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A project I designed to study biodiesel production in algae

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  1. 1. A  chemical  genetics  approach  to  study  regulation  of  lipid  accumulation   and  cell  growth  in  C.  reinhardtii       Summary     Algae  have  the  potential  to  be  a  green  and   renewable  source  for  biodiesel  production   but   in   order   to   make   it   economically   sustainable   and   competitive   many   scientific   challenges   need   to   be  undertaken.   One  of   them  most   relevant   issues   is   the   necessity   to   manipulate   algal   metabolism   in   order   to   maximize   the   yield   of   neutral   lipids,   in   particular  tryacilyglycerols  (TAG),  the  biodiesel  precursors.   The   unveiling   of   regulative   mechanisms   controlling   cell   growth   and   neutral   lipid   biosynthesis  at  molecular  level  is  an  essential  key  factor  in  algal  biodiesel  research.     To  accomplish  this  goal  I  propose  to  use  a  chemical  genetic  approach  screening  diverse   compound   libraries   in   order   to   identify   “in   vivo”   chemical   modulators   of   lipid   accumulation  and  cell  growth.   Deconvolution   strategies   will   be   applied   to   identify   proteins   targeted   by   the   most   efficient  molecules  and  hence  the  putative  regulative  genes  involved  in  these  biological   processes.       The   results   of   this   approach  have   the   potential   to   allow   researchers   to   manipulate   algal   metabolism   with   two   different   complementary   strategies:   direct   “pharmacological”   employment  of  these  chemical  modulators  in  algal  cultures  and  mutagenesis/silencing   of  regulatory  genes  in  engineered  algal  strains.  In   the  future  a   combination  of  these  two   strategies  could  be  the  best   solution  to  guarantee  simultaneous  growth  robustness  and   high  lipid  content,  maximizing  biodiesel  productivity.   Carrying   out   this   research   in   an   american   lab   will   give   me   the   possibility   to   work   in   a   stimulating  scientific  environment  where  the  confront  of  different  ideas  and  the  access   to   material   and   intellectual   resources   will   help   me   to   develop   not   only   the   project   itself,   but   also   my   ability   of   critical   thinking.   This   will   strongly   contribute   to   my   professional   and  personal  enrichment.     Furthermore  I  am  interested  in  exploring  the  dynamics  that  are  turning  the  San  Diego   area   in   a   “hub”   for   algal   biodiesel   research,   where   academia   and   industry   work   in   synergy   with   a   continuous   exchange   of  resources  and   knowledge   to   turn   biodiesel   from   algae   in   a   reality.   The   lack   of   this   common   effort   and   synergistic   exchange   between   universities   and   private   companies   is   one   of   major   factors   preventing   the   italian   research  to  become  more  competitive  in  the  global  contest.                
  2. 2. Research  Plan     a. Specific  aims     The  long-­‐term  goal  of  this  research  is  to  maximize  the  yield  of  neutral  lipids  in  algae  to   make  these  organisms  a  competitive  and  sustainable  source  for  biodiesel  production.     This   goal   will   be   accomplished   identifying   and   characterizing   chemical   and/or   genetic   modulators   of   neutral   lipid   accumulation   and   cell   growth   promotion,   using   a   forward   chemical  genetic  approach  coupled  to  a  proteomic-­‐based  target  deconvolution  strategy.     A  chemical  high  throughput  screening  will  be  performed  to  identify  small  molecules  able   to  trigger  either  lipid  accumulation  or  stimulate  cell  growth.     In   order   to   maximize   sustainable   oil   production   will   be   tested   if   the   activity   of   single   molecules   inducing   different   phenotypes   (e.g.   lipid   accumulation   and   cell   growth)   can   be   maintained   and   combined   in   vivo   when   cells   are   exposed   to   such   molecules   simultaneously.   A   key   goal   of   the   research   is   to   identify   the   putative   targets   of   the   most   effective   molecules   and   hence   unveiling   one   or   more   genes   involved   in   the   regulation   of   lipid   accumulation  and  cell  growth.  