulation of key metabolic genes, including several involved in the mitochondrial electron transport chain and AMPK signaling pathways, alongside an increase in markers associated with senescence and a secretory inflammatory profile. In parallel, mice with loss of Prkaa1 function exhibited normal postnatal development; however, by one month of age, tendons demonstrated widespread transcriptional alterations related to cell cycle regulation and ECM organization. By three months, AMPK1-deficient tendons showed significant reductions in mechanical strength and increased expression of senescence markers p21 and p16, progressing to prominent ectopic calcification with age. In vitro studies further confirmed that tendon fibroblasts lacking AMPK1 have altered ECM substrate adhesion profiles. Importantly, voluntary exercise partially rescued these deficits by enhancing ECM organization and reducing senescence marker expression. Collectively, our findings demonstrate that AMPK1 is critical for maintaining energy balance, regulating ECM remodeling, and preventing premature cellular senescence in tendon. These insights highlight AMPK signaling as a promising therapeutic target and underscore the beneficial role of exercise in mitigating tendinopathic changes.

pathology↗

bioRxiv2024

LUMIC: Latent diffUsion for Multiplexed Images of Cells

The rapid advancement of high-content, single-cell technologies like robotic confocal microscopy with multiplexed dyes (morphological profiling) can be leveraged to reveal fundamental biology, ranging from microbial and abiotic stress to organ development. Specifically, heterogeneous cell systems can be perturbed genetically or with chemical treatments to allow for inference of causal mechanisms. An exciting strategy to navigate the high-dimensional space of possible perturbation and cell type combinations is to use generative models as priors to anticipate high-content outcomes in order to design informative experiments. Towards this goal, we present the Latent diffUsion for Multiplexed Images of Cells (LUMIC) framework that can generate high quality and high fidelity images of cells. LUMIC combines diffusion models with DINO (self-Distillation with NO labels), a vision-transformer based, self-supervised method that can be trained on images to learn feature embeddings, and HGraph2Graph, a hierarchical graph encoder-decoder to represent chemicals. To demonstrate the ability of LUMIC to generalize across cell lines and treatments, we apply it to a dataset of[~] 27,000 images of two cell lines treated with 306 chemicals and stained with three dyes from the JUMP Pilot dataset and a newly-generated dataset of[~] 3,000 images of five cell lines treated with 61 chemicals and stained with three dyes. To quantify prediction quality, we evaluate the DINO embeddings, Kernel Inception Distance (KID) score, and recovery of morphological feature distributions. LUMIC significantly outperforms previous methods and generates realistic out-of-sample images of cells across unseen compounds and cell types.

molecular biology↗

bioRxiv2023

Predicting the Structural Impact of Human Alternative Splicing

Protein structure prediction with neural networks is a powerful new method for linking protein sequence, structure, and function, but structures have generally been predicted for only a single isoform of each gene, neglecting splice variants. To investigate the structural implications of alternative splicing, we used AlphaFold2 to predict the structures of more than 11,000 human isoforms. We employed multiple metrics to identify splicing-induced structural alterations, including template matching score, secondary structure composition, surface charge distribution, radius of gyration, accessibility of post-translational modification sites, and structure-based function prediction. We identified examples of how alternative splicing induced clear changes in each of these properties. Structural similarity between isoforms largely correlated with degree of sequence identity, but we identified a subset of isoforms with low structural similarity despite high sequence similarity. Exon skipping and alternative last exons tended to increase the surface charge and radius of gyration. Splicing also buried or exposed numerous post-translational modification sites, most notably among the isoforms of BAX. Functional prediction nominated numerous functional differences among isoforms of the same gene, with loss of function compared to the reference predominating. Finally, we used single-cell RNA-seq data from the Tabula Sapiens to determine the cell types in which each structure is expressed. Our work represents an important resource for studying the structure and function of splice isoforms across the cell types of the human body.

bioinformatics↗

bioRxiv2023

CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair

Elucidating gene function is a major goal in biology, especially among non-model organisms. However, doing so is complicated by the fact that molecular conservation does not always mirror functional conservation, and that complex relationships among genes are responsible for encoding pathways and higher-order biological processes. Co-expression, a promising approach for predicting gene function, relies on the general principal that genes with similar expression patterns across multiple conditions will likely be involved in the same biological process. For Cryptococcus neoformans, a prevalent human fungal pathogen greatly diverged from model yeasts, approximately 60% of the predicted genes in the genome lack functional annotations. Here, we leveraged a large amount of publicly available transcriptomic data to generate a C. neoformans Co-Expression Network (CryptoCEN), successfully recapitulating known protein networks, predicting gene function, and enabling insights into the principles influencing co-expression. With 100% predictive accuracy, we used CryptoCEN to identify 13 new DNA damage response genes, underscoring the utility of guilt-by-association for determining gene function. Overall, co-expression is a powerful tool for uncovering gene function, and decreases the experimental tests needed to identify functions for currently under-annotated genes.

genetics↗

bioRxiv2023

Imaging-based screening identifies modulators of the eIF3 translation initiation factor complex in Candida albicans

Fungal pathogens like Candida albicans can cause devastating human disease. Treatment of candidemia is complicated by the high rate of resistance to common antifungal therapies. Additionally, there is host toxicity associated with many antifungal compounds due to the conservation between essential mammalian and fungal proteins. An attractive new approach for antimicrobial development is to target virulence factors: non-essential processes that are required for the organism to cause disease in human hosts. This approach expands the potential target space while reducing the selective pressure towards resistance, as these targets are not essential for viability. In C. albicans, a key virulence factor is the ability to transition to hyphal morphology. We developed a high-throughput image analysis pipeline to distinguish between yeast and filamentous growth in C. albicans at the single cell level. Based on this phenotypic assay, we screened the FDA drug repurposing library of 2,017 compounds for their ability to inhibit filamentation and identified 33 compounds that block the hyphal transition in C. albicans with IC50 values ranging from 0.2 to 150 {micro}M. Multiple compounds showed a phenyl vinyl sulfone chemotype, prompting further analysis. Of these phenyl vinyl sulfones, NSC 697923 displayed the most efficacy, and by selecting for resistant mutants, we identified eIF3 as the target of NSC 697923 in C. albicans.

microbiology↗

bioRxiv2023

High-Content Screening to Identify Inhibitors of Dengue Virus Replication

Dengue Virus (DENV) causes dengue fever, a pandemic-potential disease with currently no FDA-approved antivirals. Additionally, the available vaccine for DENV can increase the risk of severe dengue fever for those who have never had a DENV infection due to antibody-dependent enhancements. Thus, there is an urgent need to identify dengue virus antivirals. Antivirals that target NS4B, the replication compartment forming protein of DENV and the flavivirus family, are a promising new drug class that minimize cytotoxic effects to host cells. Drug-repurposing and high-content screening were leveraged to efficiently identify antivirals likely to inhibit NS4B. Using high-content screening, we quantified the morphological patterns of NS4B and envelope (E) protein expression versus time and developed a viral pseudotime model that was able to predict the infection progression to enable drug screening. We then developed a single cell infection classifier for antiviral efficacy and performed high-throughput drug screening of 960 compounds. We identified four concentration-dependent inhibitors of DENV with nanomolar potencies including: Nexium, Pralatrexate, GW4064, and LY411575. LY411575, a gamma secretase inhibitor, exhibited an IC50 of 72nM and reduced percent infection to levels indistinguishable from the mock infection control.

cell biology↗

bioRxiv2022

Human commensal Candida albicans strains demonstrate substantial within-host diversity and retained pathogenic potential

Candida albicans is a frequent colonizer of human mucosal surfaces as well as an opportunistic pathogen. C. albicans is remarkably versatile in its ability to colonize diverse host sites with differences in oxygen and nutrient availability, pH, immune responses, and resident microbes, among other cues. It is unclear how the genetic background of a commensal colonizing population can influence the shift to pathogenicity. Therefore, we undertook an examination of commensal isolates from healthy donors with a goal of identifying site-specific phenotypic adaptation and genetic variation associated with these phenotypes. We demonstrate that healthy people are reservoirs for genotypically and phenotypically diverse C. albicans strains, and that this genetic diversity includes both SNVs and structural rearrangements. Using limited diversity exploitation, we identified a single nucleotide change in the uncharacterized ZMS1 transcription factor that was sufficient to drive hyper invasion into agar. However, our commensal strains retained the capacity to cause disease in systemic models of infection, including outcompeting the SC5314 reference strain during systemic competition assays. This study provides a global view of commensal strain variation and within-host strain diversity of C. albicans and suggests that selection for commensalism in humans does not result in a fitness cost for invasive disease.

microbiology↗

bioRxiv2022

In Vitro Evaluation and Mitigation of Niclosamide Liabilities as a COVID-19 Treatment.

