Microbiology/Metabolomics Core John Cronan and Jonathan Sweedler Enzyme Function Initiative (EFI)

1. Microbiology/Metabolomics Core John Cronan and Jonathan Sweedler Enzyme Function Initiative (EFI) Advisory Committee Meeting November 30, 2011

2. Outline • Experimental scope • Infrastructure • Targets • YidA from E. coli (HAD) • YghU, YfcF, and YfcG from E. coli (GST) • RuBisCO-like protein from R. rubrum (ENO) • Future Directions

3. Experimental Scope Phenomics Transcriptomics Metabolomics Verification of hypothesized enzyme-catalyzed reaction and/or evidence from relevant pathway Conditions for target expression (We now do qRT-PCR on each gene of interest) Bochner, B.R. (2003) New technologies to assess genotype-phenotype relationships, Nature Rev. Genetics. 4, 309-314.

4. Infrastructure Personnel Instrumentation Microbiology BiologOmnilog phenotype microarray plate reader/incubator Growth curve-ometer, BioscreenC E. coli single gene KO collection (Keio collection) Metabolomics 11 Tesla LTQ-FT LC-MS High resolution QTOF LC-MS Custom XCMS LCMS data analysis platform for untargeted metabolomics • John Cronan (Microbiology) • Jonathan Sweedler (Metabolomics) • Brad Evans (Metabolomics) • McKay Wood (Micro/Meta) • Kyuil Cho (Metabolomics) • Ritesh Kumar (Micro) • Amy Jones (Micro)

5. Targets from around the EFI • AHS: • E. coli • SsnA, Php, TatD, YahJ, YjjV, HyuA, YcdX, Ade • B. halodurans • LisM-RP • ENO: • E. coli • GudX, RspA, YcjG, YfaW • B. cereus • NSAAR • S. enterica • ManD-RP • A. tumefaciens • 1RVK, 2NQL, GlucDRP, Atu0270, Atu4120, Atu3139, Atu4196… • GST: • E. coli • YfcG, YghU, YqjG, YliJ, YfcF, YncG, YibF, YecN • HAD: • E. coli • YidA, YigB, YbjI, NagD • P. fluorescens • 3M9L • IS: • A. tumefaciens • IspB • C. glutamicum • gi# 19551716 • B. fragilis • gi# 53711383

6. HAD SF: YidA from E. coli dgoT dgoD dgoA dgoK dgoR yidA YidA kcat = 2 s-1 KM = 250 μM kcat/KM = 8 x 103 M-1s-1 Toxic if concentration builds in the cell! Courtesy of D. Dunaway-Mariano

7. YidA (HAD): no effect after addition of galactonate glycerol + galactonate succinate + galactonate glucose + galactonate

8. YidA KO likely mutated during lag YidA(HAD): long lag when cells are resuspended in galactonate

9. YidA (HAD): LCMS results for KDGP Validated with standard from Hua Huang in the DDM Lab

10. YidA from E. coli (HAD): Results and Conclusions • Phenomics is difficult with HAD SF members, as many are promiscuous housekeeping phosphatases • An abrupt shift from a relatively poor carbon source to galactonate as sole carbon source causes the YidA KO to display a growth lag • The “abruptness” may be important for quickly building levels of the toxic metabolite, KDGP • Growth of YidA following the lag may be due to mutation • Metabolomics efforts so far do not support the connection between YidA KO lag with elevated KDGP levels

11. GST SF in E. coli: a role in oxidative stress response?

12. YfcF and YfcG (GST): NO sensitivity in null mutants

13. GST SF in E. coli: secreted to the periplasm? Modeling/docking by Backy Chen, Computation Core

14. GST SF in E. coli: protein localization via gene fusion yghU-phoA yfcG-phoA empty vector yqjG-phoA treA-phoA gapA-phoA Periplasm Cytoplasm yghU-lacZ yfcG-lacZ empty vector yqjG-lacZ treA-lacZ gapA-lacZ

15. YghU (GST): protein localization via proteomics

16. YfcF(GST): culture labeling and metabolite extraction

17. Ions from WT Ions from mutant YfcF (GST): differential labeling provides higher accuracy

18. YfcF (GST): contaminant peaks remain unlabeled

19. YfcF (GST): affect of nitric oxide on metabolites

20. GST SF: results and conclusions • YfcF and YfcG are implicated in reduction of nitric oxide • NO sensitivity phenotype identified • YfcF metabolomics with cutting-edge labeling protocol allows measurement of small changes in metabolites • Cellular localization is an important aspect of enzyme function • YghU and YfcG appear to remain in the cytoplasm

21. RuBisCO-like protein, RLP, from R. rubrum (ENO) Canonical methionine salvage pathway (e.g. B. subtilis) Seemingly incomplete MSP (R. rubrum) RLP ? Work with Tobias Erb, Gerlt Lab

22. RLP: evidence for novel fate of methionine sulfur Work with Tobias Erb, Gerlt Lab (ENO)

23. RLP: whole cell untargeted metabolomics Work with Tobias Erb, Gerlt Lab (ENO)

24. RLP: whole cell untargeted metabolomics Data Processing Peak Grouping Formula Prediction Pathway Activity Profiling Formula modeling DB Search Perturbation Exp. LC-MS Analysis Isotope Pattern Analysis • Primary peaks used first • Round Robin • Recursive Backtracking • 2ppm mass tolerance • Top hits formula • Mass check • Retention time check • Intensity ratio check Seed Metabolites Preprocessing (XCMS) • Isotope pattern • High intensity change • Exist in current DB Theoretical Isotope Pattern Modeling Peak Grouping • Peak detection/alignment • Retention time correction • Noise filtering Pathway Analysis • First order Markov • Forward Trellis • Primary Peaks • Isotope pattern • ≥ 20% intensity change • Seed metabolites info. • DB Hits mono. peaks • Shared pathways detection Bayesian Statistics Activity Profiling Data Quality Control • Secondary Peaks • Isotope pattern • < 20% intensity change • Isotope pattern comparison • experimental v.s theoretical • Sort detected peaks upon fold change • p-values by MSEA • Retention time filter • Adducts/Salt filter • Missing value imputation Deisotoping Active Pathways Heuristics Data Normalization • Monoisotopic peaks • Pathways: p < 0.05 Potential Target Peaks • Prior prob. for C, N, S • 6 Golden rules • Time-wise, condition specific • Mean-, Z-value … • Highly up- or down-regulated, but not yet annotated peaks • Further experiments are needed Top 3 hits Work with Tobias Erb, Gerlt Lab (ENO)

25. RLP: whole cell untargeted metabolomics Control Control +MTA +MTA 8 1.0 2 16 2 8 4 0.5 8 1 1 4 0 0 0 0 0 0 p-value = 0.02 Butanoate metabolism p-value = 4.8 x 10-4 Metabolite intensity ( x 106 ) MTA met-salvage pathway MTR-1P p-value = 7.3 x 10-4 Purine metabolism MTRu-1P p-value = 0.048 0min 0min 0min 0min 10min 10min 10min 10min 20min 20min 20min 20min Glutathione metabolism Metabolite intensity ( x 106 ) p-value = 1.2 x 10-3 DXP up-regulated pathway Isoprenoid pathway down-regulated pathway CDP-MEP pathway showing no big difference metabolite c-MEPP Work with Tobias Erb, Gerlt Lab (ENO)

26. RuBisCO-like protein from R. rubrum RLP Cupin Work with Tobias Erb, Gerlt Lab (ENO)

27. RuBisCO-like protein (ENO): Results and Conclusions • Perfect starting point for Micro./Metabolomics Core • Collaboration with ENO bridging project • Phenotype was known • High profile project (Ashida, et.al. Science, 2003) • Genome context and measured thiol release suggested novel fate of MTA • Key enzymes in known MSP missing from genome • Cell extracts mixed with MTA produced methanethiol • LC-MS-based metabolomics uncovered connection between MTA feeding and isoprenoid biosynthesis • Untargeted metabolite profiling of R. rubrum uncovered: • Predicted MTA degradation products • Unexpected isoprenoid biosynthesis intermediates

28. Taking advantage of existing samples… Noncovalent Protein: Ligand Interactions Measured by Native ESI-MS (from test cases to EFI samples…) Future work will use the samples stored in the Protein / Structure Core Microbiology/Protein/Structure Core Collaboration

29. Micro./Metabolomics Core: future directions • Application of Biolog and custom phenotype microarrays to null mutants of targets from additional organisms • Transcriptional analysis coupled to growth condition screens to gain complementary evidence for when target genes are expressed • Further improvements in XCMS software to better detect metabolites of low abundance • Application of differential labeling and multiple chromatographies for each metabolomics experiment to increase accuracy • Continued and increasing collaboration with the BPs and Cores