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A systems biology approach to understanding the energetic balance in sugarcane
Dr. Renato Vicentini
Systems Biology Laboratory
Center for Molecular Biology and Genetic Engineering
State University of Campinas
Workshop on Interdisciplinary Plant Science, FAPESP, December 2013
Biological NetworksScaling Genotype to Phenotype
• Predictive 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 reactionState transition, catalysis
or inhibition
Strategy RNA-seq
In silico kinetic modeling and
Metabolic control analysis
Metabolite Profiling
Enzymes activity
determination and
allosteric regulation
Our Research Goals to Understanding Regulation of Sucrose Metabolism and Storage in Sugarcane
• Elucidate which genes in sugarcane leaves are responsive to changes in the sink:source ratio.
• Investigate the allosteric regulation of key enzymes.
We propose to develop an approach which integrates molecular and systems
biology to investigate these questions in sugarcane.
Why do some sugarcane genotypes accumulate more sucrose in internodes than
others ?
State of the art
• There are evidences that sink tissues exert an influence on the photosynthetic rates and carbohydrate levels of source organs.
• The activity 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 negative feedback regulation of photosynthesis.
• The signal feedback system indicating sink sufficiency to regulate source activity may be a significant target for manipulation to increase sugarcane sucrose yield.
• Currently, a model that predicts that sucrose accumulation is dependent on a system in which SPS activity exceeds that of acid invertase.
INV Hex
Sink demand
Negative feedback
Allosteric regulation of the SPS enzyme networkPhosphoproteomics approach
Sugarcane extended
night experiment
Schematic representation of the
system that module the rate of
sucrose synthesis by modifications
in the key enzyme SPS.
Manipulation of Sink Capacity
• Nine month-old field-grown plants of two genotypes of Saccharum (L.) spp. contrasting for sucrose accumulation.
• To modify plant source–sink balance, all leaves except leaf +3 were enclosed(simulated effect of internode maturation).
• RNA-seq analysis of control and perturbed system are in progress.
14d* 0d**1d
* Start** End
Sunlight
Enclosed
6d 3d
4 m
6 x 10 m plot
per genotypeUnshaded
leaf +3
Initial ResultsManipulation of Sink Capacity
• 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.
Chlorophyll fluorescence parameters (Fv/Fm; Fo/FM; Fv/Fo)
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.
Source-sink differential expressed genes
High sucrose content Low sucrose content
Sink
Source
~1% of transcripts
~5% of transcripts
Results
• More than ten thousand sugarcane coding-genes remain undiscovered (RNA-Seq).
• More than 2,000 ncRNAsconserved between sugarcane and sorghum was revealed.
• ~18% of the conserved
ncRNA presented a
perfect match with at
small RNA.
Cardoso-Silva, CB et al. PLOS One, submitted
Ortologous relationship
Phylexpress
Grasses PoGOs
SugarcanePoGOs
Networks
Carbohydrate biosynthesis
pathways
Gene-regulatory networks
Transcripts, genes and genomes source databases
Sorghum and rice genomes and genes
Transcription assembler of grasses
Angiosperm genomes (arabidopsis, rice,
populus, and sorghum)
Arabidopsis genome
Sugarcane transcripts collection
Microarray andRNA-seq data
Expression normalization and data correlation
Expressions data
Number of sugarcane genes, redundancy in ESTs database (PoGOs) and gene evolution
(dN/dS)
Sugarcane genes overview
SIM4/Blast algorithms
Similarity search
MapMan catalogue annotation
Annotation
Scaling from Genotype to Phenotype
PhosphopeptidesMetabolics Physiological parameters
Vicentini et al 2012. Tropical Plant Biology
Vicentini et al 2012. Tropical Plant Biology
Cardoso-Silva et al 2013. Plos ONE
• 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 interconnectivity between the nodes above threshold 0.04.
Sugarcane co-expression network
Regulatory complexes that are conserved in evolution
• 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 differential expressed genes identified in the source-sink experiments we can detect more than 50% genes inside regulatory complex conserved across sugarcane and rice.
• When Arabidopsis thaliana was included, only two complex still occurring.
Six significant complex were
discovered
Cellulose synthases
Gene Regulatory Network – A Bayesian ApproachThe source-sink experiment
• We detected several gene clusters, including many hubs, that incorporate different regulatory genes (ncRNAs, siRNAs, miRNAs, etc).
Landscape maps sugarcane metanetwork
Young Maturing Mature
Source Sink
decrease
increase
Relative transcriptional
activity
Landscape maps sugarcane metanetworkSpatial evolution
Maturation stageMature plantsSource-sink unbalanced
decrease
increase
Relative
transcriptional
activity
Hive plots panel co-expression network with lincRNAs, de novo sorghum genes and genes inside different taxonomical groups
• Dr. Antonio Figueira
– Dr. Joni Lima
• Dra. Adriana Hemerly
– Flavia
– MSc. Thais
• Dr. Fabio Nogueira
• Dra. Marie-Anne Van Sluys
• Dr. Renato Vicentini
– MSc. Raphael Mattos (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 Bottcher
– 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
We are open to cooperation in thephosphoproteomic/metabolomic analysisand in the enzymatic activity studies.
Supported by: