METABOLOMICS: ADDING A POWERFUL TOOL FOR SUGAR CANE CHEMICAL PROFILE STUDIES

BIOEN Workshop on Metabolomics of Sugar CaneSão Paulo, December 07th, 2009

Alberto José Cavalheiro, Vanderlan da Silva Bolzanialbjcava@iq.unesp.br; bolzaniv@iq.unesp.br

Instituto de Química, Universidade Estadual Paulista, Araraquara, SP

Núcleo de

Bioensaios,

Biossíntese &

Ecofisiologia

de Produtos

Naturais

Part 2

Looking at the NuBBe Expertise -

Current Status Research

Part 1

Metabolomics, the Chemical

Characterization of a Phenotype

Part 3

Sugar Cane Chemical

Profile Project

Metabolomics – is a promising new natural products tool:“It aims to facilitate and improve a better understanding of thedynamic biochemical composition with living systems (Ridley, et al,2006”

Holistic Approach

Reductionist

Approach

HumansAnimals

Cells

PresentPast

Organs

Molecules

R. Verpoorte et al., J. Ethno-Pharmacol. 100 (2005) 53-56

Primary metabolism

Growth

◊ Nutritional value

Secondary metabolism

◊ Health

◊ Medicines

◊ Taste

◊ Smell

◊ Color

◊ Resistance against pests

and diseases

•About 30,000 compounds

•Large range of relative quantities

•Broad polarity range

•Unstable compounds

“Metabolomics – the study of global

metabolite profiles in a system (cell, tissue,

organism) under a given set of conditions”

Plant metabolome

Analysis

TLC, LC, GC, MS, NMR, IR

Identification unknowns

Map

metabolic

network

Biosynthesis

Biological

activity

Regulation,

genes,

proteins

Metabolome

Role

In plant

Challenge

To establish robust methods for analyzing

large number of metabolites (primary and

secondary) in very small concentrations

and different samples.

METABOLOMICS – Due to its rich chemical information Instrumental Analysis

is crucial for processing and collecting a large amount of chemical

information

Dunn & Ellis, Trends Anal. Chem.,

24:285, 2005

Metabolomic (or metabonomics) has been

labeled one the new “omics”

Systems BiologySugars,

Amino acids

GlycosidesOrganic acids

Terpenoids

Steroids

Flavonoids

Lignoids

Xanthones

Tannins

Genomics;

♦ Transcriptomics;

♦ Proteomics;

Aim Metabolomics - Identification and quantification of all

metabolites in an organism;

Requirements Methods for Metabolomics:

Highly reproducible to make public database

Fast for high throughput screening

Easy sample preparation

Identification of compounds

Easy to quantify

Sensitive

High resolution

Main Methods for Metabolomics Studies Chromatography

LC-MS

GC-MS

Mass Spectrometry (MS)

Nuclear Magnetic Resonance Spectrometry (NMR)

FTIR and HPLC-UV HPLC-MS, has also been used

Fluorescence microscopy;

250

200

150

100

50

0

1975 1995 2000 2001 2002 2003 2004 2005 2007

1994 1999

Number of metabolomic related papers published from 1975 to

September 2007 (databases searched Analytical Abstracts, CAB

Abstract 2002/2007, Current contents, Science Direct and others

(term used – metabolomics or metabolite profiling

Metabolomics – the study of global

metabolite profiles in a system (cell,

tissue, organism) under a given set of

conditions

Multidisciplinary Research, with

strong insertion of Chemistry,

Biology, Pharmaceutical Sciencesand Medicine

Industry

Research &

Development

*Biodiversity

Bioproducts

Patent

Prototype

*Sustainable uses

Academic

Research

Mo

lec

ula

r B

iolo

gy:

Meta

bo

lom

ics

THE RESEARCH DEVELOPED AT NuBBE HAS BEEN BASED ON

BIOACTIVE SECONDARY METABOLITES – THUS, HAVING A STRONG

CONNECTION WITH METABOLOMICS

N

H

H3C

HOO

12

1998 Phytochemistry and chemosystematics of Rubiaceae was the starting-

point of NuBBE’s research group; phytoallexin induction in Rubiaceae was

the landmark for systematic search for biologically active compounds,

specially phytopathogenic fungi;

◊ Isolation and Bioassay-guided fractionation is consolidated;

1998 ◊ Submission of a thematic project on bioprospecting to the new Biota

Program created by FAPESP;

2003 ◊ The establishment of strategic national and international collaborations;