Niclosamide, an FDA-approved oral anthelmintic drug, has broad biological activity including anticancer, antibacterial, and antiviral properties. Niclosamide has also been identified as a potent inhibitor of SARS-CoV-2 infection in vitro, generating interest in its use for the treatment or prevention of COVID-19. Unfortunately, there are several potential issues with using niclosamide for COVID-19, including low bioavailability, significant polypharmacology, high cellular toxicity, and unknown efficacy against emerging SARS-CoV-2 variants of concern. In this study, we used high-content imaging-based immunofluorescence assays in two different cell models to assess these limitations and evaluate the potential for using niclosamide as a COVID-19 antiviral. We show that despite promising preliminary reports, the antiviral efficacy of niclosamide overlaps with its cytotoxicity giving it a poor in vitro selectivity index for anti-SARS-CoV-2 inhibition. We also show that niclosamide has significantly variable potency against the different SARS-CoV-2 variants of concern and is most potent against variants with enhanced cell-to-cell spread including B.1.1.7. Finally, we report the activity of 33 niclosamide analogs, several of which have reduced cytotoxicity and increased potency relative to niclosamide. A preliminary structure-activity relationship analysis reveals dependence on a protonophore for antiviral efficacy, which implicates nonspecific endolysosomal neutralization as a dominant mechanism of action. Further single-cell morphological profiling suggests niclosamide also inhibits viral entry and cell-to-cell spread by syncytia. Altogether, our results suggest that niclosamide is not an ideal candidate for the treatment of COVID-19, but that there is potential for developing improved analogs with higher clinical translational potential in the future. ImportanceThere is still an urgent need for effective anti-SARS-CoV-2 therapeutics due to waning vaccine efficacy, the emergence of variants of concern, and limited efficacy of existing antivirals. One potential therapeutic option is niclosamide, an FDA approved anthelmintic compound that has shown promising anti-SARS-CoV-2 activity in cell-based assays. Unfortunately, there are significant barriers for the clinical utility of niclosamide as a COVID-19 therapeutic. Our work emphasizes these limitations by showing that niclosamide has high cytotoxicity at antiviral concentrations, variable potency against variants of concern, and significant polypharmacology as a result of its activity as a nonspecific protonophore. Some of these clinical limitations can be mitigated, however, through structural modifications to the niclosamide scaffold, which we demonstrate through a preliminary structure activity relationship analysis. Overall, we show that niclosamide is not a suitable candidate for the treatment of COVID-19, but that structural analogs with improved drug properties may have higher clinical-translational potential.

microbiology↗

bioRxiv2022

Multiple ParA/MinD ATPases coordinate the positioning of disparate cargos in a bacterial cell

In eukaryotes, linear motor proteins govern intracellular transport and organization. In bacteria, where linear motors are absent, the ParA/MinD (A/D) family of ATPases spatially organize an array of genetic- and protein-based cellular cargos. ParA is well known to segregate plasmids and chromosomes, as is MinD for its role in divisome positioning. Less studied is the growing list of ParA/MinD-like ATPases found across prokaryotes and involved in the spatial organization of diverse protein-based organelles, such as Bacterial Microcompartments (BMCs), flagella, chemotaxis clusters, and conjugation machinery. Given the fundamental nature of these processes in both cell survival and pathogenesis, the positioning of these cargos has been independently investigated to varying degrees in several organisms. However, it remains unknown whether multiple A/D ATPases can coexist and coordinate the positioning of such a diverse set of fundamental cargos in the same cell. If so, what are the mechanistic commonalities, variation, and specificity determinants that govern the positioning reaction for each cargo? Here, we find that over a third of sequenced bacteria encode multiple A/D ATPases. Among these bacteria, we identified several human pathogens as well as the experimentally tractable organism, Halothiobacillus neapolitanus, which encodes seven A/D ATPases. We directly demonstrate that five of these A/D ATPases are each dedicated to the spatial regulation of a single cellular cargo: the chromosome, the divisome, the carboxysome BMC, the flagellum, and the chemotaxis cluster. We identify putative specificity determinants that allow each A/D ATPase to position its respective cargo. Finally, we show how the deletion of one A/D ATPase can have indirect effects on the inheritance of a cargo actively positioned by another A/D ATPase, stressing the importance of understanding how organelle trafficking, chromosome segregation, and cell division are coordinated in bacterial cells. Together, our data show how multiple A/D ATPases coexist and function to position a diverse set of fundamental cargos in the same bacterial cell. With this knowledge, we anticipate the design of minimal autonomous positioning systems for natural- and synthetic-cargos in bacteria for synthetic biology and biomedical applications.

