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Combining CRISPRi and metabolomics for functional annotation of compound libraries

Nature Chemical Biology volume 18, pages 482–491 (2022)Cite this article

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An Author Correction to this article was published on 08 April 2022

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Abstract

Molecular profiling of small molecules offers invaluable insights into the function of compounds and allows for hypothesis generation about small-molecule direct targets and secondary effects. However, current profiling methods are limited in either the number of measurable parameters or throughput. Here we developed a multiplexed, unbiased framework that, by linking genetic to drug-induced changes in nearly a thousand metabolites, allows for high-throughput functional annotation of compound libraries in Escherichia coli. First, we generated a reference map of metabolic changes from CRISPR interference (CRISPRi) with 352 genes in all major essential biological processes. Next, on the basis of the comparison of genetic changes with 1,342 drug-induced metabolic changes, we made de novo predictions of compound functionality and revealed antibacterials with unconventional modes of action (MoAs). We show that our framework, combining dynamic gene silencing with metabolomics, can be adapted as a general strategy for comprehensive high-throughput analysis of compound functionality from bacteria to human cell lines.

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Fig. 1: Metabolome profiling of essential gene knockdown.
Fig. 2: Functional associations between gene knockdowns and major antibiotic classes.
Fig. 3: Metabolic profiling of chemical and genetic perturbations in M. smegmatis and A549 cancer cells.
Fig. 4: Chemical versus genetic interference.
Fig. 5: Gene–drug associations.
Fig. 6: Chloroxine resistance.

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Data availability

All data generated or analyzed during this study are included in this published article as Supplementary Data. A detailed description of all data analysis steps is published in this article (Methods and Supplementary Information). Matlab code is available for download at https://www.imsb.ethz.ch/research/zampieri-group/resources.html. Metabolome data and results from data analysis can be found as Supplementary Datasets. The MS proteomics raw data and analysis results have been deposited through the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD024133. Genome sequence data have been deposited through SRA BioProject under accession number PRJNA776168 (also see Supplementary Notes). Source data are provided with this paper.

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Acknowledgements

We thank U. Sauer for supporting this work and providing laboratory facilities, N. de Souza for helpful feedback and discussions, the Gross and K.C. Huang groups at UCSF and Stanford for discussions and sharing the E. coli CRISPRi library ahead of publication and T.J. de Wet and D.F. Warner at the University of Cape Town for sharing the CRISPRi mutants in M. smegmatis. This work was supported by an NCCR AntiResist project funding to M.Z., U.J. and P.P. (180541); P.P. and L.G. were additionally funded by the European Research Council (grant agreement number 866004), EPIC-XS, project number 823839, funded by the Horizon 2020 programme of the European Union and through a Personalized Health and Related Technologies grant (PHRT-506). C.M. and M.B. were supported by NIH grant AI133191.

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Author notes

  1. These authors contributed equally: Miquel Anglada-Girotto, Gabriel Handschin.

Authors and Affiliations

  1. Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland

    Miquel Anglada-Girotto, Gabriel Handschin, Karin Ortmayr, Adrian I. Campos, Ludovic Gillet, Paola Picotti & Mattia Zampieri

  2. Biozentrum, University of Basel, Basel, Switzerland

    Pablo Manfredi & Urs Jenal

  3. Department of Microbiology and Immunology, Albert Einstein College of Medicine, New York, NY, USA

    Claire V. Mulholland & Michael Berney

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Contributions

M.Z. designed the project. M.A.-G. and G.H. performed the metabolic profiling of the E. coli mutant library. A.I.C. performed the drug metabolome profiles. K.O. performed the metabolic profiling in human cell lines. C.M. performed the metabolic profiling of M. smegmatis under the supervision of M.B. P.M. performed the genome sequencing and proteome profiling of resistant strains under the supervision of U.J. L.G. performed the limited proteolysis experiments and analyses under the supervision of P.P. M.A.-G., G.H. and M.Z. performed data analysis. All authors contributed to preparing the manuscript.

