High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds

Abstract

Rapidly spreading antibiotic resistance and the low discovery rate of new antimicrobial compounds demand more effective strategies for early drug discovery. One bottleneck in the drug discovery pipeline is the identification of the modes of action (MoAs) of new compounds. We have developed a rapid systematic metabolome profiling strategy to classify the MoAs of bioactive compounds. The method predicted MoA-specific metabolic responses in the nonpathogenic bacterium Mycobacterium smegmatis after treatment with 62 reference compounds with known MoAs and different metabolic and nonmetabolic targets. We then analyzed a library of 212 new antimycobacterial compounds with unknown MoAs from a drug discovery effort by the pharmaceutical company GlaxoSmithKline (GSK). More than 70% of these new compounds induced metabolic responses in M. smegmatis indicative of known MoAs, seven of which were experimentally validated. Only 8% (16) of the compounds appeared to target unconventional cellular processes, illustrating the difficulty in discovering new antibiotics with different MoAs among compounds used as monotherapies. For six of the GSK compounds with potentially new MoAs, the metabolome profiles suggested their ability to interfere with trehalose and lipid metabolism. This was supported by whole-genome sequencing of spontaneous drug-resistant mutants of the pathogen Mycobacterium tuberculosis and in vitro compound-proteome interaction analysis for one of these compounds. Our compendium of drug-metabolome profiles can be used to rapidly query the MoAs of uncharacterized antimicrobial compounds and should be a useful resource for the drug discovery community.

Figure 1. Antibiotic-induced metabolome responses in M. smegmatis.

(A) Metabolomics workflow. Cells were grown in 700 μl volumes in 96 well plates to an OD₅₉₅ of approximately 0.4, before addition of 10 μl of the antimicrobial compound. 80 μl of cell culture was withdrawn from each well at each sampling time. 40 μl was used to determine cell density, and the remaining 40 μl was added to cold extraction buffer. Supernatant was directly injected into a time of flight (TOF) mass spectrometer and relative changes in metabolite intensities were extrapolated from processing of the metabolome data. (B) Compounds tested. Almost half of the compounds tested included different concentrations of reference antimicrobials (yellow) and chemical stress agents (green) with known MoAs; the remainder were compounds from a GSK library used at 10 µM concentration (blue). (C) Distribution of MoAs for the 62 reference compounds. (D) Schematic representation of the drug-metabolome response data set. For each antimicrobial compound tested, the dynamic profile of 1006 metabolites was interrogated. As an example, the top graph illustrates the response of the folic acid biosynthesis intermediate 4-aminobenzoic acid to the antimicrobial para-aminosalicylic acid (104 µM, red; 41 µM, grey, 25 µM, blue). The bottom graph shows the response of the bacterial metabolite mycobactin to the known antimicrobial isoniazid (1.5 mM, red; 0.22 mM, grey; 0.11 mM, blue). Thick lines represent the results from the impulse model fitting analysis for the three drug concentrations. Metabolic profiles of 4-aminobenzoic acid and mycobactin across all conditions are shown in light grey. (E) Distribution of metabolic response onset times for antibiotics belonging to the seven main antibiotic categories tested in this study. The onset time is defined as the time at which metabolite changes reached half of their maximum change after treatment of M. smegmatis with the compounds. For each perturbation (treatment with compound), metabolites with a model fitting R²≥0.6 and a maximum absolute log₂ fold-change≥2 were retained.

Figure 2. Commonalities among metabolite changes in response to antimicrobial treatment.

(A) Correlation between growth rate and metabolite abundance in M. smegmatis after treatment with antimicrobial compounds. Each dot represents a metabolite. The two axes represent the mean R² across all tested conditions and the mean of maximum Z-scores across all tested conditions and time points. Color reflects the degree of Spearman correlation between maximum Z-score and growth inhibition across all tested conditions. Metabolites with an average R²≥0.5 and log₂ Z-score ≥0.5 are shown. (B) Pathway enrichment for metabolome responses to antibiotics with seven known MoAs: (1) - cell wall synthesis inhibitors; (2) - DNA cleavage, (3) folic acid biosynthesis inhibitors, (4) quinolones (5) mycolic acid biosynthesis inhibitors, (6) protein synthesis inhibitors, (7) RNA synthesis inhibitors. Enrichment was performed with the 50 most frequently identified genes for each antibiotic class. The heatmap shows enriched KEGG metabolic pathways with q-values≤0.01.

Figure 3. Pair-wise similarity of antimicrobials with respect to metabolic changes induced in M. smegmatis.

(A) Similarity heatmap for 62 reference antimicrobials. Similarity calculated between each drug-perturbed condition is represented as a symmetric heatmap. Diagonal values are not taken into account and are set to not-a-number (grey). Highlighted boxes correspond to the seven main MoAs of the 62 reference compounds. (B) Magnification of panel in A showing antimicrobial compounds that blocked gyrase and cell wall synthesisin M. smegmatis. (C) Receiver Operating Characteristic (ROC) curve measuring the ability of metabolome-based predictions using the iterative hypergeometric test (45) to discriminate antibiotics sharing similar MoAs. Notably, we considered only MoAs that applied to more than one antimicrobial reference compound. CPR, ciprofloxacin; LVX, levofloxacin; MFL, – moxifloxacin; NAL, nalidixic acid; NFL,norfloxacin; OFL, ofloxacin; AMX,amoxicillin; AMP, ampicillin; CCL,cefaclor; CTX,ceftriaxone; OCI,oxacillin; BAC, bacitracin; CYC,D-cycloserine; EMB, ethambutol; FOS,fosfomycin.

Figure 4. Metabolome-based predictions of MoAs for 212 GSK compounds.

(A) Grouping of metabolome similarity-based predictions for the 212 GSK compounds into known MoAs. (B) Impact of antimicrobial drugs on normalized FolA in vitro activity. The measured dihydrofolate conversion rate was normalized to the activity measured with DMSO vehicle only. All tested compounds were dissolved in DMSO: 40 μM of streptomycin (STR), 40 μM and 1333 μM of trimethoprim (TRM, TRM-H), 40 μM para-aminosalicylic acid (PAS) and 40 μM of six GSK compounds. (C) Shown is RecA promoter activity in exponentially growing E. coli treated with the following: four GSK compounds predicted to be quinolone-like agents (BRL-7940SA, BRL-10988SA, GSK1066288A, GSK695914A), norfloxacin, ampicillin, DMSO and three GSK compounds (GSK2534991A, GSK1826825A, GSK1518999A) predicted to be a protein synthesis inhibitor, a folic acid biosynthesis inhibitor or with an unknown MoA, respectively. (D) Gyrase activity of M. tuberculosis measured using an in vitro supercoiling assay at different concentrations of moxifloxacin (MFL) or GSK1066288A. (E) Gyrase activity of E. coli in the presence of DMSO, MFL, STR or GSK1066288A. Activity of denatured gyrase was used as a negative control (ø).

Figure 5. Analysis of metabolite changes in M. smegmatis after treatment with the GSK compound GSK2623870A.

(A) Pairwise similarity between M. smegmatis metabolome response profiles to 16 GSK compounds with no similarity to known MoAs. (B) Schematic representation of trehalose monomycolate exporter protein MmpL3. Transmembrane segments are represented in violet. Circles indicate the locations of amino acid changes associated with resistance of M. tuberculosis to antimycobacterial lead compounds previously found to select for resistance mutations in MmpL3. These compounds include SQ109 N=-(2-adamantyl)-N-[(2E)-3,7-dimethylocta-2,6-dienyl]ethane-1,2-diamine (blue), THPP tetrahydropyrazolo[1,5- a]pyrimidine-3-carboxamide (orange) and SPIROS N-benzyl-69,79-dihy-drospiro[piperidine-4,49-thieno[3,2-c]pyran] (purple). The black star indicates the amino acid change associated with resistance to the GSK compound GSK2623870A. The genomes of the M. tuberculosis H37Rv and Beijing GC1237 mutant strains contain an A to G single nucleotide polymorphism at position 755, which resulted in a tyrosine to cysteine missense mutation at position 252 of the MmpL3 protein. (C) 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. Proteins highlighted in red are known to physically interact with fatty acid synthase FAS-I (Fig. S17, S18). For each protein, the size of the dot reflects the number of interacting partners (73) with significant conformational changes. (D) Shown are rapid metabolic changes induced by the GSK antimicrobial compound GSK2623870A. Each dot corresponds to the R² and Z-score values of the metabolite 5 minutes after exposure of M. smegmatis to the antimicrobial compound. Metabolites highlighted in red are involved in fatty acid metabolism.