From Genotype to Phenotype in Sugarcane: a systems biology approach to understanding the sucrose synthesis and accumulation

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Event / Evento: II Workshop on Sugarcane Physiology for Agronomic Applications

Speaker / Palestrante: Renato Vicentini (University of Campinas - Unicamp)
Date / Data: Oct, 29-30th 2013 / 29 e 30 de outubro de 2013
Place / Local: CTBE/CNPEM Campus, Campinas, Brazil
Event Website / Website do evento: www.bioetanol.org.br/sugarcanephysiology

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From Genotype to Phenotype in Sugarcane: a systems biology approach to understanding the sucrose synthesis and accumulation

  1. 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. 2. Systems  Biology    
  3. 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. 4. Sugarcane  Produc9on  Situa9on     Moore, P.H. personal communication
  5. 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. 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. 7. Source-­‐sink  rela9onship  in  sugarcane     Sink Source
  8. 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. 9. Sugarcane  extended  night  experiment   Sucrose  metabolism  -­‐  Circadian  regula9on   Day Night
  10. 10. Sucrose  metabolism   Circadian  regula9on  
  11. 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. 12. Manipula9on  of  Sink  Capacity     Chlorophyll  content  (SPAD)  of  sugarcane  leaves.  
  13. 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. 14. Ini9al  Results   Manipula9on  of  Sink  Capacity  
  15. 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.
  16. 16. Source-­‐sink  differen9al  expressed  genes     ~5% of transcripts High  sucrose  content   Low  sucrose  content   Source   ~1% of transcripts Sink  
  17. 17. Gene  regulatory  network    
  18. 18. 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.  
  19. 19. Lignifica9on  in  sugarcane     Bottcher, A et al. Plant Physiology, in press
  20. 20. Large-­‐scale  transcriptome  analysis  of  two  sugarcane  cul9vars  contras9ng   for  lignin  content  
  21. 21. 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.
  22. 22. 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.  
  23. 23. 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  
  24. 24. Survey  of  the  sugarcane  genome  for  genes     General  overview  of  the  sRNA  mapping  against  the  sugarcane  BACs.  
  25. 25. 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.  
  26. 26. 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
  27. 27. Sugarcane  co-­‐expression  network    
  28. 28. 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.  
  29. 29. 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
  30. 30. 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).  
  31. 31. Landscape  maps  sugarcane  metanetwork     Young Maturing Mature Relative transcriptional activity increase decrease Source Sink
  32. 32. Source-­‐sink  unbalanced   Matura9on  stage   Mature  plants   Relative transcriptional activity increase decrease Landscape  maps  sugarcane  metanetwork   Spa9al  evolu9on  
  33. 33. Source-­‐sink  unbalanced   Matura9on  stage   Mature  plants   Relative transcriptional activity increase decrease Source-­‐sink  gene  expression  network   Spa9al  evolu9on  
  34. 34. 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
  35. 35. •  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:  
  36. 36. 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

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