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The Eli and Edythe L. Broad Institute A Collaboration of Massachusetts Institute of Technology, Harvard University and affiliated Hospitals, and Whitehead.

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Presentation on theme: "The Eli and Edythe L. Broad Institute A Collaboration of Massachusetts Institute of Technology, Harvard University and affiliated Hospitals, and Whitehead."— Presentation transcript:

1 The Eli and Edythe L. Broad Institute A Collaboration of Massachusetts Institute of Technology, Harvard University and affiliated Hospitals, and Whitehead Institute for Biomedical Research Lessons learned from the Genome- scale metabolic reconstruction and curation of Neurospora crassa Jeremy Zucker Jonathan Dreyfuss Heather Hood James Galagan

2 Capture Metabolic Knowledge Pathway-tools/BioCyc KEGG Reactions Interactions Literature

3 Visualizing ‘omics Data Provide a visually intuitive, metabolic framework for interpreting large ‘omics datasets

4 in silico Predictions Algorithmically Interpret Expression Data in a Metabolic Context?

5 Example: Plasmodium Validation KO Phenotype Predictions – 90% Accuracy External Metabolite Changes – 70% Accuracy New Predictions 40 Enzymatic drug targets Experimental validation of novel target Eflux* *Colijn, C., A. Brandes, J. Zucker, et al. (2009). PLoS Comput Biol

6 Modeling in the Neurospora PO1 ClockVisualization and Analysis Profiling RNA-Seq ChIP-Seq Interpretation of Expression Profiling and Regulatory Network Data in a Metabolic Context – Inform Experiments

7 BUILDING THE MODEL

8 Manual reconstruction protocol Nature Protocols, Vol. 5, No. 1. (07 January 2010), pp

9 Automated Model SEED reconstruction pipeline Nature biotechnology, Vol. 28, No. 9. (29 September 2010), pp

10 Genome sequence to metabolic model PathwaysLiterature Nutrient media (Vogels) NeurosporaCyc ElementsMetadata Complexes Reactions Transporters Biomass composition

11 EFICAz2 predicts enzymes … Decision tree Databases HMMs FDR SVM 9934 protein sequences 1993 enzymes 1770 reactions BMC Bioinformatics 2009, 10:107

12 Protein Complex editor 182 reactions with isozymes or complexes 31 complexes experimentally validated through literature search 2-oxoisovalerate alpha subunit 2-oxoisovalerate beta subunit … fatty acid synthase beta subunit dehydratase fatty acid synthase alpha subunit reductase Identify multiple genes of reaction Allow curator to validate potential complexes 2-oxoisovalerate complex Present all possible combinations of complexes Fatty acid synthase complex …

13 Transport inference parser (TIP) 9934 free-text Protein annotations 176 transporters assigned to 97 transport reactions MFS glucose transporter ATP synthase … sucrose transporter Filter proteins for transporters Infer multimeric complex Infer substrate Infer energy-coupling mechanism … Bioinformatics (2008) 24 (13): i259-i267.

14 Pathologic predicts pathways 1770 enzyme- catalyzed reactions 265 Pathways … … X = #rxns in metacyc pwy Y = #rxns with enzyme evidence Z = #unique rxns in pwy P(X|Y|Z) = prob of pwy in Neurospora Science 293:2040-4, 2001.

15 Literature curation validates predictions … 1212 citations associated with 307 pathways 31 complexes 168 genes …

16 Neurospora Cellular overview

17 NEUROSPORACYC

18 New feature on Broad website

19 NeurosporaCyc Cellular overview

20 NeurosporaCyc cellular overview

21 Googlemaps-like zoomable interface

22 Highlight genes on overview

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25 NeurosporaCyc Omics Viewer

26 Omics data mapped onto metabolism

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29 Omics data mapped onto Genome

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32 DEBUGGING THE BUG

33 The problem with EC numbers Reaction classNumber of reactions neurospora (metacyc) Balanced normal reactions993 (4585) Generic reactions198 (688) Protein modification reactions:82 (469) Reactions with instanceless classes:80 (228) Generic redox reactions36 (212) Polymeric reactions24 (91) Polymerization pathway reactions11 (17)

34 Generic Reactions

35 instance of ?

36 Protein Modification reactions

37 Reactions with instanceless classes

38 Solution: Instantiate classes

39 Generic Redox reactions

40 Polymeric reactions

41 Polymerization Pathway reactions

42 Solution: Instantiate polymerization steps POLYMER-INST-Fatty-Acids-C16 + coenzyme A + ATP -> POLYMER-INST-Saturated-Fatty-Acyl-CoA- C16 + diphosphate + AMP + H+ POLYMER-INST-Fatty-Acids-C14 + coenzyme A + ATP -> POLYMER-INST-Saturated-Fatty-Acyl-CoA- C14 + diphosphate + AMP + H+ … POLYMER-INST-Fatty-Acids-C0 + coenzyme A + ATP -> POLYMER-INST-Saturated-Fatty-Acyl-CoA- C0 + diphosphate + AMP + H+