2006 ◊ Development of hyphenated analytical methods, e.g. HPLC-DAD-MS/MS;

Analysis of complex bioactive secondary metabolites in plants and fungi

is introduced into the NuBBE

A glance at the origins

O

H

HHO

R2 R1

OH

O

O

H

HOGlu

H

OH

HO2C

HHO

O

H

HHOCO2Me

CO2Me

OGlu

Bolzani et al. (1991) Phytochemistry 30, 2089; Young et al. (1992) Phytochemistry 31, 3422;

Bolzani et al. (1997) Phytochemistry 46, 305; Bolzani et al. (1996) J. Braz. Chem. Soc. 7, 157;

Young et al. (1998) J. Nat. Prod. 61, 936;

Looking at the NuBBe Expertise - Current Research

Lines at NuBBE on Natural Products Chemistry

Bio-guided-isolation of plant species, and of associated microorganisms from plant

and marine species aiming the discovery of antifungal, Antitumoral, antimalarial, CNS

active compounds.

Intra- and inter-specific metabolic/chemical variability in plant species from

Cerrado and Atlantic Rainforest.

Development of hyphenated analytical methods, e.g. HPLC-DAD-MS, MS-MS,

NMR, for analysis of bioactive secondary metabolites from plants and endophyte

fungi – Metabolomic profiles (identifying the all secondary metabolites of

Cerrado and Atlantic plant species for further organization of a metabolomic

datamine);

Biosynthesis and molecular biology.

Cyclic peptides and cyclotides from Brazilian plants: Isolation, synthesis,

ecological function and bioactive evaluation.

Medicinal chemistry studies of drug leads from plant species from Cerrado and

Atlantic Rainforest.

One Example Performed at NuBBE with Leaves

of Cryptocarya mandioccana

FOTO: ERNESTO L. BASTOS Jr.

Intra-specific Chemical Diversity of C. mandiocana from

Atlantic Forest Region, was evaluated qualitatively and

quantitatively by LC-DAD method.

Cavalheiro, et. al. Chromatographia 2009, 70, 1455-1560.

LC chromatogram from crude hydroethanolic extract of leaves from C. mandioccana

using the optimized gradient condition – secondary metabolic profile clearly identified

Carlos Botelho (PECB-NSM)

17 trees

Juréia-Itatins (EEJI-NA)

20 trees

Picinguaba (PESM-NP)

20 trees

- 3 populations

- 57 trees - mapped

Cryptocarya mandioccana (Lauraceae)

