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Large-scale chemical–genetics yields new M. tuberculosis inhibitor classes

Nature volume 571, pages 72–78 (2019)Cite this article

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Abstract

New antibiotics are needed to combat rising levels of resistance, with new Mycobacterium tuberculosis (Mtb) drugs having the highest priority. However, conventional whole-cell and biochemical antibiotic screens have failed. Here we develop a strategy termed PROSPECT (primary screening of strains to prioritize expanded chemistry and targets), in which we screen compounds against pools of strains depleted of essential bacterial targets. We engineered strains that target 474 essential Mtb genes and screened pools of 100–150 strains against activity-enriched and unbiased compound libraries, probing more than 8.5 million chemical–genetic interactions. Primary screens identified over tenfold more hits than screening wild-type Mtb alone, with chemical–genetic interactions providing immediate, direct target insights. We identified over 40 compounds that target DNA gyrase, the cell wall, tryptophan, folate biosynthesis and RNA polymerase, as well as inhibitors that target EfpA. Chemical optimization yielded EfpA inhibitors with potent wild-type activity, thus demonstrating the ability of PROSPECT to yield inhibitors against targets that would have eluded conventional drug discovery.

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Fig. 1: Generating large-scale chemical–genetic interaction profiles from primary screening.
Fig. 2: Chemical–genetic interactions of bioactive compounds with known MOA.
Fig. 3: New inhibitor classes of well-validated targets in M. tuberculosis.
Fig. 4: Predicting inhibitors of folate biosynthesis from a 50,000-compound screen.
Fig. 5: Discovery of inhibitors in the absence of reference data, including inhibitors of a novel target in M. tuberculosis.

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Data availability

Source Data for Figs. 1a, b, 2, 3a, c, e, 4a, 5b and Extended Data Figs. 1b, 2a, c, d, 4a, 5, 7e, 8, 9a, b, c, f, i, l are provided with the online version of the paper. The raw primary screen data, calculated fold changes and P values, and compound annotations, are available online at https://broad.io/cgtb.

Code availability

ConcensusGLM is available at https://doi.org/10.5281/zenodo.3235787 and on GitHub at http://github.com/broadinstitute/concensusGLM. Other computer code is available from the corresponding author upon request.

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Acknowledgements

Funding was provided by the Bill and Melinda Gates Foundation, Broad Institute TB Gift Donors and Pershing Square Foundation.

Reviewer information

Nature thanks Terry Roemer and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. Emily LaVerriere

    Present address: Biological and Biomedical Sciences Program, Harvard Medical School, Boston, MA, USA

  2. Mary Stanley

    Present address: Rush University Medical Center, Chicago, IL, USA

  3. Elisabeth Meyer

    Present address: Department of Biochemistry, Stanford University, Stanford, CA, USA

  4. Rebecca E. Audette

    Present address: Saint Louis University School of Medicine, St Louis, MO, USA

  5. Natalia Betancourt

    Present address: Regeneron Pharmaceuticals, Tarrytown, NY, USA

  6. Joshua Davis

    Present address: Regulatory and Quality Solutions LLC, Braintree, MA, USA

  7. Christina Gallo

    Present address: Boston University School of Medicine, Boston, MA, USA

  8. Caitlin E. Moss

    Present address: Yale School of Medicine, New Haven, CT, USA

  9. Matthew Thompson

    Present address: Caribou Biosciences, Berkeley, CA, USA

  10. Christopher Watson

    Present address: College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA

Authors and Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Eachan O. Johnson, Emily LaVerriere, Emma Office, Mary Stanley, Elisabeth Meyer, Tomohiko Kawate, James E. Gomez, Nirmalya Bandyopadhyay, Kayla Delano, Joshua Davis, Christina Gallo, Aaron J. Golas, Sofia Kennedy, Rebecca Korn, Raymond M. Nietupski, Matthew Thompson, Christopher Watson, Eric S. Lander, Brian K. Hubbard, Michael H. Serrano-Wu, Michael Fitzgerald & Deborah T. Hung

  2. Department of Molecular Biology and Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA

    Eachan O. Johnson, Tomohiko Kawate & Deborah T. Hung

  3. Department of Genetics, Harvard Medical School, Boston, MA, USA

    Eachan O. Johnson, Tomohiko Kawate & Deborah T. Hung

  4. Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Rebecca E. Audette, Michelle Gardner, Kristine M. Guinn, Jessica T. Pinkham, Shoko Wakabayashi & Eric J. Rubin

