Using GC-MS analysis to study central metabolism – plant autophagy as a test

case Tamar Avin-Wittenberg

Department of Plant and Environmental Sciences

The Hebrew University of Jerusalem

12.7.2017

Energy supply and demand vary throughout plant life

Adapted from Graciet and Wellmer, 2010, Trends Plant Science.

Storage compounds Photosynthesis Degradation Supply

Demand Vegetative development & growth Flower development Seed production

Plants face many types of stresses which cause energy deprivation

Adapted from "Abiotic Stress in Plants - Mechanisms and Adaptations“ 2011

Autophagy as a model system for nutrient remobilization How to break down a big question to smaller, bite-size questions?

Autophagy Autophagy (‘self eating’) is a conserved eukaryotic mechanism for the intracellular degradation of cytoplasmic components in the lytic organelle.

Li and Vierstra, 2012, Trends Plant Sci.

Autophagy plays a major role in health and disease

Beau et al, 2011, Int J Biochem Cell Biol

Vacuole Phagophore

Autophagosome

Autophagic body

Macroautophagy

AuTophaGy related (ATG) genes compose the autophagic machinery

Li and Vierstra, 2012, Trends Plant Sci.

The roles of autophagy in plants

Constitutively active in low levels

Induced by: Carbon and nitrogen starvation

Senescence

Abiotic stress

Biotic stress

Functions in selective degradation of proteins and organelles

Yoshimoto et al, 2004, Plant Cell

atg mutant plants are sensitive to nutrient starvation

Thompson et al, 2005, Plant physiol.

Li et al, 2015, Plant Cell

Carbon starvation

Nitrogen starvation

Li and Vierstra, 2012, Trends Plant Sci.

Aim of study: To elucidate the impact of autophagy on plant metabolism during developmental stages and stress conditions

Autophagy functions in nutrient recycling

Carbon starvation – whole plant or individual leaves?

Weaver and Amasino, 2001, Plant Physiology

The experimental system: individually darkened leaves (IDL)

• Physiologically relevant

• Severe Phenotype

• What happens to the metabolites? Where are they going?

The experimental setup (IDL) -5 -3 -1 0

collection

• Lines used: wt col, atg5-1, atg5-3.

• 2 weeks in short day, 16 days in long day (starting to bolt).

• Leaves #5&6 were treated and young leaves were collected as systemic.

• 3 plants per sample, 6 biological replicates for GC-MS analysis.

Darkened

Systemic

Time for metabolomics… Is there a connection between the morphological phenotype and a metabolic phenotype?

Primary metabolites

Produced by all organisms

Essential for survival

Include building blocks such as sugars, amino acids and lipids

Techniques to measure primary metabolites

Colorimetric methods

• Direct measurement of absorbance, Enzyme coupling assays

Chromatographic methods

Spectrometric methods

Hyphenated methods

Gas Chromatography –Mass Spectrometry (GC-MS)

Why use the GC-MS?

Robust and high throughput analysis of 100-400 metabolites.

Suitable for low molecular weight metabolites (50-800 Da):

• Primary metabolites :sugars, sugar alcohols, organic acids, amino acids

• selected secondary metabolites

• vitamins

Largely independent of ion-suppression effects.

The steps of GC-MS analysis

Sample preparation

GC separation MS detection Evaluation &

quantification

A little bit about experimental design

Plants are sensitive to small environmental changes

• Randomize the pots/plates in the growth room

Use between 4-6 biological replicates

Plan the samples size according to the collection capacity

• Sample number doubles very easily

Sampling and Extraction

Requirements

• Fast quenching of in vivo metabolic reactions

• Fast inactivation of metabolic enzymes

• Complete extraction of metabolites

• Enrichment of target metabolites

Sample preparation

GC separation MS detection Evaluation &

quantification

Why is derivatization performed?

Halket et al, 2005, J Exp. Bot.

The problems:

In order to detect metabolites by GC-MS they need to be rendered volatile

Metabolites can come in several physical conformations, complicating the detection

The solution - two derivatization procedures:

Trimethylsilylation (TMS) – confers volatility

Methoximation – renders metabolites (derivatants) in a single conformational structure and simplifies chromatography

Sample preparation

GC separation MS detection Evaluation &

quantification

Separation by gas chromatography Two major forces:

• Volatility

• interaction with film inside the column

Sample preparation

GC separation MS detection Evaluation &

quantification

What is mass spectrometry?

The separation of ions on the basis of their mass/charge ratio

Sample preparation

GC separation MS detection Evaluation &

quantification

Metabolite identification

1000000

Retention tim

e index

Fragment (M

/Z)

Ion c

urr

ent

1320

1330

1340

1350100

200

300400

500

600

500000

AC

D

F

E

B

Every metabolite can be identified using two traits:

• Retention time index

• Mass spectra

Sample preparation

GC separation MS detection Evaluation &

quantification

Metabolite profiling by GC-MS results in a ‘3D data’

Comparing with standard compound library data

Detected spectrum

Spectrum of standard compound

Similar RT

Sample preparation

GC separation MS detection Evaluation &

quantification

Resources for GC-MS Mass Spectral Metabolite Identification

NIST (http://www.nist.gov/srd/nist1a.cfm)

FiehnLib (http://fiehnlab.ucdavis.edu/Metabolite-Library-2007/)

Golm metabolic database (http://gmd.mpimp-golm.mpg.de/)

Mass Bank (http://www.massbank.jp/index.html?lang=en)

Chromatograms of WT and an autophagy mutant

WT atg5-1

WT

atg5-1

additional corrections:

• Normalization to internal standard

• Normalization to fresh/dry weight

Absolute quantification of metabolites

0

20000

40000

60000

80000

100000

120000

140000

160000

0 200 400 600 800 1000 1200

Fra

gm

ent count

Absolute amount (ng)

125 ng

250 ng

500 ng

1000 ng

Results of systemic leaves are not conclusive

0 1 3 5 0 1 3 5 0 1 3 5

WT atg5-1 atg5-3

0 1 3 5 0 1 3 5 0 1 3 5

WT atg5-1 atg5-3

0 1 3 5 0 1 3 5 0 1 3 5 WT atg5-1 atg5-3

Amino acids Organic acids

Sugars

The metabolic profile of dark treated leaves is affected by the treatment

4,682 -9,36

7,878

-5,42

PC1 (31,2%)

PC

2 (

19

,5%

)

atg5-3 0

atg5-1 5 atg5-1 0

WT 0

atg5-3 3

atg5-1 3

WT 5

atg5-3 1

atg5-3 5

WT 3

atg5-1 1

WT 1

The metabolic profile of dark treated leaves is affected by the treatment

0 1 3 5 0 1 3 5 0 1 3 5

WT atg5-1 atg5-3

Amino acids

0 1 3 5 0 1 3 5 0 1 3 5

WT atg5-1 atg5-3

Organic acids

0 1 3 5 0 1 3 5 0 1 3 5

WT atg5-1 atg5-3

Sugars

The metabolic profile of dark treated leaves is affected by the treatment

0 1 3 5 0 1 3 5

atg5-1 atg5-3

Amino acids

0 1 3 5 0 1 3 5

atg5-1 atg5-3

Organic acids

0 1 3 5 0 1 3 5

atg5-1 atg5-3

Sugars

The experimental system – etiolated Arabidopsis seedlings

Seeds are sown on plates with or without 1% sucrose.

The plants were germinated and grown in the dark for 5-7 days.

Why? A „closed system“

A model system for carbon starvation, a known inducer of autophagy

Physiologically relevant

Etiolation – growing a plant without light

Quail, 2002, Nat Rev Mol Cell Biol.

Autophagic flux is increased in etiolated seedlings under carbon starvation

Nakatogawa et al, 2009, Nat Rev Mol Cell Biol.

Incorporation into autophagosomes

Degradation in the vacuole

Atg8 HA GFP

Atg8 GFP

GFP

1% suc - +

Avin-Wittenberg et al, 2015, Plant Cell

autophagy mutant seedlings are shorter under carbon starvation

+1% sucrose No sucrose

* *

*

0

2

4

6

8

10

12

14

16

WT atg5-1 atg5-3 atg7-2

hyp

oco

tyl l

en

gth

(m

m)

0

2

4

6

8

10

12

14

16

WT atg5-1 atg5-3 atg7-2

hyp

oco

tyl l

en

gth

(m

m)

Avin-Wittenberg et al, 2015, Plant Cell

Some controls…

The problem:

autophagy mutants display an early senescence phenotype stemming from salicylic acid (SA) accumulation.

The solution:

crosses between autophagy mutants and SA deficient stay-green lines.

Yoshimoto et al, 2009, Plant Cell.

Preventing SA accumulation does not rescue the autophagy mutant phenotype

0

2

4

6

8

10

12

14

16

WT NahG atg5.NahG

hyp

oco

tyl l

en

gth

(m

m)

b

c

a

Avin-Wittenberg et al, 2015, Plant Cell

The primary metabolite profile of autophagy mutants is different compared to WT

5.662 -10.4

5.175

-3.25

PC1 (46.1%)

PC

2 (

11.1

%)

WT

atg5-1

atg5-3

atg7-2 NahG

atg5.NahG

Avin-Wittenberg et al, 2015, Plant Cell

Amino acids

Organic acids

Sugars

atg mutants have less free amino acids

Avin-Wittenberg et al, 2015, Plant Cell

Amino acids can be used as substrates for the Mitochondrial electron transport chain

Galili et al, 2014, Front Plant Sci.

atg mutants divert flux to the TCA cycle

Dark respiration

Days after transition to darkness

0 3 6 9

m

ol C

O2 m

-2 s

-1

0.0

0.2

0.4

0.6

0.8

1.0

WT

atg5-1

atg7-2

atg9-1

**

*

*

*

*

*

Barros et al, accepted for publication

WT

atg7-2

atg9-1

atg5-1

0 DAY

WT

atg7-2

atg9-1

atg5-1

9 DAY

Nutrient remobilization can be examined in several ways

Test the nutrient content under the experimental conditions – what compounds are there and how much of them?

Test nutrient flux – what is the rate in which nutrients are produced and used?

Metabolic flux analysis Metabolic flux analysis (MFA) determines in vivo rates of conversion (fluxes) through active pathways. The aim of MFA is the detailed quantification of (all) metabolic fluxes in central metabolism. Dieuaide-Noubhani Ana Alonso “Plant Metabolic Flux Analysis Methods and Protocols” 2014

Isotopic tracer labeling methods for MFA

14C feeding and fractionation

Adapted from Fernie et al, 2001, Planta

External Glucose

Internal Glucose

Hexose phosphates

Others CO2, solubles (amino acids, organic acids, other sugars) and insolubles

(proteins, starch, cell wall etc.)

14C labeled glucose

CO2

atg mutants display reduced protein flux

Adapted from Fernie et al, 2001, Planta

0

500

1000

1500

2000

2500

WT atg5-1

Bq

/gFW

Total label uptake & metabolism

label uptake label metabolized

0

500

1000

1500

2000

2500

3000

3500

WT atg5-1

Me

tab

olic

flu

x (n

mo

l he

xose

e

qu

ival

en

ts/F

W*h

)

Metabolic flux

Protein Sucrose Starch

Avin-Wittenberg et al, 2015, Plant Cell

*

*

*

External Glucose

Internal Glucose

Hexose phosphates

Others CO2, solubles (amino acids, organic acids, other sugars) and insolubles

(proteins, starch, cell wall etc.)

14C-Glucose fractionation

Detecting heavy isotopes by MS

m+0

m+1

m+2

m+3

Sign

al in

ten

sity

m/z

m+0

m+1

m+2

m+3

m/z

Types of MFA and required metabolic status

Dieuaide-Noubhani and Alonso, 2014, Method Mol Biol

Metabolic steady state

Isotopic steady state

Steady state MFA

Dynamic labelling MFA (Isotopic non-steady state MFA)

Applicable for estimation of fluxes at branch points

Isotopic steady state is required (limited application)

High resolution of fluxes

Kinetic experiment is required

Isotopic steady state is not a prerequisite

Steady-state MFA

Duckwall et al, 2013, J Carcinog.

Kinetic labeling experiments

Szecowka et al, 2013, Plant Cell

Isotope labeling experiment

atg5-1 WT

13C-Lys feeding Metabolic analysis Label redistribution

- Incubation 40,80,120min - Non-treated control - 4 replicates

GC-TOF-MS

m+0

m

+1

m+2

m

+3

Sign

al in

ten

sity

m/z

m+0

m

+1

m+2

m

+3

m/z

Heavy isotope feeding

13C-Lysine feeding

Metabolic analysis Label redistribution

Autophagy mutants divert flux into the TCA cycle

13C-Lysine feeding

0

2

4

6

8

10

12

0 40 80 120 13C

su

m a

ccu

mu

lati

on

(n

mo

l/m

g)

FW)

Time (min)

Lysine

wt

atg5-1

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

0 40 80 120 13C

su

m a

ccu

mu

lati

on

(n

mo

l/m

g)

FW)

Time (min)

Glutamate

wt

atg5-1 *

*

L-lysine L-saccharopine (S)-2-amino-6-oxohexanoate L-2-aminoadipate 2-oxoadipate

glutaryl-CoA (E)-glutaryl-CoA crotonyl-glutaryl-CoA (R)-3-hydroxybutanoyl-CoA

acetoacetyl-CoA 2-acetyl-CoA

H+ NADPH

2-oxoglutarate

NADP+

H2O

NAD+

H2O H+

NADH L-glutamate

NAD+

H2O NADH

2H+

2-oxoglutarate L-glutamate

Coenzyme A NAD+

NADH

CO2

An oxidized electron-transfer flavoprotein

A reduced electron-transfer flavoprotein

H+ CO2 H2O NAD+

H+ NADH

Coenzyme A

Avin-Wittenberg et al, 2015, Plant Cell

Autophagy mutants divert flux into the TCA cycle

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0 40 80 120 13C

su

m a

ccu

mu

lati

on

(n

mo

l/m

g)

FW)

Time (min)

Malate

wt

atg5-1

0

0.001

0.002

0.003

0.004

0.005

0 40 80 120 13C

su

m a

ccu

mu

lati

on

(n

mo

l/m

g)

FW)

Time (min)

Aspartate

wt

atg5-1

13C-Lysine feeding

*

*

*

L-aspartate oxaloacetate (S)-malate

2-oxoglutarate L-glutamate NAD+ NADH H+

Avin-Wittenberg et al, 2015, Plant Cell

Summary

It is very important to find the right tool to answer your biological question

GC-MS analysis is a great tool to study central metabolism

A scientifically meaningful result depends on a well planned experiment

Using labeled isotopes can increase our knowledge of the metabolic flux in the system

Hila Mizrahi Noa Bibi

Nofar Pinker Kama Lee-Tal

Alisdair R. Fernie Fernie group Toshihiro Obata Takayuki Tohge Saleh Alseekh Aida Maric

Ralph Bock Gal Wittenberg

Patrick Giavalisco Krzysztof Bajdzienko

Wagner L. Araújo Jessica A. S. Barros