Flux  balance  analysis  to  improve  butanol  productivity  in  Synechocystis  PCC  6803  

   

Master  of  Science  Thesis    

Kiyan  Shabestary        

   Supervisor:  Josefine  Anfelt,  PhD  student,  KTH    Examiner:  Paul  Hudson,  Assistant  Professor,  KTH    Degree  Project  in  Biotechnology  BB202X  

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 Abstract    Engineering  microorganisms  at   the  systems   level   is   recognized   to  be   the   future  of  metabolic   engineering.   Thanks   to   the   development   of   genome   annotation,  microorganisms   can   be   understood,   as   never   before,   and   be   reconstructed   in   the  form   of   computational  models.   Flux   balance   analysis   provides   a   deep   insight   into  cellular  metabolism   and   can   guide  metabolic   engineering   strategies.   In   particular,  algorithms  can  assess  the  cellular  complexity  of  the  metabolism  and  hint  at  genetic  interventions   to   improve   product   productivity.   In   this   work,   Synechocystis   PCC  6803   metabolism   was   investigated   in   silico.   Genetic   interventions   could   be  suggested   to   couple   butanol   synthesis   to   growth   as   a   way   to   improve   current  productivities.   Cofactor   recycling   and,   in   particular,   buffering   mechanisms   were  shown   to  be   important   targets.  Creating  a   cofactor   imbalance  and   removing   these  buffering   mechanisms   is   an   important   driving   force.   This   forces   a   carbon   flux  through  butanol  synthesis  to  maintain  cofactor  balance  and  sustain  growth.    Objective    The  objective  of  the  present  work  is  to  identify  gene  targets  in  silico  at  the  systems  level  to  improve  n-­‐butanol  and  isobutanol  productivities  in  Synechocystis  PCC  6803.  An  emphasis   is  put  on   coupling  product   to   growth  and  understanding   the  driving  forces  behind  it.      

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Contents    Introduction  ………………………………………………………………………………………4  

Theory……………………………………………………………………………………………….6                        6  

Genome-­‐scale  metabolic  models…….…………………………………………………………..6  

Flux  balance  analysis  …………………………………………………………………………………7  

Optimization  algorithms  ……………………………………………………………………………9                9  

n-­‐butanol  and  isobutanol  pathways  …………………………………………………………11                                  11  

Metabolic  engineering  in  cyanobacteria  …………………………………………………...14  

Results  …………………………………………………………………………………………….18    

OptKnock  to  predict  reaction  deletions  ………………………………………………........18  

OptForce  to  predict  reaction  modulations    .………………………………………………23  

Identifying  buffering  mechanisms  for  cofactor  balancing  ..……………………28  

Discussion  ……………………………………………………………………………………….30  

Conclusion  ……………………………………………………………………………………….33  

Future  directions  ……………………………………………………………………………..33  

References  ……………………………………………………………………………………….34  

 

   

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Introduction    Industrial  biotechnology  is  an  emerging  and  promising  discipline  for  the  production  of   a   wide   range   of   compounds   including   fine   chemicals,   biopolymers,   biofuels   or  biopharmaceuticals.   Using   living   organisms   as   ‘cell   factories’,   mainly   micro-­‐refineries  that  perform  a  specific   task,  represents  the  cornerstone  of   this   field  and  lay   the   foundations   towards   a   future   bio-­‐based   society.   The   apparition   of   high-­‐throughput   sequencing   to   read   cellular   genetic   information   has   appeared   as   a  revolution  leading  biotechnology  into  a  new  era.  Understanding  the  cellular  genetic  code   enable   an   unprecedented   insight   into   microorganisms   physiology   and  functions.  As  a  result,  tools  to  genetically  engineer  cell  factories  have  evolved  to  gain  in  accuracy.      Previous  methods  to  seek  for  high-­‐producing  strains  were  mainly  based  on  chance  where   random  mutagenesis   created  strains  diversity  and  good  screening  methods  enabled   selection   of   the   best   producers.   However,   these   methods   were   time-­‐consuming  and  did  not  find  the  optimal  strains.  Recently,  genomics  paved  the  way  for   other   –omics   techniques   such   as   transcriptomics,   proteomics   and   finally  metabolomics.   This   high   amount   of   data   assessable   with   newly   available   high-­‐throughput  analytical  techniques  can  be  translated  into  gene-­‐protein-­‐reaction  (GPR)  associations.   New   methods,   mainly   rational   design,   accurately   target   genes   to  modulate   cellular   functions   through   this   relationship   for   production   purposes.  Metabolic   engineering   is   the   field   aiming   at   understanding   how   genes   influence  cellular  metabolism  and  further  exploit  them  to  shape  microorganisms  metabolism  to   meet   engineering   objectives.   Metabolic   engineer   may   count   on   tools   including  gene   deletions,   over-­‐expression,   down-­‐regulation   or   even   heterologous   gene  insertion  from  other  organisms  as  way  to  improve  strains.      Initial  metabolic  engineering  strategies  aimed  at  redirecting  carbon  flux  to  product  synthesis   regardless   of   the   metabolic   burden   created   in   the   cell.   In   particular,  cofactors,  widely  connected  metabolites  helping  reactions   to  go   forward,  were  not  taken   into   account   and   their   imbalance   resulted   in   such   bottlenecks.   It   is   now  commonly   thought   that   understanding   the   cell   as   a  whole   is   an   important   key   to  metabolic  engineering  success.  However,  biological  organisms  are  complex  systems  involving   hundreds   if   not   thousands   of   biochemical   reactions.   For   metabolic  engineers,   this   means   a   high   number   of   combinatorial   gene   targets   to   reach   an  optimal  overproduction  phenotype.  Therefore,  to  efficiently  assess  the  high  number  of  possibilities  a  cell  can  offer,  computational  methods  are  very  welcoming  tools  to  support   experiments.   Genome-­‐scale   metabolic   models   aim   to   represent   cellular  metabolism  as  a  whole  using  GPR  associations  and  can  be  used  for  simulations.  Flux  balance   analysis   (FBA)   is   a   computational   method   aiming   at   solving   flux  distributions   (phenotype)   in   cellular  metabolism   for   a   given  genotype.  Algorithms  can  further  be  used  to  provide  strategies  (mainly  reaction  deletions  or  modulations)  in  order  to  reach  a  desired  overproduction  phenotype.    

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An   important   issue  met   in   engineering   strategies   is   cellular   behavior.   Very   often,  engineering  strategies  are   in  opposition  to  the  cellular  ones.  Most  microorganisms  optimize   their   growth   as   a   mean   of   survival.   The   ones   growing   the   fastest  outcompete  slower  organisms.  This  forms  the  basis  for  natural  selection.  Organisms  have   evolved   for   millions   of   year   to   perfectly   fit   their   environment.   Coupling  product  synthesis  to  growth  is  a  way  to  bypass  this  issue.  A  successful  strategy  is  to  modify  the  stoichiometric  system  in  a  way  that  the  cell  needs  to  produce  a  product  in   order   to   achieve   growth.   Therefore   at   maximal   growth   rate,   the   product   is  synthesized  as  a  growth-­‐related  by-­‐product.      The  aim  of  this  project  is  to  perform  FBA  on  cyanobacteria  Synechocystis  PCC  6803  genome   scale   model   to   identify   metabolic   engineering   strategies   for   enhanced  biofuel   production.   n-­‐butanol   and   isobutanol   are   target  molecules   in   this   project.  Cyanobacteria  are  prokaryotic  algae  and  promising  cell  factories  for  the  production  of  a  wide  variety  of  compounds   including  biofuels.  There  are  two  types  of  process  using   algea   for   biofuel   production.   One   is   to   use   high-­‐biomass   content   eukaryotic  algae  such  as  chlamydomonas  species  for  biomass  production.  The  biomass  is  then  decomposed   into   sugars   and   fermented   into   biofuel   by   microorganisms   such   as  yeast.   The   other   is   the   direct   use   of   algae   as   a   chassis   for   biofuel   production.  Cyanobacteria  species  are  preferred  cell  factories  in  this  case  due  to  easier  genetic  modifications.   Nevertheless,   algae   only   require   nitrogen,   carbon   dioxide   and  sunlight   to   grow.   This   low   requirement   makes   them   promising   cell   factories.  Moreover  they  do  not  compete  with  food  production  as  they  do  not  require  arable  land.                                    

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Theory    Genome-­‐scale  metabolic  models      Genome-­‐scale   metabolic   models   (GSMMs)   have   been   the   catalysts   for   the  development  of  flux  balance  analysis.  It  is  a  bottom-­‐up  approach  where  the  model  is  created  from  its  smallest  constituents,  starting  from  genes  to  establish  the  metabolic  map  (Thiele  and  Palsson,  2010;  Fig.  1).  Based  on  extensive  research  from  literature  and   databases   (KEGG,   Brenda),   a   draft   reconstruction   can   be   first   established.   It  should  be  noted  that  this  might  include  previous  GSMMs  to  be  upgraded.  Software  such   as   the   RAVEN   toolbox   (Agren   et   al.,   2013)   can   perform   reconstructions   in   a  semi-­‐automated   way.   This   reconstruction   often   results   in   gaps   i.e.   some   missing  reactions   in   a   pathway   (a   missing   serine   pathway   for   Synechocystis   PCC   6803;  Knoop  et  al.,  2013).  For  further  readability,  confidence  scores  are  assigned  for  each  reaction.  Reactions  without  any  physiological  evidence,  introduced  in  the  model  for  functional  purposes  solely,  take  the  lowest  confidence  value.  Finally,  a  model  can  be  generated   into   a   mathematical   form,   the   stoichiometric   matrix   (S   matrix).     All  stoichiometric   coefficients   for   each   reaction   and   metabolite   are   stored   in   the   S  matrix.   One   important   part   of   the  model   is   the   addition   of   artificial   reactions   for  modeling  purposes.  This   includes  addition  of  maintenance  and  a  biomass  reaction.  The  biomass  reaction  aims  to  link  precursors  to  biomass  constituent  such  as  lipids,  proteins   and   other  macromolecules   to  model   growth.   The   flux   going   through   this  reaction  is  scaled  in  order  to  represent  the  growth  rate  (μ).    

 The   reconstructed   model   can  then   be   saved   in   the   Systems  Biology   Markup   Language  (SBML)   format   (Hucka   et   al.,  2003).   This   provides   a  standardized   way   for   model  usage   and   improvement.  Genome-­‐scale   models   can   be  used  for  two  purposes  mainly.  First,   they   can   be   used   as   a  way   to   provide   a   way   to  contextualize   high   trough-­‐put  omics  data.  Secondly,  they  can  be   used   alongside   algorithms  in   order   to   find   optimal  engineering   strategies.   The  latter  is  the  aim  of  the  present  work.    Synechocystis  PCC  6803,  often  labeled   as   the   “green  Escherichia   coli”   (E.   coli),   is   a  

Fig.   1.   Genome-­‐scale   model   reconstruction   procedure.   Figure  from  Feist  et  al.  (2008).  

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model   organism   for   phototrophs.   However,   lack   of   genome   annotation   and  extensive  in  vivo  gene  essentiability  have  slowed  down  the  development  of  accurate  cyanobacteria  genome-­‐scale  models  in  comparison  to  well-­‐studied  organisms  like  E.  coli   or   Saccharomyces   cerevisiae   (S.   cerevisiae).   The   apparition   of   a   gene   database  (CyanoBase;  http://genome.microbedb.jp/cyanobase/)  for  all  known  Synechocystis  PCC  6803  and  Anabaena  PCC  7120  ORFs  (Nakamura  et  al.,  1999;  Nakao  et  al.,  2010)  paved   the   way   for   numerous   genome-­‐scale   reconstructions   (Shastri   and   Morgan,  2005;  Montagud   et   al.,   2011;   Saha   et   al.,   2012;   Nogales   et   al.,   2012;   Knoop   et   al.,  2013).        Synechocystis  sp.  PCC  6803  model  iJN678  (Nogales  et  al.,  2012)  was  used  to  perform  flux   balance   analysis.   The   reconstructed   network   incorporates   678   genes,   863  reactions  and  795  metabolites.  The  model  supports  heterotrophic,  autotrophic  and  mixotrophic  conditions  simulation  by  constraining  uptakes.  Autotrophic  conditions  were   simulated   as   “light   limited”   where   photon   flux   was   constrained   to   -­‐18.7  mmol/gDW/h  (both  bounds)  and  carbon  uptake   (HCO3)  constrained   to  a  maximal  value   of   3.8   mmol/gDW/h.   In   heterotrophic   conditions,   glucose   uptake   was  constrained   to   0.8   mmol/gDCW/h   in   order   to   match   the   photoautotrophic  maximum  growth  rate.  Photon  flux  was  set  to  0.      In  this  project,  iJN678  was  used  because  it  offers  a  standardized  metabolite,  reaction  and  transporter  annotation  comparable  to  E.  coli  genome-­‐scale  models.  The  biomass  objective   function   is   refined   in   comparison   to   other   models.   Respiration   and  photosynthesis  are  also  well  modeled,   in  particular  cyclic  electron  flows  which  are  important  targets  in  this  study.      Results  have  been  shown  to  be  sensitive  to  the  model  used,  cofactor  preference  and  reaction  promiscuity.  Since  cofactor  preference  is  not  known  for  a  large  number  of  reactions,  models  can  show  high  differences   in  cofactor  assignment.  This  has  been  shown   to   be   a   real   issue   when   investigating   suitable   strategies   for   target  overproduction.   For   this   reason,   the   model   from   Knoop   et   al.   (2013)   is   used   to  contrast  and  discuss  the  validity  of  obtained  results.    Flux  balance  analysis    Flux   balance   analysis   (FBA)   is   a   mathematical   method   assuming   steady-­‐state  growth   to   simulate   flux   distribution   for   a   given   genome-­‐scale   model   (Orth   et   al.,  2010).   Mainly,   internal   metabolites   such   as   the   ones   found   in   the   central  metabolism   are   assumed   not   to   accumulate.   For   each   internal   metabolite,  production   and   consumption   rates   equalize.   This   then   leads   to   a   set   of   equations  that   can   be   solved   using   linear   programming   (LP).   Since   the   system   is  underdetermined,  multiple  distributions  are  possible.  It  should  be  noted  that  this  is  a   fundamental   difference   with   metabolic   flux   analysis   (MFA)   where   exchange  reactions  are  measured  such  as  the  system  is  determined.  A  single  flux  distribution  can   then   be   obtained   for   MFA.   Using   an   objective   function   to   be   maximized   or  minimized   (a   reaction   in   the  model   needs   to   be   chosen),   a   certain   distribution   is  

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picked   up.   For   the   flux   distribution   to   be   realistic,   the   objective   function   to   be  optimized   should   fit   the   organism   objective.   Natural   selection   has   compelled  microorganisms   to   aim   for   increased   fitness.   Therefore,   maximization   of   growth  (biomass  equation)  is  often  chosen  as  objective  function,  especially  for  prokaryotes.    Alternatives  to  FBA  include  minimization  of  metabolic  adjustment  (MOMA;  Segrè  et  al.,  2002)  and  regulatory  on/off  minimization  (ROOM;  Shlomi  et  al.,  2005).  Both  are  used   to   find   distribution   in   mutants   cells   based   on   WT   distributions.   They   both  minimize   flux   distribution   differences   between   the  WT   and   the  mutant.  MOMA   is  used  to  find  immediate  flux  distribution  whereas  FBA  and  ROOM  give  the  final  one  (Segrè  et  al.,  2002).  MOMA  and  ROOM  are  relevant   tools  when  mutants  are   tested  experimentally.      FBA   is   often   referred   to   as   a   constraint-­‐based   tool.   Constraints  present   in   current  GSMMs   can   be   classified   into   two   types:   (1)   Balance   constraints   due   to   the  stoichiometry  of   the   system.   (2)  Flux   constraints  where   each   flux   is   limited  by   an  upper   and   lower   bound   (i.e   irreversible   reaction   cannot   carry   a   negative   flux).  Additional   constraints   on   the   system   can   greatly   influence   the   solution   space,  mainly,   the   set   of   all   mathematical   possible   flux   distributions   that   satisfy   the  constraints  (Fig.  2).  The  more  constraints  one  adds,  the  more  restricted  the  solution  space.  This  results  in  predicting  an  optimal  flux  distribution  with  greater  accuracy.      

 Fig.  2.  The  conceptual  basis  of  constraint-­‐based  modeling.  In  this  representation,  a  three  reactions  model  is  assumed   for   simplicity.  Without   any   constraints,   all   mathematical   distributions   are   possible   solutions   of   the  system.  When  constraints  are  added,  the  solution  space  is  restricted  to  a  constrained  area  (in  blue).  An  objective  function  can  be  used  to  get  an  optimal  flux  distribution  for  (v1,v2,v3).  In  this  example  v3  is  the  objective  function  to  be  optimized.  Adapted  from  Orth  et  al.  (2013).  

The  main  constraint   in  FBA   is   the  stoichiometry  of   the  system.  Net   formation  of  a  given  metabolite  is  calculated  as  the  sums  of  all  flux  leading  to  this  metabolite  minus  the  sums  of  all  flux  consuming  this  metabolite,  over  the  whole  system.  Depending  on  the   stoichiometry,   some  reactions   can  weigh  more   than  others.  For  a  given   flux,   a  reaction   whose   stoichiometry   is   2   moles   of   metabolites   formed   per   mole   of  substrate  leads  to  higher  formation  rate  than  if  only  1  mole  was  formed.  Thus,  net  

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metabolite   formation   rate  !!!!"  can   be   simplified   to   the   sum   of   reaction   rates  

multiplied  by  the  reaction-­‐specific  stoichiometry  coefficient  of  the  metabolite  :    

𝑑𝑥!𝑑𝑡  =   𝑆!"  𝑣!

!

!!!

 

 N   is   the   total   number   of   reactions,  𝑆!"  and   vj   are   stoichiometric   coefficient   and  reaction   rate   for   the   ith   metabolite   and   jth   reaction.   This   equation   can   be   further  generalized   for   all   metabolites   M   of   the   system.   A   vector   of   net   formation   rates  dx/dt  can  be  obtained  as  the  S  matrix  multiplied  by  a  vector  v   containing  the  flux  distribution.   Under   steady-­‐state,   the   net   rate   for   each   metabolites   is   zero   (no  accumulation).    

𝑑𝑥𝑑𝑡 = 𝑆𝑣 = 0  

 Other  types  of  constraints  not  integrated  (for  now)  in  current  genome-­‐scale  models  are  regulatory  constraints  or  advanced  kinetic  constraints  such  as  enzyme  activity  or  kinetic  parameter  values.        Similarly,  all  possible  mathematical  distributions  can  be  further  plot  in  two  or  three  dimensions.  One  reaction  flux  can  be  plotted  against  another  one  giving  a  particular  solution  space.  This  is  useful  to  determine  possible  phenotypes  for  a  given  genotype.  Ibarra   et   al.   (2002)   successfully   demonstrated   FBA   accuracy   for   long-­‐term  phenotypes.  After  40  days,  E.   coli   evolved   from  a   sub-­‐optimal  phenotype   to   reach  optimal  growth  as  predicted  by  FBA.    The   most   widely   used   platform   to   perform   FBA   is   the   COBRA   toolbox  (Schellenberger  et  al.,  2011).  It  can  be  used  on  MATLAB  or  python  platforms  and  its  high  modularity   and   documentation  makes   COBRA   toolbox   the   best   choice   in   the  scientific   community.  Most   simulations  were   performed   using   the   COBRA   toolbox  2.0  on  MATLAB.    Optimization  algorithms    As   discussed   before,   FBA   offers   a   genuine   insight   into   the   flux   distribution   for   a  given  network.  As  an  extension,  perturbations  can  be  applied   to   the  system   in   the  form  of  reaction  deletions  or  modulations.  Flux  distributions  can  be  computed  and  differences   can   be   analyzed   to   understand   the   cellular   metabolism.   Additionally,  algorithms  can  be  employed  to  go  the  other  way  round.  For  a  particular  phenotype  –let’s   say   an   overproduction   phenotype-­‐,   algorithms   predict   associated   reaction  deletions  or  modulations  (genotype  alteration).      

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OptKnock    The   most   common   and   widely   used   algorithm   is   the   bi-­‐level   optimization  framework  OptKnock  (Burgard  et  al.,  2003).  It  identifies  reaction  deletion  strategies  that   couple   the   biomass   objective   function   with   a   biochemical   overproduction  target.    This  bi-­‐level  formulation  can  be  simplified  into  a  single-­‐level  mixed  integer  linear   programming   (MILP),   which   consists   of   problems   with   both   discrete   and  continuous  variables.      maximize 𝑣!"#$%&' (OptKnock) 𝑦! subject to maximize 𝑣!"#$%&& (Primal) 𝑣! subject to 𝑆!"  𝑣!!

!!! = 0 𝑣!!!"!#_!"#$%& =  𝑣!!!"!#_!!_!"#$%&%"#'   𝑣!"#!!  ≤  𝑣!"#!  !"#

!                 𝑣!"#$!""  ≥ 𝑣!"#$%&&

!"#$%!           𝑣!!"# ∙ 𝑦!  ≤  𝑣!  ≤  𝑣!!"# ∙ 𝑦! ,        ∀𝑗 ∈ 𝑁    

1 − 𝑦! ≤ 𝐾          𝑤𝑖𝑡ℎ  𝑦!  !∈! =   0,1 , ∀𝑗 ∈ 𝑁  𝑎𝑛𝑑  𝐾, 𝑛𝑢𝑚𝑏𝑒𝑟  𝑜𝑓  𝑘𝑛𝑜𝑐𝑘𝑜𝑢𝑡𝑠  𝑎𝑙𝑙𝑜𝑤𝑒𝑑      However,  due  to  the  complexity  of  the  algorithm,  high  computational  time  has  often  been  an   issue.  OptKnock   screen   for   all   reactions,   and   the  more  knockout   allowed,  the  higher  number  of  combinations.  An  alternative  algorithm  is  OptGene,  a  genetic  algorithm   (GA)   to   find   knockout   strategies   quicker   (Patil   et   al.,   2005).   The   idea  behind   this   algorithm   is   to   find   individuals   with   high   fitness   values   (mainly   high  productivity).    Individuals  are  then  selected  for  mating.  Offspring  are  screened  and  the  iterative  process  continues.  A  mutation  rate  is  also  applied  each  round  in  order  to  create  diversity.    OptForce    Developped   as   an   extension   of   the   OptKnock   framework,   OptForce   finds   reaction  modulations   in   addition   to   deletions   (Ranganathan   et   al.,   2010).   The   algorithm  compares  the  flux  variability  between  an  overproducing  (OP)  phenotype  and  a  wild  type   (WT)   phenotype   (Fig.   3).   Reactions   whose   value   ranges   significantly   differ  between  OP  and  WT  phenotypes  are   listed   in  MustU  (over-­‐production)  and  MustL  (down-­‐regulation).  This  can  be  further  extended  to  reactions  pair  (MustUU,  MustLL,  MustUL).  As  a  first  output  of  the  algorithm,  these  reaction  lists  can  be  used  to  guide  strategies.   OptForce   can   further   compute   combinations   of   these   reaction  modulations  from  these  lists  alongside  reaction  knockouts.  The  obtained  strategy  is  referred   as   the   FORCE   set.   By   increasing   the   number   of   allowed   modifications  (knock-­‐out/-­‐up/-­‐down),   OptForce   can   iteratively   give   different   strategies   and  solutions  can  pictures  in  the  form  of  a  boolean  diagram.  

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 OptSwap    OptSwap  (King  and  Feist,  2013)   is  a  bi-­‐level  MILP  problem  based  on  RobustKnock  formulation  (Tepper  and  Shlomi,  2010).  In  addition  to  RobustKnock  and  OptKnock,  it   incorporates  possible   swaps   in   cofactor   specificity   for  oxidoreductase   reactions.  Since   cofactor   usage   is   an   essential   feature   of   coupling,   this   algorithm   provides   a  genius  tool  to  achieve  high  productivity.      

                             Fig.   3.   Comparing   flux   ranges   between   normal   and   overproducing   phenotype   in   OptForce  (Ranganathan  et  al.,  2010).  

There  are  other  similar  algorithms  developed  for  specific  purposes  but  not  extended  to   the  COBRA   toolbox   community   (OptORF,  Kim  &  Reed,  2010;  CASOP,  Hädicke  &  Klamt,   2010).   Nevertheless,   results   using   different   algorithms   are   useful   tools   to  compare  with  results  from  OptForce,  OptKnock  and  OptSwap.    n-­‐butanol  and  isobutanol  pathways    n-­‐butanol   (hereafter   butanol)   is   a   fermentative   product   produced   from   the  precursor   acetyl-­‐CoA   through   the   ABE   fermentation   process.   Traditionally,  production  of  fermentative  products  has  been  produced  under  anaerobic  conditions  in  facultative  or  obligate  fermentative  organisms.  Earlier  efforts  to  produce  butanol  have  been  achieved  in  the  native  butanol  producer  and  obligate  anaerobic  bacteria  Clostridia,  where  butanol  production  was  first  reported  (Pasteur,  1862).  Clostridium  species   are   not   great   hosts   due   to   their   slow   growth   and   thus   low   volumetric  productivity.   Therefore,   Clostridia   butanol   pathway   has   further   been   adapted   to  other   organisms   (Nielsen   et   al.,   2009).   Expressing   a   pathway   from  a   fermentative  organism   into   an   autotrophic   organism   such   as   cyanobacteria   raise   few   concerns.  Among  them,  oxygen  sensitivity  of  pathway  enzymes  (Atsumi  et  al.,  2008;  Boynton  et  al.,  1996)  and  reversibility  of  trans-­‐enoyl-­‐CoA  reductase  (Bond-­‐Watts  et  al.,  2011)  have   been   targets   for   improvement.   To   solve   these   problems,   chimeric   pathway  using  enzymes  from  other  organisms  have  been  employed  (Lan  &  Liao,  2012;  Bond-­‐Watts   et   al.,   2011;   Anfelt   et   al.,   2015).   Alternative   pathways   to   produce   butanol  include  modification  of  the  clostridia  pathway  at  one  or  several  steps.  For  instance,  Lan  and  Liao  (2012)  replaced  the  first  condensation  step  by  an  ATP-­‐driven  two-­‐step  process   using   beta-­‐oxidation   reversal   in   Synechococcus   elongatus   PCC   7942.   One  

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malonyl-­‐CoA   and   one   acetyl-­‐CoA   ultimately   condense   to   one   acetoacetyl-­‐CoA.  Pasztor  et  al.   (2014)  also  used  beta-­‐oxidation  reversal  and  extended   it   to  butyryl-­‐ACP  further  transformed  to  butanol  using  the  TPC  pathway  in  E.  coli  (Fig.  4).    Although  isobutanol  is  similar  to  butanol,  it  is  less  soluble  and  has  an  energy  density  close   to  gasoline  making   it  a  better  biofuel.   Isobutanol  synthesis   takes  place  along  the   2-­‐keto-­‐acid   (or   Ehrlich)   pathway.   Amino   acid   precurssors   in   the   form   of   keto  acids  are  decarboxylated  into  aldehydes  and  reduced  to  the  corresponding  alcohols.  Isobutanol   can   be   obtained   from   valine   whereas   propanol   from   isoleucine.  Isobutanol   has   been   successfully   produced   in   E.   coli   (Yan   and   Liao,   2009)   and  Synechococcus  elongatus  PCC  7942  (Atsumi  et  al.,  2009).  All  pathways  are  studied  in  this   project   (Fig.   4).   They   are   hereafter   referred   to   as   LL   pathway   (Lan   and   Liao,  2012),  TPC  pathway  (Pasztor  et  al.,  2014),   isobutanol  pathway,  NADH  and  NADPH  pathway.    FBA  was  used  to  determine  the  maximal  theoretical  butanol  (or   isobutanol)  yields  for   all   these   pathways   (Table   1).   Yields   were   calculated   as   production   rate   over  carbon   uptake   rate.   Under   light-­‐limited   conditions,   the   carbon   uptake   is   the   sole  limiting-­‐factor.   It   is   calculated   as   bicarbonate   input  minus   carbon   dioxide   output.  Even   though   most   pathways   have   the   same   maximal   rate   towards   the   product,  yields  slightly  differ  because  of  different  uptake  rates.    Table   1.   Maximum   rates   and   yields   for   the   different   pathways   in   light-­‐limited  conditions.  

 

Maximum  production  rate  

Bicarbonate  uptake  rate  

Carbon  dioxide    export  rate  

Maximum  theoretical  yield  YHCO3-­‐,P  

Pathway    mmol  Prod.  /  gDW  h    mmol  HCO3-­‐  /  gDW  h    mmol  CO2  /  gDW  h   mol  Prod.  /  mol  HCO3-­‐  

Std  NADH   0.39    1.55   0   0.252  

Std  NADPH   0.39    1.55   0   0.252  

TPC      0.375   3.7                  2.19   0.248  

LL   0.39   3.7              2.14   0.250  

Isobutanol   0.39      1.55   0   0.252    It  is  interesting  to  see  that  for  TPC  and  LL  pathways,  HCO3-­‐  is  taken  at  maximal  rate  even   though  one   third   is   required  as   represented  by  a  massive  CO2-­‐  export.  These  two  pathways  differ  from  the  others  in  using  ATP.  One  explanation  could  be  that  this  extra   demand   requires   higher   ATP   production.   The   main   ATP   source   is   ATP  synthase,   whose   activity   is   dependent   on   LEF,   fixed   in   this   case   (light-­‐limiting).  Based  on  these  calculations,  each  pathway  can  achieve  similar  maximal  theoretical  yield   due   to   similar   stoichiometry.   However   TPC   and   LL   pathway   requires   more  carbon  dioxide.   It  was  mentioned   that   carbon  sinks   such  as  decarboxylation  steps  increased  carbon  uptake  (Oliver  and  Atsumi,  2015).  This  can  be  a  disadvantage  for  the  cost  of  the  process  with  a  high  amount  of  carbon  not  efficiently  used.  Production  of  isobutanol  is  suggested  here  since  it  a  better  fuel  and  has  a  similar  maximal  yield  than  butanol.  

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Fig.  4.  Pathways  leading  to  butanol  and  isobutanol  synthesis  from  pyruvate  precursor  as  implemented  in  the  model.  Plain  and  dashed  arrows  indicate  native  and  non-­‐native  reactions  respectively.  In  blue,  the  2-­‐keto-­‐acid   route   for   isobutanol.   In   red,   standard  Clostridium   butanol   pathway.   In   this   project,   last   three   steps  were  studied  with  NADH  or  NADPH.     In  green,  ATP-­‐driven  pathway  reported  by  Lan  and  Liao  (2012).  The  first   two  steps  and  conversion  of  crotonyl-­‐CoA  to  butyryl-­‐CoA  useing  NADH  are  specific  for  this  pathway.  In  yellow,  TPC  pathway  using  β-­‐oxidation  reversal  reported  by  Pazstor  et  al.  (2014).  Metabolite  names  next  to  arrows  indicate  reaction  requirements.  

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Metabolic  engineering  in  cyanobacteria    Generic  approaches  /  Introduction    Regardless  of   the  organism  used,  metabolic   engineering   can  be  divided   into   three  levels   with   increased   complexity   and   predictive   power   (Fig.   5).   Initial   strategies  have  mainly   focused  on  gene   level.  Rerouting   carbon   flux   to  a   certain  pathway  by  simple   amplification   using   high   gene   copy   number,   optimized   codon   usage   or  increased   promoter   strength   was   common.   Quickly   it   was   realized   that  productivities   did   not   meet   expectations   due   to   cellular   balancing   omission.  Stepping   back   to   the   pathway   level   granted   a   better   global   understanding   of   the  process.  The  “push  and  pull”  concept  aiming  at  increasing  pathway  precursors  and  minimizing   competing   pathways   is   an   example.   Balancing   the   pathway   using  cofactor   usage   and   the   notion   of   driving   force   enabled   to   achieve   higher  productivity.  The  current  state  of  metabolic  engineering  focuses  on  these  two  levels.  Generic   strategies   to   increase   productivities   include   most   notably:   By-­‐product  production   inhibition,   removal   of   competing   reactions,   target   pathway  overexpression,   cofactor   usage,   product   degradation/uptake   removal,   feedback  inhibition  removal  or  toxicity  tolerance.  Built  on  the  previous  levels,  systems  level  is  at   its   infancy   due   to   its   high   level   of   complexity.   It   requires   high   computational  power  and  high  understanding  of  the  organism.    

 Fig.   5.   Systemic   approach   of   strain   development   (Lan   and   Liao,   2013).   From   right   to   left,   increased   in  productivity  often  means  increased  in  complexity.  

In   cyanobacteria,   early   strategies   on   individual   gene   level   have   been   performed  extensively.  Expression  of  different  native  and  foreign  promoters  has  been  achieved.  Native   promoters   used   are   usually   found   in   housekeeping   genes   (light-­‐inducible  PpsbA2  from  PSII;  Prbc  in  CO2  fixation).  Strong  foreign  IPTG-­‐inducible  promoter  Ptrc  is  widely  used  in  pathway  engineering  (Atsumi  et  al.,  2009;  Huang  et  al.,  2010;  Lan  and  Liao,  2011).  Codon  usage  for  heterologous  gene  could  also  be  optimized.  IspS  gene  encoding   isoprene  synthase  from  a  plant  specie  was  successfully  codon-­‐optimized,  resulting  in  a  10-­‐fold  increase  in  gene  expression  (Lindberg  et  al,  2010).  Recent  advances  have  mainly  focus  on  the  pathway  level  with  the  notion  of  a  driving  force   pushing   towards   product   synthesis.   Decarboxylation   and   ATP   usage   could  provide  such  a  driving  force  (Lan  and  Liao,  2012).  As  discussed  earlier,  reversibility  

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pathway  reactions  has  also  been  investigated  as  a  way  to  push  forward  the  carbon  flux  (Bond-­‐Watts  et  al.,  2011).  Enzyme  kinetic  data  can  also  be  integrated  to  have  an  overview  of  the  rate-­‐controlling  step  in  a  pathway  using  metabolic  control  analysis  (Angermayr  et  al.,  2013).  Engineering  tolerance  to  product  is  also  an  important  part  to  achieve  high  productivity  (Kaczmarzyk  et  al.,  2014).    Future   studies   aim   at   building   on   these   driving   forces   to   improve   current  productivity.   Genetic   engineering   of   non-­‐obvious   targets   aims   at   creating   such  driving   forces   over   the   whole   organism   rather   than   the   pathway   level.   Since  genome-­‐scale   reconstructions   involve   hundreds   of   reactions,   powerful  computational   techniques   are   therefore   essential.   In  E.  coli,   it  was   possible   to   use  OptKnock  to  couple  lactate  production  to  growth  (Fong  et  al.,  2005).  After  60  days,  experimental   results  were   in  good  agreement  with  simulations  and  approximately  90%   of   maximal   theoretical   lactate   production   rate   was   achieved.   Through   this  project,  analysis  of  driving  forces  that  lead  to  growth-­‐coupled  butanol  synthesis  is  a  recurring  theme.      Cofactor  recycling    One  important  issue  in  metabolic  engineering  is  cofactor  balance.  It  has  been  many  times  the  case  that  adding  and  removing  pathways  results  in  a  cofactor  imbalance,  equivalent   to   a   bottleneck.   Due   to   interconnections   between   photosynthesis   and  respiration,   cyanobacteria   have   a   highly   regulated   cofactor   balance   making   it  difficult  to  engineer.    Cofactor   recycling   is   important   in   order   to   couple   biomass   to   product   synthesis.  Since   cell   factories   internal   objectives   are   to   optimize   their   growth,   linking   it   to  engineering   objective   represents   a   genuine   driving   force   towards   overproduction  phenotype.  A  phenotypic  phase  plane  (PPP  or  production  envelope)  can  be  plot  to  illustrate  that.  Biomass  is  iteratively  forced  at  different  flux  values.  For  each  of  them,  flux  towards  product  synthesis  is  maximized  and  minimized.  For  a  given  genotype,  the  PPP  represents  all  mathematically  possible  phenotypes.  We  assume  that  the  cell  will  optimize  its  growth.  Thus,  the  cellular  flux  distribution  will  be  pinpointed  as  the  point  with   the   highest   growth   rate   (far   right;   Fig.   6).   Now   if   product   synthesis   is  coupled   to   growth,   at   maximal   growth   rate,   product   synthesis   will   occur   as   a  necessary   by-­‐product   to   growth.   This   notion   is   perhaps   the   central   aspect   of   this  work  and  provides  a  powerful  strategy  for  product  synthesis.    Coupling  biomass  and   fermentative  products  requires   few  gene  deletions   in  E.  coli  or  S.  cerevisiae.  Lactate  production   in  E.  coli   is  a  good  example.  Biomass   formation  results   in  an  excess  of  reducing  equivalents  through  the  central  metabolism  in  the  form   of   NADH.   Under   anaerobic   conditions,   the   TCA   cycle   is   not   active.  Fermentation  pathways  leading  to  lactate,  ethanol  or  succinate  (all  recycling  NADH  to  NAD+)  are  thus  the  main  remaining  mechanisms  to  re-­‐oxidize  excess  NADH.  This  recycling  is  essential  for  the  cell  to  sustain  growth.  Without  any  NAD+  available,  flux  through  the  central  metabolism  cannot  go  forward.  As  a  result,  lactate,  ethanol,  and  

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succinate   are   all   potential   growth-­‐coupled   product   through   cofactor   recycling.  Strong   coupling   between   one   of   these   products   and   growth   can   be   achieved   by  knocking-­‐out  other  fermentative  pathways.      

Fig.  6.  Simple  trade-­‐off  versus  growth-­‐coupled  phenotypic  phase  plane.  A.  In  the  simple  trade-­‐off  design,  no  product   is   synthesized   at   maximal   growth   rate.   B.   In   the   growth-­‐coupled   design,   product   is   synthesized   at  maximal  growth  rate.    Another   key   concept   related   to   cofactor   recycling   as   driving   force   is   the  ATP/NADPH   ratio,   a   central   theme   in   cyanobacteria.   For   instance,   knocking   out  ethanol   dehydrogenase   and   acetate   kinase   lead   to   growth-­‐coupled   synthesis   of  lactate   in  silico  (Burgard  et  al.,  2003)  and   in  vivo  (Fong  et  al.,  2005).  This  suggests  that  single  NADH  recycling  does  not  achieve  coupling  in  this  case.  ATP  generation  is  also  an  important  factor.      Coupling   product   with   biomass   has   been   shown   to   be   difficult   in   cyanobacteria  (Nogales   et   al.,   2013).  Under   phototrophic   conditions,   presence   of  mechanisms   to  balance   cofactor   redox   state   hinders   coupling   (Fig.   7).   Cyclic   electron   flows   (or  alternate   electron   flows;   CEF)   support   the   cell   in   ATP/NADPH   ratio   modulation  depending   on   environmental   conditions.   Because   carbon  dioxide   fixation   requires  an   ATP/NADPH   ratio   of   1.5,   higher   than   the   1.28   ratio   provided   by   the   linear  electron   flow,   CEF   decreases   the   ATP/NADPH   ratio   by   indirectly   increasing   ATP  production   at   the   expense   of   direct   NADH/NADPH   oxidation.   It   is   believed   that  biomass   requires   a   ratio   larger   than   1.51   or   2   (Erdrich   et   al.,   2014;   Knoop   and  Steuer,   2015).   For   this   reason,   a   successful   strategy  would   aim   at   decreasing   the  ATP/NADPH  ratio.  With  a  higher  ratio  requirement  needed  to  produce  biomass,  an  excess   NADPH   would   then   be   available   for   our   product.   NADPH   would   then   be  recycled  back  to  NADP+  using  product  synthesis  pathway.  This  would  provide  a  way  to  couple  product  with  growth.        

0  0.05  0.1  0.15  0.2  0.25  0.3  0.35  0.4  0.45  

0   0.01   0.02   0.03   0.04  

Product  m

mol/gDW/h  

Growth  rate  1/h  

A.  Simple  trade-­‐off  

0  0.05  0.1  0.15  0.2  0.25  0.3  0.35  0.4  0.45  

0   0.01   0.02   0.03   0.04  

Product  m

mol/gDW/h  

Growth  rate  1/h  

B.  Growth-­‐coupled  

  17  

 

Fig.   7.   A.   Overview   of   photosynthetic   and   respiratory   electron   transport   chains.   In   most   cyanobacteria,  photosynthesis  only  occurs  in  thylakoids,  and  respiration  takes  place  in  both  cellular  and  thylakoid  membrane.  As   a   result   photosynthesis   and   respiration   closely   interact   even   sharing   some   membrane   components  (plastoquinone  PQ  pool,   cytochrome  b6f).   Linear   electron   flow   includes  photosystem   II   (PSII),   cytochrome  b6f  (CYTBF),  photosystem  I  (PSI),   ferredoxin  NADP+  oxidoreductase  (FNR),  connected  trough  plastoquinone  (PQ),  cytochrome   c   (cytC),   and   ferredoxin,   respectively.   Cyclic   electron   flows   include   the   ferredoxin   PQ   reductase  (FQR),   the  NAD(P)H  dehydrogenase  complexes,   the  aa3-­‐type  terminal  oxidase  (CYO),   the  PQ  oxidase  (CydBD),  the  MEHLER  reaction,  and  the  hydrogenase  (H2ase).  B.  Simple  scheme  showing  how  ATP/NADPH  requirements  can  be  engineered  to  provide  a  driving  force  for  product  synthesis.  

     

ATP NADPH

1.3   LEF ≥  2   Biomass  

     

Fixed  ATP/NADPH  ratios  

Extra  NADPH  for product  synthesis

B

A

 

  18  

Results    OptKnock  and  OptForce  were  applied  to  iJN678.  OptKnock  was  performed  using  the  COBRA   toolbox   2.0   on   MATLAB   (Schellenberger   et   al.,   2011).   OptForce   was  performed   using   the   GAMS   modeling   environment   (General   Algebraic   Modeling  System,   www.gams.com).   CPLEX   solver   was   used   for   both   algorithms.   Transport  reactions   were   removed   from   consideration   using   OptKnock.   For   OptForce,  transport   and   peripherical   reactions   associated   with   biomass   formation  (carotenoid,   riboflavin,   sterol,   nucleic   acid   components...)  were   removed   from   the  pool  as  much  as  possible  (reduced  set).  Two  approaches  were  used,  one  iteratively  removing   these   reactions   from   the  solution  and  another   removing   these   reactions  before   running   the   algorithm.   Transporters   or   other   peripherical   reactions   were  allowed   to   be   knockout   mainly   for   informative   reasons.   Understanding   the   logic  behind  these  knockouts  is  an  important  aspect  to  understand  driving  forces  behind  growth-­‐coupled  butanol  production.  In  many  cases,  strategies  could  not  be  found  for  the  reduced  set.  In  both  cases,  reaction  deletions  found  using  OptKnock  were  used  to  guide  the  algorithm  and  decrease  the  computation  time.  Simulations  were  limited  to  few  hours.     Identifying  the  driving  force    Identifying   the   reason   behind   coupling   is   an   important   part   of   understanding   to  create  high-­‐producing   strain.   For   each   strategy,   sensitivity   to   cofactor   recycling   is  tested  to  identify  which  cofactor  balance  requirements  push  the  flux  though  product  synthesis.   Implementing   a   reaction   that   re-­‐oxidize   a   specific   cofactor   and   test   its  influence  on   the  coupling  design   is   informative.   If   the   coupling   is  not  affected,   the  strategy   is   not   sensitive   to   this   cofactor.   Conversely,   no   coupling   means   that   the  reaction   is   used   instead   of   the   pathway   because   it   is   less   a   burden   for   the   cell   to  simply   recycle   cofactors   using   a   single   reaction   than   producing   butanol.   For  instance,  a  reaction  converting  NADH  to  NAD+  can  be  added  for  a  given  strategy  to  test  sensitivity  to  NADH  recycling.  If  the  PPP  shows  uncoupling  between  growth  and  product,   then  the  strategy   is  said   to  be  sensitive   to  NADH  consumption.  Strategies  were   sensitive   to   NADH   consumption,   NADPH   consumption,   and/or   ATP  production.    OptKnock  to  predict  reaction  deletions    OptKnock   provides   the   first   step   towards   identifying   suitable   gene   targets.   High  computational  time  or  even  existence  of  a  possible  solution  makes  it  difficult  to  find  targets.   OptKnock   hinted   strategies   for   both   heterotrophic   and   autotrophic  conditions.   For   simplicity   and   also   because   autotrophic   conditions   are   target  conditions   for   cyanobacteria   growth,   autotrophic   strategies   are   considered   here.  Strategies  were  found  for  the  NADH  pathway  and  for  the  LL  pathway.      

  19  

Strategy  for  NADH  pathway    The   initial   list  proposed  by  OptKnock   involved  more  than  10  reaction  deletions   to  achieve  coupling.  Refining  the  list  involved  successive  removal  of  each  target  to  get  a  minimal   set   required   to   reach   coupling.   The   reduced   list   involves   7   reaction  deletions   (Table   2).   To   compare   the   effect   of   each   reaction   deletion   on   the   flux  distribution,   simulation  with   the   restored   reaction  was   computed   for   each   target  (mutant+).   A   visualization   tool   was   used   to   easily   see   global   changes   in   the  metabolism  (Maarleveld  et  al.,  2014).  A  phenotypic  phase  plane  permits  to  visualize  growth-­‐coupled   production   of   butanol   (Fig.   8).   This   strategy   was   shown   to   be  sensitive  to  NADH  consumption.    

 Fig.  8.  Phenotypic  phase  plane  for  the  standard  NADH  pathway.  In  the  modified  strain,  approximately  0.13  mmol/gDW/h  butanol  production  is  predicted  at  maximal  growth.  

Among  the   targets,  hindering   the  circular  electron   flow  was  shown  to  be  essential  for   coupling.   NADH   dehydrogenases   (type   1   and   type   2)   were   required   to   be  knocked   out.   This   is   consistent  with   other   studies   from  Erdrich   et   al.   (2014)   and  Reed  et  al.   (2013),  where   they  used  other  algorithms  such  as  CASOP  and  OptORF,  respectively.   The   most   obvious   reason   behind   these   knockouts   is   the   removal   of  competing  reactions   for  NADH  re-­‐oxidation.  Since  biomass   is  optimized,  NADH  re-­‐oxidation  will   preferentially  occurs   in   the  ETC,  where  ATP  can  be  produced  as   an  end  product   rather   than  being   ‘wasted’   for   a   product  without   interest   for   the   cell  (butanol  is  excreted  and  cannot  be  re-­‐used  after  all).    

