Functional landscape of SARS-CoV-2 cellular restriction.

A deficient interferon (IFN) response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been implicated as a determinant of severe coronavirus disease 2019 (COVID-19). To identify the molecular effectors that govern IFN control of SARS-CoV-2 infection, we conducted a large-scale gain-of-function analysis that evaluated the impact of human IFN-stimulated genes (ISGs) on viral replication. A limited subset of ISGs were found to control viral infection, including endosomal factors inhibiting viral entry, RNA binding proteins suppressing viral RNA synthesis, and a highly enriched cluster of endoplasmic reticulum (ER)/Golgi-resident ISGs inhibiting viral assembly/egress. These included broad-acting antiviral ISGs and eight ISGs that specifically inhibited SARS-CoV-2 and SARS-CoV-1 replication. Among the broad-acting ISGs was BST2/tetherin, which impeded viral release and is antagonized by SARS-CoV-2 Orf7a protein. Overall, these data illuminate a set of ISGs that underlie innate immune control of SARS-CoV-2/SARS-CoV-1 infection, which will facilitate the understanding of host determinants that impact disease severity and offer potential therapeutic strategies for COVID-19.

Discovery of SARS-CoV-2 antiviral drugs through large-scale compound repurposing.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019 has triggered an ongoing global pandemic of the severe pneumonia-like disease coronavirus disease 2019 (COVID-19)1. The development of a vaccine is likely to take at least 12-18 months, and the typical timeline for approval of a new antiviral therapeutic agent can exceed 10 years. Thus, repurposing of known drugs could substantially accelerate the deployment of new therapies for COVID-19. Here we profiled a library of drugs encompassing approximately 12,000 clinical-stage or Food and Drug Administration (FDA)-approved small molecules to identify candidate therapeutic drugs for COVID-19. We report the identification of 100 molecules that inhibit viral replication of SARS-CoV-2, including 21 drugs that exhibit dose-response relationships. Of these, thirteen were found to harbour effective concentrations commensurate with probable achievable therapeutic doses in patients, including the PIKfyve kinase inhibitor apilimod2-4 and the cysteine protease inhibitors MDL-28170, Z LVG CHN2, VBY-825 and ONO 5334. Notably, MDL-28170, ONO 5334 and apilimod were found to antagonize viral replication in human pneumocyte-like cells derived from induced pluripotent stem cells, and apilimod also demonstrated antiviral efficacy in a primary human lung explant model. Since most of the molecules identified in this study have already advanced into the clinic, their known pharmacological and human safety profiles will enable accelerated preclinical and clinical evaluation of these drugs for the treatment of COVID-19.

Transcription Elongation Can Affect Genome 3D Structure.

How transcription affects genome 3D organization is not well understood. We found that during influenza A (IAV) infection, rampant transcription rapidly reorganizes host cell chromatin interactions. These changes occur at the ends of highly transcribed genes, where global inhibition of transcription termination by IAV NS1 protein causes readthrough transcription for hundreds of kilobases. In these readthrough regions, elongating RNA polymerase II disrupts chromatin interactions by inducing cohesin displacement from CTCF sites, leading to locus decompaction. Readthrough transcription into heterochromatin regions switches them from the inert (B) to the permissive (A) chromatin compartment and enables transcription factor binding. Data from non-viral transcription stimuli show that transcription similarly affects cohesin-mediated chromatin contacts within gene bodies. Conversely, inhibition of transcription elongation allows cohesin to accumulate at previously transcribed intragenic CTCF sites and to mediate chromatin looping and compaction. Our data indicate that transcription elongation by RNA polymerase II remodels genome 3D architecture.

Massively Parallel Sequencing of Peritoneal and Splenic B Cell Repertoires Highlights Unique Properties of B-1 Cell Antibodies.

B-1 cells are a unique subset of B cells that are positively selected for expressing autoreactive BCRs. We isolated RNA from peritoneal (B-1a, B-1b, B-2) and splenic (B-1a, marginal zone, follicular) B cells from C57BL/6 mice and used 5'-RACE to amplify the IgH V region using massively parallel sequencing. By analyzing 379,000 functional transcripts, we demonstrate that B-1a cells use a distinct and restricted repertoire. All B-1 cell subsets, especially peritoneal B-1a cells, had a high proportion of sequences without N additions, suggesting predominantly prenatal development. Their transcripts differed markedly and uniquely contained VH11 and VH12 genes, which were rearranged only with a restricted selection of D and J genes, unlike other V genes. Compared to peritoneal B-1a, the peritoneal B-1b repertoire was larger, had little overlap with B-1a, and most sequences contained N additions. Similarly, the splenic B-1a repertoire differed from peritoneal B-1a sequences, having more unique sequences and more frequent N additions, suggesting influx of B-1a cells into the spleen from nonperitoneal sites. Two CDR3s, previously described as Abs to bromelain-treated RBCs, comprised 43% of peritoneal B-1a sequences. We show that a single-chain variable fragment designed after the most prevalent B-1a sequence bound oxidation-specific epitopes such as the phosphocholine of oxidized phospholipids. In summary, we provide the IgH V region library of six murine B cell subsets, including, to our knowledge for the first time, a comparison between B-1a and B-1b cells, and we highlight qualities of B-1 cell Abs that indicate unique selection processes.

Chemical Proteomics Identifies Druggable Vulnerabilities in a Genetically Defined Cancer.

The transcription factor NRF2 is a master regulator of the cellular antioxidant response, and it is often genetically activated in non-small-cell lung cancers (NSCLCs) by, for instance, mutations in the negative regulator KEAP1. While direct pharmacological inhibition of NRF2 has proven challenging, its aberrant activation rewires biochemical networks in cancer cells that may create special vulnerabilities. Here, we use chemical proteomics to map druggable proteins that are selectively expressed in KEAP1-mutant NSCLC cells. Principal among these is NR0B1, an atypical orphan nuclear receptor that we show engages in a multimeric protein complex to regulate the transcriptional output of KEAP1-mutant NSCLC cells. We further identify small molecules that covalently target a conserved cysteine within the NR0B1 protein interaction domain, and we demonstrate that these compounds disrupt NR0B1 complexes and impair the anchorage-independent growth of KEAP1-mutant cancer cells. Our findings designate NR0B1 as a druggable transcriptional regulator that supports NRF2-dependent lung cancers.

Disease tolerance mediated by microbiome E. coli involves inflammasome and IGF-1 signaling.

Infections and inflammation can lead to cachexia and wasting of skeletal muscle and fat tissue by as yet poorly understood mechanisms. We observed that gut colonization of mice by a strain of Escherichia coli prevents wasting triggered by infections or physical damage to the intestine. During intestinal infection with the pathogen Salmonella Typhimurium or pneumonic infection with Burkholderia thailandensis, the presence of this E. coli did not alter changes in host metabolism, caloric uptake, or inflammation but instead sustained signaling of the insulin-like growth factor 1/phosphatidylinositol 3-kinase/AKT pathway in skeletal muscle, which is required for prevention of muscle wasting. This effect was dependent on engagement of the NLRC4 inflammasome. Therefore, this commensal promotes tolerance to diverse diseases.

CoVaMa: Co-Variation Mapper for disequilibrium analysis of mutant loci in viral populations using next-generation sequence data.

Next-Generation Sequencing (NGS) has transformed our understanding of the dynamics and diversity of virus populations for human pathogens and model systems alike. Due to the sensitivity and depth of coverage in NGS, it is possible to measure the frequency of mutations that may be present even at vanishingly low frequencies within the viral population. Here, we describe a simple bioinformatic pipeline called CoVaMa (Co-Variation Mapper) scripted in Python that detects correlated patterns of mutations in a viral sample. Our algorithm takes NGS alignment data and populates large matrices of contingency tables that correspond to every possible pairwise interaction of nucleotides in the viral genome or amino acids in the chosen open reading frame. These tables are then analysed using classical linkage disequilibrium to detect and report evidence of epistasis. We test our analysis with simulated data and then apply the approach to find epistatically linked loci in Flock House Virus genomic RNA grown under controlled cell culture conditions. We also reanalyze NGS data from a large cohort of HIV infected patients and find correlated amino acid substitution events in the protease gene that have arisen in response to anti-viral therapy. This both confirms previous findings and suggests new pairs of interactions within HIV protease. The script is publically available at http://sourceforge.net/projects/covama.

