Signal Transduction Laboratory

Institute of Chemistry

University of São Paulo

Glaucia Mendes Souza

http://sucest-fun.org

Sugarcane Signaling and Regulatory Networks

Team

Instituto de Química - USP Dr. Glaucia Mendes Souza Dr. Carlos Hotta Carolina Gimiliani Lembke (PhD student) Maximiller Dal-Bianco L. Costa (PhD student) Paloma Mieko Sato (PhD student) Rodrigo Fandino (Master student) Abdalla Almeida (Master student) Fabio Fernandes da Rocha Vicente (PhD student) Sávio Ferreira (PhD student) Marcelo Camacho (PhD student) Milton Yutaka Nishiyama Jr. (PhD student) Edwin Delgado (Bioinformatician)

ESALQ – USP Dr. Helaine Carrer Centro de Ciências Agrárias - UFSCAR Dr. Hermann Hoffmann Dr. Monalisa Carneiro Dr. Marcos Sanches Instituto de Biologia – UNICAMP Dr. Marcelo Menossi CBMEG – UNICAMP Dr. Anete Pereira de Souza Instituto de Biociências – USP Dr. Marie-Anne Van Sluys Dr. Marcos Buckeridge IME-USP Dr. Alan Durham Dr. João Eduardo Ferreira

UFV – Minas Gerais Dr. Marcio Barbosa Dr. Marcelo Loureiro UFAL - Alagoas Dr. Laurício Endres Cambridge University Paul Dupree Texas A&M Jorge da Silva

Ohio-State University - USA Dr. Erich Grotewold Georgia University - USA Dr. Andrew Patterson Life Technologies Dr. Antje Taliana Dr. Pei-Zhong Tang Microsoft Research Institute Dr. Dave Heckerman Dr. Bob Davidson Dr. Gabriel Margarido

Motivation: improving yield has become essential for the sustainability of biofuel production

We need to develop tools for

sugarcane biotechnology

Goals

Identification of genes associated to sucrose, fiber quality and content and

drought tolerance

Evaluation of gene function through the generation and analysis of transgenic

sugarcane plants

Sugarcane gene promoter sequence and regulatory motif identification

Development of the SUCEST-FUN Database and computational tools

Need to understand the brazilian germplasm

and develop tools for the biotechnological route of sugarcane improvement

Approach: evaluate at the sequencing, expression and physiological level

50 cultivars 4 ancestor genotypes 8 progenies

RB867515 S. officinarum S. robustum S. sinensis S. spontaneum

Agronomic traits of interest: brix, yield, drought and fiber quality and content

SP80-3280

4x44 K - 45220 oligos

1217 controls Agilent

43803 oligos representing SAS

14522 sense

7380 antisense

Oligo design

350

50 5' 3'

390

310

90 10 Start site

448 hybridizations

10,262 differences in gene expression when cultivars and tissues with contrasting sucrose

content were compared

12,249 changes related to drought stress

3,524 when ancestral sugarcane species were compared to a commercial sugarcane cultivar

with differing fiber deposition patterns

Total Number of Differentially Expressed data points by treatment

(crossing biological replicates)

S. spontaneum as an energy crop?

Plants X mass X % dry matter S .spontaneum 273 x 0,223kg x (100-63,1) = 2246,4 kg of total dry matter RB867515 73 x 1,563 x (100-83,7) = 1859,8 kg of total dry matter

22

3

156

3

22

3

73

Savio Ferreira

Expression pattern analysis of ancestral species

- Few cell wall related genes were

differentially expressed

- S. spontaneum presented an up

regulation of catabolic pathways

(mainly carbohydrates) and down

regulation of anabolic pathways

- S. officinarum showed a down

regulation of carbohydrate

degradation genes; in leaf, few cell

wall genes were down regulated

(C3H, XTH, CesA, Csl)

- S. robustum showed a pattern

similar to S. spontaneum, presenting

an up regulation of carbohydrate

degradation (mainly starch) and

krebs Cycle.

