Supplementary Materials

Supplementary Material for:

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

Mattia Zampieri,* Balazs Szappanos, Maria Virginia Buchieri, Andrej Trauner, Ilaria Piazza, Paola Picotti, Sébastien Gagneux, Sonia Borrell, Brigitte Gicquel, Joel Lelievre, Balazs Papp, Uwe Sauer

*Corresponding author. Email: zampieri{at}imsb.biol.ethz.ch

Published 21 February 2018, Sci. Transl. Med. 10, eaal3973 (2018)
DOI: 10.1126/scitranslmed.aal3973

This PDF file includes:

  • Materials and Methods
  • Fig. S1. Schematic description of MS data normalization and analysis.
  • Fig. S2. Analysis of correlations across biological replicates.
  • Fig. S3. Distribution of responsive metabolites.
  • Fig. S4. Number of affected metabolites per MoA.
  • Fig. S5. Distribution of growth inhibitory activities.
  • Fig. S6. Pathway enrichment for metabolome responses to antibiotics with seven major MoA.
  • Fig. S7. Pairwise drug similarity.
  • Fig. S8. Metabolome-based similarity.
  • Fig. S9. Similarity between compounds with equivalent MoAs as a function of the difference in growth inhibition.
  • Fig. S10. Distribution of predicted MoAs.
  • Fig. S11. Pairwise compound chemical distance.
  • Fig. S12. M. tuberculosis gyrase assay.
  • Fig. S13. M. tuberculosis and E. coli gyrase assay.
  • Fig. S14. Distribution of growth inhibitory activities for GSK compounds with classified and unclassified MoAs.
  • Fig. S15. Pathway enrichment analysis for compounds with potential unconventional MoAs.
  • Fig. S16. Common metabolic responses across GSK compounds with potential new MoAs.
  • Fig. S17. Protein-protein interactions among proteins with significant conformational changes detected by limited proteolysis analysis.
  • Fig. S18. Robustness of results from limited proteolysis.
  • Fig. S19. Similarity between compounds with equivalent MoAs as a function of growth inhibition.
  • Fig. S20. Data normalization.
  • Fig. S21. Schematic representation of the procedure used to estimate pairwise similarity among tested compounds.
  • References (8186)

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Other Supplementary Material for this manuscript includes the following:

  • Table S1 (Microsoft Excel format). Antibiotic perturbation list.
  • Table S2 (Microsoft Excel format). Metabolome data (perturbation name versus metabolites data matrix).
  • Table S3 (Microsoft Excel format). Impulse model fitting results (maximum fold change and R2 matrices).
  • Table S4 (Microsoft Excel format). MoA predictions (list of top predicted MoAs and complete matrix of enrichment q values).
  • Table S5 (Microsoft Excel format). Gene-drug assignments with P values coming from the network locality analysis.
  • Table S6 (Microsoft Excel format). List of metabolites used to annotate peaks in the mass spectra.
  • Table S7 (Microsoft Excel format). Comparison of similarity metrics.
  • Table S8 (Microsoft Excel format). Results from analysis of limited proteolysis data.