Dr. Renato Vicentini from the State University of Campinas presented his research using systems biology approaches to understand sucrose synthesis and accumulation in sugarcane. His laboratory investigates gene regulatory, metabolic, and protein networks in sugarcane. They are developing predictive models to scale from genotype to phenotype. Their goals are to understand how some sugarcane genotypes accumulate more sucrose than others and to investigate allosteric regulation of key enzymes. Their approaches include RNA sequencing, metabolic profiling, and phosphoproteomics. They are also manipulating source-sink relationships in sugarcane to study differential gene expression and developing a sugarcane transcriptome. The talk provided an overview of their work using multi-omics data to build biological networks

1. From Genotype to Phenotype in Sugarcane: a
Systems Biology Approach to Understanding the
Sucrose Synthesis and Accumulation
Dr. Renato Vicentini
Systems Biology Laboratory
Center for Molecular Biology and Genetic Engineering
State University of Campinas
II Sugarcane Physiology for Agrnomic Applications – CTBE
October 2013

2. Systems
Biology

3. Biological
Networks
Scaling
Genotype
to
Phenotype
•
Predic9ve
methods
capable
of
scaling
from
genotype
to
phenotype
can
be
developing
through
systems
biology
coupled
with
genomics
data.
•
Three
types
of
biological
networks
are
of
major
interest
in
our
laboratory.
Class
Gene-regulatory network
Metabolic network
Protein network
Node
Genes / transcripts
Metabolites
Protein species
Edge
Induction or repression
Biochemical reaction
State transition, catalysis
or inhibition
RNA-seq
In silico kinetic modeling and
Metabolic control analysis
Metabolite Profiling
Enzymes activity
determination and
allosteric regulation
Strategy

4. Sugarcane
Produc9on
Situa9on
Moore, P.H. personal communication

5. Our
Research
Goals
to
Understanding
Regula9on
of
Sucrose
Metabolism
and
Storage
in
Sugarcane
Why do some sugarcane genotypes accumulate more sucrose in internodes than
others ?
•
•
Elucidate
which
genes
in
sugarcane
leaves
are
responsive
to
changes
in
the
sink:source
ra9o.
Inves9gate
the
allosteric
regula9on
of
key
enzymes.
We propose to develop an approach which integrates molecular and systems
biology to investigate these questions in sugarcane.

6. State
of
the
art
•
•
•
•
There
are
evidences
that
sink
9ssues
exert
an
influence
on
the
photosynthe9c
rates
and
carbohydrate
levels
of
source
organs.
The
ac9vity
of
photosynthesis-­‐related
enzymes
are
modified
by
the
local
levels
of
sugar
and
hexoses
that
will
be
transported
to
sink.
As
observed
in
sugarcane,
a
decreased
hexose
levels
in
leaf
may
act
as
a
signal
for
increased
sink
demand,
reducing
a
nega9ve
feedback
regula9on
of
photosynthesis.
The
signal
feedback
system
indica9ng
sink
sufficiency
to
regulate
source
ac9vity
may
be
a
significant
target
for
manipula9on
to
increase
sugarcane
sucrose
yield.
Sink demand
INV
Hex
Negative feedback
•
Currently,
a
model
that
predicts
that
sucrose
accumula9on
is
dependent
on
a
system
in
which
SPS
ac9vity
exceeds
that
of
acid
invertase.

7. Source-­‐sink
rela9onship
in
sugarcane
Sink
Source

8. Allosteric
regula9on
of
the
SPS
enzyme
network
Phosphoproteomics
approach
Sugarcane extended
night experiment
Schematic representation of the
system that module the rate of
sucrose synthesis by modifications
in the key enzyme SPS.

9. Sugarcane
extended
night
experiment
Sucrose
metabolism
-­‐
Circadian
regula9on
Day
Night

10. Sucrose
metabolism
Circadian
regula9on

11. Manipula9on
of
Sink
Capacity
•
•
Nine
month-­‐old
field-­‐grown
plants
of
two
genotypes
of
Saccharum
(L.)
spp.
contras9ng
for
sucrose
accumula9on.
To
modify
plant
source–sink
balance,
all
leaves
except
leaf
+3
were
enclosed
(simulated
effect
of
internode
matura9on).
RNA-­‐seq
analysis
of
control
and
perturbed
system
are
in
progress.
14d*
6d
3d
1d 0d**
4m
•
*
Unshaded
leaf +3
6 x 10 m plot
per genotype
Start
** End
Sunlight
Enclosed

12. Manipula9on
of
Sink
Capacity
Chlorophyll
content
(SPAD)
of
sugarcane
leaves.

13. Manipula9on
of
Sink
Capacity
Chlorophyll
fluorescence
parameters
(Fv/Fm;
Fo/FM;
Fv/Fo)
•
The
lowest
sucrose
content
genotype
(SP83-­‐2847)
shows
the
highest
levels
of
chlorophylls
and
a
highest
efficiency
in
the
photosystem
II
(Fv/Fo),
specially
in
the
middle
of
the
day.

