Measurement uncertainty of isotopologue fractions ... · Measurement uncertainty of isotopologue fractions determined via mass spectrometry for metabolic flux analysis Teresa Mairinger1,2,

$Page 1: Measurement uncertainty of isotopologue fractions ... · Measurement uncertainty of isotopologue fractions determined via mass spectrometry for metabolic flux analysis Teresa Mairinger1,2,$
Measurement uncertainty of isotopologue

fractions determined via mass spectrometry

for metabolic flux analysis

Teresa Mairinger1,2, Hedda Drexler1, Chu Dinh Binh1, Stefan Neubauer2, Christina Troyer1,2, Karin Ortmayr1,3, Gunda Koellensperger2,3 und Stephan Hann1,2

1 Division of Analytical Chemistry, Department of Chemistry, BOKU Vienna, Austria2 acib - austrian centre of industrial biotechnology, Vienna, Austria3 Institute of Analytical Chemistry, University of Vienna, Austria

„Design, optimisation and application of cell fabricsfor replacement of conventional (synthetic) productionprocesses”

Primary Metabolite Organism Significance tons per year Ethanol Saccharomyces cerevisiae

Kluyveromyces fragilis fuel ethanol, alcoholic beverages

Citric acid Aspergillus niger food industry Acetone and butanol Clostridium acetobutyricum solvents Lysine Corynebacterium nutritional additive Glutamic acid glutamacium flavour enhancer Riboflavin Ashbya gossipii,

Eremothecium ashbyi nutritional

Vitamin B12 Pseudomonas denitrificans Propionibacterium shermanii

nutritional

Dextran Leuconostoc mesenteroides industrial Xanthan gum Xanthomonas campestris industrial

59,000,000

2,000,000

1,500,000 3,000,000

12

500110,000

4,000

Courtesy of D. Mattanovich, BOKU Vienna

Industrial biotechnology

Examples of primary metabolite production

3

Biocyc Metabolic map of Saccharomyces cerevisiae

Metabolomics and fluxomics in industrial biotechnology

…link between metabolism production efficiency and product quality of cell fabrics

Genome scale

metabolic model

under-determined system of equations

1. Flux balance

analysis (extracellular parameters)

2. 13C-metabolic flux analysis

3. Non-targeted

differential analysis

• Correction of reactions by thermodynamic constraints

Optimization of CHO cell fabrics with metabolomics and fluxomics

Genome scale

metabolic model


1. Flux balance



3. Non-targeted



• Calculation of intracellular fluxes /mmol g-1 h-1


Genome scale

metabolic model


1. Flux balance



3. Non-targeted




• Reduction of number of allowed reactions and degrees of freedom

• Determination of branching points


Genome scale

metabolic model


1. Flux balance



3. Non-targeted




• Reduction of number of allowed reactions and degrees of freedom

• Determination of branching points• Reduction of complexity of

solution space by discovery of novel pathways und metabolites


Metabolic profiling for flux balance analysis

Selection of pathway(s)Selection of pathway(s)

Sampling, and sample

preparation of cellular samples

Sampling, and sample

preparation of cellular samples

Sampling and sample

preparation of supernatants

Sampling and sample

preparation of supernatants

Selection of organism

Selection of organism

Selection of analytes

Selection of analytes

Absolute quantification via LC-MS/MS and GC-MS/MS analysis

(U13C internal standardization)

Absolute quantification via LC-MS/MS and GC-MS/MS analysis

(U13C internal standardization)

Major intracellular targets:Glycolysis + pentose phosphate pathway (sugars, sugar phosphates)

TCA -cycle (organic acids)

Amino acid biosynthesis(amino acids and intermediates)

Targeted metabolite quantification with mass spectrometric techniques

Major targets in supernatants (CHO):Constituents of culture medium(amino acids, organic acids, glucose, etc.)

13C-metabolic flux analysis

modified from Wiechert W., MetabolicEngineering (2001) 3: 195- 206

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3

21

3

3

12

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123

3

12

3211

23

Isotopologuemolecules differ in terms of their isotope composition

A C3-metabolite consist of 4 13C-Isotopologue

M+0 M+1 M+2 M+312C13C

13C-isotopologue analysis

21

3

21

3

3

12

321

123

3

12

3211

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Isotopomermolecules have same isotope composition BUT differ in the position of the isotope

1 Isotopologue, but 3 Isotopomers

M+0 M+1 M+2 M+312C13C

A C3-metabolite consist of 4 13C-Isotopologe

Isotopologuemolecules differ in terms of their isotope composition


Tandem Mass Isotopomer Distribution (TMID)

123

23

1M+2

M+0

M+1

M+3Precursor Fragment

IF1 = %+ + +

IF2 = %+ + +

IF3 = %+ + +

IF4 = %+ + +

Isotopologue Distribution (ID)

IF...Isotopologue Fraction

Guerrasio R, Haberhauer-Troyer C, Steiger M, Sauer M, Mattanovich D, Koellensperger G, Hann S; Anal.Bioanal.Chem.(2013), 15, 405: 5133- 5146


LC or GC Tandem MS analysis

Tandem mass isotopomer distribution

Sauer U, Molecular Systems Biology 2, 1-10 (2006)

Analysis of tandem mass isotopomer distribution




Correction for contribution of natural abundant isotopes

H+

OSi

SiO

OOO

Si

OO

Si

SiO

O

C+

O

OSiCE 10

-1 Cof C-backbone

Citrate 4 TMSm/z 481.1924 m/z 273.0973TMID M0.0

0,0

5,0

10,0

15,0

20,0

M0.0 M1.0 M1.1 M2.1 M2.2 M3.2 M3.3 M4.3 M4.4 M5.4 M5.5 M6.5

% T

MID

TMID ofDerivative Interferences from natural

abundant isotopes and isotopes from derivatization reagents (especially in GC-MS/MS) are distorting the original isotopologue ratios

Correction of signals from 13C, 18O, 29Si, 30Si, etc. is indispensable

H+

OSi

SiO

OOO

Si

OO

Si

SiO

O

C+

O

OSiCE 10

-1 Cof C-backbone

Citrate 4 TMSm/z 481.1924 m/z 273.0973TMID M0.0

0,0

5,0

10,0

15,0

20,0

M0.0 M1.0 M1.1 M2.1 M2.2 M3.2 M3.3 M4.3 M4.4 M5.4 M5.5 M6.5

% T

MID

TMID ofDerivative

TMIDcorrected

Interferences from natural abundant isotopes and isotopes from derivatization reagents (especially in GC-MS/MS) are distorting the original isotopologue ratios

Correction of signals from 13C, 18O, 29Si, 30Si, etc. is indispensable

Calculation of multinominal distribution and correction in Pearl

In cooperation with Christian Jungreuthmayerand Jürgen Zanghellini (acib)

Correction for contribution of natural abundant isotopes

Trueness of measured isotopologue fractions

Replicates RSD IFmeas IFtheor BiasG6P 1 2 3 4 5 6

M+0 0.5067 0.5163 0.5307 0.5499 0.5154 0.5541 3.7% 52.9% 50.2% 2.7%M+1 0.3366 0.2925 0.2900 0.2703 0.3100 0.2721 8.5% 29.5% 30.2% -0.7%M+2 0.1567 0.1912 0.1792 0.1798 0.1746 0.1738 6.4% 17.6% 19.6% -2.0%

Cit 1 2 3 4 5 6M+0 0.6243 0.6247 0.6272 0.6242 0.6221 0.6227 0.3% 62.4% 63.4% -1.0%M+1 0.2207 0.2195 0.2262 0.2228 0.2257 0.2228 1.2% 22.3% 22.3% 0.0%M+2 0.1154 0.1148 0.1165 0.1146 0.1141 0.1158 0.8% 11.5% 11.4% 0.1%

Leu 1 2 3 4 5 6M+0 0.8598 0.8643 0.8626 0.8609 0.8615 0.8613 0.2% 86.2% 86.7% -0.5%M+1 0.1057 0.1023 0.1049 0.1046 0.1045 0.1040 1.1% 10.4% 10.4% 0.0%M+2 0.0303 0.0294 0.0289 0.0304 0.0300 0.0299 2.0% 3.0% 2.9% 0.1%

Mal 1 2 3 4 5 6M+0 0.7144 0.7163 0.7173 0.7152 0.7156 0.7152 0.1% 71.6% 73.0% -1.5%M+1 0.1788 0.1796 0.1790 0.1809 0.1802 0.1808 0.5% 18.0% 18.2% -0.2%M+2 0.0866 0.0847 0.0842 0.0846 0.0845 0.0842 1.1% 8.5% 8.8% -0.3%

(1) Comparison with theoretical ratios of standard substances

Isotopologue IFmeas IFtheor Bias3PG M+0 63.2% 63.0% -0.2%

M+1 23.4% 23.7% 0.3%M+2 13.4% 13.3% -0.1%

E4P M+0 57.9% 58.9% 1.0%M+1 25.2% 24.1% -1.1%M+2 12.6% 13.3% 0.7%M+3 4.3 3.7% -0.6%

R5P M+0 51.3% 50.8% -0.5%M+1 26.0% 25.8% -0.2%M+2 14.1% 15.4% 1.3%M+3 6.9% 5.2% -1.7%M+4 1.7% 1.8% 0.1%

G6P M+0 49.3% 45.5% -3.8%M+1 24.5% 27.4% 2.9%M+2 15.1% 17.8% 2.7%M+3 6.7% 7.0% 0.3%M+4 3.1% 2.6% -0.5%M+5 1.2% 0.7% -0.5%

(2) Comparison with theoretical ratios in real samples (P. pastoris)

Chu D.B., Troyer C., Mairinger T., Ortmayr K., Neubauer S., Koellensperger G. and Hann S., Anal. Bioanal. Chem, 2015, in press


(3) inter-comparison with LC-MS/MS data in real flux samples

Tandem mass isotopomer fractions of citrate in cell extract of filamentous fungi


cut-off applied for Fractions: >0.5%

Dependence of trueness of isotopo-logue fractions from fraction height

Dependence of fraction precision from intensity

Sources of uncertainty in fluxomics

Model equation and budget for M+2 isotopologue fraction of 3-phospho-glycerate

Model equation


Model equation and budget for M+2 isotopologue fraction of 3-phospho-glycerate

Model equation Contribution of major input quantities

In cooperation with Wolfhard Wegscheider(Univ. of Leoben)

Metabolic model with optimum fluxes

Courtesy of Michael Hanscho

Optimization function shows optimum flux distribution

Enabled determination of external factors influencing yield

Allow the identification of genetic optimization potentials

Cooperation partnersFUNDING INSTITUTIONS

COMETCompetence Centers for Excellent Technologies

FFGAustrian Research Promotion Agency

BMWFJFederal Ministry for Economy, Family and Youth

BMVITFederal Ministry for Transport, Innovation and Technology

SFGStyrian Business Promotion Agency

Styrian Provincial GovernmentEconomic Affairs and Innovation

zitTechnology promotion agency of the City of Vienna

Standortagentur Tirol

INDUSTRIAL PARTNERS

Thank you for your attention!

Documents

Measurement uncertainty of isotopologue fractions ... · Measurement uncertainty of isotopologue fractions determined via mass spectrometry for metabolic flux analysis Teresa Mairinger1,2,