Secondary metabolite variety

Google map ©

F1 F2 F3 F4 F5 F6 S1 NI1NI2NI3NI4 S2 S3 S4 NI5NI6NI7 S5 S6 S7

0

1x107

2x107

3x107

4x107

1.2x108

1.4x108

Pe

ak A

rea

Chemotype FS1

F1 F2 F3 F4 F5 F6 S1 NI1NI2NI3NI4 S2 S3 S4 NI5NI6NI7 S5 S6 S70

1x107

2x107

3x107

4x107

5x107

Peak A

rea

Chemotype FS2

F1 F2 F3 F4 F5 F6 S1 NI1 NI2 NI3 NI4 S2 S3 S4 NI5 NI6 NI7 S5 S6 S7

0

1x107

2x107

3x107

4x107

5x107

Pe

ak A

rea

Chemotype F

F1 F2 F3 F4 F5 F6 S1 NI1NI2NI3NI4 S2 S3 S4 NI5NI6NI7 S5 S6 S7

0

1x107

2x107

3x107

4x107

5x107

Compound

Pe

ak A

rea

Chemotype FS3

F

FS1

FS2

FS3

NP025/99

CB354/99

CB283/99

NP012/99

NP007/99

NP012/98

JU2016/99

CB314/99

JU2009/99

CB354/98

CB231/98

NP022/99

NP001/99

NP001/98

NP006/98

NP003/98

NP018/99

NP018/98

NP014/99

NP007/98

NP015/99

NP015/98

NP014/98

NP005/99

NP005/98

NP017/99

NP004/98

NP002/99

NP006/99

NP011/98

NP002/98

NP016/99

NP016/98

NP013/99

NP013/98

JU2020/99

JU2011/99

JU2010/99

JU2006/99

JU2005/99

CB301/99

CB325/98

CB301/98

CB283/98

JU2002/99

CB314/98

CB270/98

NP021/99

NP019/99

NP008/98

JU2021/99

JU2017/99

JU2012/99

JU2019/99

JU2018/99

JU2007/99

CB316/99

CB302/99

JU2015/99

CBXCM/99

JU2014/99

JU2008/99

JU2013/99

JU2003/99

CBXCM/98

CB316/98

CB300/98

JU2001/99

CB313/99

CB356/98

CB248/98

CB356/99

CB317/99

CB300/99

CB248/99

CB353/98

CB230/99

CB230/98

O

OR

OH O

OH

OH

F1 R=galactopyranoside

F2 R=glucopyranide

F3 R=xylopyranoside

F4 R=arabinopyranoside

F5 R=allopyranoside

F6 R=rhamnopyranoside

OH

O O

Cin + 3 Ac

O O

OH OH OH

OH OH OH

O O

O O

OH OH

Cin + 5 ou 6 Ac

OH OH

O O

Cin + 4 Ac

FLAVONOIDS STYRYLPIRONES

O O O

HO

OH

Nehme et al. Bioch. Syst. Ecol. 2008:602-611.

Moraes et al. Bioch. Syst. Ecol. 2007: 233-244.

Cryptocarya mandioccana

ChemotypesF

FS1

FS2

FS3

Atlantic Rain ForestRemnants from São Paulo State

Source: Fundação SOS Mata Atlântica/INPE, 1998

.

100 Km

São Paulo State

41 40

85

0 0

15

47

60

0

12

0 0

0,0

20,0

40,0

60,0

80,0

100,0

PECB-NSM EEJI-NA PESM-NP

Dis

trib

uti

on

(%

)

F

FS1

FS2

FS3

Cryptocarya mandioccana

Chemotypes –genetic variability

Skdh: shiquimate desydrogenase

INSTITUTO DE QUÍMICA

UNESP

Atlantic Rain Forest

Remnants from São Paulo State

Source: Fundação SOS Mata Atlântica/INPE, 1998

.

100 Km

São Paulo State

41 40

85

0 0

15

47

60

0

12

0 0

0,0

20,0

40,0

60,0

80,0

100,0

PECB-NSM EEJI-NA PESM-NP

Dis

trib

uti

on

(%

)

F

FS1

FS2

FS3

Cryptocarya mandioccana

Chemotypes – geographic distribution

Nehme et al. Bioch. Syst. Ecol. 2008:602-611.

Instituto de Química

INSTITUTO DE QUÍMICA

UNESP

Flavonoids

Styrylpyrones

O

O-Gluc

OOH

HO

OH

Dados não publicados.

Dynamic metabolic – two specimen monitored

Cm 354

Cm 353

Instituto de Química

Monthly collected material for one year.

Every 15th day : sampling every 3 hours for 24 hours

Cryptocarya mandioccanaOH OH

O O

Styrylpirones flavonoids

flavonoids

METABOLOMICS IN THE CONTEXT OF

BIOENERGY PRODUCTION FROM PLANT BIOMASSPROJECT THEMATIC – PROC # 2008/56250-7

ALBERTO JOSÉ CAVALHEIRO (Coordinator)

Vanderlan da S. Bolzani (Vice-coordinator)

National Team

Dulce helena Siqueira Silva (IQ-Unesp)

Ian Castro-Gamboa (IQ-Unesp)

Norberto Peporine Lopes(IQ-Unesp)

Mary-Anne Van Sluys (IB-USP, SP)

Marcos Buckeridge (IB-USP, SP)

Marco Aurélio SilvaTine (IBot –SMA)

Maria Cláudia Mrax Young (IBot –SMA)

Luce Maria Brandrão (IBot –SMA)

Márcio de Castro Silva Filho (Esalq - Setor Fitopatologia)

Nelson Sidnei Massola Júnior (Esalq - Setor Fitopatologia)

International Collaboration

Arthur Edison (University of Florida, USA)

Rob Verpoorte (Leiden University,

Holland)

Leslie Gunatilaka (University of Arizona,

USA)

SPECIFIC AIMS OF THE PROJECT

-To establish methodology using RMN and MS data and multivariate

analysis to screening and identify the metabolomic profile of several sugar

cane cultivars, in order to prepare a sugarcane metabolomic datamine;

-Study the metabolic dynamics of cultivars susceptible and resistant to

pathogens, aiming to understand the role of metabolites in different

matrices, under the influence of several pathogens;

-Identify micromolecular biochemical markers/signalizers associated with

infection by pathogens, aiming the early diagnosis;