14. Ini9al
Results
Manipula9on
of
Sink
Capacity

15. Sugarcane
de
novo
assembling
transcriptome
De novo assembling workflow. The numbers indicates the amount of
sequences; K, hash-length in base pairs; Dashed arrows, unused
sequences; Gray boxes, comprises the sequences used in the final
transcriptome.

17. Source-­‐sink
differen9al
expressed
genes
~5% of transcripts
High
sucrose
content
Low
sucrose
content
Source
~1% of transcripts
Sink

18. Gene
regulatory
network

19. Orthologous
rela9onship
across
grasses
Phylexpress
-­‐
a
bioinforma9cs
tool
for
large
scale
orthology
establishment
•
•
•
•
Iden9fica9on
of
orthologs
is
cri9cally
important
for
gene
func9on
predic9on
in
newly
sequenced
genomes
and
for
gene
informa9on
transfer
between
species.
Can
integrates
expression
informa9on
across
orthologs
intended
to
find
conserved
hub
within
gene9c
networks.
Help
understanding
gene9c
networks
evolu9onary
plas9city.
Phylexpress
was
used
to
established
the
orthology
of
all
available
ESTs
from
grasses.
We
also
transferred
all
grasses
unigenes
to
the
MapMan
BIN
system.

20. Lignifica9on
in
sugarcane
Bottcher, A et al. Plant Physiology, in press

21. Large-­‐scale
transcriptome
analysis
of
two
sugarcane
cul9vars
contras9ng
for
lignin
content

22. Results
•
More
than
ten
thousand
sugarcane
coding-­‐genes
remain
undiscovered
(RNA-­‐
Seq).
•
More
than
2,000
ncRNAs
conserved
between
sugarcane
and
sorghum
was
revealed.
•
~18% of the conserved
ncRNA presented a
perfect match with at small
RNA.

23. A
phased
distribu9on
of
sRNAs
in
sugarcane
ncRNAs
•
•
•
~18%
of
the
sugarcane/sorghum
conserved
ncRNA
presented
a
perfect
match
with
at
least
one
23-­‐25nt
small
RNA.
Some
of
these
siRNAs
shows
perfect
match
against
func9onal
proteins.
These
puta9ve
ncRNAs:
precursors
of
the
perfect
matched
sRNAs
(cis
ac9on);
or
they
are
produced
by
other
loci
and
act
in
trans.

24. Transcripts,
genes
and
genomes
source
databases
Sugarcane
transcripts
collec9on
Sorghum
and
rice
genomes
and
genes
Angiosperm
genomes
(arabidopsis,
rice,
populus,
and
sorghum)
Transcrip9on
assembler
of
grasses
Similarity
search
Annota9on
MapMan
catalogue
annota9on
SIM4/Blast
algorithms
Ortologous
rela9onship
Sugarcane
genes
overview
Number
of
sugarcane
genes,
redundancy
in
ESTs
database
(PoGOs)
and
gene
evolu9on
(dN/dS)
Vicentini et al 2012. Tropical Plant Biology
Phosphopep9des
Expressions
data
Microarray
and
RNA-­‐seq
data
Expression
normaliza9on
and
data
correla9on
Phylexpress
Networks
Vicentini et al 2012. Tropical Plant Biology
Grasses
PoGOs
Sugarcane
PoGOs
Scaling
from
Genotype
to
Phenotype
Metabolics
Arabidopsis
genome
Physiological
parameters
Carbohydrate
biosynthesis
pathways
Gene-­‐regulatory
networks

25. Survey
of
the
sugarcane
genome
for
genes
General
overview
of
the
sRNA
mapping
against
the
sugarcane
BACs.

26. Gene
Regulatory
Network
–
A
Bayesian
Approach
The
example
of
lignin
biosynthesis
•
•
The
genes
ShHCT-­‐like,
ShCCoAOMT1,
and
ShCCR1
showed
a
posi9ve
correla9on
with
S/G
(syringyl
and
guaiacyl
)
ra9o
.
In
the
regulatory
network
analysis,
ShPAL1
was
directly
related
with
the
central
(pith)
regions
of
sugarcane
stem.
Bottcher, A et al. Plant Physiology, in press
YR
=
rind
(peripheral)
of
young
internode,
YP
=
pith
of
young
internode,
IR
=
rind
of
intermediary
internode,
IP
=
pith
of
intermediary
internode,
MR
=
rind
of
mature
internode,
MP
=
pith
of
mature
internode.

27. Gene
Regulatory
Network
–
A
Bayesian
Approach
The
example
of
lignin
biosynthesis
•
•
•
The
genes
ShCAD2,
ShCOMT1,
ShC3H2,
ShCCR1,
ShCAD8,
ShC4H2
and
ShC4H4
showed
strong
correla9on
with
lignols.
According
the
network
analysis,
ShPAL2
is
nega9vely
correlated
with
lignin
precursors.
Many
studies
have
demonstrated
the
importance
of
C4H
ac9vity
in
monolignol
biosynthesis:
– downregula9on
of
C4H
had
the
deposi9on
levels
of
lignin
and
the
S/G
ra9o
decreased
(tobacco)
– high
expression
of
C4H
was
correlated
with
lower
fiber
diges9bility
of
the
stems
in
Panicum
maximum.
Bottcher, A et al. Plant Physiology, in press

28. Sugarcane
co-­‐expression
network

29. Sugarcane
co-­‐expression
network
•
Sugarcane
meta-­‐network
of
coexpressed
gene
clusters
generated
by
HCCA
clustering
method
(85
clusters
with
381
edges).
Nodes
in
the
meta-­‐network,
represent
clusters
generated
by
HCCA.
Edges
between
any
two
nodes
represent
interconnec9vity
between
the
nodes
above
threshold
0.04.

30. Regulatory
complexes
that
are
conserved
in
evolu9on
•
•
By
comparing
networks
from
different
species
it
is
possible
to
reduce
measurement
noise
and
to
reinforce
the
common
signal
present
in
the
networks.
Using
the
differen9al
expressed
genes
iden9fied
in
the
source-­‐sink
experiments
we
can
detect
more
than
50%
genes
inside
regulatory
complex
conserved
across
sugarcane
and
rice.
Six
significant
complex
were
discovered
•
When
Arabidopsis
thaliana
was
included,
only
two
complex
s9ll
occurring.
Cellulose
synthases

31. Gene
Regulatory
Network
–
A
Bayesian
Approach
The
source-­‐sink
experiment
•
We
detected
several
gene
clusters,
including
many
hubs,
that
incorporate
different
regulatory
genes
(ncRNAs,
siRNAs,
miRNAs,
etc).

32. Landscape
maps
sugarcane
metanetwork
Young
Maturing
Mature
Relative transcriptional
activity
increase
decrease
Source
Sink

33. Source-­‐sink
unbalanced
Matura9on
stage
Mature
plants
Relative
transcriptional
activity
increase
decrease
Landscape
maps
sugarcane
metanetwork
Spa9al
evolu9on

34. Source-­‐sink
unbalanced
Matura9on
stage
Mature
plants
Relative
transcriptional
activity
increase
decrease
Source-­‐sink
gene
expression
network
Spa9al
evolu9on

35. Role
of
lncRNAs
in
Gene
Regulatory
Network
Clear pattern of
separation between
genotypes from the
different Breeding
Programs
Plant lncRNAs displays elevated intraspecific expression variation.
Cardoso-Silva, CB et al. PLOS One, in press

37. •
Dr.
Renato
Vicen.ni
– MSc.
Raphael
Majos
(miRNAs
network,
PhD)
– MSc.
Natália
Murad
(Gen2Phe,
Phd)
– Msc.
Leonardo
Alves
(Circadian
clock,
PhD)
– Elton
Melo
(Phosphoproteomics,
Msc)
– Lucas
Canesin
(lncRNA,
Birth/death
of
genes,
Msc)
Dr.
Michel
Vincentz
– Dr.
Luiz
Del
Bem
Dr.
Paulo
Mazzafera
– Dra.
Alexandra
Sawaya
– Dra.
Paula
Nobile
– Dr.
Michael
dos
Santos
Brito
– Dr.
Igor
Cesarino
– Dra.
Alexandra
Bojcher
– Adriana
Brombini
dos
Santos
Dra.
Anete
de
Souza
•
•
Dra.
Sabrina
Chabregas
Dra.
Juliana
Felix
•
•
•
Dr.
Marcos
Landell
Dr.
Ivan
Antônio
dos
Anjos
Dra.
Silvana
Creste
•
•
•
Team
and
collaborators
•
Dr.
Antonio
Figueira
– Dr.
Joni
Lima
•
Dra.
Adriana
Hemerly
– Flavia
– MSc.
Thais
•
Dr.
Fabio
Nogueira
– MSc.
Fausto
Or9z-­‐Morea
– MSc.
Geraldo
Silva
•
Dra.
Marie-­‐Anne
Van
Sluys
– Guilherme
Cruz
– Dr.
Douglas
Domingues
We
are
open
to
coopera9on
in
the
phosphoproteomic/metabolomic
analysis
and
in
the
enzyma9c
ac9vity
studies.
Supported
by:

38. Contact
Supported
by:
Dr. Renato Vicentini
shinapes@unicamp.br
http://sysbiol.cbmeg.unicamp.br
Group leader
Systems Biology Laboratory
Center for Molecular Biology and Genetic Engineering
State University of Campinas