Acriflavine, a clinically approved drug, inhibits SARS-CoV-2 and other betacoronaviruses

The COVID-19 pandemic caused by SARS-CoV-2 has been socially and economically devastating. Despite an unprecedented research effort and available vaccines, effective therapeutics are still missing to limit severe disease and mortality. Using high-throughput screening, we identify acriflavine (ACF) as a potent papain-like protease (PLpro) inhibitor. NMR titrations and a co-crystal structure confirm that acriflavine blocks the PLpro catalytic pocket in an unexpected binding mode. We show that the drug inhibits viral replication at nanomolar concentration in cellular models, in vivo in mice and ex vivo in human airway epithelia, with broad range activity against SARS-CoV-2 and other betacoronaviruses. Considering that acriflavine is an inexpensive drug approved in some countries, it may be immediately tested in clinical trials and play an important role during the current pandemic and future outbreaks. © 2021 The Author

Machine learning classifies ferroptosis and apoptosis cell death modalities with TfR1 immunostaining

Determining cell death mechanisms occurring in patient and animal tissues is a longstanding goal that requires suitable biomarkers and accurate quantification. However, effective methods remain elusive. To develop more powerful and unbiased analytic frameworks, we developed a machine learning approach for automated cell death classification. Image sets were collected of HT-1080 fibrosarcoma cells undergoing ferroptosis or apoptosis and stained with an anti-transferrin receptor 1 (TfR1) antibody, together with nuclear and F-actin staining. Features were extracted using high-content-analysis software, and a classifier was constructed by fitting a multinomial logistic lasso regression model to the data. The prediction accuracy of the classifier within three classes (control, ferroptosis, apoptosis) was 93%. Thus, TfR1 staining, combined with nuclear and F-actin staining, can reliably detect both apoptotic and ferroptosis cells when cell features are analyzed in an unbiased manner using machine learning, providing a method for unbiased analysis of modes of cell death

CellDeathPred: a deep learning framework for ferroptosis and apoptosis prediction based on cell painting

Abstract Cell death, such as apoptosis and ferroptosis, play essential roles in the process of development, homeostasis, and pathogenesis of acute and chronic diseases. The increasing number of studies investigating cell death types in various diseases, particularly cancer and degenerative diseases, has raised hopes for their modulation in disease therapies. However, identifying the presence of a particular cell death type is not an obvious task, as it requires computationally intensive work and costly experimental assays. To address this challenge, we present CellDeathPred, a novel deep-learning framework that uses high-content imaging based on cell painting to distinguish cells undergoing ferroptosis or apoptosis from healthy cells. In particular, we incorporate a deep neural network that effectively embeds microscopic images into a representative and discriminative latent space, classifies the learned embedding into cell death modalities, and optimizes the whole learning using the supervised contrastive loss function. We assessed the efficacy of the proposed framework using cell painting microscopy data sets from human HT-1080 cells, where multiple inducers of ferroptosis and apoptosis were used to trigger cell death. Our model confidently separates ferroptotic and apoptotic cells from healthy controls, with an average accuracy of 95% on non-confocal data sets, supporting the capacity of the CellDeathPred framework for cell death discovery

Role of the ubiquitin-like protein Urm1 as a noncanonical lysine-directed protein modifier

The ubiquitin (Ub)-related modifier Urm1 functions as a sulfur carrier in tRNA thiolation by means of a mechanism that requires the formation of a thiocarboxylate at the C-terminal glycine residue of Urm1. However, whether Urm1 plays an additional role as a Ub-like protein modifier remains unclear. Here, we show that Urm1 is conjugated to lysine residues of target proteins and that oxidative stress enhances protein urmylation in both Saccharomyces cerevisiae and mammalian cells. Similar to ubiquitylation, urmylation involves a thioester intermediate and results in the formation of a covalent peptide bond between Urm1 and its substrates. In contrast to modification by canonical Ub-like modifiers, however, conjugation of Urm1 involves a C-terminal thiocarboxylate of the modifier. We have confirmed that the peroxiredoxin Ahp1 is such a substrate in S. cerevisiae and found that Urm1 targets a specific lysine residue of Ahp1 in vivo. In addition, we have identified several unique substrates in mammalian cells and show that Urm1 targets at least two pathways on oxidant treatment. First, Urm1 is appended to lysine residues of three components that function in its own pathway (i.e., MOCS3, ATPBD3, and CTU2). Second, Urm1 is conjugated to the nucleocytoplasmic shuttling factor cellular apoptosis susceptibility protein. Thus, Urm1 has a conserved dual role by integrating the functions of prokaryotic sulfur carriers with those of eukaryotic protein modifiers of the Ub family

New small molecules targeting apoptosis and cell viability in osteosarcoma.