-Relate such markers/signalizers with transcriptome, aiming the indication

of genes to activated or suppresses in the development of new cultivars

resistant to pathogens;

-Prospection of novel antifungal and insecticides from plant extracts;

1. RUST (FERRUGEM)

Puccinia melanocephala (Basidiomycete )

Reduces photosynthesis

Reduces productivity (7-10 ton / ha)

2. SMUT (CARVÃO)

Ustilago scitaminea (fungus)

Asymptomatic Systemic Infection

Stress can trigger epidemics leading to

total damage of all infected area

3. SUGAR CANE BORER (Diatraea saccharalis,

(Lepidoptera: Crambidae)

Brazil's major sugar cane pest

Why Bioprospecting for novel antifungal and insecticides

secondary metabolites from sugar cane extracts?

The first collecting plant material was done in Araraquara

Region, at Usina Zanin plantation, cultivar RB85-5453.

SUGAR CANE LEAVES

2. 1 GC-MS analysis Sugars, amino acids, carboxylic acids etc

Derivative preparation1. Methoxyamination (MeONH2 / HCl / Py)

2. Silylation of amine, hydroxyl, carboxyl and

thiol groups with MTBSTFA

2. 2 HPLC-DAD-ESI-MS analysis Secondary metabolites etc

Ethanol

WaterIsopropanol

(0

,0

,1

)

(2/3,1/

6,1/6)

(0,1/2,

1/2)

(1/2,0,

1/2)

(1/2,1/

2,0)

(1

,0

,0

)

(0

,1

,0

)

(1/6,2/

3,1/6)

(1/6,1/

6,2/3)

(1/3,1/

3,1/3)

3

56

7

1

0

98

241

1. Optimization of extraction conditions

2. 3 NMR analysisAll metabolites etc

Water 100%

2. 1 GC-MS analysis Sugars, amino acids, carboxylic acids etc

2. 1.1 GC-MS analysis Apolar compounds

Rt (min) Compound

31,118 Hexadecan

34,877 Heptadecan

38,454 Octadecan

41,866 Nonadecan

45,136 Eicosan

51,254 Docosan

56,871 Tetracosan

Rt (min) Compound

39,840 2-pentadecanone,

6,10,14 trimethyl

42,740 Haxadecanoic acid,

methyl ester

45,020 Heptdecanoic acid,

methyl ester

47,950 9,12 octadienoic acid,

methyl ester

49,180 9 octadecenoic acid,

methyl ester

Hexanic extract

Hex+EtOAc extract

2. 2 HPLC-DAD analysis Secondary metabolites etc

IPA/EtOH/W 0,20:0,45:0,35

HPLC-ESI-MS Secondary metabolites etc

HPLC-DAD Secondary metabolites etc

m/z 463

m/z 565

m/z 595

m/z 639

Isoschaftoside

Schaftoside

Luteolina-8-C-(Rham Glu)

Orientin

Tricin-7-O-neohesperidoside

m/z 163

m/z 191

m/z 363

Cafeoylquinic acid

Caffeic acid

Coumaric acid

Quinic acid

m/z 179

HPLC-ESI-MS analysis

Castro-Gamboa et al, 2009, unpublished data

S. officinarum virtual 1H NMR simulation of reported metabolites

All major reported metabolites from S. officinarum were:

◊ Accounted for structure minimal energy effects and solvent interaction

using MOPAC 2009.

◊ Reported chemical shift were compared and standardized with the

simulated data for 1H using the HOSE (Hierarchically Ordered Spherical

Description of Environment), code as well as the neural algorithm available

in Mnova ver. 6.0.2-5475 through Modgraph NMRPredict desktop.