RNAi  silencing  of  the  candidate  target  genes  will  provide   additional  information  regarding  the  regulative  mechanisms  of  the  biological  processes   investigated.   The   great   advantage   of   this   experimental   approach   is   that   it   will   potentially   enable   characterization   of   genes   involved   in   a   lipid   accumulation   and   cell   growth   and   at   the   same  time  will  provide  chemical  tools  to  modulate  their  functions.         b. Background  and  significance         Neutral   lipids,   and   in   particular   triacylglycerols   are   the   raw   material   for   biodiesel   production.  Currently  biodiesel  is  produced  mainly  out  of  field  crops  but  this  source  is   not  sustainable  to  satisfy  the  increasing  demand  of  the  market,  especially  if  the  goal  is   replacing  fossil  fuels.  Algae  have  the  potential  to  overcome  many  of  the  limits  affecting   crops   in   biodiesel   production,   but   in   order   to   become   a   competitive   and   commercial   reality  technologic  breakthroughs  are  needed  in  different  scientific  fields  [1].  Metabolic   engineering   of   algal   strains   in   order   to   re-­‐direct   as   much   as   possible   of   the   captured   solar   energy   into   the   highly   energetic   chemical   bounds   of   lipids   is   one   of   the   most   critical   issues.   To   accomplish   this   goal   many   biological   questions,  in   particular   related   to   biosynthesis   and   regulation   of   fatty   acids   and   triacylglycerols   (TAG),   need   to   be   addressed  [2].   The  pathways  and  the  enzymes  involved  in  fatty  acid  and  TAG  biosynthesis  are  poorly   studied   in   algae   but   are   very   well   characterized  in   higher   plants.   Computational   analysis   of  integrated  genomic,  proteomic  and  metabolomic  data  is  a  powerful  tool  that  can  help   mapping   the   metabolic   network   of   algae,   revealing   pathways   in   common   with   higher   plants  or  animals,  and  new  and  unique  ones  not  present  in  other  eukaryotic  organisms   [3,4].   If   charting   the   metabolic   maps   in   an   essential   knowledge   to   identify   structural  
  3. 3. genes  involved  in  fatty  acids  and   TAG  biosynthesis,  this  knowledge  may  not  be  sufficient   to  efficiently  manipulate  a  given  metabolic  pathway  through  overexpression   of   one  or   more   structural   genes.   This   issue   was   highlighted   by   lack   of   significant   effect   on   lipid   accumulation   after   overexpression   in   Cyclotella   criptica   of   Acetyl-­‐CoA   Carboxylase   (ACCA)  a  key  enzyme  in  fatty  acid  biosynhesis  [5].   Therefore   the   “Holy   Grail”   in   biodiesel   research   is   to   understanding   regulation   of   TAG   biosynthesis  at  molecular  level,  and  in  particular  how  the  cells  direct  the  metabolic  flux   of   photosynthetically   fixed   carbon   towards   fatty   acids   biosynthesis   first,   and   towards   TAG   biosynthesis   afterwards.   Regulative   genes   involved   in   such   control   need   to   be   identified  in  order  to  create  rational  engineered  algae  with  superior  lipid  content  [2].     It  is  well  known  that  many  algal  species,  even  belonging  to  different  taxonomic  groups,   alter   their   lipid   metabolism   in   response   to   changes   in   environmental   factors,   such   as   nutrient,   pH,   temperature,   salinity   and   light   intensity.   In   particular   specific   stress   conditions,   such   as   nutrient   limitation,   lead   to   an   increase   in   de   novo   biosynthesis   of   neutral  lipids  and  a  conversion  of  membrane  polar  lipids  in  TAG.  In  many  cases  the  net   result  is  an  increase  in  total   lipid  content  of  two  or  three  folds.  The  most  critical  nutrient   whose   limitation   triggers   this   metabolic   shift   is   nitrogen.   The   drawback   is   that   under   stress  such  as  nitrogen  limitation  algae  slowly  decrease  their  growth  until  they  reach  a   complete  arrest  in  cell  cycle  and  a  quiescent  state  [5].   