1. The document discusses a study analyzing the metabolite profiles of maize stem extracts using direct infusion electrospray ionization mass spectrometry (DIESI-MS). Samples were collected from different maize genotypes grown under varying environmental conditions. 2. Metabolite profiling was conducted to better understand the function of the maize stem and how metabolites change under different field conditions. DIESI-MS was used for its high-throughput capabilities. 3. Preliminary results found differences in metabolite signatures between genotypes and environmental treatments. Amino acids like lysine, valine, and serine showed clear signals. Further analysis of the metabolic profiles may help explain the genotype-phenotype relationship in maize

1. 1st. National Workshop on
Fodder Maize
“Metabolic phenotyping
of maize stem extracts
using DIESI-MS”
MC Martín García Flores
Supervisor: Dr. Axel Tiessen
Lab Metabolomics & Molecular Physiology
Irapuato, Gto., January 23, 2013

2. Outline
• Introduction
– Forage maize
– Genotype-Phenotype dilemma
• Experimental strategy and Methods
– Workflow Metabolomics
– Harvesting and sample prep
– Mass spectrometry (DIESI-MS)
• Control experiments
• Results
– Spectral comparisons, heatmaps

3. Forage production
2012 Year Forage production
1600000
1400000
1200000
1000000
Ton
800000
600000
400000
200000
0 QUERETARO
AGUASCALIENTES
PUEBLA
MEXICO
GUANAJUATO
ZACATECAS
CHIHUAHUA
DURANGO
COAHUILA
JALISCO
State
http://www.siap.gob.mx/
22/01/2013

4. Average Yield
2012 year.
Yield maize
70 62 61
60 51
48
50 44
40 40
40 35
Ton/ha
31 30
30
20
10
0
MEXICO
GUERRERO
HIDALGO
DURANGO
QUERETARO
COAHUILA
OAXACA
GUANAJUATO
AGUASCALIENTES
BAJA CALIFORNIA
SUR
State
http://www.siap.gob.mx/
22/01/2013

5. Phenotype dilemma
Genotype DNA
Environment
Agronomy
Phenotype
Biomass
Grain Yield
Tiessen, 2009

6. Genotype
• Genetic information
• DNA (Nuclear. Mit, Chl) Epi-genotype
Heredable modifications
• Sequence (Base order) Methylation
Histone-Acetylation
• Unique identity microRNAs, etc
• Variety, Species, Genus Protein-states, etc

7. Phenotype
• Measurable characteristics
• Agronomic traits
• Morphological traits
• Physiological traits
• Biochemical traits
• Depends on both Gen,Env, and Gen x
Env

8. Phenotype dilemma
Genotype DNA Genes
Epigenetic Transcription
modifications
Signal RNA
transduction
Environment Translation
Agronomy Proteins
Photosynthesis Activity
Phenotype Growth
Development
Partitioning Metabolites
Biomass
Physiology
Grain Yield
Tiessen, 2009

9. Metabolomics
• Characterization, identification and
quantification of all metabolites
• Functional genomics (function of genes)
• Physiology
• Phenotyping, Breeding
• QTL analysis
• Metabolic Engineering

10. Throughput
Analytic methods:
• HPLC-DAD (20 sa/day)
• GC-FID (30 sa/day)
• GC-EI-MS (18 sa/day)
High through-put.
Sample pre-treatment.
• DIESI-MS (80 sa/day) Omitted fractioning and
separation.
Reduction of analysis time.
Minimizes influence of sample
manipulation.
Brings down costs.
• Enzymatic and colorimetric assays (384 sa/day)

11. Typical workflow in Metabolomics
Koek et al., 2011

12. Biological questions
What is the function of the stem in
the maize plant?
Which metabolites change under
different field conditions?
Experimental design (Lattice)
Texcoco: 11 Genotypes, 6 biological samples, 3 technical
samples (Low Nitrogen and Normal Nitrogen)
Tlaltizapan:14 Genotypes, 6 biological samples, 3
technical samples (Water stress and Well watered)

13. Genetic material
CIMMYT; experimental fields
Texcoco: Low Nitrogen Tlaltizapan: Water Stress
Entry Pedigree Entry Pedigree
1 H-55 *8 DTPYC9-F143-5-4-1-2-B*5/CML312SR (WW and WS)
6 HID-15 20 DTPWC9-F24-2-3-1-1-B*5/CML312SR
26 CML323-BB/CML312SR
8 CMT 099001 (CML457/CHWE235)//CHWE231
69 LaPostaSeqC7-F78-2-1-1-1-B*4/CML312SR
4 P1684
72 CML486-BB/CML312SR
*9 CMT 099003 (CML457/CHWL147)//CHWE229 (NN)
87 CML311/MBRC3BcF95-2-2-1-B*7/CML312SR
7 (CML457/CML459)//IML-6
89 LaPostaSeqC7-F96-1-2-1-3-B*4/CML312SR
5 ASPROS-823
[[KILIMAST94A]-30/MSV-03-1-10-B-1-BB-1xP84c1F27-4-1-6-B-5-B]F8-3-2-2-
104 1xG16SeqC1F47-2-1-2-1-B*5-xP84c1F26-2-2-6-B-3-B]-3-1-
2 H-57 B/CML395]-1-1-BB/CML312SR
CML311xCML311/CML311xMBRC1BcF94-3-1-1-B*4-1-1-
11 TESTIGO LOCAL#! 125 B*7/CML312SR
10 CMT 099027 (CML457/CHWE235)//CHWE233 CLQ-RCWQ83=(CML146xCML150)-B-32-1-2-B-1-
132 B*4/CML312SR
*3 CV-702 (LN)
143 CL02450Q/CML451QLocalCheckQPM2
LaPostaSeqC7-F64-2-6-2-2-
147 BB/CML495=Check2IntermediateMaturity
91 DTPYC9-F69-3-5-1-1-B*4/CML312SR
99 LaPostaSeqC7-F64-2-6-2-2-BB/CML312SR

14. Sampling
Experimental field trial
Maize stem sampling
Physiological data recording
Grounding and collection of extracted
juice sample, 1 ml
Freezing in 96-well microplates, dry ice
for 30 s. and storaged in liquid “N”
(-80 °C).

15. Sample preparation
Filter 0.45 µm mesh PVDF; 10 µL sample and
990 µL De-Ionizade H2O (1:100 dilution).
Add 25 µL formic acid to 475 µL sample, mix 3
min and Read: DIESI-MS.
Defreezing in ice bath for 1 hr
Centrifugate for 10 min, 4000 rpm, 4 °C.
Supernatant filtration: 0.45µm mesh
PVDF, activated carbon treatment, 100
µL aliquotes in 96-well microplates
Freezing (-18 °C) before DIESI-MS
García-Flores et al., 2012

16. Data acquisition
DIESI Conditions
Water micromass Q/Z
spectrometer
ES (+) source.
Voltages: Capillary (Kv) 3.0 Temperatures.
Cone (V) 60 source ·C 80
Extractor (V) 3 desolvation ·C 150
Rf lens (V) 0.5
Gas flow.
Desolvation (L/hr) 250
Cone (L/hr) 50
Syringe.
Pump flow (µl/min) 10
Analyzer.
LM resolution 15
HM resolution 15
Ion energy 0.5
Multiplier 650
García-Flores et al., 2012

17. Direct infusion electron spray ionization
mass spectrometry (DIESI-MS).
Waters micromass (Z/Q).
Mass spectrometer (m/z).

18. Mass Spectrometer components
Warwick, 2007

19. Electrospray ionization
Capillary column, small quantities of sample,continuos flow, vacuum
interlock. Electro-ionization.
Martín, 2012

20. Data processing
TOPPAS Software
Data acquisition and Output
files
spectrum analysis
Input File Spectra NoiseFilt Output
files Convert Merger ersgolay files
er
File Peak
Output Text Output
Convert Picker
files Exporter files
er wavelet
Winkler, 2011

21. Data processing
Average of 6 spectra

22. Control experiments

23. Default machine peaks
Top Peak: (m/z) 43.26, Intensity: Top Peak: (m/z) 29.62, Intensity:
217896000 8164352
Solvent Water
Drought stress (SS)
CLQ-RCWQ83=(CML146xCML150)-B-
32-1-2-B-1-B*4/CML312SR
Tlaltizapan

24. Deinonized Water
Formic acid

25. Peak discrimination
Green: 1:100
Dilutions Red: 1:1000
Blue: 1:10000

26. Metabolites of interest
Aminoacid (M.W.)+H
Arginine 175.01
Asparagine 133.00
Glutamine 147.06
Proline 116.06
Serine 106.04
Tryptophan 205.08
Threonine 120.05
Tyrosine 182.07
Leucine 132.17

27. Lysine REP 1
Fragments
Lysine
m/z intensity C6H14N2O2
147.14 6 600 192
Mass positive: 147.10
REP 2 REP 3
Lysine Lysine

28. Valine
Valine: (m/z) 118.08
Solvent Peak: (m/z) 43.32

29. AAs:Most common signals
m/z Histidine Isoleucine Lysine Methionine Valine Serine Threonine Cysteine Tryptophan Phenylalanine
39.451 6.89E+06 5.09E+06 7.55E+06 1.47E+07 4.76E+06 7.01E+06 5.74E+06 7.44E+06 2.10E+06 5.97E+06
47.4482 3.93E+08 3.97E+08 4.83E+08 4.05E+08 4.32E+08 4.92E+08 4.45E+08 4.20E+08 3.74E+08 3.88E+08
48.5103 8.85E+06 6.08E+06 2.22E+07 6.85E+06 1.68E+07 2.18E+07 1.74E+07 1.42E+07 6.08E+06 6.05E+06
59.3195 8.44E+07 3.48E+07 4.29E+07 3.59E+07 3.75E+07 4.59E+07 3.74E+07 3.05E+07 3.13E+07 2.80E+06
61.3814 3.13E+07 1.77E+07 2.18E+07 2.23E+07 2.00E+07 2.45E+07 2.12E+07 1.80E+07 1.59E+07 1.59E+07
69.1918 6.18E+06 4.37E+06 1.39E+07 1.04E+07 8.09E+06 1.59E+07 8.23E+06 8.84E+06 3.50E+06 4.97E+06
75.1904 3.86E+07 2.27E+08 4.37E+07 1.77E+08 5.75E+07 6.10E+07 1.75E+08 1.50E+08 2.18E+08 1.68E+08
89.0626 1.53E+07 1.05E+07 1.11E+07 1.15E+07 1.06E+07 1.29E+07 1.20E+07 1.01E+07 9.94E+06 9.92E+06
89.9376 1.52E+06 7.38E+05 1.09E+06 1.63E+06 8.09E+05 1.04E+06 9.51E+05 8.96E+05 6.84E+05 6.83E+05
93.0619 5.30E+08 5.98E+08 6.49E+08 6.03E+08 6.14E+08 6.82E+08 6.42E+08 6.27E+08 5.85E+08 5.86E+08
94.3742 2.53E+07 3.12E+07 3.86E+07 3.52E+07 3.25E+07 4.27E+07 3.97E+07 3.53E+07 3.07E+07 3.00E+07
97.1238 4.59E+06 3.34E+07 4.33E+06 4.98E+07 7.11E+06 8.88E+06 2.97E+07 3.40E+07 2.90E+07 2.83E+07
101.1232 5.28E+07 4.13E+07 3.48E+07 4.55E+07 3.90E+07 4.56E+07 4.55E+07 3.84E+07 3.92E+07 3.71E+07
102.873 4.02E+06 2.57E+06 2.70E+06 3.09E+06 2.58E+06 3.09E+06 3.43E+06 2.54E+06 2.73E+06 2.44E+06
105.0602 1.93E+07 1.16E+07 1.10E+07 1.31E+07 1.08E+07 1.04E+07 1.30E+07 1.18E+07 1.15E+07 1.07E+07
107.2475 1.19E+07 8.35E+06 9.39E+06 9.74E+06 8.45E+06 1.10E+07 9.67E+06 8.48E+06 8.01E+06 7.38E+06
117.1841 6.74E+07 5.23E+07 4.31E+07 6.18E+07 4.40E+07 5.64E+07 5.94E+07 4.86E+07 5.14E+07 4.79E+07
121.0588 1.07E+07 1.35E+08 1.03E+07 1.13E+08 2.21E+07 2.02E+07 1.02E+08 9.64E+07 1.40E+08 1.05E+08
131.9958 1.70E+06 6.48E+07 1.10E+06 2.60E+06 9.43E+05 1.07E+06 1.02E+06 9.49E+05 8.62E+05 0.00E+00
171.0599 1.05E+07 8.64E+06 1.83E+07 1.29E+07 6.67E+06 1.72E+07 1.29E+07 1.22E+07 9.88E+06 7.95E+06
191.9376 1.33E+07 2.13E+07 1.50E+07 1.66E+07 2.75E+07 2.59E+07 2.31E+07 1.96E+07 1.82E+07 2.01E+07
238.0731 7.07E+06 1.62E+07 1.01E+07 1.35E+07 1.93E+07 1.88E+07 1.86E+07 1.78E+07 1.47E+07 1.64E+07
Higher
Lower

30. AAs most common signals Plot
AAs Surface Plot
AAs Surface Plot
Phe C10
7.00E+08 Phe
Try C9
Try
Cys
6.00E+08 C8 Cys
Thr
C7 Thr
5.00E+08 Ser
Ser
Val C6
4.00E+08 Val
Int Met
C5 Met
3.00E+08 Lys Lys
Iso C4
2.00E+08
Iso
C10 His C3 His
C9
C8
1.00E+08 C7 C2
C6
C5 AAs
0.00E+00 C4 Intensity C1 AAs
C3
C2
39.45
48.51
61.38
75.19
89.06
93.06
97.12
47.44
59.31
69.19
89.93
94.37
102.87
191.93
101.12
105.06
107.24
117.18
121.05
131.99
171.05
238.07
C1
m/z
m/z
0.00E+00-1.00E+08 1.00E+08-2.00E+08 2.00E+08-3.00E+08
3.00E+08-4.00E+08 4.00E+08-5.00E+08 5.00E+08-6.00E+08
6.00E+08-7.00E+08 0.00E+00-2.00E+08 2.00E+08-4.00E+08 4.00E+08-6.00E+08 6.00E+08-8.00E+08
Excel 2003