  5. Department of Microbiology and Immunology, Weill Cornell Medical College, New York, NY, USA

    Natalia Betancourt, Israel Da Silva, Jennifer A. McConnell, Paula A. Pino, Nadine Ruecker, Naomi Song, Carolina Trujillo, Joshua B. Wallach, Sabine Ehrt & Dirk Schnappinger

  6. Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, MA, USA

    Caitlin E. Moss, Kenan C. Murphy, Kadamba G. Papavinasasundaram, Megan K. Proulx & Christopher M. Sassetti

  7. Department of Computer Science, Texas A&M University, College Station, TX, USA

    Thomas R. Ioerger

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  1. Eachan O. Johnson
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Contributions

The manuscript was written by E.O.J., E.S.L. and D.T.H. Statistical analysis was carried out by E.O.J. Computational pipelines were written by E.O.J. and N. Bandyopadhyay. Experiments were designed as follows. Strain construction: K.M.G., K.C.M., T.R.I., S.E., E.J.R., C.M.S., D.S. Assay development and screening: E.O.J., J.E.G., M.F., D.T.H. Medicinal chemistry: T.K., B.K.H., M.H.S.-W., D.T.H. Mechanism of action follow-up: E.O.J., J.E.G., D.T.H. Experiments were carried out as follows. Strain construction: R.E.A., N. Betancourt, I.D.S., M.G., J.A.M., C.E.M., K.G.P., J.T.P., P.A.P., M.K.P., N.R., N.S., C.T., S.W., J.B.W. Assay development: E.O.J., M.T. Compound screening: E.O.J., E.L., M.S., K.D., J.D., C.G., A.J.G., R.K., R.M.N., M.T., C.W. Mechanism of action follow-up: E.O.J., J.E.G., E.O., E.M., S.K.

Corresponding author

Correspondence to Deborah T. Hung.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Mtb hypomorph strain creation.

a, Hypomorph strains were constructed by introducing a DAS tag at the 3ʹ end of the gene of interest, with concomitant introduction of a 20-nucleotide barcode and an episomally encoded, regulated SspB gene to control the level of protein depletion. b, Degradation of a DAS-tagged target gene product was mediated by SspB, the expression of which was driven by an ATC-inducible TetON promoter. To allow individualized degrees of knockdown for each gene product, a series of TetON promoters with varying strengths was generated. Regulated promoter strength was quantified by fusion to a luciferase gene and measuring luminescence in the presence and absence of the ATC inducer. In the screen, strains containing the TetON-1, -2, -6, -10 and -18 promoters were used for subsequent strain construction. Independent biological replicates (n = 8) are shown as open circles; means are shown as horizontal bars; error bars denote 95% confidence intervals. c, A range of up to five different knockdown levels was attempted using five different promoters for each target gene, allowing the generation of 2,014 hypomorphs. d, Barcoded hypomorph strains were pooled and distributed into 384-well plates containing the compound library and incubated for 14 days. e, Chromosomal strain barcodes were inserted into each engineered hypomorph, thus allowing PCR amplification by an array of primers containing 5′ overhangs encoding screen location (well and plate) barcodes. For census enumeration of pooled strains, PCR products were combined and subjected to Illumina NGS. f, Dose response of trimethoprim, a DHFR inhibitor, against wild-type Mtb and the DHFR hypomorph, showing the hypersensitivity of the hypomorph. Growth was calculated as OD600 normalized to untreated controls. Independent biological replicates (n = 4) are shown as open circles; means are shown as filled circles; error bars denote 95% confidence intervals.

Source data

Extended Data Fig. 2 Strain and compound collection characterization.

a, Histogram of growth of the individual hypomorphs in the screening pool over the 14-day duration of the screen. Most hypomorphs were within a tenfold window of growth rates. b, Histogram of hypomorph Zʹ factors between the untreated wells and rifampin control wells of the screening assay for the hypomorphs. All Zʹ factors were greater than 0.5, indicating an excellent screening assay. c, The composition of the bioactive compound library. The right bar shows the broad classes within the segment of known drugs. d, The bioactive compound library was tested up to 50 µM against a GFP-expressing Mtb strain. Most compounds had detectable activity for at least one concentration tested. e, Volcano plot (maximum likelihood mean fold change from n = 2 biologically independent samples against the unadjusted two-sided Wald test P value) of chemical–genetic interactions from the bioactive library. Each point represents a single strain–compound interaction at a single concentration. Some interactions of interest are highlighted: compounds are designated by colour, with wild-type Mtb interactions shown as open circles and hypomorphs interactions of interest shown as solid circles. Most interactions were inhibitory, because the compound library was confirmed to be enriched for antitubercular activity.