While  most  of  the  research  in  this  field  has  been  focusing  in  individuating  algal  strains   and   culture   conditions   leading   to   the   highest   yield   of   neutral   lipid   accumulation,   no   attempts  to  shed  light  into  the  molecular  biology  of  such  phenomena  have  been  made   yet.   Individuating  the  molecular  actors  involved  in  control  mechanisms  of  lipid  metabolism  is   an   essential   goal   to   design   rational   engineered   algal   strains   that   constitutively   synthesize  and  store  high  level  of  neutral   lipids.  Parallel  and  integrative  studies  aimed  to   increase   the   rate   of   cell   growth   are   required   to   further   boost   the   productivity   of   biodiesel  from  algae.   Complete   genomic   sequences   of   several   algal   species   are   available   [4]   allowing   classic   forward   and   reverse   genetic   studies,   where   mutagenesis   is   a   mean   to   elucidate   the   relationship  between  genes  and  phenotypes.   Chemical   genetics   is   an   emerging   powerful   technology   which   employs   diverse   small-­‐ molecule   compounds   (replacing   mutagenesis)   acting   as   “perturbers”   in   a   biological   system,  in  order  to  elucidate  a  biological  process  of  interest  and  identify  gene  products   involved   in   that   process.   This   approach   offers   several   advantages   over   classic   forward   and  reverse  genetics  potentially  also  in  relation  to  the  algal  biodiesel  research.  First  of   all   small   molecules   work   rapidly   and   often   reversibly,   commercial   compound   libraries   are   available   in   formats   that   allow   a   relatively   fast   analysis   and   possibility   of   automation,  effectively   reducing  the  time   of  the   screening  (especially  when  compared   with   the   time   required   to   create   and   screen   mutant   libraries).   Furthermore   while   mutagenesis   strategies   rely   on   complete   inactivation   of   a   gene,   chemicals   have   the   potential   to   block   only   a   specific   function   of   a   multifunctional   protein,   potentially   generating  phenotypes  not  reproducible  via  mutagenesis  or  silencing  [6].  Indeed  several   cases   where   small   molecules   and   mutations   targeting   the   same   proteins   produced  
  4. 4. radically  different  phenotypes  have  been  reported  [7].  Another  important  advantage  is   that   active   compounds   may   be   tested   for   functionality   across   different   species   where   genomic   data   are   not   available   or   efficient   transgenic   technologies   are   not   fully   developed.   Last   but   not   least   the   discovery   of   compounds   with   biological   desired   activities  may  lead  to  industrial  applications  (so  far  applied  mainly  in  the  pharmaceutical   field)   and   this   might   be   a   crucial   benefit   in   biodiesel   research   as   well.   Based   on   their   efficacy   small   bioactive   molecules   could   potentially   find   a   practical   employment   in   biomass  and  biodiesel  precursor  production,  or  at  least  to  be  the  base  to  design  drugs   with   high   efficacy   and   specificity   in   modulating   the   biological   processes   of   interest   (lipid   accumulation   and   cell   growth   promotion).   Chemical   modulators   could   indeed   be   an   alternative   way   to   transgenesis,   in   order   to   increase   biodiesel   productivity   in   algae,   especially   given   the   reluctance   of   large   part   of   the   public   opinion   and   the   scientific   community  to  introduce  GMOs  in  the  environment.   On   the   other   side   identification   of   regulative   genes   could   allow   the   design   of   rational   metabolic   engineered   algal   strains,   able   to   accumulate   neutral   lipids   possibly   with   improved  cell  growth  performances,  in  standard  culture  conditions.   In   the   future   the   combination   of   transgenic   strains   and   chemical   modulators   could   be   the  best  strategy  to  maximize  the  yield  of  biodiesel  precursors  in  algae.   These  are  the  reasons  why  I  propose  to   use  this  approach  in  studying  lipid  accumulation   and  cell  growth  in  a  model  algal  species.       c. Preliminary  studies   No  preliminary  studies  are  available.         d. Research  design  and  methods     The   project   is   based   on   a   phenotype   driven   chemical   proteomic   approach,   which   consists  in  introducing  small  molecules  in  a  system  and  selecting  the  ones  able  to  induce   a  particular  phenotype.  The  only  algal  organism  for  which  extensive  biological  genomic   and   proteomic   data   exist   is   Chlamydomonas   reinhardtii   [4];   different   mutants   characterized   by   different   phenotypes   are   available,   among   these   the   mutant   CC-­‐503   cw92   mt+   is   characterized   by   the   absence   of   a   cell   wall   and   was   used   for   genomic   sequencing.   A   growth   inhibition-­‐based   drug   screening   performed  in   parallel   in   wild   type   and   wall   less   C.reinhardtii   cells   showed   that   the   latter   were   less   sensitive   to   drugs   compared  to  the  former.  The  authors  hypothesized  a  better  uptake  and  internalization   of   the   molecules   in   the   wall   less   cells   [8].   Given   these   premises   I   propose   to   use   C.   reinhardtii  cw92  mt+  as  experimental  model.  The  research  will  be  essentially  divided  in   two  stages:     1) High   throughput   screening:   I   intend   to   use   the   fluorescent   dye   Nile   Red   to   screen   one   or   more   bioactive   compound   libraries   in   order   to   identify   molecules   that   are   able   to   trigger   neutral   lipid   accumulation   in   the   model   species.   Nile   Red   is   a   lipid  
  5. 5. extrinsic  fluorescent  dye  whose  maximum  emission  is  blue-­‐shifted  as  the  polarity  of   the  surrounding  environment  decreases.  Recently  Chen  et  al.  proposed  an  optimized   protocol   with   increased   accuracy   and   sensitivity,   suitable   for   high   throughput   quantitative  screening  of  neutral  lipid  content  in  algae.  Reliability  of  the  method  was   demonstrated   by   a   direct   comparison   with   the   conventional   gravimetric   technique   [9].   This   protocol   can   be   easily   formatted   for   96   well   plates   and   used   with   a   microplate   reader   to   screen   compound   libraries.   In   addition   to   the   selected   fluorescence  emission,  cell  proliferation  will  be   monitored  either  checking  the  O.D.   at   750   nm   or   using   one   of   the   several   commercially   available   cell   viability/proliferation   assay   (colorimetric   or   fluorescent)   formatted   for   96   well   plates.   The   screening   will   be   performed   in   parallel   using   cells   growing   in   standard   condition  and  cells  growing  under  nitrogen  limitation.  For  each  condition  and  each   molecule  data  regarding  lipid  accumulation  and  cell  growth  will  be  acquired  at  least   at   three   different   time   points   (to  be   determined  empirically   and   very   likely   different   for   the   two   conditions)   in   a   time   course   manner.   Untreated   cells   growing   under   standard   condition   or   nitrogen   limitation   will   be   used   as   controls.   Dose-­‐response   studies  will  be  carried  out  to  analyze  the  potency  of  the  positive  compounds  using   fluorescence   intensity   and   standard   growth   curves,   respectively   for   neutral   lipid   accumulation   and   cell   growth.   During   the   screening   I   expect   to   find   molecules   affecting  lipid  metabolism  that  can  be  divided  in  3  main  categories  according  to  their   activities:   a. Able   to   promote   lipid   accumulation  in   standard  culture   conditions   but   not   to   further  increase  lipid  accumulation  in  nitrogen  starving  cells   b. Able   to   promote   lipid   accumulation   in   standard   culture   conditions   and   to   further  increase  lipid  accumulation  in  nitrogen  starving  cells   c. Able  to  inhibit  lipid  accumulation  in  nitrogen  starving  cells   In  the  same  way  I  am  interested  in  individuate  molecules  affecting  cell  growth  with   two  distinct  activities:   d. Able   to   promote   cell   growth   under   standard   conditions   but   not   in   nitrogen   starved  cells   e. Able  to  rescue  the  growth  defective  phenotype  of  nitrogen  starving  cells  but   not  to  promote  growth  of  cells  cultured  in  standard  conditions.   f. Able   to   rescue   the   growth   defective   phenotype   of   nitrogen   starving   cells   and   to  promote  growth  of  cells  cultured  in  standard  conditions   While   finding   single   molecules   exerting   both   desired   activities   (cell   growth   promotion  and  lipid  accumulation)  seems  unlikely,  the  combination  of  two  or  more   moelcules   characterized   by   different   biological   activities   will   be   tested   in   vivo   to   study   if   the   two   different   activities   can   be   combined   and   maintained  in   a   additive   or   synergistic   way.   Several   factors   may   affect   the   result   of   all   high-­‐throughput   screenings.  First  of  all  failure  in   identifying  compound  with  the  desired  activity  might   be   due   to   an   incorrect   design   of   the   screening   itself.   In   order   to   avoid   this   possibility   control   measurement   will   be   acquired   for   each   time   point   and   for   each   condition   and  replicates  will  be  used.  The  two  critical  factors  that  may  have  the  greater  impact   in   the   identification   of   molecules   inducing   the   desired   phenotype   are   drug  
  6. 6. concentration  and  drug  exposure  time.  While  the  former  should  be  kept  as  low  as   possible   (low   µmolar   range)   to   increase   the   stringency   of   the   screening   and  limit  off   target   effects,   the   latter   should   be  long   enough   (at   least   72h)   to   allow   accumulation   of  lipids.  A  prescreening  setup  to  optimize  time   points  using  control  cells  grown  in   the   two   different   conditions   will   be   essential.   Another   critical   factor   affecting   the   success   of   a   chemical   screening   is   the   number   of   compounds   and   their   structural   variability.   A   starting   candidate   could   be   the   Diverset   library   (ChemBridge,   San   Diego)  since  many  successful  screenings  have  been  reported,  and  in  particular  two   independent   groups   were   recently   able   to   identify   new   auxin-­‐like   compounds   affecting  plant  growth  within  this  library  [10,11].  In  the  hypothesis  that  C.  reinhardtii   shares   with   higher   plants   the   auxin   response   pathway,   some   of   the   molecules   identified  in   these   screenings   could   stimulate   algal   growth   too   and   could  be   used   as   putative   positive   controls.   The   most   interesting   compounds   or   a   combination   of   them   can   be   tested   in   other   algal   species   of   particularly   intereste   for   biofuel   production,  to  assess  if  they  can  exert  the  same  effect.     2) The  following  step  consists  in  individuating  the  target/targets  of  the  compound  and   understanding   their   role   in   the   biological   process   investigated.   This   process   called   “target  deconvolution”  remains  the  most  challenging  part  of  every  chemical  genetics   experiment.  In  particular  in  this  case  the  low  stringency  of  the  screening  conditions   could   lead   to   identifications   of   a   broad   spectrum   of   compound   acting   on   different   pathways  and  acting  with  different  mechanisms.   The  problem  of  the  low  stringency   is   mainly   due   to   the   lack   of   information   at   molecular   level   regarding   lipid   accumulation   and   cell   growth   in   algae,   gap   that   this   project   could   fill   at   least   partially.   A   first   step   in   target   deconvolution   is   database   mining,   to   verify   if   the   molecule   of   interest   has   already   been   characterized   and   which   are   the   putative   target  proteins  [12].  Several  deconvolution  strategies  have  been  proposed  but  each   of  them  can  be  either  time  consuming  or  lead  to  false  positives  [13].  In  alternative  I   propose   a   novel   chemicalproteomic   quantitative   method   to   identify   drug   targets,   based  on  the   principle  that  the   binding   of  a  small  molecule  to  a  protein  affects  its   sensitivity   to   protease   digestion   [14].   Briefly   cell   lysates   are   prepared   in   non-­‐ denaturing   conditions,   aliquots   remain   untreated   while   others   are   incubated   with   different  concentrations  of  the  drug  and  finally  all  lysates  are  digested  with  one  or   more   proteases.   