43 What happens when the metabolic network is infeasible? Add a “reaction” with the smallest number of reactants and products that results in a feasible model minimize card(r) subject to Sv + r = 0 l ≤ v ≤ u

44 Fast Automated Reconstruction of Metabolism Input: – EFICAz probabilities for each reaction – Biomass components – Experimental growth / no growth phenotypes in different nutrient conditions – Gene essentiality – Manual curation of pathways Output: – Metabolic network of MetaCyc reactions maximally consistent with input

45 VALIDATING THE MODEL WITH IN SILICO KNOCKOUT PREDICTIONS

46 Neurospora phenotypes for validation Neurospora e-Compendium – 29 Mutants essential on minimal media – Non-essential on supplemental media PO1 Phenotype Collection – 79 non-essential KOs under minimal media – Additional phenotypes are observed. Used FBA with Neurospora model to simulate gene knockouts in minimal medium

47 Neurospora phenotype prediction results Predicted EssentialNon-Essential ObservedEssential22 (TN)7 (FP) Non-Essential14 (FN)65 (TP) PrecisionTP/ (TP+FP) 90% RecallTP/ (TP+FN) 82% SpecificityTN/ (TP+FP) 76% Accuracy(TP+TN)/ (TP+TN+FP+FN) 81%

48 Comparison of model organisms under minimal media Yeast (iND750) 1 E.Coli (iAF1260) 2 Neurospora Viable Predicted/ Observed 439/455=96%993/1022=97%65/79=82% Essential Predicted/ Observed 35/109=32%159/238=67%22/29=76% Overall accuracy84%91%81% [1] Genome Res : [2] Molecular Systems Biology :121

49 MODELING THE EFFECT OF OXYGEN LIMITATION ON XYLOSE FERMENTATION

50 Biofuels from Neurospora? Growing interest for obtaining biofuels from fungi Neurospora crassa has more cellulytic enzymes than Trichoderma reesei N. crassa can degrade cellulose and hemicellulose to ethanol [Rao83] Simultaneous saccharification and fermentation means that N. crassa is a possible candidate for consolidated bioprocessing Xylose Ethanol

51 Effects of Oxygen limitation on Xylose fermentation in Neurospora crassa Zhang, Z., Qu, Y., Zhang, X., Lin, J., March Effects of oxygen limitation on xylose fermentation, intracellular metabolites, and key enzymes of Neurospora crassa as Applied biochemistry and biotechnology 145 (1-3), Xylose Pyruvate TCAEthanol RespirationFermentation Glycolysis Oxygen level (mmol/L*g) Ethanol conversion (%) Low O 2 Intermediate O 2 High O 2

52 Pentose phosphate Aerobic respiration Fermentation TCA Cycle Model of Xylose Fermentation Xylose Oxygen Ethanol ATP Two paths from xylose to xylitol

53 Pentose phosphate Aerobic respiration Fermentation TCA Cycle Oxygen=5 ATP=16.3 NADPH Regeneration NADPH & NAD + Utilization High Oxygen NAD + Regeneration

54 Pentose phosphate Aerobic respiration Fermentation TCA Cycle Ethanol Low Oxygen Oxygen=0

55 Pentose phosphate Aerobic respiration Fermentation TCA Cycle Ethanol Intermediate Oxygen Optimal Ethanol NADPH & NAD Utilization Oxygen=0.5 ATP=2.8 NAD Regeneration NADPH Regeneration All O 2 used to regenerate NAD used in first step

56 Pentose phosphate Aerobic respiration Fermentation TCA Cycle Ethanol Intermediate Oxygen Optimal Ethanol NADPH & NAD Utilization Oxygen=0.5 ATP=2.8 NAD Regeneration NADPH Regeneration All O 2 used to regenerate NAD used in first step Bottleneck Pyruvate decarboxylase Improve NADH enzyme

57 USING E-FLUX TO PREDICT DRUG TARGETS BY INTEGRATING EXPRESSION DATA WITH FBA

58 E-Flux explanation

59 Application of E-flux to TB

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62 Next Steps Annotation: use phenotype predictions to improve model NeurosporaCyc: Use E-flux to interpret the effect of clock genetic regulatory program on metabolism. Validation: add additional phenotypes

63 Acknowledgements Neurospora P01 Project Heather Hood Jonathan Dreyfuss James Galagan SRI Peter Karp Mario Latendresse Markus Krumenacker Ingrid Kesseler Tomer Altman Suzanne Paley Ron Caspi Mike Travers

64 Fast Automated Reconstruction of Metabolism (FARM) Gene Calls (Broad) Protein Complex prediction Transport predictor (TIP) Pathway prediction (Pathologic) Enzyme prediction (EFICAz) Literature curation (CAP) Nutrient media (Vogels) NeurosporaCyc

65 C C Fast Automated Reconstruction of Metabolism (FARM) 846 Reactions 640 Metabolites 564 Genes EFICAz predictions Pathway predictions Nutrient conditions Biomass composition Protein complexes Transport


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