Deep sequencing of protease inhibitor resistant HIV patient isolates reveals patterns of correlated mutations in Gag and protease.

While the role of drug resistance mutations in HIV protease has been studied comprehensively, mutations in its substrate, Gag, have not been extensively cataloged. Using deep sequencing, we analyzed a unique collection of longitudinal viral samples from 93 patients who have been treated with therapies containing protease inhibitors (PIs). Due to the high sequence coverage within each sample, the frequencies of mutations at individual positions were calculated with high precision. We used this information to characterize the variability in the Gag polyprotein and its effects on PI-therapy outcomes. To examine covariation of mutations between two different sites using deep sequencing data, we developed an approach to estimate the tight bounds on the two-site bivariate probabilities in each viral sample, and the mutual information between pairs of positions based on all the bounds. Utilizing the new methodology we found that mutations in the matrix and p6 proteins contribute to continued therapy failure and have a major role in the network of strongly correlated mutations in the Gag polyprotein, as well as between Gag and protease. Although covariation is not direct evidence of structural propensities, we found the strongest correlations between residues on capsid and matrix of the same Gag protein were often due to structural proximity. This suggests that some of the strongest inter-protein Gag correlations are the result of structural proximity. Moreover, the strong covariation between residues in matrix and capsid at the N-terminus with p1 and p6 at the C-terminus is consistent with residue-residue contacts between these proteins at some point in the viral life cycle.

Rapid deep sequencing of patient-derived HIV with ion semiconductor technology.

The development of next-generation sequencing technologies has facilitated the study of HIV drug resistance evolution. However, the high capacity and per-run cost of many sequencers is not ideal for viral sequencing unless many samples are analyzed simultaneously. Ion semiconductor sequencing has recently emerged as a flexible, lower-cost alternative with short runtime. This paper describes the use of Ion Torrent devices for deep sequencing of drug resistant HIV samples. High levels of sequencing coverage were obtained in HIV Gag and protease, allowing the detection of mutations at low frequencies.

Identification of HIV-1 inhibitors targeting the nucleocapsid protein.

The HIV-1 nucleocapsid (NC) is a RNA/DNA binding protein encoded within the Gag polyprotein, which is critical for the selection and chaperoning of viral genomic RNA during virion assembly. RNA/DNA binding occurs through a highly conserved zinc-knuckle motif present in NC. Given the necessity of NC-viral RNA/DNA interaction for viral replication, identification of compounds that disrupt the NC-RNA/DNA interaction may have value as an antiviral strategy. To identify small molecules that disrupt NC-viral RNA/DNA binding, a high-throughput fluorescence polarization assay was developed and a library of 14,400 diverse, druglike compounds was screened. Compounds that disrupted NC binding to a fluorescence-labeled DNA tracer were next evaluated by differential scanning fluorimetry to identify compounds that must bind to NC or Gag to impart their effects. Two compounds were identified that inhibited NC-DNA interaction, specifically bound NC with nanomolar affinity, and showed modest anti-HIV-1 activity in ex vivo cell assays.

Virtual screening for HIV protease inhibitors: a comparison of AutoDock 4 and Vina.

BACKGROUND: The AutoDock family of software has been widely used in protein-ligand docking research. This study compares AutoDock 4 and AutoDock Vina in the context of virtual screening by using these programs to select compounds active against HIV protease. METHODOLOGY/PRINCIPAL FINDINGS: Both programs were used to rank the members of two chemical libraries, each containing experimentally verified binders to HIV protease. In the case of the NCI Diversity Set II, both AutoDock 4 and Vina were able to select active compounds significantly better than random (AUC = 0.69 and 0.68, respectively; p<0.001). The binding energy predictions were highly correlated in this case, with r = 0.63 and iota = 0.82. For a set of larger, more flexible compounds from the Directory of Universal Decoys, the binding energy predictions were not correlated, and only Vina was able to rank compounds significantly better than random. CONCLUSIONS/SIGNIFICANCE: In ranking smaller molecules with few rotatable bonds, AutoDock 4 and Vina were equally capable, though both exhibited a size-related bias in scoring. However, as Vina executes more quickly and is able to more accurately rank larger molecules, researchers should look to it first when undertaking a virtual screen.