Source data

Extended Data Fig. 3 Feature weights of Lasso classifiers.

a, Feature weights of the hypomorphs used by the Lasso binary classifier to predict DNA gyrase inhibitors. We trained on the fluoroquinolones, with the GyrA hypomorph being the most predictive strain. Features are denoted as the target gene of the hypomorph and compound concentration separated by an underscore. b, As in a for the Lasso binary classifier to predict inhibitors of mycolic acid biosynthesis. We trained on known InhA inhibitors, with the MshC hypomorph being a prominent discriminator. c, As in a for the Lasso binary classifier to predict inhibitors of folate biosynthesis. We trained on the sulfonamides, with the TrpG hypomorph being a prominent discriminator. d, As in a but trained on chemical–genetic interaction profiles from the sulfonamides and confirmed new folate biosynthesis inhibitors. e, As in a but trained on chemical–genetic interaction profiles of the newly identified inhibitors of tryptophan biosynthesis.

Extended Data Fig. 4 Validation of predicted DNA gyrase inhibitors.

a, Actual compound performance of predicted DNA gyrase inhibitors in an agarose gel-based in vitro assay of DNA gyrase supercoiling inhibition. The ratio of imaged pixel intensities (Supplementary Fig. 1a–l) for supercoiled and relaxed bands was indicative of inhibition, shown by the ciprofloxacin control. Eight of the twenty-seven compounds with the greatest effect sizes that showed statistically significant (P < 0.05, two-sided Wald test) inhibition are shown. Open circles show the independent samples (n = 23 for ciprofloxacin and novobiocin, n = 2 for all other conditions); filled circles indicate the mean pixel intensity ratio; error bars denote the 95% confidence interval of the mean. b, Agarose gel showing increasing inhibition of Mtb DNA gyrase supercoiling activity with increasing tryptanthrin concentration. DNA gyrase catalyses supercoiling of pBR322; inhibitors prevent the accumulation of supercoiled gel bands. This experiment was repeated independently once with similar results. The uncropped version is shown in Supplementary Fig. 1m.

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Extended Data Fig. 5 Validation of predicted inhibitors of mycolic acid biosynthesis.

Actual performance of predicted inhibitors of mycolic acid biosynthesis in an in vitro assay of inhibition of incorporation of14C-acetic acid into mycolic acid. The ratio of imaged pixel intensities for fatty acid methyl ester (FAME) and mycolic acid methyl ester (MAME) bands was indicative of inhibition activity, as shown by the isoniazid and ethionamide controls. Open circles show the independent biological replicates (n = 5 for vehicle control, n = 4 for BRD-1728 and BRD-7564, n = 3 for BRD-4384 and BRD-9942, n = 1 for ethionamide control, and n = 2 for all other conditions); filled circles indicate the mean pixel intensity ratio; error bars denote the 95% confidence interval of the mean (statistical significance determined by P < 0.05, two-sided Wald test).

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Extended Data Fig. 6 New classes of inhibitors of folate and tryptophan biosynthesis.

a, Schematic of the folate and tryptophan biosynthesis pathways. TrpG is an amphibolic enzyme, upstream of both PABA and tryptophan. Biosynthetic enzymes mentioned in the text are indicated in their metabolic context. 3-IGP, 3-indoleglycerol phosphate; ADC, 4-amino-4-deoxychorismate; DHF, dihydrofolate; DHFR, dihydrofolate reductase; DHFS, dihydrofolate synthase; DHP, dihydropteroate; DHP-PAS: adduct of DHP and PAS; DHPS, dihydropteroate synthase; HMDP-P2, 6-hydroxymethyl-7,8-dihydropterin diphosphate; MTX: methotrexate; PABA, para-amino-benzoic acid; PAS, para-aminosalicylic acid; Sulfa drugs, sulfonamide antibiotics. b, Dose–response curves of known inhibitors of folate biosynthesis and the validated tryptophan biosynthesis inhibitor scaffold, 3-indole propionic acid, supplemented with PABA, folic acid or tryptophan. Chemical structures of the known inhibitors are shown. Independent biological replicates (n = 4) are shown as open circles; means are shown as filled circles; error bars show 95% confidence intervals. c, Actual performance of predicted inhibitors of folate biosynthesis in a metabolite rescue assay. Mtb was treated with predicted inhibitors in the presence or absence of tryptophan, folate or PABA. The effect of BRD-7721, a 3-indole propionic acid ester, is abolished by supplementation with tryptophan, indicating it is an inhibitor of tryptophan biosynthesis. By contrast, the effect of the nitrothiophene BRD-2550 is abolished by folate and PABA, and that of BRD-8884 is abolished by folate alone, showing that they are inhibitors of folate biosynthesis with distinct mechanisms. Independent biological replicates (n = 4) are shown as open circles; means are shown as filled circles; error bars denote the 95% confidence intervals. d, Chemical structures of predicted and subsequently validated inhibitors of folate biosynthesis, including the nitrothiophenes and para-aminosalicylic acid derivative BRD-9819.