Corresponding author

Correspondence to Mattia Zampieri.

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Competing interests

P.P. is an advisor at Biognosys AG (Schlieren, Switzerland), the company that licenses the LiP technology.

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Extended data

Extended Data Fig. 1 Metabolome profiles of CRISPRi mutant library.

A) Distribution of significant metabolic changes per gene knockdowns/time points. On average, 24 metabolites underwent significant changes (absolute Z-score ≥ 1 and p value ≤ 1e−5) per mutant and time point, and most of the metabolites (95%) exhibited a significant response in at least one gene knockdown. B) Distribution of significant changes for each metabolite across knockdowns/time points. C) Relative distribution of collected time points per gene knockdown. D) Distribution of Optical Densities (OD600) at sampling. E) Distribution of locality scores for essential (blue) and non-essential knockdowns (red). F) Distribution of the number of significant affected metabolites by essential (blue) and non-essential (red) gene knockdowns in each time point. G) Estimates of growth rates of CRISPRi mutants grown in M9 glucose + 1 mM IPTG. Each dot corresponds to mean ± standard deviation (SD) across three biological replicates. Average Wild-type growth rate ± SD are represented by solid and dashed red lines, respectively. Each dot is color-coded according to the significance in growth rate difference with respect to wild-type (that is t-test analysis). Pie chart reports on the fraction of knockdown strains growing significantly slower than wild type (pvalue ≤ 0.01). H) Median absolute Z-score of significantly affected metabolites (|Z-score|≥ 1 and p value ≤ 1e − 5) in each mutant/time point. Median values for each timepoint across all mutant strains are used to fit a sigmoidal curve (red curve). The blue shaded area identifies the confidence region (95% confidence interval). Green dashed lines report the 95% confidence interval for the time necessary to reach 90% of the maximum estimated changes (that is plateau).

Source data

Extended Data Fig. 2 Analysis of growth phenotypes.

A) Growth dynamics of each individual mutant. Each line corresponds to the OD averaged across 3 biological replicates. In orange, mutants exhibiting a sudden drop in OD are highlighted. B) Median absolute Z-score of significantly affected metabolites in each mutant/time point vs estimated growth rate. As expected the slower the growth rate the larger is the impact of gene knockdowns on metabolism7, although the dependency is mild: Spearman correlation of -0.19. C) Distribution of growth rates for essential and non-essential gene knockdowns. D) Distribution of growth rates for gene knockdowns across KEGG pathways.

Source data

Extended Data Fig. 3 Metabolome-based predictions of functional gene-gene similarity.

A) ROC analysis comparing the performance of different similarity metrics in detecting pair of genes that are either encoding for subunits of the same protein complex or for iso-enzymes. We used previously published data27 profiling the metabolome across 3873 gene knockout mutants. We selected only genes encoding for proteins complexes and isoenzymes with at least one significant metabolic changes as defined in27 (|Z-score| >5). Area Under the Curve (AUC) is reported for Spearman correlation, mutual information, context likelihood of relatedness (CLR)60 applied on mutual information, iterative similarity and CLR applied on iterative similarity (iSim). The best performance was obtained with iSim (Table S1). To cope with the fact that similarity metrics, like mutual information, can be biased by hidden global patterns in the data (in our case likely to reflect indirect and general type of effects (for example growth rates)), and to take into account the typical patterns of interaction of multivariate datasets, the authors in60 developed an effective and simple approach to normalize pair wise mutual information. The CLR algorithm applies an adaptive background correction step to the matrix of pair-wise similarity scores to eliminate indirect global similarities between drug/gene knockdown metabolome profiles. After computing the similarity between drug/gene pairs, the algorithm compares the similarity between drug/gene A and drug/gene B to the background distribution of similarity scores calculated for all possible drug/gene pairs that include either A or B. The pairs with the most probable functional associations are those whose similarity scores is larger than the background distribution of similarity scores. This step, when applied on mutual information, improves predictions by eliminating ‘promiscuous’ cases, where one gene weakly co-varies with a large numbers of genes. The improvement of CLR applied on iSim are minor. Hence, in this work iSim is used instead of iSim+CLR. B) For each gene, we ranked gene-gene metabolic similarity and performed KEGG-functional enrichment analysis -that is KEGG pathways that exhibit a significant (qvalue ≤ 0.01) enrichment of gene knockdowns exhibiting similar metabolic profiles. In blue the ROC curve obtained by considering only similarities between gene pairs from different operons. In purple, we report ROC analysis of KEGG functional enrichment without accounting for operon structure. D) Each corresponds to a KEGG metabolic pathway and the respective AUC values estimated from gene-gene similarity. Only KEGG pathways with an AUC ≥ 0.6 are reported.