Treated   and   untreated   peptide  mixtures   will  be   labeled   with  iTRAQ   (isobaric  tags  for  relative  and  absolute  quantification),  separated  by  OFFGEL  [15]  and   analyzed   by   mass   spectrometry   to   identify   quantitative   differences   among   the   samples   [16].   Peptides   showing   a   stechiometric   enrichment   or   depletion   after   the   drug  exposure  should  be  indicative  of  proteins  interacting  with  the  drug  itself.  This   method   has   only   been   validate   as   proof   of   concept   to   verify   predicted   targets   of   know   drugs,   and  its   application  in   identifying   novel   targets   of   uncharacterized  drugs   has   not   been   tested   yet   especially   coupled   to   quantitative   proteomics.   Nevertheless   the   fact   that   doesn’t   require   expensive,   labor  intensive   and   time   consuming   studies,   and  it  can  potentially  be  used  in  a  high  throughput  is  critical    then  the  potential  to   become   the   method   of   choice   in   target   deconvolution   analysis.   The   setup   and  
  7. 7. optimization   of   the   quantitative   proteomic   analysis,   possibly   using   characterized   drug  will  be  essential  and  can  be  carried  out  in  parallel  with  the  screening  process.   The   success   of   this   method   could   allow   individuation   of   targets   of   several   small   molecules   in   a   relatively   short   time.   Finally   additional   information   about   mechanisms   of   action   of   candidate   target   proteins   can   be   collected   studying   the   effect   of   gene   silencing   on   the   phenotype.   Recentely   a   method   for   efficient   knockdown  emplying  miRNA  has  been  developed  in  C.  reinhardtii  [17]               References     [1] Dismukes   GC,   Carrieri   D,   Bennette   N,   Ananyev   GM,   Posewitz   MC.   Aquatic   phototrophs:   efficient   alternatives   to   land-­‐based   crops   for   biofuels.   Curr   Opin   Biotechnol  2008;19:235-­‐40.   [2] Hu   Q,   Sommerfeld   M,   Jarvis   E,   Ghirardi   M,   Posewitz   M,   Seibert   M,   Darzins   A.   Microalgal  triacylglycerols  as  feedstocks  for  biofuel  production:  perspectives   and  advances.  Plant  J  2008;54:621-­‐39.   [3] May   P,   Wienkoop   S,   Kempa   S,   Usadel   B,   Christian   N,   Rupprecht   J,   Weiss   J,   Recuenco-­‐Munoz   L,   Ebenhöh   O,   Weckwerth   W,  Walther   D.   Metabolomics-­‐   and   proteomics-­‐assisted   genome   annotation   and   analysis   of   the   draft   metabolic   network  of  Chlamydomonas  reinhardtii.  Genetics,  2008;179:157-­‐66.   [4] Merchant   SS,   Prochnik   SE,   Vallon   O,   Harris   EH,   Karpowicz   SJ,   Witman   GB,   Terry   A,  Salamov  A,  Fritz-­‐Laylin  LK,  Maréchal-­‐Drouard  L,  Marshall  WF,  Qu  LH,  Nelson   DR,   Sanderfoot   AA,   Spalding   MH,   Kapitonov   VV,   Ren   Q,   Ferris   P,   Lindquist   E,   Shapiro  H,  Lucas  SM,  Grimwood  J,  Schmutz  J,  Cardol  P,  Cerutti  H,  Chanfreau  G,   Chen   CL,   Cognat   V,   Croft   MT,   Dent   R,   Dutcher   S,   Fernández   E,   Fukuzawa   H,   González-­‐Ballester  D,  González-­‐Halphen  D,  Hallmann  A,  Hanikenne  M,   Hippler   M,  Inwood  W,  Jabbari  K,  Kalanon  M,  Kuras  R,  Lefebvre  PA,  Lemaire  SD,  Lobanov   AV,  Lohr  M,  Manuell  A,  Meier  I,  Mets  L,  Mittag  M,  Mittelmeier  T,  Moroney  JV,   Moseley   J,   Napoli   C,   Nedelcu   AM,   Niyogi   K,   Novoselov   SV,   Paulsen   IT,   Pazour   G,   Purton  S,  Ral  JP,  Riaño-­‐Pachón  DM,  Riekhof  W,  Rymarquis  L,  Schroda  M,  Stern   D,  Umen  J,  Willows  R,  Wilson  N,  Zimmer  SL,  Allmer  J,  Balk  J,  Bisova  K,  Chen  CJ,   Elias  M,  Gendler  K,  Hauser  C,  Lamb  MR,  Ledford  H,  Long  JC,  Minagawa  J,  Page   MD,  Pan  J,  Pootakham  W,  Roje  S,  Rose  A,  Stahlberg  E,  Terauchi  AM,  Yang  P,  Ball   S,   Bowler   C,   Dieckmann   CL,   Gladyshev   VN,   Green   P,   Jorgensen   R,   Mayfield   S,   Mueller-­‐Roeber  B,  Rajamani  S,  Sayre  RT,  Brokstein  P,  Dubchak  I,  Goodstein  D,   Hornick   L,   Huang   YW,   Jhaveri   J,   Luo   Y,   Martínez   D,   Ngau   WC,  Otillar   B,   Poliakov   A,   Porter   A,   Szajkowski   L,   Werner   G,   Zhou   K,   Grigoriev   IV,   Rokhsar   DS,   Grossman   AR.   The   Chlamydomonas   genome   reveals   the   evolution   of   key   animal  and  plant  functions.  Science,  2007;318:245-­‐50.  
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