Extended Data Fig. 7 Performance of a large, unbiased compound library.

a, ROC curve showing that primary data were predictive of activity in a confirmatory secondary growth assay (n = 4 biologically independent experiments). We retested 454 compound–strain interactions using a resazurin, growth-based colorimetric assay. Taking 75% inhibition in the secondary assay as the ground truth, we demonstrated the primary assay as predictive of real activity that could be detected by conventional methods. Using 50% and 90% inhibition as the ground-truth assumption, the ROC AUC values were 0.61 and 0.69. b, Volcano plot (maximum likelihood mean fold change from n = 2 biologically independent samples against the unadjusted two-sided Wald test P value) of chemical–genetic interactions from the larger unbiased chemical library. Each point represents a single strain–compound interaction at a single concentration. c, Compounds in the library of bioactive compounds generally hit more strains than compounds in the unbiased library. Empirical cumulative distribution functions of number of hypomorphs hit by compounds in the two screens is plotted. Shown by the dotted lines, 36% of compounds in the bioactive library and 75% of compounds in the larger library hit 10 strains or fewer, suggesting that activity detected in the larger screen was generally more hypomorph-specific. d, Clustering of chemical–genetic interaction profiles. The number of chemical–genetic interaction profile clusters in the two libraries was determined by finding the minimum Gap statistic, a measure of within-cluster similarity compared to clustering at random. The minimum, denoted by the dotted lines, shows the estimate of the true number of clusters in the unbiased and bioactive libraries, with the unbiased library containing many more unique chemical-genetic clusters. e, Chemical–genetic clusters (n = 1,864) are enriched for chemically similar compounds in the unbiased library. The y axis shows the frequency of chemical–genetic clusters with a particular Tanimoto WSS Z score (x axis), which is an indicator of in-cluster chemical similarity. A total of 221 (12%) of the clusters have meaningful structure–activity relationships—that is, compounds within a cluster have significantly greater chemical similarities than by chance—as indicated by a one-sided permutation test (unadjusted P < 0.05, 10,000 permutations). f, Actual compound performance of predicted inhibitors of folate biosynthesis in a metabolite rescue assay. Mtb was treated with predicted inhibitors in the presence or absence of folate or PABA. The effect of BRD-1242 is abolished by PABA alone, and the effects of BRD-4308 and BRD-9309 are abolished by folate alone, suggesting that they are inhibitors of folate biosynthesis with distinct mechanisms. Independent biological replicates (n = 3) are shown as open circles; means are shown as filled circles; error bars denote 95% confidence intervals.

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Extended Data Fig. 8 Validation of predicted RNAP inhibitors.

Actual compound performance of predicted RNAP inhibitors in an in vitro assay for inhibition of RNA synthesis by E. coli RNAP. Three compounds that showed statistically significant inhibition are shown with a rifampin control (P < 0.05, two-tailed Wald test). Open circles show the independent replicates (n = 36 for vehicle control, n = 4 for BRD-6652, n = 8 for BRD-8565 and rifampin, n = 12 for actinomycin D), filled circles indicate the mean ratio of treatment-to-vehicle fluorescence, and error bars show the 95% confidence interval of the mean. Act. D, actinomycin D.