Source data

Extended Data Fig. 4 Metabolic profiles of genetic perturbations in Mycobacterium Smegmatis.

A) Growth dynamics of each individual M. smegmatis CRISPR mutant after ATC induction. Each line and errorbar corresponds to the OD600 average and standard deviation across 3 biological replicates. B) Heatmap of metabolome profiles 24 hours after CRISPR induction.

Source data

Extended Data Fig. 5 Metabolic profiles of chemical perturbations in A549 cancer cells.

A) Dynamic monitoring of cell confluence (% well area covered by cells) in A549 cancer cell line in the presence of sgRNAs targeting three different sections of thymidylate synthase, in addition to a control sgRNA inducing cell death (lethal control, gLethal) by targeting multiple sections in the genome. B) Dynamic monitoring of cell confluence in A549 cancer cell line treated with different drugs. C) The volcano plots illustrate metabolic changes (that is log2 fold-change and p-value significance) for 1593 putatively annotated metabolites in A549 cells 48 hours after treatment with 14 different drugs. Pvalues are estimated by two-sided t-test analysis. 3 drugs are targeting TYMS (methotrexate, pemetrexed and 5-fluorouracil). The remaining drugs act on largely different cellular processes, such as central metabolism (oxamate, dichloroacetate, carbonyl cyanide, 2-deoxyglucose, UK5099), nucleotide biosynthesis (mercaptopurine), mevalonate pathway (atorvastatin), DNA repair (camptothecin, cisplatin, doxorubicin) and dopamine receptors (trifluoperazine).

Source data

Extended Data Fig. 6 Gene-drug associations.

A) Disk diffusion assay testing the susceptibility to ofloxacin (10 μg) in wild-type E. coli and pyrG knockdown. Violin plot represents mean ± SD across 3 biological replicates. Two representative petri dishes are reported on the lower panel. B) Imipramine gene-drug associations. Volcano plot of similarities between drug and knockdowns time dependent metabolic profiles. Notably, we found genes exhibiting more rare but strong associations to chemically induced metabolic changes, such as the association between the gene mediating chromosome partitioning during division mukF, and imipramine, a drug typically used as antidepressant, potentially hinting at the toxic side effects of imipramine as DNA damaging agent; C) Vatalanib gene-drug associations. Vatalanib, a tyrosine kinase inhibitor that interferes with the ATP-binding site of vascular endothelial growth factor receptors (VEGFR1-3), induces similar metabolic changes to purA knockdown, a key gene in the adenosine ribonucleotides de novo biosynthesis. D) fraction of compounds inhibiting at least 50% of the growth-rate at 100 µM for (i) compounds within the top 1% of drug-gene knockdown similarities, (ii) compounds within the top 1% of drug-gene knockout similarities (data reported in27), (iii) all tested compounds. We found that drugs with strong similar metabolic profiles to at least one essential gene knockdown (that is top 1% of all drug-genes associations) are significantly enriched (pvalue = 2.3071e-183 ≤ 0.001 ***, from 13% to 60%) for drugs inducing Growth Inhibition (GI) greater than 50%. On the contrary when selecting the top 1% of similarities between drugs and the ~3800 non-essential gene knockout7,27, we found no significant enrichment and the majority of drugs exhibits relatively low growth inhibition (GI < 50%). E-F-G) Volcano plot of similarities between tegaserod, suloctidil, thiethylperazine and the remaining 1341 drugs. Pvalues are estimated by hypergeometric test analysis. H-J-K) Volcano plot of proteome changes in three tegaserod resistant strains (Table 1 of the main text) with respect to the ancestor wild-type E. coli (Table S7). Proteins with significant (qvalue ≤ 0.05 and |FC|≥ 1) changes with respect to the wild-type are highlighted in red. Pvalues are estimated by two-sided t-test analysis and corrected for multiple tests.

