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The Eli and Edythe L. Broad Institute

Project 2: Bioinformatics and Systems Modeling. Jeremy Zucker. 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. Specific Aims of Project 2.

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The Eli and Edythe L. Broad Institute

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  1. Project 2: Bioinformatics and Systems Modeling Jeremy Zucker 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

  2. Specific Aims of Project 2 • Finish the Neurosporaassembly • Continue to improve the structural annotation. • Curate the functional annotation, with particular emphasis on metabolic enzymes • Develop scientific insights by using system models to integrate data from Projects 1 and 3

  3. New Assembly/Annotation • NC7 =>NC10 • Contigs: 251 =>20 • Genes: 9826=>9734 • Transcripts: 9846=>9908 • Exons: 27208=>26625 • New genes: 530 • Removed genes: 631 • Merged genes: 24 • Split genes: 86 • 3869 UTR changes This is the final assembly! Heather Hood, Zehua Chen

  4. But the annotation continues… • RNAseq! • New genes • UTR’s • Alternative splicing • NoncodingRNAs Matt Sachs, Brian Haas

  5. From genome annotation to functional annotation Enzyme predictor Pathway predictor • 267 Pathways • 1701 enzymatic reactions • 1455 compounds • 4000+ community annotations (thanks Heather and CAP participants!) • 853 literature citations fungicyc.broadinstitute.org:1555

  6. Summary of BioCyc Capabilities • Knowledge Management System for Neurospora Community • Literature citations • Evidence codes • Metabolic Pathways and gene regulation • Omics viewer • RNA expression data • Flux predictions • Metabolite measurements • Protein expression • Enables system modeling

  7. From functional annotation to system modeling Enzyme predictor Pathway predictor Model Omics data Predictions

  8. Biofuels from Neurospora? • Growing interest for obtaining biofuels from fungi • Neurosporacrassahas more cellulytic enzymes than Trichodermareesei • N. crassacan degrade cellulose and hemicellulose to ethanol [Rao83] • Simultaneous saccharification and fermentation means that N. crassais a possible candidate for consolidated bioprocessing Xylose Ethanol

  9. Effects of Oxygen limitation on Xylose fermentation in Neurosporacrassa Intermediate O2 Xylose Ethanol conversion (%) Glycolysis Pyruvate Respiration Fermentation Low O2 High O2 TCA Ethanol Oxygen level (mmol/L*g) Zhang, Z., Qu, Y., Zhang, X., Lin, J., March 2008. Effects of oxygen limitation on xylose fermentation, intracellular metabolites, and key enzymes of Neurosporacrassaas3.1602. Applied biochemistry and biotechnology 145 (1-3), 39-51.

  10. Pentose phosphate Xylose degradation Xylose Two paths from xylose to xylitol Glycolysis Model of Xylose Fermentation Aerobic respiration Fermentation Oxygen Ethanol TCA Cycle ATP

  11. Pentose phosphate Xylose degradation High Oxygen NADPH Regeneration Glycolysis NADPH & NAD+ Utilization Aerobic respiration Fermentation Oxygen=5 NAD+ Regeneration TCA Cycle ATP=16.3

  12. Pentose phosphate Xylose degradation Low Oxygen Glycolysis Aerobic respiration Fermentation Oxygen=0 Ethanol TCA Cycle

  13. Pentose phosphate Xylose degradation Intermediate Oxygen Optimal Ethanol NADPH Regeneration Glycolysis NADPH & NAD Utilization Aerobic respiration Fermentation Oxygen=0.5 All O2 used to regenerate NAD used in first step Ethanol NAD Regeneration TCA Cycle ATP=2.8

  14. Pentose phosphate Xylose degradation Intermediate Oxygen Optimal Ethanol NADPH Regeneration Glycolysis NADPH & NAD Utilization Improve NADH enzyme Bottleneck Pyruvate decarboxylase Aerobic respiration Fermentation Oxygen=0.5 All O2 used to regenerate NAD used in first step Ethanol NAD Regeneration TCA Cycle ATP=2.8

  15. Future Goals • Proceed pathway by pathway with the genome-scale metabolic reconstruction • Apply gene expression and gene regulation data under a variety of different conditions • Focus on a short list of high confidence predictions that can be experimentally validated.

  16. Acknowledgements Broad Institute James Galagan Bruce Birren Brian Haas Aaron Brandes Matt Henn Li Jun Ma Christina Cuomo Carsten Russ Broad Genome Sequencing Platform Finishing Team Margaret Priest HarindraArachchi Lynne Aftuck Mike Fitzgerald Genome Assembly Sarah Young Sean Sykes Annotation Team Brian Haas Mike Koehrsen QianZeng Tom Walk Program Project Heather Hood Jay Dunlap Kathy Borkovich Louise Glass Mary-Anne Nelson Matt Sachs Gloria Turner Dick Weiss Mark Farman Many others…

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