Massively scaled-up testing for SARS-CoV-2 RNA via next-generation sequencing of pooled and barcoded nasal and saliva samples.

Frequent and widespread testing of members of the population who are asymptomatic for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for the mitigation of the transmission of the virus. Despite the recent increases in testing capacity, tests based on quantitative polymerase chain reaction (qPCR) assays cannot be easily deployed at the scale required for population-wide screening. Here, we show that next-generation sequencing of pooled samples tagged with sample-specific molecular barcodes enables the testing of thousands of nasal or saliva samples for SARS-CoV-2 RNA in a single run without the need for RNA extraction. The assay, which we named SwabSeq, incorporates a synthetic RNA standard that facilitates end-point quantification and the calling of true negatives, and that reduces the requirements for automation, purification and sample-to-sample normalization. We used SwabSeq to perform 80,000 tests, with an analytical sensitivity and specificity comparable to or better than traditional qPCR tests, in less than two months with turnaround times of less than 24 h. SwabSeq could be rapidly adapted for the detection of other pathogens.

Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical-Genetic Interactions.

A common strategy for identifying molecules likely to possess a desired biological activity is to search large databases of compounds for high structural similarity to a query molecule that demonstrates this activity, under the assumption that structural similarity is predictive of similar biological activity. However, efforts to systematically benchmark the diverse array of available molecular fingerprints and similarity coefficients have been limited by a lack of large-scale datasets that reflect biological similarities of compounds. To elucidate the relative performance of these alternatives, we systematically benchmarked 11 different molecular fingerprint encodings, each combined with 13 different similarity coefficients, using a large set of chemical-genetic interaction data from the yeast Saccharomyces cerevisiae as a systematic proxy for biological activity. We found that the performance of different molecular fingerprints and similarity coefficients varied substantially and that the all-shortest path fingerprints paired with the Braun-Blanquet similarity coefficient provided superior performance that was robust across several compound collections. We further proposed a machine learning pipeline based on support vector machines that offered a fivefold improvement relative to the best unsupervised approach. Our results generally suggest that using high-dimensional chemical-genetic data as a basis for refining molecular fingerprints can be a powerful approach for improving prediction of biological functions from chemical structures.

Swab-Seq: A high-throughput platform for massively scaled up SARS-CoV-2 testing.

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is due to the high rates of transmission by individuals who are asymptomatic at the time of transmission1,2. Frequent, widespread testing of the asymptomatic population for SARS-CoV-2 is essential to suppress viral transmission. Despite increases in testing capacity, multiple challenges remain in deploying traditional reverse transcription and quantitative PCR (RT-qPCR) tests at the scale required for population screening of asymptomatic individuals. We have developed SwabSeq, a high-throughput testing platform for SARS-CoV-2 that uses next-generation sequencing as a readout. SwabSeq employs sample-specific molecular barcodes to enable thousands of samples to be combined and simultaneously analyzed for the presence or absence of SARS-CoV-2 in a single run. Importantly, SwabSeq incorporates an in vitro RNA standard that mimics the viral amplicon, but can be distinguished by sequencing. This standard allows for end-point rather than quantitative PCR, improves quantitation, reduces requirements for automation and sample-to-sample normalization, enables purification-free detection, and gives better ability to call true negatives. After setting up SwabSeq in a high-complexity CLIA laboratory, we performed more than 80,000 tests for COVID-19 in less than two months, confirming in a real world setting that SwabSeq inexpensively delivers highly sensitive and specific results at scale, with a turn-around of less than 24 hours. Our clinical laboratory uses SwabSeq to test both nasal and saliva samples without RNA extraction, while maintaining analytical sensitivity comparable to or better than traditional RT-qPCR tests. Moving forward, SwabSeq can rapidly scale up testing to mitigate devastating spread of novel pathogens.

Reliable and accurate diagnostics from highly multiplexed sequencing assays.

Scalable, inexpensive, and secure testing for SARS-CoV-2 infection is crucial for control of the novel coronavirus pandemic. Recently developed highly multiplexed sequencing assays (HMSAs) that rely on high-throughput sequencing can, in principle, meet these demands, and present promising alternatives to currently used RT-qPCR-based tests. However, reliable analysis, interpretation, and clinical use of HMSAs requires overcoming several computational, statistical and engineering challenges. Using recently acquired experimental data, we present and validate a computational workflow based on kallisto and bustools, that utilizes robust statistical methods and fast, memory efficient algorithms, to quickly, accurately and reliably process high-throughput sequencing data. We show that our workflow is effective at processing data from all recently proposed SARS-CoV-2 sequencing based diagnostic tests, and is generally applicable to any diagnostic HMSA.

CRISPR screens in cancer spheroids identify 3D growth-specific vulnerabilities.

Cancer genomics studies have identified thousands of putative cancer driver genes1. Development of high-throughput and accurate models to define the functions of these genes is a major challenge. Here we devised a scalable cancer-spheroid model and performed genome-wide CRISPR screens in 2D monolayers and 3D lung-cancer spheroids. CRISPR phenotypes in 3D more accurately recapitulated those of in vivo tumours, and genes with differential sensitivities between 2D and 3D conditions were highly enriched for genes that are mutated in lung cancers. These analyses also revealed drivers that are essential for cancer growth in 3D and in vivo, but not in 2D. Notably, we found that carboxypeptidase D is responsible for removal of a C-terminal RKRR motif2 from the α-chain of the insulin-like growth factor 1 receptor that is critical for receptor activity. Carboxypeptidase D expression correlates with patient outcomes in patients with lung cancer, and loss of carboxypeptidase D reduced tumour growth. Our results reveal key differences between 2D and 3D cancer models, and establish a generalizable strategy for performing CRISPR screens in spheroids to reveal cancer vulnerabilities.

Using BEAN-counter to quantify genetic interactions from multiplexed barcode sequencing experiments.

The construction of genome-wide mutant collections has enabled high-throughput, high-dimensional quantitative characterization of gene and chemical function, particularly via genetic and chemical-genetic interaction experiments. As the throughput of such experiments increases with improvements in sequencing technology and sample multiplexing, appropriate tools must be developed to handle the large volume of data produced. Here, we describe how to apply our approach to high-throughput, fitness-based profiling of pooled mutant yeast collections using the BEAN-counter software pipeline (Barcoded Experiment Analysis for Next-generation sequencing) for analysis. The software has also successfully processed data from Schizosaccharomyces pombe, Escherichia coli, and Zymomonas mobilis mutant collections. We provide general recommendations for the design of large-scale, multiplexed barcode sequencing experiments. The procedure outlined here was used to score interactions for ~4 million chemical-by-mutant combinations in our recently published chemical-genetic interaction screen of nearly 14,000 chemical compounds across seven diverse compound collections. Here we selected a representative subset of these data on which to demonstrate our analysis pipeline. BEAN-counter is open source, written in Python, and freely available for academic use. Users should be proficient at the command line; advanced users who wish to analyze larger datasets with hundreds or more conditions should also be familiar with concepts in analysis of high-throughput biological data. BEAN-counter encapsulates the knowledge we have accumulated from, and successfully applied to, our multiplexed, pooled barcode sequencing experiments. This protocol will be useful to those interested in generating their own high-dimensional, quantitative characterizations of gene or chemical function in a high-throughput manner.

Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions.

Chemical-genetic interactions-observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes-contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes.

MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action.