microbiology↗

bioRxiv2022

Prioritizing virtual screening with interpretable interaction fingerprints

Machine learning-based drug discovery success depends on molecular representation. Yet traditional molecular fingerprints omit both the protein and pointers back to structural information that would enable better model interpretability. Therefore, we propose LUNA, a Python 3 toolkit that calculates and encodes protein-ligand interactions into new hashed fingerprints inspired by Extended Connectivity Finger-Print (ECFP): EIFP (Extended Interaction FingerPrint), FIFP (Functional Interaction FingerPrint), and Hybrid Interaction FingerPrint (HIFP). LUNA also provides visual strategies to make the fingerprints interpretable. We performed three major experiments exploring the fingerprints use. First, we trained machine learning models to reproduce DOCK3.7 scores using 1 million docked Dopamine D4 complexes. We found that EIFP-4,096 performed (R2 = 0.61) superior to related molecular and interaction fingerprints. Secondly, we used LUNA to support interpretable machine learning models. Finally, we demonstrate that interaction fingerprints can accurately identify similarities across molecular complexes that other fingerprints over-look. Hence, we envision LUNA and its interface fingerprints as promising methods for machine learning-based virtual screening campaigns. LUNA is freely available at https://github.com/keiserlab/LUNA.

bioinformatics↗

bioRxiv2021

A Multi-Omics Human Liver Organoid Screening Platform for DILI Risk Prediction

Background and AimsDrug-induced liver injury (DILI), both intrinsic and idiosyncratic, causes frequent morbidity, mortality, clinical trial failures and post-approval withdrawal. This suggests an unmet need for improved in vitro models for DILI risk prediction that can account for diverse host genetics and other clinical factors. In this study, we evaluated the utility of human liver organoids (HLOs) for high-throughput DILI risk prediction and in an organ-on-chip system. MethodsHLOs were derived from 3 separate iPSC lines and benchmarked on two platforms for their ability to model in vitro liver function and identify hepatotoxic compounds using biochemical assays for albumin, ALT, and AST, microscopy-based morphological profiling, and single-cell transcriptomics: 1) HLOs dispersed in 384-well formatted plates and exposed to a library of compounds. 2) HLOs adapted to a liver-on-chip system. ResultsO_LIDispersed HLOs derived from the 3 iPSC lines had similar DILI predictive capacity to intact HLOs in a high-throughput screening format allowing for measurable IC50 values of compound cytotoxicity. Distinct morphological differences were observed in cells treated with drugs exerting differing mechanisms of toxicity. C_LIO_LIOn-chip HLOs significantly increased albumin production, CYP450 expression, and ALT/AST release when treated with known DILI drugs compared to dispersed HLOs and primary human hepatocytes. On-chip HLOs were able to predict the synergistic hepatotoxicity of tenofovir-inarigivir and showed steatosis and mitochondrial perturbation via phenotypic and transcriptomic analysis with exposure to FIAU and acetaminophen, respectively. C_LI ConclusionsThe high throughput and liver-on-chip system exhibit enhanced in vivo-like function and demonstrate the potential utility of these platforms for hepatotoxicity risk assessment. Tenofovir-inarigivr associated hepatotoxicity was observed and correlates with the clinical manifestation of DILI observed in patients. LAY SUMMARYIdiosyncratic (spontaneous, patient-specific) drug-induced liver injury (DILI) is difficult to study due to the lack of liver models that function as human liver tissue and are adaptable for large-scale drug screening. Human liver organoids grown from patient stem cells respond to known DILI-causing drugs in both a high-throughput and on a physiological "chip" culture system. These platforms show promise in their use as predictive model for novel drugs before entering clinical trials.

pharmacology and toxicology↗

bioRxiv2021

Crystal structures of the σ2 receptor template large-library docking for selective chemotypes active in vivo