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Extended Data Fig. 9 Inhibitors of EfpA, in M. tuberculosis.

a, Dose–response analysis of BRD-8000 on the growth of wild-type Mtb, the EfpA hypomorph, and a mutant overexpressing EfpA (pUV15::efpA), demonstrating hypersensitivity of the hypomorph. Independent biological replicates (n = 4) are shown as open circles; means are shown as filled circles; error bars denote 95% confidence intervals. b, BRD-8000.2 is bactericidal as demonstrated by reducing colony-forming units over time. Independent biological replicates (n = 8) are shown as open circles; means are shown as filled circles; error bars denote 95% confidence intervals. c, Toxicity and bioavailability measurements of BRD-8000.2 and BRD-8000.3. hERG, human ether a-go-go related gene. d, Cytochrome P450 (CYP) inhibition measurements of BRD-8000.2 and BRD-8000.3. e, Schematic of the EtBr efflux assay. Bacteria were loaded with EtBr and its efflux was monitored by change in fluorescence. f, Example kinetic time courses of EtBr fluorescence decay for Msm with BRD-8000.3. The concentration of EtBr used for pre-incubation is indicated in colour, with two different inhibitor concentrations shown. Numerically integrated Michaelis–Menten best-fit time courses are shown in red. Experiments were repeated independently once with similar results. g, Kinetic time course of EtBr fluorescence decay for Mtb. Numerically integrated Michaelis–Menten best-fit time courses are shown in red. The table shows best-fit Michaelis–Menten parameters and Fick’s diffusion constant for wild-type Mtb, an EfpA-overexpressor (pUV15::efpA), and the BRD-8000 resistant mutant (efpAV319F). Although the in vivo apparent maximal efflux rate (Vmax) of the EfpA overexpressor is higher than the wild-type Mtb, that of the BRD-8000 resistant mutant is not, indicating that the resistant mutant is not hyperactive for efflux. Experiments were repeated independently once with similar results. h, Dose–response analysis of isoniazid against wild-type Mtb and the BRD-8000 resistant mutant (efpAV319F). Because isoniazid is a substrate of EfpA, no shift in the MIC90 value for isoniazid with the BRD-8000 resistant mutant indicates that EfpA(V319F) is not hyperactive for efflux. Mean growth (n = 4 biologically independent replicates) is shown, with error bars indicating 95% confidence intervals. i, Results of an assay for intracellular accumulation of BRD-8000.3 in MsmΔefpA complemented with either Mtb wild-type efpA or efpAV319F. Fluorescence of BRD-8000.3 in bacterial lysates was measured, and lysate background fluorescence was subtracted. Although verapamil, a general efflux pump inhibitor, caused statistically significant intracellular accumulation of BRD-8000.3, there was no significant difference in accumulation between the different strains in the absence of verapamil, indicating that EfpA(V319F) is not hyperactive for efflux, and that BRD-8000.3 is not a substrate of EfpA. Independent biological replicates (n = 9 for wild-type control without verapamil; n = 3 for other conditions) are shown as open circles; means are shown as filled circles; error bars denote s.e.m. j, Additional compounds predicted and validated to be efflux inhibitors. k, Dose–response analysis of kinetic time courses of EtBr fluorescence decay for compounds in h. Increasing inhibitor concentration is denoted by colour. Experiments were repeated independently once with similar results. l, Example kinetic time courses of EtBr fluorescence decay for Msm at one concentration (6 µM) of BRD-9327. Numerically integrated Michaelis–Menten best-fit time courses are shown in red. The table shows global best-fit kinetic inhibition parameters across ten concentrations for this inhibitor. Experiments were repeated independently once with similar results.

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Extended Data Table 1 Minimum inhibitory concentrations of BRD-8000 series compounds against non-tubercular strains
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Supplementary information

41586_2019_1315_MOESM1_ESM.pdf

Supplementary Information Supplementary Notes 1-8: Additional detail on statistical methods and chemical characterization.

Reporting Summary

Supplementary Figure 1 Agarose gel electrophoresis for

in vitro DNA gyrase assay. a-l, Gel images used to validate predicted DNA gyrase inhibitors using an in vitro assay of DNA gyrase supercoiling and decatenation activity. Analysis of supercoiling band intensity is presented in Extended Data Fig. 4a. i, was rotated to be square. Detailed lane annotations are in Supplementary Table 3. m, Uncropped agarose gel image for the dose response of tryptanthrin inhibition of DNA gyrase supercoiling and decatenation activity. The white box indicates the cropped region used in Extended Data Fig. 4b.

Supplementary Data 1

Strains and genotypes used in chemical genetic screens.

Supplementary Data 2

Ground truth reference set of compounds used for training machine learning models for mechanism of action prediction.

Supplementary Data 3

Annotation of Supplementary Figure 1.

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Johnson, E.O., LaVerriere, E., Office, E. et al. Large-scale chemical–genetics yields new M. tuberculosis inhibitor classes. Nature 571, 72–78 (2019). https://doi.org/10.1038/s41586-019-1315-z

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