O O

OH

HO OH

HO

OHO

OHHO

HO O OH

OH

HO OH

OH

O OH

OH

HO OH

O OH

OH

HO OH

HO

O+

OH

O

O

OH

HO

OHO

HO

OH

OH

HO

HO

HOOH

O

HO

O

O

OH

HOOH

OHHO

O

HO

O

OHHO

HO

O

OHO

HO HO

O

HO

O

O

O

OH O

O

OH

O

O

HO

O

OH

O

OHHO

HO

OH

O

H2N NH2

HO

O

O

OH

OH

O

HO

NH2H2N

O

OHO

OH

OHHO

HO

OH

O

OH

O

OH O

O

OH

O

O

HO

HO

OH

O

OH

HO

OH O

O

OHO

OHO

OH

OH

HO

OH

HO

HO

O

O

O

HO

HO OH

O

OH

HO

OH

OH

OH

HO

OH O

O

OH

OH

O

O O

HO

OH

OH

HO

HO

OH

HO

OH O

O

OHO

O

OH

OH

HO

OH

HO

HO

OH

S. officinarum virtual 1H NMR simulation of reported metabolites

Castro-Gamboa et al, 2009, unpublished data

Virtual 1H NMR spectra after the

application of algorithms treatment

to suppress interchangeable

deuterium/protium nuclei

Castro-Gamboa et al, 2009, unpublished data

S. officinarum virtual 13C NMR simulation of reported metabolites

◊ Accounted for structure minimal energy effects and

solvent interaction using MOPAC 2009.

◊ Reported chemical shift were compared and standardized

with the simulated data For 13C simulation we used the

HOSE (Hierarchically Ordered Spherical Description of

Environment), code as well as the neural algorithm

available in Mnova ver. 6.0.2-5475 through Modgraph

NMRPredict desktop.

1H NMR simulation of crude EtOAc extract of S. officinarum

leaves ( cultivar RB85-5453 )

Castro-Gamboa et al, 2009, unpublished data

-Each individual spectra was generated and processed

with the same experimental values from the acquired

real samples Showed slides presented before.

SOLVENT d6-DMSO,

5mm cryoprobe

1H NMR experimental data of crude EtOAc extract of the leaves of S. officinarumafter subtraction of C6-C3 virtual standards from original spectra

Castro-Gamboa et al, 2009, unpublished data

Acid hydrogenQuinic acid

moietyAromatic and olefinic

region

Caffeoyl quinic acid

d6-DMSO solvent5mm cryoprobe)

Final Remarks1. Current development of CG, LC, MS and NMR methodologies are

being performed using the leaves of sugar cane samples of one

cultivar RB85-5453.

Perspective

1. Conclude all experimental methodology using LC, CG, RMN and MS

data, and multivariate analysis to screening and identify the metabolomic

profile of several sugar cane cultivars, aiming a sugarcane metabolomic

datamine of several cultivars around Araraquara;

2. Identify micromolecular markers/signalizers associated with infection by

pathogens, aiming the early diagnosis; Prospection of biologically

secondary metabolites aiming to identify new antifungal and insecticides

prototype from sugar cane extracts for further SAR studies;

3. Relate such markers/signalizers with transcriptome, aiming the

indication of genes to activated or suppresses in the development of new

cultivars resistant to pathogens;

NuBBE Financial Supports: FAPESP, FINEP, CNPq and CAPES

Pharmaceutical Companies APSEN; Natura Cosméticos;

Rob Verpoorte

Leiden University

Cana boys

Master Students:

Gabriel Mazze - FAPESP fellowship ????

Paulo R. Ussoni Tomaz – FAPESP fellowship

Undergraduate:

Ana Paula D. Silva – CNPq

Ian Castro-Gamboa UNESP

“New Frontiers Program

FAPESP Ph.D Fellowship”

through process number

2008/03207-7 (University of

Florida, USA)

Acknowledgments

Special thanks to Prof. Arthur S. Edison (University of Florida,

USA for allowing the use of their NMR facilities through National

High Magnetic Field Laboratory Visiting Scientist Program, Ian

Castro, under project # 12415

“Hoje, a questão básica já não é mais se podemos produzir alimentos, fármacos, energia, produtos em quantidade

suficiente, mas quais as conseqüências ambientais disso. Na velocidade vertiginosa da destruição da biosfera, da

atmosfera e até da estratosfera, dentro de algumas dezenas de anos custará uma inconcebível fortuna à pesquisa

básica visando a uma extensão do período remanescente do homem no planeta. O drama não consiste

tanto na capacidade do homem de alterar o ambiente, mas no desejo de alterá-lo

antes de entender com precisão os fatores químicos e biológicos que governam a

estrutura e o funcionamento da biodiversidade. Tal observação só leva à

compreensão do fenômeno se sua causa é baseada em numa conjunção de fatores

incluindo comportamento ou forma. Portanto, engrenar biologia e química é uma

medida de defesa das gerações futuras” (1988)

Otto R. Gottlieb

Thank You Your Attention

The Nobel Laureate, Roald Hoffmann, in the

Science article quoted: “He is the premier

Brazilian organic chemist and one of the

world’s outstanding phytochemists and

biogeochemists as well. His work deserves

the highest honors of our profession,

including the Nobel Prize.”