Summary: Chemical-genomic approaches that map interactions between small molecules and genetic perturbations offer a promising strategy for functional annotation of uncharacterized bioactive compounds. We recently developed a new high-throughput platform for mapping chemical-genetic (CG) interactions in yeast that can be scaled to screen large compound collections, and we applied this system to generate CG interaction profiles for more than 13 000 compounds. When integrated with the existing global yeast genetic interaction network, CG interaction profiles can enable mode-of-action prediction for previously uncharacterized compounds as well as discover unexpected secondary effects for known drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, bioprocesses (Gene Ontology terms) and genes for CG information including direct CG interactions, bioprocesses and gene-level target predictions. MOSAIC also provides access to chemical structure information of screened molecules, chemical-genomic profiles and the ability to search for compounds sharing structural and functional similarity. This resource will be of interest to chemical biologists for discovering new small molecule probes with specific modes-of-action as well as computational biologists interested in analysing CG interaction networks. Availability and implementation: MOSAIC is available at http://mosaic.cs.umn.edu. Contact: hisyo@riken.jp, yoshidam@riken.jp, charlie.boone@utoronto.ca or chadm@umn.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Errata: Functional annotation of chemical libraries across diverse biological processes.

This corrects the article DOI: 10.1038/nchembio.2436.

Errata: Functional annotation of chemical libraries across diverse biological processes.

This corrects the article DOI: 10.1038/nchembio.2436.

Coculture of Marine Invertebrate-Associated Bacteria and Interdisciplinary Technologies Enable Biosynthesis and Discovery of a New Antibiotic, Keyicin.

Advances in genomics and metabolomics have made clear in recent years that microbial biosynthetic capacities on Earth far exceed previous expectations. This is attributable, in part, to the realization that most microbial natural product (NP) producers harbor biosynthetic machineries not readily amenable to classical laboratory fermentation conditions. Such "cryptic" or dormant biosynthetic gene clusters (BGCs) encode for a vast assortment of potentially new antibiotics and, as such, have become extremely attractive targets for activation under controlled laboratory conditions. We report here that coculturing of a Rhodococcus sp. and a Micromonospora sp. affords keyicin, a new and otherwise unattainable bis-nitroglycosylated anthracycline whose mechanism of action (MOA) appears to deviate from those of other anthracyclines. The structure of keyicin was elucidated using high resolution MS and NMR technologies, as well as detailed molecular modeling studies. Sequencing of the keyicin BGC (within the Micromonospora genome) enabled both structural and genomic comparisons to other anthracycline-producing systems informing efforts to characterize keyicin. The new NP was found to be selectively active against Gram-positive bacteria including both Rhodococcus sp. and Mycobacterium sp. E. coli-based chemical genomics studies revealed that keyicin's MOA, in contrast to many other anthracyclines, does not invoke nucleic acid damage.

Functional annotation of chemical libraries across diverse biological processes.

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound's mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

Chemical Genomics, Structure Elucidation, and in Vivo Studies of the Marine-Derived Anticlostridial Ecteinamycin.

A polyether antibiotic, ecteinamycin (1), was isolated from a marine Actinomadura sp., cultivated from the ascidian Ecteinascidia turbinata. 13C enrichment, high resolution NMR spectroscopy, and molecular modeling enabled elucidation of the structure of 1, which was validated on the basis of comparisons with its recently reported crystal structure. Importantly, ecteinamycin demonstrated potent activity against the toxigenic strain of Clostridium difficile NAP1/B1/027 (MIC = 59 ng/µL), as well as other toxigenic and nontoxigenic C. difficile isolates both in vitro and in vivo. Additionally, chemical genomics studies using Escherichia coli barcoded deletion mutants led to the identification of sensitive mutants such as trkA and kdpD involved in potassium cation transport and homeostasis supporting a mechanistic proposal that ecteinamycin acts as an ionophore antibiotic. This is the first antibacterial agent whose mechanism of action has been studied using E. coli chemical genomics. On the basis of these data, we propose ecteinamycin as an ionophore antibiotic that causes C. difficile detoxification and cell death via potassium transport dysregulation.

Accumulation of heme biosynthetic intermediates contributes to the antibacterial action of the metalloid tellurite.