Savio Ferreira

BB AB BB AB BB AB0

5

10

15

20 *** *** ***

_______ _______ _______2 4 5

Brix

7 9 1110

15

20

25

BB

AB

***

Meses

Brix

(Média

)

B)

A)90 High and Low Brix Genotypes selected

RIDESA Breeding Program – Monalisa Sampaio

Photosynthesis

Sucrose

Reducing Sugars

Fiber

Plant Height

Number of internodes

Diameter

Biomass

Yield

Physiology, Morphology, Biochemistry

61 genotypes

Paloma Mieko Sato & Rodrigo F. Andrade

Transcriptome at 7 months

Expression pattern analysis of genotypes contrasting for sucrose content

Photosynthesis slightly negative correlation with sucrose

Photosynthesis no correlation with fiber, yield or biomass

Paloma Mieko Sato & Rodrigo F. Andrade

Sucrose positive correlation with brix and internode number

Sucrose negative correlation with reducing sugars

10

12

14

16

18

20

22r = 0.9874

P value < 0.0001

oB

rix

0.5

1.0

1.5

2.0

2.5

3.0

r = -0.8765

P value < 0.0001

% R

ed

uc

ing

su

ga

rs

4 6 8 10 12 14 16 18 20

4

6

8

10

12

14

16r = 0.6993

P value < 0.0001

% Sucrose

Inte

node n

um

ber

4 6 8 10 12 14 16 18 20

0.5

1.0

1.5

2.0

2.5 r = 0.5661

P value < 0.0001

% Sucrose

Pla

nt

he

igh

t (m

)

A)

D)C)

B)

Transgenics for Protein Kinases and Phosphatases

C. Lembke, P. M. Sato

Transgenics Transcriptome: SnRK1, SnRK2, SnRK3

GMRG_Term Pop_freq Pop_frac Study_frac Raw_es e-score Description Contributing_genes

GO:0009733 2,27E+01 33/14522 14/1941 3,75E-01 1,30E+02 response to auxin stimulus

1122 genes altered, 360 unknown

SCSGFL5C08F02.g

Auxin responsive factor

LB SS HB SS LB AS HB AS

0.00

0.05

0.10

0.15

0.20

0.25

Crossing 2

mR

NA

ta

rge

t/ U

BE

2 m

RN

A

SCEPAM1018E02.g

cane UPK-20

LB SS HB SS LB AS HB AS

0.0000

0.0002

0.0004

0.0006

0.03

0.06

0.09

0.12

0.15

Crossing 5

mR

NA

ta

rge

t/G

AP

DH

mR

NA

SCEPAM2014B12.g

Sucrose transporter

153 SS 238 SS 153 AS 238 AS

0.0000

0.0001

0.0002

0.0003

0.0004

0.0005

0.0006

0.0007

mR

NA

ta

rge

t/G

AP

DH

mR

NA

SCCCLR1001E04.g

Rubisco

321 SS 318 SS 321 AS 318 AS

0.00000

0.00005

0.00010

0.00015

0.00020

0.00120.00140.00160.00180.0020

0.0070

0.0075

0.0080

mR

NA

ta

rge

t/G

AP

DH

mR

NA

SCCCRZ3001D04.g

Triose phosphate translocator

714 SS 710 SS 714 AS 710 AS

0.000

0.001

0.002

0.003

0.004

0.005

0.012

0.014

0.016

0.018

0.020

mR

NA

ta

rge

t/G

AP

DH

mR

NA

A) B)

C) D)

E)

*

*

*

**

*

*

*

*

*

Regulation of the antisense may be important!

Alignment of SnRK1 Sugarcane X Sorghum

Motivo Sequência Motivo Sequência

ABRELATERD1 ACGT AGL3 ATTA

ABRERATCAL AGCAGC ANAERO2CONSENSUS GAAAAT

ACGTATERD1 GGATT ANT AAACCA

ACGTATERD1 TGACG Athb-1 CATCTG

AGCBOXNPGLB CCGCGG ATHB-5 CAACA

AP1 AAACCA CDC5 TGGGCC

ARR10 CAACGG MYB1AT ACTTTG

ARR1AT AACTAAC P1BS GTATATGC

ASF1MOTIFCAMV AACGG RAV1 gagCAACAacgc

CGCGBOXAT TGGGCC RAV1-A CAACA

Core TTTTTAG SITEIIATCYTC TGGGCC

ERF1 GCCGCC SORLIP1AT GCCAC

GATABOX GATAAG SV40COREENHAN GTGGAAAG

GCCCORE CCGAC TBOXATGAPB ACTTTG

GT1CONSENSUS GCCAC TGBOXATPIN2 AACGTG

IBOX GCCAC

LTRECOREATCOR15 GTGGAAAG

MYB2CONSENSUSAT CATCTG

MYB4 AACTAAC

MYBCORE CCGTTG

MYBCOREATCYCB1 AACGG

MYCATERD1 CATGTG

RAV1AAT CAACA

Light Sugars

Drought

and ABA

Drought

and ABA

JA and

auxin

1,807 1,729 1,1361,338 1,946

6,775

20,778

15,747

3,964

0,0

5,0

10,0

15,0

20,0

25,0

SCEN 1024 E12 PUB

% I

NP

UT

IgG 7`` REAMP Pol II 7`` REAMP Pol II 7`` 2 REAMP

Chromatin Precipitation: 13-fold

enrichment, 1.56 Gb of data

Sugarcane Genome 8.7 Gb of data

Gene promoter analysis

Infrastructure: Transformation Pipeline, green houses SP, Araras and ESALQ

Monalisa Carneiro: Sugarcane cultivation and harvest Helaine Carrer: Technical improvements Biolistics and Agrobacterium Regeneration from calli or leaf disk Great improvement in regeneration time

Glaucia Souza, Helaine Carrer, Marcelo Menossi: 31 gene targets introduced into the pipeline Silencing (8 constructs) Over-expression (19 constructs) (3 constructs into Brachypodium and 4 into tobacco) 72 plants confirmed for expression 8 to 15% efficiency biolistics and 4 to 10% Agrobacterium

Infrastructure: SUCEST-FUN Database

Milton Nishiyama-Jr.

Bioinfo Support for 16 BIOEN projects

8 thousand accesses/month (public site)

96 registered datamining users (private site)

Sucest-Fun Web Services

http://sucest-fun.org

Milton Nishiyama-Jr., Edwin Delgado

Time in continuous light (h)

0 24 48 72 96 120

Lum

inescence (

photo

ns/1

800 s

)

0,0

6,0e+5

1,2e+6

1,8e+6

ZT8

ZT20

2011/00818-8 - Development of alternative

biological models for the study of sugarcane

regulatory networks Dr. Carlos Hotta (coordinator) jun/11 –

ongoing

Aims:

1- To develop tools to use other biological models as

alternative to sugarcane

LUCIFERASE under a circadian promoter is a tool developed for Arabidopsis

that is being adapted to use in Brachypodium and sugarcane

2011/00818-8 Dr. Carlos Hotta jun/11 –

ongoing

Aims:

2- To associate the sugarcane circadian clock with its high

productivity and ability to accumulate sucrose >30% of the sense transcripts and > 20% of the antisense transcripts are

controlled by the circadian clock

Pathways like carbohydrate metabolism, photosynthesis, protein synthesis

and degradation, sugar and starch synthesis and degradation are controlled

by the circadian clock

Supressor of induced cell death by Bax protein and

negative regulator of cell death in plants, animals and

yeast

FUNCTIONAL GENOMICS OF PHOTOSYNTHETIC GENES IN SUGARCANE Helaine Carrer – ESALQ

Transgenic Sugarcane Tolerant to Drought

Program Cell Death Inhibitor Protein

Gene

AtBi-1 (Programmed cell death and stress)

COMT (sugarcane)

C4-PEPC (sugarcane)

β- Carbonic Anydrase (Arabidopsis)

FS I-Sub. L (sugarcane)

FS-II Sub. K (sugarcane)

COMT-AS lines COMT

expression Total lignin means

(%) Total lignin

Holocellulose Extractives

WT-SP803280 1.00 15.54±0.17a 100 61.05± 23.75

P9-SP803280 -13.79 13.96±0.09c 89,8 61.53± 24.51

P16-SP803280 -4.843 14.89±0.26ab 95,8 60.72± 24.39

P20-SP803280 -14.43 14.93±0.14ab 96,1 62.48± 22.59

P32-SP803280 -14.41 14.57±0.26ab 93,7 61.12± 24.31

WT-RB835486 1.00 15.46±0.29a 99,5 59.03± 25.61

P4-RB835486 -16.84 13.97±0.06c 89,9 59.71± 26.32

P9-RB835486 -12.18 14.51±0.16ab 93,3 59.99± 35.49

Table1. COMT gene expression and Total lignin means of sugarcane transgenic lines, varieties SP80-3280 and RB 835486 transformed with COMT antisense fragment

COMT silencing: 4% to 11% reduction in lignin

II I

I II III IV V

28cm

I WT

17cm WT

WT III

IV

A B C

D

Figure 1. COMT-AS transgenic lines of sugarcane: A) I – Transgenic line without support; WT – Wild type as control; B) WT – Wild type as control, III – line P20-SP803280 with gentle inclination and IV – severe inclination; C ) I – P9-SP803280, II - P20-SP803280, III - P16-SP803280, IV and V – P32-SP803280 transgenic lines showing different down-inclination; D) I – P4-RB835496 and II – P9-RB835486 transgenic lines showing different down-inclination

COMT Transgenic Plants

INFORMATION TECHNOLOGY APPLIED TO BIOENERGY GENOMICS:PROBABILISTIC

ANNOTATION USING ARTIFICIAL INTELIGENCE – Ricardo Vêncio - FACULDADE DE MEDICINA

DE RIBEIRÃO PRETO/USP

Genomics

(Microsoft/FAPESP):

1) Develop an IT methodology and software framework for automatic

probabilistic annotation;

2) Apply 1) to re-annotation of sugarcane:

* transcriptome, (SUCEST, 2003)

* genome annotation, to be sequenced by the BIOEN

Metabolomics (BIOEN):

Develop a probabilistic model for metabolite identification from

complex spectra;

100%

5% 0%

100%

Next: Start

manuscript

drafting

Next: Start manuscript

drafting

Next: from tests to results

Genomics – Probabilistic Annotation

D.Almeida-e-Silva, master candidate; Prof. Vêncio, LabPIB-USP

Adapted from: Rogers et al. (2008)

Metabolomics – Probabilistic Annotation

R.Silva, PhD candidate; Prof. Vêncio, LabPIB-USP

Sugarcane Analysis: METABOLOMICS AND PROTEOMICS OF

SUCROSE ACCUMULATION IN SUGARCANE Laboratory Max Feffer of Genetics of Plants, ESALQ-USP, Piracicaba

Coordinator: Prof. Carlos Alberto Labate – Fapesp Process 2008/56100-5

Group members: Camila Ribeiro; Danielle Silva; Fabrício Moraes; Fernando Silva; Ilara Budzinski; Simone Gonzalez; Thais Regiani

Leaf Nucleus Isolation

Histones

DNA regulation

RNA regulation

Transcriptions factors

• UDP-Glucose pyrophosphorylase

• Sucrose phosphate synthase

• RUBISCO

• Phospholipase

• Ubiquitin

• GAPDH

Leaves

phosphoproteins

1 - Understand the mecanisms related to sucrose accumulation.

2 - Undestand drought stress.

Main Objectives:

Is 4 months old sugarcane leaves different from 12 months old?

Sucrose accumulation gene expression qPCR Metabolomics (GC-MS) PCA

Proteomics: Non-irrigated sugarcane leaves Proteomics: Control sugarcane leaves

Functional category: Response to stress

14-3-3 like protein

peptidyl-prolyl cis-trans isomerase

glioxalase I

betaine aldehyde dehydrogenase

abscisic stress ripening protein

drought inducible 22 kDa

Photosynthesis: soil water content

-5

0

5

10

15

20

25

0 500 1000 1500 2000

100%

58%

23%

Reidratadarehydrated

Photo

synte

sis

(mg C

O2·d

m-2

·h-1

) PAR (Photosynthetically active radiation)

Sugarcane Metabolomics (LC-MS): Drought Stress

Methanol + Formic acid (N)

Leaves +1

stalks 5 e 9

Extraction: Two buffers

1 2 1 – Methanol + Formic acid (N)

2 – Methanol + Chloroform + Water (G)

SP-3280

Is N buffer different from G

buffer?

-70

-60

-50

-40

-30

-20

-10

0

10

20

30

40

50

60

-200 -100 0 100 200

t[2]

t[1]

S cores C omp[1] vs . C omp[2] colored by S ample G roup

IG

IN

SG

SN

INININ

IG

IG

IG

SNSN

SN

SG

SG

SG

INININ

IG

IG

IG

SNSNSN

SG

SG

SG

INININ

IG

IG

IG

SNSNSN

SG

SGSG

INININ

IG

IG

IG

SN SN

SN

SG

SGSG

INININ

IG

IG

IG

SNSN

SN

SG

SG

SG

Hotelling’s T2 Ellipse (95%) = (196; 55.63)

R2X[1] = 0.779

R2X[2] = 0.06277

EZinf o 2 - Simone_Folha_51 (M4: PCA-X) - 2012-10-17 16:54:02 (UTC-3)

N G

Are irrigated plants different from

non-irrigated ?

Methanol + Chloroform+ Water (G)

S

I

I

S

To understand the differential sucrose yield in Brazilian sugarcane

genotypes, using an integrate approach in which plant physiology,

morphology and anatomy are considered as the causes of variation in

sugarcane performance.

Basic question:

Why do some sugarcane genotypes accumulate more sucrose in stalks than others?

Derived questions:

Question #1- Do they have higher photosynthetic capacity and/or higher leaf area and/or

higher sucrose storage capacity and/or any difference in carbohydrate metabolism in

leaves or stalks?

Question #2- Does canopy architecture favor the plant metabolism, especially

photosynthesis? Does the high solar energy availability at the basal canopy positions

change the functional leaf area and then the plant photosynthesis and carbohydrate

production in sugarcane genotypes with contrasting canopy architecture and yield?

Question #3- Is the sugarcane genotype with high sucrose yield less affected by

environmental constraints during the winter season? How do sugarcane cultivars with

differential sucrose yield respond to cold and/or drought stress? Does this response

change during the plant cycle?

Integrating Physiological, Morphological And Anatomical Traits

To Understand The Differential Sucrose Yield In Sugarcane

Genotypes

Eduardo Caruso Machado - IAC

100 150 200 250 300 350 400 450 500 550

0

500

1000

1500

2000

2500

SP79-1011 IACSP94-2094 IACSP95-50000

50

100

150

200

250

300

350

Pro

ductio

n (

ton

ha

-1)

Stalk

TotalA

0

5

10

15

20

25

IACSP95-5000IACSP94-2094

Sta

lks m

-1

SP79-1011

SP79-1011

IACSP94-2094

IACSP95-5000

Sta

lk v

olu

me

(cm

3)

Days after planting

D

0

500

1000

1500

2000

2500

3000

3500

Le

af are

a d

ura

tio

n (

d-1)

B

0

20

40

60

80

100

120

140

160

SP79-1011 IACSP94- 2094 IACSP95-5000

Ne

t P

ho

tosyn

tesis

[m

ol m

-2 (

30

0d

)-1]

SP79-1011 IACSP94- 2094 IACSP95-5000

C

Question #1- Do they have higher photosynthetic capacity and/or higher leaf

area and/or higher sucrose storage capacity and/or any difference in

carbohydrate metabolism in leaves or stalks?

