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

Systems  Biology    

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

Sugarcane  Produc9on  Situa9on    

Moore, P.H. personal communication

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 ?

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

Source-­‐sink  rela9onship  in  sugarcane    

Sink

Source

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.

Sugarcane  extended  night  experiment  Sucrose  metabolism  -­‐  Circadian  regula9on  

Day Night

Sucrose  metabolism  Circadian  regula9on  

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

Manipula9on  of  Sink  Capacity    

Chlorophyll  content  (SPAD)  of  sugarcane  leaves.  

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)  

Ini9al  Results  Manipula9on  of  Sink  Capacity  

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  differen9al  expressed  genes    

High  sucrose  content   Low  sucrose  content  

Sink  

Source  

~1% of transcripts

~5% of transcripts

Gene  regulatory  network    

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.  

Lignifica9on  in  sugarcane    

Bottcher, A et al. Plant Physiology, in press

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

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.

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.  

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

Survey  of  the  sugarcane  genome  for  genes    

General  overview  of  the  sRNA  mapping  against  the  sugarcane  BACs.  

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

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

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.  

Sugarcane  co-­‐expression  network    

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

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).  

Landscape  maps  sugarcane  metanetwork    

Young Maturing Mature

Source Sink

decrease

increase

Relative transcriptional activity

Landscape  maps  sugarcane  metanetwork  Spa9al  evolu9on  Matura9on  stage  Mature  plants  Source-­‐sink  unbalanced  

decrease

increase

Relative transcriptional

activity

Source-­‐sink  gene  expression  network  Spa9al  evolu9on  Matura9on  stage  Mature  plants  Source-­‐sink  unbalanced  

decrease

increase

Relative transcriptional

activity

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

•  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  

 

Contact    

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

Supported  by: