1. Metabolomics PCB 5530 Tom Niehaus Fall 2014

2. Learning Outcomes - Learn the basics of metabolomics - Understand the limitations of metabolomics - Things to consider when using metabolomics for your own research Finish lecture Day 1 Day 2 Lecture Activity 1: Identifying an unknown peak Activity 2: Analyzing a metabolomics dataset

3. Definitions and Background Metabolome = the total metabolite pool All low molecular weight (< 2000 Da) organic molecules in a sample such as a leaf, fruit, seedling, etc. Sugars Nucleosides Organic acids Ketones Aldehydes Amines Amino acids Small peptides Lipids Steroids Terpenes Alkaloids Drugs (xenobiotics)

4. Metabolomics = high-throughput analysis of metabolites Definitions and Background Metabolomics is the simultaneous measurement of the levels of a large number of cellular metabolites (typically several hundred). Many of these are not identified (i.e. are just peaks in a profile). Not hypothesis driven snapshot

5. Definitions and Background

6. Metabolomics Metabolic profiling Targeted analysis -measure many compounds -measure a set of related compounds -measure a specific compound ScopeAccuracy

7. Definitions and Background History and Development Metabolic profiling is not new. Profiling for clinical detection of human disease using blood and urine samples has been carried out for Centuries. This urine wheel was published in 1506 by Ullrich Pinder, in his book Epiphanie Medicorum. The wheel describes the possible colors, smells and tastes of urine, and uses them to diagnose disease. Nicholson, J. K. & Lindon, J. C. Nature 455, 1054–1056 (2008).

8. Definitions and Background History and Development Advanced chromatographic separation techniques were developed in the late 1960’s. Linus Pauling published “Quantitative Analysis of Urine Vapor and Breath by Gas- Liquid Partition Chromatography” in 1971 Chuck Sweeley at MSU helped pioneer metabolic profiling using gas chromatography/ mass spectrometry (GC-MS) Plant metabolic biochemists (e.g. Lothar Willmitzer) were among other early leaders in the field. Metabolomics is expanding to catch up with other multiparallel analytical techniques (transcriptomics, proteomics) but remains far less developed and less accessible.

9. Definitions and Background Plant Metabolome Size It is estimated that all plant species contain 90,000 - 200,000 compounds. Each individual plant species contains about 5,000 – 30,000 compounds. e.g. ~ 5,000 in Arabidopsis The plant metabolome is much larger than that of yeast, where there are far fewer metabolites than genes or proteins (<600 metabolites vs. 6000 genes). The size of the plant metabolome reflects the vast array of plant secondary compounds. This makes metabolic profiling in plants much harder than in other organisms.

10. Definitions and Background The Power of Metabolomics Silent Knockout Mutations. ~90% of Arabidopsis knockout mutations are silent – i.e. have no visible phenotype and so provide no clues to gene function. (The search for some sort of visible phenotype therefore often becomes desperate.) The situation in yeast is similar – up to 85% of yeast genes are not needed for survival. When there is little or no change in growth rate (visible phenotype) of a knockout mutant, the pool sizes of metabolites have altered so as to compensate for the effect of the mutation, leaving metabolic fluxes are unchanged. Thus – intuitively – mutations that are silent when scored for metabolic fluxes or growth rate (growth rate is the sum of all metabolic fluxes) should have obvious effects on metabolite levels. There is a firm theoretical basis for this in MCA.

11. Definitions and Background The Power of Metabolomics Example. In the Chloroplast 2010 project (phenotype analysis of knockouts of Arabidopsis genes encoding predicted chloroplast proteins): Various knockouts showed essentially normal growth and color but highly abnormal free amino acid profiles, e.g. At1g50770 (‘Aminotransferase- like’)

12. Definitions and Background Limitations of metabolomics High biological variance in metabolite levels (i.e., high variation between genetically identical plants grown in the same conditions) Unlike nucleic acids and proteins, metabolites have a vast range of chemical structures and properties. Their molecular weights span two orders of magnitude (20–2000 Da). Therefore no single extraction or analysis method works for all metabolites. (Unlike DNA sequencing, microarrays, MS analysis of proteins – all are general methods.) The concentrations of various metabolites can vary dramatically from mM to pM concentrations. Some metabolites are labile and won’t survive extraction and analysis Issues with chromatography, detection, and data analysis

13. Metabolomics Steps in metabolomics sample preparationsample extraction chromatography detection data analysis

14. Sample Preparation Growth/Sample Size Grow organisms (e.g. plants or bacteria) under identical conditions Randomize the treatment groups (Make sure the effects you measure are due to the variable being testing) number of replicates… depends on what you want to find -Large differences = small replication needed -Small differences = large replication needed In general, six replicates for each treatment are needed (due to high biological variability)

15. Sample Preparation Sample collection Uniform sample sizes (e.g. hole punches in leaves) Be consistent- similar tissue - time of day Quickly freeze sample in liquid nitrogen, store samples at -80 ° C Fast-harvesting method for bacteria (~30 sec)

16. Sample Extraction Choosing an extraction method No universal extraction method exists Some solvents may degrade certain compounds Its good to have some idea of what metabolites you want to extract

17. Sample Extraction Sample extraction The method should be consistent and reproducible Further workup may be required (e.g. solid phase extraction) SPEX SamplePrep Grinder

18. Y Chromatography introduction Invented in 1900 by Mikhail Tsvet (used to separate plant pigments) There are several types of chromatography, but all consist of a stationary phase and a mobile phase. Compounds are separated based on differential partitioning between the two phases. Types include: - TLC (thin-layer chromatography) - GC (gas chromatography) - LC (liquid chromatography) GC and LC are routinely used in metabolomics

19. Chromatography Gas Chromatography GC = ‘good chromatography’ optimized over several decades ~5 columns routinely used high reproducibility Limitations: - high temperatures can destroy labile compounds - polar compounds cannot ‘fly’ on GC columns and must first be derivatized (5% diphenyl/95% methyl siloxane) Identification based on RT

20. 20 1) Methoximation of aldehyde and keto groups (primarily for opening reducing ring sugars) 2) Silylation of polar hydroxy, thiol, carboxy and amino groups with silylation agent MSTFA A single compound with multiple active groups will result in multiple peaks (1TMS, 2TMS, 3TMS) GC-MS can distinguish between stereoisomers Step 1) MethoximationStep 2) Silylation Gas chromatography requires volatile compounds (two step derivatization in vial) Z/E isomer have same mass spectrum but differ 2 seconds in retention time Anal Chem. 2009 Dec 15;81(24):10038-48. doi: 10.1021/ac9019522. FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Kind T, Wohlgemuth G, Lee do Y, Lu Y, Palazoglu M, Shahbaz S, Fiehn O. Chromatography Sample derivatization

21. Chromatography Liquid Chromatography LC = ‘Lousy chromatography’ fairly new, recent advances infinite solvent systems possible Advantages: - compound can be collected after separation - derivatization not necessary - a separation protocol can be optimized for nearly any compound low reproducibility thousands of columns available - normal phase-ion exchange - reverse phase-HILIC

22. Detection Mass Spectrometry mass spectrometry is a technique to measure the mass of ions (m/z) All mass spectrometers perform three main tasks: 1) Ionize molecules: 2) Use electric and magnetic fields to accelerate ions and manipulate their flight: 3) Detect ions (convert to electronic signal):

23. Detection Mass Spectrometry Example mass spectrum: m/z relative abundance

24. Detection Mass Spectrometry Time [min] Normalized Intensity m/z Normalized Intensity Chromatogram (GC-MS) 166 97 129 83 61 47 35 119 11270 Mass spectrum (EI) Peak selector

25. Mass Spectrometry Ionization: chemical vs electon Chemical Ionization (+) Electron Ionization (+) [M+H] + [M+28] + [M+40] + [M+H] + is very abundant in chemical ionization (CI) Different ionization gases can be used such as NH 3, methane, butane Example picture: adduct ions at M+28.02=[M+C2H5] + and M+40.04=[M+C3H5] + are used for verification of [M+H] + Accurate mass [u]397.1690 Mass accuracy [ppm]5 Isotopic abundance error [%]5 A+1 [%]37.90 A+2 [%]17.84 A+3 [%]5.03

26. 26 Adduct formation – expect the unexpected Statistics: Adducts in NIST12 MS/MS DB (80,000 spectra) Most common adducts for LC-MS ([M+H]+ [M+Na]+ [M+NH4]+ [M+acetate]+) …around 290 different adducts

27. There are several types of mass spectrometers: - TOF (time of flight)- Q, QQQ (quadrupole) - Ion Trap- Orbitrap - FTICR (Fourier transform ion cyclotron resonance) Mass Spectrometry Mass Spectrometers QuadTOF

28. Mass Spectrometry Definitions and concepts isomer- compounds with the same chemical formula isobar- compounds with similar masses e.g. CO (27.9949) and C 2 H 4 (28.0313) e.g. propanol and isopropanol (C 3 H 8 O) C 8 H 10 N 2 O has 100,082,479 isomers isotopes- compounds with different numbers of neutrons in their nuclei e.g. 12 C vs 13 C

29. Mass Spectrometry Definitions and concepts Resolution (resolving power) Accuracy Mass range RP(FWHM) = measured mass / peak width at 50% peak intensity Difference in true mass and measured mass Range of ions that can be detected (typically 50-1000 m/z)

30. Mass Spectrometry Why is resolution important? High resolution is needed to determine the accurate mass High resolution is also needed to determine accurate isotopic patterns Note: -monoisotopic vs ave mass -accurate mass can distinguish isobars, not isomers

31. Mass Spectrometry Definitions and concepts Dynamic range- the concentration range over which a linear response is obtained Sensitivity- the lowest amount an instrument can detect Speed- the number of spectra or scans that can be acquired in one second Determines the capability of an instrument to do quantitative analysis 1 scan/ sec = very slow 500 scans/sec = very fast matrix effects- signal is muted due to complex sample or other unknown processes

32. 32 In order to deconvolute (separate/clean) overlapping peaks, enough mass spectra have to be acquired to perform the mathematical calculations. With only one spectrum per second this is impossible. That requires: a) fast scanning detectors like time-of-flight (TOF) b) fast data acquisition hardware/software (DAC/ADC) The LECO TOF can acquire up to 500 mass spectra per second. For GC-MS 20 spectra/second sufficient for comprehensive GC (GCxGC) up to 200 spectra/sec needed Source: LECO ChromaTOF HelpfileLECO ChromaTOF Mass Spectrometry Why is high speed important?

33. Mass Spectrometry Properties of various mass spectrometers TOFQuadIon TrapOrbitrapFT-ICR Resolving Powervery goodfair very goodexcellent Dynamic Rangevery goodexcellentfair Sensitivityexcellent Speedexcellentgoodexcellentgoodfair Cost150-300K100K 500K1M Maintenanceave very high

34. Data Analysis Goals Identify all peaks In practice this is very difficult if not impossible quantification or semi-quantification of compounds Often involves comparing -fold changes in samples or groups of samples e.g. wild-type vs knockout plant Various statistical tests to look for differences in the treatment groups e.g. PCA, MCA, ANOVA Huge data files

35. Data Analysis Identifying peaks MS libraries can identify peaks (mostly GC/MS), especially when combined with RT information (GC/MS only): e.g. NIST library

36. Data Analysis Activity 1: Identifying peaks Can you find sucrose in a MS dataset? Example: sucrose (C 12 H 22 O 11 )

37. Data Analysis Activity 1: Identifying peaks Accurate mass can help determine the chemical formula: Example: sucrose (C 12 H 22 O 11 ) -Determine monoisotopic mass at http://www.chemspider.com/http://www.chemspider.com/ (342.116211 Da) -Determine M+H from MS adduct excel sheet (class website) (343.123487 Da) Lets say you find that mass in the dataset, but is it really sucrose? -Download Molecular weight calculator at http://www.alchemistmatt.com/mwtwin.html http://www.alchemistmatt.com/mwtwin.html -Open formula finder under tools -enter molecular weight target: 342.116211 -how many isobars are at 2 ppm? 0.1 ppm -enter 342.116211 at chemspider, how many isomers?

38. Data Analysis Example output of a metabolomics experiment Open GC-TOF-MS dataset from class website: -How many compounds identified? How many significant -fold changes -Pathway analysis at http://www.metaboanalyst.ca/MetaboAnalyst/http://www.metaboanalyst.ca/MetaboAnalyst/ -enter compound names or KEGG IDs for significant -fold changes -choose organism ‘E. coli’ and submit -Which pathways are affected in this dataset? Open HILIC-TOF-MS dataset from class website: -How many compounds identified? How many significant -fold changes -How many unidentified peaks? -Can you identify an unknown peak with a significant fold change