Increased

production in

IAC95-5000

may be

associated to

increased Net

Photosynthesis,

increased LAD

and increased

stalk volume

IACSP93-2060 IACSP95-30280

500

1000

1500

2000

2500

Dry

ma

tte

r (g

)

Sugarcane varieties

Stalk

Leaves

IACSP93-2060 IACSP95-30280

5

10

15

20

25

30

Ph

oto

sy

nth

es

is (

mg

m-2

d-1)

Sugarcane varieties

Upper canopy position

Bottom canopy positionA B

0 500 1000 1500 2000 2500-5

0

5

10

15

20

25

30

35

40

0 500 1000 1500 2000

Photosynthetic photon flux density (mol m-2 s

-1)

IACSP95-3028

Upper canopy position

Ph

oto

sy

nth

es

is (m

ol

m-2 s

-1)

Bottom canopy position

IACSP93-2060

C D

SP79-1011 (erect leaves)

Actual Changed0

4

8

12

16

Ph

oto

syn

thesis

(m

g C

O2 m

-2 d

-1)

Canopy morphology

Canopy position 1 2

Ca

no

py

po

sit

ion

1

2

E

F

Question #2- Does canopy architecture favor the plant metabolism, especially

photosynthesis? Does the high solar energy availability at the basal canopy positions

change the functional leaf area and then the plant photosynthesis and carbohydrate

production in sugarcane genotypes with contrasting canopy architecture and yield?

Higher photosynthesis in inferior leaves is

related to higher biomass production

0.1

1

10 ψ

Pn

E

gs

Fv/Fm

ΦPSII

ETR NPQ

SS

Suc

Sta

NSC

Pro

Ref

IACSP94-2094

IACSP96-2042

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

10 20 30 40 50Stalkyield(kg)

IntegratedCO2assimila on(molm-2)

Ref WDRef WD

IACSP94-209416.6±5.2IACSP96-204238.0±1.6

Genotypes/condi onsCRPN(%)

*n.s.

By R.V. RIBEIRO, R.S. MACHADO, E.C. MACHADO, D.F.S.P. MACHADO, J.R. MAGALHÃES FILHO and M.G. A. LANDELL (2013) Revealing drought resistant and productive patterns in sugarcane genotypes by evaluating both physiological responses and stalk yield. Experimental Agriculture (in press)

Question #3- Is the sugarcane genotype with high sucrose yield less affected by

environmental constraints during the winter season? How do sugarcane

cultivars with differential sucrose yield respond to cold and/or drought stress?

Does this response change during the plant cycle?

The cultivar with more photosynthesis had more stalk yield but was much more sensitive to drought

BIOEN Plataforms

SUCEST-FUN Database e Bioinfo Tools

Sugarcane Transgenics

Transcriptome

ChIP-Seq

Promoter Identification

Nuclear Proteome

Metabolomics

Morphological, physiological trait analysis

Main Findings

Genes associated to sucrose content, fiber, clock and drought identified

Regulatory and metabolic pathways for sucrose content and drought tolerance found

Morphological and physiological traits associated to yield, drought and sucrose content

NEXT

Systems Biology Experiment

Whole genome analysis of promoter regions (allelic variation)

Regulatory and metabolic pathway annotation and manipulation (Promoter Discovery)

RNA-Seq Plataform

Organ-specific regulatory networks (diurnal rhythms)

Interactions between regulatory networks (drought and circadian clock)

Impact of different alleles in regulatory networks – Arabidopsis tests with different clock

alleles