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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
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Systems Biology
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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
Strategy RNA-seq In silico kinetic modeling and
Metabolic control analysis Metabolite Profiling
Enzymes activity determination and allosteric regulation
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Sugarcane Produc9on Situa9on
Moore, P.H. personal communication
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Our Research Goals to Understanding Regula9on of Sucrose Metabolism and Storage in Sugarcane
• 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.
Why do some sugarcane genotypes accumulate more sucrose in internodes than others ?
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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.
• Currently, a model that predicts that sucrose accumula9on is dependent on a system in which SPS ac9vity exceeds that of acid invertase.
INV Hex Sink demand
Negative feedback
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Source-‐sink rela9onship in sugarcane
Sink
Source
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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.
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Sugarcane extended night experiment Sucrose metabolism -‐ Circadian regula9on
Day Night
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Sucrose metabolism Circadian regula9on
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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* 0d** 1d
* Start ** End
Sunlight Enclosed
6d 3d
4 m
6 x 10 m plot per genotype Unshaded
leaf +3
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Manipula9on of Sink Capacity
Chlorophyll content (SPAD) of sugarcane leaves.
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Manipula9on 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)
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Ini9al Results Manipula9on of Sink Capacity
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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.
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Source-‐sink differen9al expressed genes
High sucrose content Low sucrose content
Sink
Source
~1% of transcripts
~5% of transcripts
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Gene regulatory network
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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.
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Lignifica9on in sugarcane
Bottcher, A et al. Plant Physiology, in press
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Large-‐scale transcriptome analysis of two sugarcane cul9vars contras9ng for lignin content
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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.
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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.
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Ortologous rela9onship
Phylexpress
Grasses PoGOs
SugarcanePoGOs
Networks
Carbohydrate biosynthesis pathways
Gene-‐regulatory networks
Transcripts, genes and genomes source databases
Sorghum and rice genomes and genes
Transcrip9on assembler of grasses
Angiosperm genomes (arabidopsis, rice,
populus, and sorghum)
Arabidopsis genome
Sugarcane transcripts collec9on
Microarray and RNA-‐seq data
Expression normaliza9on and data correla9on
Expressions data
Number of sugarcane genes, redundancy in ESTs database (PoGOs) and gene evolu9on
(dN/dS)
Sugarcane genes overview
SIM4/Blast algorithms
Similarity search
MapMan catalogue annota9on
Annota9on
Scaling from Genotype to Phenotype
Phosphopep9des Metabolics Physiological parameters
Vicentini et al 2012. Tropical Plant Biology
Vicentini et al 2012. Tropical Plant Biology
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Survey of the sugarcane genome for genes
General overview of the sRNA mapping against the sugarcane BACs.
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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.
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.
Bottcher, A et al. Plant Physiology, in press
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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
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Sugarcane co-‐expression network
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• 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.
Sugarcane co-‐expression network
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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.
• When Arabidopsis thaliana was included, only two complex s9ll occurring.
Six significant complex were discovered
Cellulose synthases
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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).
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Landscape maps sugarcane metanetwork
Young Maturing Mature
Source Sink
decrease
increase
Relative transcriptional activity
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Landscape maps sugarcane metanetwork Spa9al evolu9on Matura9on stage Mature plants Source-‐sink unbalanced
decrease
increase
Relative transcriptional
activity
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Source-‐sink gene expression network Spa9al evolu9on Matura9on stage Mature plants Source-‐sink unbalanced
decrease
increase
Relative transcriptional
activity
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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
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• 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
We are open to coopera9on in the phosphoproteomic/metabolomic analysis and in the enzyma9c ac9vity studies.
Supported by:
• 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
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Contact
Dr. Renato Vicentini [email protected] http://sysbiol.cbmeg.unicamp.br Group leader Systems Biology Laboratory Center for Molecular Biology and Genetic Engineering State University of Campinas
Supported by: