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MPDB Presentation

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Description of the MPDB metabolic profiling database application

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MPDB Presentation

  1. 1. MPDB - Integrated system for storage and analysis of metabolomic data Design and implementation of the data acquisition and analysis pipeline Alexander Raskind, SFRES MTU
  2. 2. Omics data availability http://www.ncbi.nlm.nih.gov/Genbank/genbankstats.html Transcriptomics data: ArrayExpress – 3670 experiments, 109666 hybridizations http://www.ebi.ac.uk/microarray-as/aer/ Proteomics data: PRIDE – 3,537 Experiments 645,869 Identified Proteins http://www.ebi.ac.uk/microarray-as/aer/ Metabolomics data: MMCD – 20.306 compounds http://mmcd.nmrfam.wisc.edu/ Human Metabolome Database – 2500 compounds http://www.hmdb.ca/
  3. 3. Shifting research paradigm genome.uiowa.edu http://www.shimadzu.com Targeted analysis High-throughput analysis
  4. 4. Populus as model system • Wide ecological range • Small genome relative to other trees • Relatively easy transformation and cloning • Belongs to Salicaceae – Willow family, produces large amount of phenolic compounds that may influence carbon sequestration
  5. 5. Project rationale • Affordable equipment generates limited amount of metabolomic data with modest quality • Proper information storage and maximal extraction of useful information are essential • Free open source laboratory information system tailored to metabolomics workflow would benefit to a large scientific community
  6. 6. System requirements • Easy access to large arrays of analytical results and biological metadata • Tools for data analysis • Addition of analysis modules • Accommodation of other types of analytical data • USER FRIENDLY
  7. 7. Analysis workflow
  8. 8. Major analytical problems • Chemical complexity of the sample o human metabolome - 2500 metabolites, plants – much more • Wide dynamic range of response o difference between most and least abundant components may be more than 10,000 • Biological variation • Matrix effects o Interactions between sample componets leading to shifts in retention time and sensitivity of detection comparative to pure compounds • Instrument effects o Shifting retention time (column wearing out and maintenance) o Changes in sensitivity
  9. 9. Data analysis pipeline • Raw data cleanup, peak detection, deconvolution and quantification • Compound identification (library search) • Export of analysis results and biological metadata to the database • Peak alignment and normalization • Final data analysis
  10. 10. System Outline Analyzer-Pro Result (XML format) MP-align GC/MS or LC/MS raw data MPDB Offline Online Data analysis Biological information
  11. 11. Compound identification • NIST 2002 database for GCMS (MS only, ~140,000 entries) • In-house database of essential metabolites (MS and retention time, ~200 entries)
  12. 12. Why we need alignment Single batch Multiple batches
  13. 13. Spectra similarity
  14. 14. Alignment algorithm Peak list RI MS Grou p Consist ency Aligned groups
  15. 15. Signal normalization Raw data Normalized to TIC
  16. 16. User interface - tasks • Data entry • New analysis • Review analysis • Quality control • Help
  17. 17. Data set definition
  18. 18. Sample groups review and annotation
  19. 19. Alignment results
  20. 20. Data export
  21. 21. Data sorting and filtering
  22. 22. Data assessment and analysis • Data for individual compound groups • Data for individual samples and compounds • Principal component analysis • Clustering of samples and compounds • Graphical maps of compound ratios
  23. 23. Individual compound group data
  24. 24. Mass spectral data for the group
  25. 25. Individual sample and peak details
  26. 26. PCA
  27. 27. Clustering
  28. 28. Compound ratios
  29. 29. Quality control
  30. 30. Sample analysis – effects of nitrogen stress on the Populus leaf metabolism • Plants grown hydroponically • N-stress for 8 weeks • Samples taken from leaves at different developmental stages (lamina and mid-vien) • Metabolites fractionated by SPE • Hydrophylic fractions additionally analyzed at 1:20 dilution • Fractions were also subjected to glucosidase hydrolysis and LPE • 3-5 biological and 1-2 technical replicas
  31. 31. Leaf hydrophilic fraction • Up-regulated by N-stress: o Galacturonic acid (X7), D-Arabinonate, o Turanose, Syringin o Ribose(?), methyl-Galactoside, 3-Hydroxy-3- methylglutaric acid (HMGA), D-(-)-3- Phosphoglyceric acid
  32. 32. Leaf hydrophilic fraction • Down-regulated by N-stress: o Most of free aminoacids and polyamines below detection level or strongly reduced. Also some sugars and polyols, but not clearly identified) o Small organic acids (fumaric, succinic, threonic, citric, malic, oxaloacetic) o Sugar phosphates (glucose, fructose) o Xylose, melibiose, cellobiose
  33. 33. Acknowledgements • Prof. Scott Harding • Prof. Chung-Jui Tsai • Dr. Changyu Hu • Prof. Meir Edelman (WIS)

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