31. AA’s fragments Summary
AA Ionize Intensity Fragments
Histidine Low 56 452 120.08, 121.09, 123.06
Glutamic acid Low 2 749 184 112.88, 114.09, 130.90
Aspartic acid Low 3 822 336 99.03, 101.03, 117.03
Isoleucine Standard 13 760 512 98.09, 99.09, 115.98
Arginine Low 3 046 144 139.97, 140.98, 157.97
Lysine Standard 6 600 192 112.19, 113.16, 130.16
Alanine High-quality 134 176 768 75.50, 87.43, 89.50
Methionine Low 3 736 320 115.91, 116.92, 132.87
Glutamine High-quality 94 728 192 128.83, 129.87, 147.06
Valine Standard 11 805 696 101.07, 116.03, 117.06
Serine High-quality 63 971 328 102.99, 103.99, 104.99
Threonine High-quality 64 405 504 102.96, 117.94, 118.85
Asparagine Low 6 648 576 111.88, 112.88, 115.85
Tyrosine Low 2 569 472 148.92, 162.94, 164.88
Cysteine Standard 19 729 408 101.98, 102.96, 104.90
Proline Standard 13 458 432 100.20, 115.11, 232.15
Glycine High-quality 103 301 120 75.43, 151.06, 226.17
Tryptophan Low 897 216 170.11, 172.09, 188.06
Phenylalanine Low 4 782 336 130.90, 146.92, 162.94
Leucine High-quality 3 208 960 100.17, 117.32, 131.40

32. Results

33. Results DIESI-MS spectrum
García-Flores et al., 2012

34. Comparison
¿Which MS peaks vary
according to the environmental
conditions?

35. Comparison of spectra
Control (WW)
Drought stress (SS)
CLQ-RCWQ83=(CML146xCML150)-B-32-1-2-B-1-B*4/CML312SR
Tlaltizapan

36. Control
(WW)
Drought stress (SS)
CLQ-RCWQ83=(CML146xCML150)-B-32-1-2-B-1-B*4/CML312SR
Tlaltizapan

37. Control-Low nitrogen spectra
Control (NN)
Low Nitrogen
CV-702 Batan

38. Control (NN)
Low Nitrogen
CV-702 HL Batan

39. Can we use the MS info for
classifying samples?
Statistical analysis with R
ANOVA analysis
Herarchical clustering
Heatmap bicluster

40. Metabolic HeatMap

41. Evaluating the physiological state of maize (Zea mays
L.) plants by direct-injection electrospray mass
spectrometry (DIESI-MS).
Martín García Flores; Sheila Juárez Colunga; Josaphat Miguel Montero Vargas; Janet
Ana Isabel López Arciniéga; Alicia Chagoya; Axel Tiessen and Robert Winkler.
Molecular Biosystems García-Flores et al., 2012

42. Conclusions
DIESI-MS has high throughput. It is the cheapest and
fastest MS strategy. We have successfully set up the
method at CINVESTAV.
We can detect >200 of different ms peaks. We can
measure more that 80 samples per day.
Some peaks vary acording to Gen and Env effects
Biochemical phenotyping of maize stem fluids enables
the rapid evaluation of the physiological state of plants.
It also allows to discriminate between genotypes (
breeding)
Metabolic heatmaps are useful for MS data
representation

43. Perspectives
The method will be applied at large scale for
investigating the metabolic stress response of various
Zea mays L. genotypes.
We hope we can detect biomarkers for selection
Derived metabolic markers can complement the DNA
based markers for breeding.

44. Acknowledgments
• CIMMYT MasAgro-IMIC
– Dr. Marc Rojas (IMIC)
– Dr. Felix San Vicente (CIMMYT)
• CONACYT Grants
• Dr. Axel and Dr. Robert
• Laboratory team: Mayela, Andrés, Adrián,
Erandi, Sheila, Obed, Iván, Julio, Viviana,
Daniel, etc

45. Laboratory
Metabolomics & Molecular Physiology

46. Questions?
Many thanks for
being here.

47. CINVESTAV
IRAPUATO
Plant Biotechnology