Despite the option of multimodal therapy in the treatment strategies of osteosarcoma (OS), the most common primary malignant bone tumor, the standard therapy has not changed over the last decades and still involves multidrug chemotherapy and radical surgery. Although successfully applied in many patients a large number of patients eventually develop recurrent or metastatic disease in which current therapeutic regimens often lack efficacy. Thus, new therapeutic strategies are urgently needed. In this study, we performed a phenotypic high-throughput screening campaign using a 25,000 small-molecule diversity library to identify new small molecules selectively targeting osteosarcoma cells. We could identify two new small molecules that specifically reduced cell viability in OS cell lines U2OS and HOS, but affected neither hepatocellular carcinoma cell line (HepG2) nor primary human osteoblasts (hOB). In addition, the two compounds induced caspase 3 and 7 activity in the U2OS cell line. Compared to conventional drugs generally used in OS treatment such as doxorubicin, we indeed observed a greater sensitivity of OS cell viability to the newly identified compounds compared to doxorubicin and staurosporine. The p53-negative OS cell line Saos-2 almost completely lacked sensitivity to compound treatment that could indicate a role of p53 in the drug response. Taken together, our data show potential implications for designing more efficient therapies in OS

Identification of Small-Molecule Frequent Hitters from AlphaScreen High-Throughput Screens

Although small-molecule drug discovery efforts have focused largely on enzyme, receptor, and ion-channel targets, there has been an increase in such activities to search for protein-protein interaction (PPI) disruptors by applying high-throughout screening (HTS)-compatible protein-binding assays. However, a disadvantage of these assays is that many primary hits are frequent hitters regardless of the PPI being investigated. We have used the AlphaScreen technology to screen four different robust PPI assays each against 25,000 compounds. These activities led to the identification of 137 compounds that demonstrated repeated activity in all PPI assays. These compounds were subsequently evaluated in two AlphaScreen counter assays, leading to classification of compounds that either interfered with the AlphaScreen chemistry (60 compounds) or prevented the binding of the protein His-tag moiety to nickel chelate (Ni2+-NTA) beads of the AlphaScreen detection system (77 compounds). To further triage the 137 frequent hitters, we subsequently confirmed by a time-resolved fluorescence resonance energy transfer assay that most of these compounds were only frequent hitters in AlphaScreen assays. A chemoinformatics analysis of the apparent hits provided details of the compounds that can be flagged as frequent hitters of the AlphaScreen technology, and these data have broad applicability for users of these detection technologies

Reducing Mutant Huntingtin Protein Expression in Living Cells by a Newly Identified RNA CAG Binder

Expanded CAG trinucleotide repeats in Huntington’s disease (HD) are causative for neurotoxicity. The mutant CAG repeat RNA encodes neurotoxic polyglutamine proteins and can lead to a toxic gain of function by aberrantly recruiting RNA-binding proteins. One of these is the MID1 protein, which induces aberrant Huntingtin (HTT) protein translation upon binding. Here we have identified a set of CAG repeat binder candidates by in silico methods. One of those, furamidine, reduces the level of binding of HTT mRNA to MID1 and other target proteins in vitro. Metadynamics calculations, fairly consistent with experimental data measured here, provide hints about the binding mode of the ligand. Importantly, furamidine also decreases the protein level of HTT in a HD cell line model. This shows that small molecules masking RNA–MID1 interactions may be active against mutant HTT protein in living cells

Multiparametric assay in OS cell lines and control cell types after compound A13 treatment.

<p>A) Immunofluorescence images show pseudo-colored nuclei (blue), mitochondria (red) and lysed cells (yellow). Cell type is indicated on the left, control (DMSO) and compound concentrations are indicated on the top of the images. Scale bars are 50 μM. Graphs in B)-E) show results from automated image quantification. The parameters cell count (B), mitochondrial (mito) mass (C), nucleus area (μm²) (D), and membrane permeability (%) (E) were determined for treatment with compound A13 at 10 μM for 24h. Unpaired t test showed *p < 0.05, **p < 0.01, ***p < 0.001, and not significant (ns) for U2OS vs. hOB and HOS vs. hOB, respectively, and in E) #p < 0.001 relative to DMSO-treated control of the respective cell type (A13^-). Data are expressed as means +/- SD from duplicates of two independent experiments and are shown as percent or as fold change relative to DMSO-treated control.</p