Source data

Extended Data Fig. 7 Chloroxine induce copper toxicity.

A-B-C-D) Results from limited proteolysis analysis. Each dot in the volcano plot represents the relative difference in peptide abundance between the treated and untreated proteome extracts. None of the 13797 detected peptides exhibit significant (pvalue < 7.2480e-07, Bonferroni corrected) conformational changes between untreated and whole proteome extracts treated with 4 different concentrations of chloroxine (Table S6). Pvalues are estimated by two-sided t-test analysis. E) Relative growth rates at different concentrations of chloramphenicol with respect to the untreated condition. Wild type E. coli was grown in LB medium with and without 200 µM of CuCl2. Growth inhibitory activity for different chloramphenicol concentrations were measured in triplicates. Errorbars are mean ± standard deviation across three biological replicates. F) 2-Isopropylmaleate z-score levels across the 1342 tested compounds. G) Average Z-score across metabolites in the branched chain amino acids biosynthesis pathway as defined in KEGG. Significance (that is pvalue estimated by permutation test) of enrichment analysis is testing for each compound an average increase of metabolite levels. Out of the 1342 tested compounds (that is Prestwick library + antibiotics), only 35 (less than 3%) induced significant changes in the levels of BCAA intermediates (pvalue ≤ 5e-4 Bonferroni corrected and mean Z-score≥0.5). In addition to chloroxine, clioquinol, CuCl2 and paraquat, we observed that several of the 35 compounds inhibit protein synthesis (for example apramacyn, tobramycin, ribostamacyn…). This is likely reflecting the indirect accumulation of amino acids, including BCAA intermediates, as a results of the inhibition of protein synethesis4,18. H) Growth of wild-type E.coli (blue) and ΔkatG (black) without and with (dashed lines) 7.8 µM of chloroxine. H) Growth of wild-type E.coli (blue), ΔrcsB (black) and rcsB++ (expression of rcsB is under the control of an IPTG-inducible promoter, here we used 1 mM of IPTG) without and with (dashed lines) 7.8 µM of chloroxine. J) Scatter plot of metabolic changes induced by chloroxine vs clioquinol. K) Scatter plot of metabolic changes induced by chloroxine vs paraquat. Some of the largest metabolic changes induced by paraquat involved metabolic intermediates of branched chain amino acids.

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Supplementary Dataset 1.

Supplementary Data 2

Supplementary Dataset 2.

Supplementary Data 3

Supplementary Dataset 3.

Supplementary Data 4

Supplementary Dataset 4.

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Supplementary Dataset 5.

Supplementary Data 6

Supplementary Dataset 6.

Supplementary Data 7

Supplementary Dataset 7.

Supplementary Data 8

Supplementary Dataset 8.

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Anglada-Girotto, M., Handschin, G., Ortmayr, K. et al. Combining CRISPRi and metabolomics for functional annotation of compound libraries. Nat Chem Biol 18, 482–491 (2022). https://doi.org/10.1038/s41589-022-00970-3

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