The {sigma}2 receptor is a poorly understood transmembrane receptor that has attracted intense interest in many areas of biology including cancer imaging, Alzheimers disease, schizophrenia, and neuropathic pain. However, little is known regarding the molecular details of the receptor, and few highly selective ligands are available. Here, we report the crystal structure of the {sigma}2 receptor in complex with the clinical drug candidate roluperidone and the probe compound PB28. These structures, in turn, templated a large-scale docking screen of 490 million make-on-demand molecules. Of these, 484 compounds were synthesized and tested, prioritizing not only high-ranking docked molecules, but also those with mediocre and poor scores. Overall, 127 compounds with binding affinities superior to 1 M were identified, all in new chemotypes, 31 of which had affinities superior to 50 nM. Intriguingly, hit rate fell smoothly and monotonically with docking score. Seeking to develop selective and biologically active probe molecules, we optimized three of the original docking hits for potency and for selectivity, achieving affinities in the 3 to 48 nM range and to up to 250-fold selectivity vs. the {sigma}1 receptor. Crystal structures of the newly discovered ligands bound to the {sigma}2 receptor were subsequently determined, confirming the docked poses. To investigate the contribution of the {sigma}2 receptor in pain processing, and to distinguish it from the contribution of the {sigma}1 receptor, two potent {sigma}2-selective and one potent {sigma}1/{sigma}2 non-selective ligand were tested for efficacy in a mouse model of neuropathic pain. All three ligands demonstrated timedependent decreases in mechanical hypersensitivity in the spared nerve injury model, supporting a role for the {sigma}2 receptor in nociception, and a possible role for {sigma}1/{sigma}2 polypharmacology. This study illustrates the opportunities for rapid discovery of in vivo active and selective probes to study under-explored areas of biology using structurebased screens of diverse, ultra-large libraries following the elucidation of protein structures.

pharmacology and toxicology↗

bioRxiv2021

Phospholipidosis is a shared mechanism underlying the in vitro antiviral activity of many repurposed drugs against SARS-CoV-2

Repurposing drugs as treatments for COVID-19 has drawn much attention. A common strategy has been to screen for established drugs, typically developed for other indications, that are antiviral in cells or organisms. Intriguingly, most of the drugs that have emerged from these campaigns, though diverse in structure, share a common physical property: cationic amphiphilicity. Provoked by the similarity of these repurposed drugs to those inducing phospholipidosis, a well-known drug side effect, we investigated phospholipidosis as a mechanism for antiviral activity. We tested 23 cationic amphiphilic drugs--including those from phenotypic screens and others that we ourselves had found--for induction of phospholipidosis in cell culture. We found that most of the repurposed drugs, which included hydroxychloroquine, azithromycin, amiodarone, and four others that have already progressed to clinical trials, induced phospholipidosis in the same concentration range as their antiviral activity; indeed, there was a strong monotonic correlation between antiviral efficacy and the magnitude of the phospholipidosis. Conversely, drugs active against the same targets that did not induce phospholipidosis were not antiviral. Phospholipidosis depends on the gross physical properties of drugs, and does not reflect specific target-based activities, rather it may be considered a confound in early drug discovery. Understanding its role in infection, and detecting its effects rapidly, will allow the community to better distinguish between drugs and lead compounds that more directly impact COVID-19 from the large proportion of molecules that manifest this confounding effect, saving much time, effort and cost. One Sentence SummaryDrug-induced phospholipidosis is a single mechanism that may explain the in vitro efficacy of a wide-variety of therapeutics repurposed for COVID-19.

pharmacology and toxicology↗

bioRxiv2021

Dual targeting of salt inducible kinases and CSF1R uncouples bone formation and bone resorption

Bone formation and resorption are typically coupled, such that the efficacy of anabolic osteoporosis treatments may be limited by bone destruction. The multi-kinase inhibitor YKL-05-099 potently inhibits salt inducible kinases (SIKs) and may represent a promising new class of bone anabolic agents. Here we report that YKL-05-099 increases bone formation in hypogonadal female mice without increasing bone resorption. Postnatal mice with inducible, global deletion of SIK2 and SIK3 show increased bone mass, increased bone formation, and, distinct from the effects of YKL-05-099, increased bone resorption. No cell-intrinsic role of SIKs in osteoclasts was noted. In addition to blocking SIKs, YKL-05-099 also binds and inhibits CSF1R, the receptor for the osteoclastogenic cytokine M-CSF. Modeling reveals that YKL-05-099 binds to SIK2 and CSF1R in a similar manner. Dual targeting of SIK2/3 and CSF1R induces bone formation without concomitantly increasing bone resorption and thereby may overcome limitations of most current anabolic osteoporosis therapies.

physiology↗