Analysis of the metabolic features of plant extremophile ...€¦ · Analysis of the metabolic...

Analysis of the metabolic features of plant extremophile species from Atacama Desert

Thomas DussarratJoint PhD project (2019-2022) France (UB) – Chili (PUC)

Supervision: D. Rolin, P. Pétriacq, R. Gutiérrez

1

Atacama is one of the harshest environments for plant life 2

Nutrient poor soil

High radiation

High salinity

Extremely low water availability

Extreme daily T°

oscillations

Driest non-polar

desert

Atacama is one of the harshest environments for plant life

50 times driest than Death Valley

Atacama: ≈610

(W/m²/day)

≈188

≈145

≈185

≈990

≈800.

≈1400

Atacama: ≈170

(mm/year)Sources: NASA, WWIS, FAO and Statista Research

3

Surprising plant diversity:≈ 70 plant species registered

Poaceae

Solanaceae

Asteraceae

Ephedraceae

Cactaceae

Fabaceae

4

F. Diaz et al 2018

Gene expression networks of 32 plant species

Phylogenomic study Metabolic features

2 31

8R. Gutiérrez – Plant Systems Biology Lab – Chili

Objective: To identify the plant processes required for adaptation in the Atacama Desert

Relevant clusters for adaptation

Nitrogen metabolism

Osmotic stresspathways

Redox metabolism

5

Defence metabolism

3300m

2400m

Puna

Prepuna

Altitude limit for plant life

No plant’s land

4000m

Steppe

4500m

150

60

590

625

mm

/year

W/m

²/day

4-5

A clear environmental gradient through the transect 6

Phyto-Health Symposium T. Dussarrat ; P. Pétriacq ; D. Rolin 11/19

8

pH

1

19

Average2

01

8 (°C

)0.24

7.39

meq

/10

0g

Credits : Pal-Gabor et al. unpublished

Minimal Nitrogen concentration for optimal crop plant growth

High Steppe Puna Prepuna

Nitrogen(mg/kg)

Sites along altitudinal cline

L1 = 4480L2 = 4370 L3 = 4270L4 = 4174L5 = 4072L6 = 3970L7 = 3870L8 = 3870L9 = 3770L10 = 3670L11 = 3570L12 = 3470L13 = 3370L14 = 3270L15 = 3170L16 = 3070L17 = 2970L18 = 2870L19 = 2770L20 = 2670L21 = 2570L22 = 2470

Plant species collected and water content (2019)TL transect

Ste

pp

eP

un

aP

rep

un

a

m.a.s.l

Diversity & Coverage

14 families, 27 species - 10 species in 2 ecosystems

7



TL transect

Ste

pp

eP

un

aP

rep

un

a

m.a.s.l

3P. bryoides3P.quadrangularis3A.spinossisima 3P.pinnatifida

3L.subinflatus3B.tola

3F.denudata ?S. metarsium3E.americana3T.multiflora

3H.doellii 3A.deserticola4A.imbricata

4A. adscensionis3C. commissuralis4A.artemisioides

COpuntia spp

3S.chilense3F.chilensis

3T.atacamensis

3J.frigida 3C.crispa3M.monocephala 3A.atacamensis

Plant families: Poaceae, Ephedraceae, Polygonaceae, Amaranthaceae, Caryophyllaceae, Zygophyllaceae,Fabaceae, Verbanaceae, Boraginaceae, Solanaceae, Apiaceae, Calyceraceae, Asteraceae, and Cactaceae


14 families, 27 species - 10 species in 2 ecosystems

Plant species collected and water content (2019)


7

TL transect

Ste

pp

eP

un

aP

rep

un

a

m.a.s.l

3P.quadrangularis

4A.imbricata

3P.quadrangularis

3P.quadrangularis3P.quadrangularis3P.quadrangularis

4A.imbricata

4A.imbricata

4A.imbricata

4A.imbricata

Focus on 3 species covering 1600m of altitude

Plant species collected and water content (2019)

Plant families: Fabaceae, Amaranthaceae, Asteraceae

7

3A.spinossisima3A.spinossisima3A.spinossisima3A.spinossisima3A.spinossisima3A.spinossisima

40 60 80

Average WC (%)

Specific objectives 2: Compare one species

for the different environments1: Compare the different species

for all environments


Complementary analyses to explore

the metabolic profiles of Atacama plants

8


Biochemical phenotyping - HitMe platform

Full scan analysis- LCMS

➢ Targeted quantitative analyses of metabolic markers involved in…

Nitrogen metabolism

Response to osmotic stress

Redox metabolism

➢ Untargeted analysis of semi-polar metabolites…

Coll. Cédric Cassan



8





Nitrogen metabolism


Redox metabolism


Coll. Cédric Cassan

1: Significant variations between species 9

Osmoregulation


✓ Significant variations of the carbohydrate levels between species

Sum

of

Glu

cose

, Fru

cto

se,

Sucr

ose

mg

/gD

W

a b b c

Sum

of

Mal

ate,

Cit

rate

mg

/gD

W

a ab bc c

Control: Tomato leavesP < 0.01

1: Significant variations between species 10

Carbon metabolism

Star

chm

g/g

DW

Ch

loro

ph

yllm

g/g

DW

a b a ca b a c

✓ Significant variations of the carbohydrate levels between species


✓ Chlorophylls and starch levels are much lower than in tomato leaves

Control: Tomato leaves

P < 0.01

1: Significant variations between species


N metabolism

11

✓ Unlike light, N is limiting in the desert

✓ Atacama plants present low poolsof N-related compounds

a a a b a b b b

Nit

rate

mg

/gD

W

Free

am

ino

acid

sm

g/g

DW

a b a c

Pro

tein

sm

g/g

DW

P < 0.01

1: Significant variations between species


Redox metabolism

12

➢ Atacama plants accumulate polyphenols

✓ Specialised (stress-responsive) pathways are likely to be induced in Atacama species

→ Specialised metabolites ? LCMS !

➢ Antioxidant assays are underway…Glutathione, ascorbate, NAD(P)/H

Tota

l po

lyp

hen

ols

mg

/gD

W

a b c d P < 0.01

Metabolic marker concentrations

in tomato leaves VS aerial tissues of extreme plants


13

Chlorophyll levels Decreased ability to capture photons.Protection ?

Carbon reservesLight is not limiting, Atacama plantscan produce biomass when otherrequired resources become available.

Nitrate N is limiting. Moderate levels of freeamino acids suggest an active turnover.

Total polyphenols Specialised metabolites as polyphenolsare likely to be crucial in survivalunder extreme conditions.



14





Nitrogen metabolism


Redox metabolism


✓ Biochemical diversity

✓ Detection of specialised metabolites

✓ Relative quantification

Coll. Amélie Flandin & Stéphane Bernillon

12

LCMS experiment workflow

15

1. Experimental design

2. Sample preparation3. Data acquisition

Full scan

4. Data processing

5. Statistical analyses

7. Data interpretation


6. Metabolite identification

Coll. Stéphane Bernillon,Amelie Flandin

Ethanolic extractionC18 separation - HRMS

≈6000 to ≈2500 detected ions

12LCMS experiment: towards the discovery of

extremophile metabolic profiles

PLS-DA – A. imbricata

A. imbricataFrom 2770m to 3470m

16


5. Statistical analyses

Co

mp

on

ent

2 (

14

.2%

)

Component 1 (43%)

3470m3370m

2970m2770m

3170m

R²= 0.90Q²= 0.82

12

RFMF T. Dussarrat 11/19

Multilinear model allowing to associate 14 metabolic markers with the altitude

Modelling: how to link environmental conditions and

metabolic variables

17

A. imbricata


Coll. Sylvain Prigent

✓ 14 mz-RT variables highly correlate with the environment

Measured altitude (m)

Fitt

edal

titu

de

(m)

28

00

3

00

0

3

20

0

34

00

2800 3000 3200 3400

R²= 0.97

12

Great prediction for a larger plant group ?


metabolic variables

18


R²= 0.92

2600 3000 3400 3800 4200

0.75

1.00

0.50

R²

dis

trib

uti

on

0.25

0.00

✓ 14 variables allow to predict altitude of plants from 4 families (8 species)

✓ Tested 500 times

→ Biochemical characterisation of these metabolic variables !

4200

3800

3400

3000

2600


Pre

dic

ted

alti

tud

e (

m)

Candidacy exam T. Dussarrat 11/18

Low levels of Chlorophylls, C reserves and N compounds

Significant variations between species

Quantitative data on central metabolism

Development of predictive multilinear models allowing to isolate 14 metabolic variables highly correlated with altitude

Conclusion19


Analyse the metabolic featuresof extreme plants from Atacama Desert

Untargeted analysis of semi-polar metabolites


➢ Associate discriminant metabolic markers to specific environmental variables

➢ Can we find these metabolic markers in other extremophile plants? Thar Desert and Californian Desert ?

Perspectives20

Work in progress

✓ Chemical identity of discriminant metabolites:- Molecular networks from MS2 data

- Screen metabolite databases

Perspectives

✓ Redox and NMR analyses


✓ José O’Brien✓ Jani Brouwer ✓ Wendy Wong Jimenez

✓ Rodrigo Gutiérrez✓ Soledad Undurraga✓ Francisca Díaz✓ Tomas Moyano✓ All the lab team

✓ Antoine De Daruvar✓ Michel Hernould✓ Dominique Rolin✓ Pierre Pétriacq✓ Yves Gibon

21


Acknowledgments

✓ Amélie Flandin✓ Stéphane Bernillon✓ Cédric Cassan✓ Sylvain Prigent✓ All the lab team


14 families, 27 species - 10 species in 2 ≠ ecosystems

Plant families: Poaceae, Ephedraceae, Polygonaceae, Amaranthaceae, Caryophyllaceae, Zygophyllaceae,Fabaceae, Verbanaceae, Boraginaceae, Solanaceae, Apiaceae, Calyceraceae, Asteraceae, and Cactaceae


2: Environmental responses of metabolic markers

for one species

13


Nit

rate

mg

/gD

W

Alin

oac

ids

mg

/gD

W

Pro

tein

sm

g/g

DW

Star

chm

g/g

DW

Ch

loro

ph

yllm

g/g

DW

Osm

oly

tes

mg

/gD

W

a a a a a a a a a b a a a a a

ab ab a ab b ab ab a ab b a a b ab a

➢ Significant changes

P. quadrangularis

Osmoregulation C metabolism

N metabolism

➢ Microbiome of the soil has been studied through the transect➢ The soil composition in macro and micronutrients is known and was evaluate for 4 years

4

Environmental data

ID Sample

➢ ID Sample document for each sample (280)

Figure 3: Significant negative correlation between Soil and Plant Water Content (Pearson test)

Negative correlation between soil WC and plant WC

12



metabolic variables

17


Coll. Sylvain Prigent

2. Resolve the equation: yi = β0 + β1xi1 + β2xi2 + … + βnxin + єiyi = predicted altitude of sample i.β1 = coefficient for variable 1xin= number of variables for sample i

3. Calculate a predicted for all samples with this equation4. Compare it with Measured altitude to determine the R² of the model

1. Define an alpha from 0 to 1 → Determine the number of variables that can use the model

Pre

dic

ted

alti

tud

e (

m)


12


20

Perspectives: Molecular networking


➢ Plot our variables of interest Comparison with libraries Identification ➢ New biosynthesis pathways – new molecules?

➢ Molecular network developed with MS² spectrum from all species

12


18Metabolic networking to give a name and a biological

sense to these highly discriminant variables


tiliroside Library

Use MS² spectrum to develop metabolic networks

12


19

Perspectives


➢ Illustrate the variations of intensity between environments within one species or a group of species

➢ Highlight the variations between Atacama species and both model and agronomic species. New biosynthesis pathways – new molecules?


27 species of Atacama extremophiles

Quantitative data on central metabolism

Characterise the genotype impact on molecular markers of the central metabolism

Compare these levels to tomato leaves

Development of predictive multilinear models allowing to isolate 14 metabolic variables highly

correlated with altitude

Conclusion19


Analyse the metabolic featuresof extreme plants from Atacama Desert

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