The metalloid tellurite is highly toxic to microorganisms. Several mechanisms of action have been proposed, including thiol depletion and generation of hydrogen peroxide and superoxide, but none of them can fully explain its toxicity. Here we use a combination of directed evolution and chemical and biochemical approaches to demonstrate that tellurite inhibits heme biosynthesis, leading to the accumulation of intermediates of this pathway and hydroxyl radical. Unexpectedly, the development of tellurite resistance is accompanied by increased susceptibility to hydrogen peroxide. Furthermore, we show that the heme precursor 5-aminolevulinic acid, which is used as an antimicrobial agent in photodynamic therapy, potentiates tellurite toxicity. Our results define a mechanism of tellurite toxicity and warrant further research on the potential use of the combination of tellurite and 5-aminolevulinic acid in antimicrobial therapy.

A global genetic interaction network maps a wiring diagram of cellular function.

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

Chemical genomic profiling via barcode sequencing to predict compound mode of action.

Chemical genomics is an unbiased, whole-cell approach to characterizing novel compounds to determine mode of action and cellular target. Our version of this technique is built upon barcoded deletion mutants of Saccharomyces cerevisiae and has been adapted to a high-throughput methodology using next-generation sequencing. Here we describe the steps to generate a chemical genomic profile from a compound of interest, and how to use this information to predict molecular mechanism and targets of bioactive compounds.

Contamination of deconjugation enzymes derived from Helix pomatia with the plant bioactive compounds 3,3'-diindolylmethane, 5-methoxypsoralen, and 8-methoxypsoralen.

Bioactive compounds from plant foods are intensely investigated for effects on disease prevention. ß-Glucuronidase/arylsulfatase from Helix pomatia (snail) is commonly used when quantifying exposure to metabolized dietary components. However, we describe here the contamination of multiple formulations of this enzyme preparation with 3,3'-diindolylmethane (DIM), 8-methoxypsoralen (8-MOP), and 5-methoxypsoralen (5-MOP), bioactives from cruciferous and apiaceous vegetables under investigation as putative cancer chemopreventive agents. We identified an Escherichia coli preparation of ß-glucuronidase as free from contamination with any of the compounds tested. These results demonstrate the importance of selecting appropriate enzyme preparations when quantifying naturally occurring, trace level compounds in biological fluids.

Targeted three-dimensional liquid chromatography: a versatile tool for quantitative trace analysis in complex matrices.

Targeted multidimensional liquid chromatography (MDLC), commonly referred to as 'coupled-column' or 'heartcutting', has been used extensively since the 1970s for analysis of low concentration constituents in complex biological and environmental samples. A primary benefit of adding additional dimensions of separation to conventional HPLC separations is that the additional resolving power provided by the added dimensions can greatly simplify method development for complex samples. Despite the long history of targeted MDLC, nearly all published reports involve two-dimensional methods, and very few have explored the benefits of adding a third dimension of separation. In this work we capitalize on recent advances in reversed-phase HPLC to construct a three-dimensional HPLC system for targeted analysis built on three very different reversed-phase columns. Using statistical peak overlap theory and one of the most recent models of reversed-phase selectivity we use simulations to show the potential benefit of adding a third dimension to a MDLC system. We then demonstrate this advantage experimentally by developing targeted methods for the analysis of a variety of broadly relevant molecules in different sample matrices including urban wastewater treatment effluent, human urine, and river water. We find in each case that excellent separations of the target compounds from the sample matrix are obtained using one set of very similar separation conditions for all of the target compound/sample matrix combinations, thereby significantly reducing the normally tedious method development process. A rigorous quantitative comparison of this approach to conventional 1DLC-MS/MS also shows that targeted 3DLC with UV detection is quantitatively accurate for the target compounds studied, with method detection limits in the low parts-per-trillion range of concentrations. We believe this work represents a first step toward the development of a targeted 3D analysis system that will be more effective than previous 2D separations as a tool for the rapid development of robust methods for quantitation of low concentration constituents in complex mixtures.