0  

0.05  

0.1  

0.15  

0.2  

0.25  

0.3  

0.35  

0.4  

0.45  

0   0.005   0.01   0.015   0.02   0.025   0.03   0.035   0.04  

Butanol  m

mol/gDW/h  

Growth  rate  1/h  

PPP  for  NADH  pathway  

initial  strain  

moditied  strain  

  20  

This   deletion   is   also   believed   to   be   sufficient   enough   to   lower   the   ATP/NAD(P)H  ratio  in  two  different  ways:  (1)  preventing  NADH  oxidation  into  NAD,  (2)  decreasing  ATP   production   through   ATP   synthase.   Decreasing   electron   flow   activity   reduces  the  number  of  proton  crossing  the  membrane  resulting  in  a  lower  proton  gradient.    Glutamate   synthase   was   also   hinted   as   a   suitable   reaction   deletion.   Glutamate  synthase   is   an   oxido-­‐reductase   converting   glutamine   to   glutamate.   Glutamate  synthetase,   involved   in   nitrogen   fixation,   goes   in   the   reverse   direction.   These  reactions   can   combine   to   form   a   cycle   where   1   mol   ATP   and   1   mol   NADH   are  consumed   per   iteration   (Fig.   9).   Glutamate   synthase  may   be   seen   as   a   competing  reaction   consuming   NADH.   Its   repression   is   thus   necessary   to   ensure   coupling.  Interestingly,   its   repression   also   down-­‐regulates   the   flux   through   glutamate  synthetase  which  otherwise  could  lower  ATP/NADPH  ratio  by  consuming  ATP.  This  deletion  shows  that  NADH  recycling  and  not  lowering  this  ration  is  the  target  effect.    

     Malate  dehydrogenase  might  have  been  suggested  for  the  same  reason.  It  catalyzes  the   inter-­‐conversion  between  oxaloacetate  and  malate  (Fig.  10).  MDH+  shows  that  the   reaction   normally   drives   through   NADH   consumption   to   form   malate.   Thus,  knocking   this   reaction   out   is   believed   to   prevent   another   competing   reaction   for  NADH  consumption.  Knocking-­‐out  MDH  also  resulted  in  a  23%  increased  flux  from  pyruvate  to  acetyl-­‐CoA,  precursor  of  1-­‐butanol.  This  deletion  may  offer  an  additional  important   feature   of   coupling;   directing   the   flux   towards   our  pathway  of   interest.  FBA   predicts   indeed   a   higher   flux   towards   pyruvate   in   the  mutant   than   in  MDH+  distribution.  However,   it   is  not   clear  whether   it   results   from   the   coupling   itself  or  this   individual   knockout.   To   further   confirm   the   influence   of   NADH   recycling,  cofactor   preference   for   malate   dehydrogenase   and   glutamate   synthase   was  swapped  from  NADH  to  NADPH.  Knocking  out  these  two  targets  were  not  essential  any  more  to  reach  coupling.    Pyruvate-­‐ferredoxin  oxidoreductase  (POR_syn)  catalyzes  pyruvate  decarboxylation  to  acetyl-­‐CoA  resulting  in  the  reduction  of  the  cofactor  ferredoxin.  Ferredoxin  can  be  oxidized   back   using   ferredoxin-­‐NADP+   reductase   (FNOR)   with   NADP+   as   electron  

Fig.  9.  Futile  cycle  between  glutamine  and  glutamate.  This  cycle  could  allow  to  balance  NADH  at  the  expense  of  ATP.  Knockout  of  glutamate  synthase  is  required  in  many  strategies.  

  21  

acceptor.   The  mechanism   behind   this   is   not   clear   since   POR_syn   indirectly   offers  more   NADPH.   Interestingly,   knocking-­‐out   POR_syn   results   in   a   10%   increase  through  FNOR  in  comparison  to  POR+  mutant.  Many  interventions  target  the  central  metabolism   as   expected.   Since   the   flux   there   is   higher   than   the   rest   of   the  metabolism,  interventions  have  the  highest  effect.    Glycolate   oxidase   (GLYCTO1),   our   last   target,   is   part   of   photorespiration.   Two  variants  exist  in  the  model.  One  uses  H2O  and  the  other  one  uses  NAD+  as  electron  acceptor.  This  knockout   is  potentially  problematic  and  represents  a   limitation  that  one  can  encounter  using  OptKnock.  In  fact,  both  versions  are  encoded  by  the  same  open  reading  frame  (ORF).  Knocking  out  both  version  results  in  a  lethal  phenotype  for   the   cell   according   to   simulations.   This   is   consistent   with   reports   that  photorespiration  is   important  for  cell  viability  (Knoop  et  al.,  2013).  To  understand  the  meaning   of   this   knockout,   the  mutant  was   compared  with   GLYCTO1+  mutant  flux   distribution.     When   the   flux   is   not   constrained,   FBA   predicts   that   the   H2O  version  of   the   reaction  will   carry  all   the   flux.   In   contrast,   the  NAD+  version   is   the  only  way  to  photorespiration  in  the  mutant,  resulting  in  coupling.      An   interesting   question   is  whether   the   knockout   it-­‐self   or   the   down-­‐regulation   of  photorespiration  is  an  essential  feature  of  coupling.  To  test  this,  flux  was  forced  to  a  low   value   for   both   versions.   This   resulted   in   coupling.   It   indicates   that   the   target  knockout   aims   at   reducing   the   flux   through  photorespiration.  But  why   should   the  flux  be  down-­‐regulated   to  ensure   coupling?  There  are   two  explanations:   (1)  More  NADH  is  produced  using  the  NAD+  way,  which  can  be  used  for  butanol  production.  (2)  Low   flux   through  photorespiration  and  steady-­‐state   constraints   imply   that   the  carbon   flux   is   redirected   to   the  central  metabolism.  Comparison  with   the  mutant+  indicates   that   accounts   for   a   12%   increase   towards   3-­‐Phosphoglycerate   (3PG).  Again,  it  is  not  clear  whether  this  feature  is  due  to  this  individual  knockout  or  due  to  the  sum  of  knockouts  resulting  in  coupling.        

Table  2.  Minimal  knockout  set  to  couple  growth  with  butanol  in  NADH  pathway  Enzyme  name   Reaction  

Glutamate  synthase     H+  +  NADH  +  2-­‐Oxoglutarate  +  L-­‐Glutamine    -­‐>  NAD+  +  2  L-­‐Glutamate  NADH  dehydrogenase  2    (tilacoide)  NADH   H+  +  NADH  +  PQtil    -­‐>  NAD

+    +  PQH2  til    NADH  dehydrogenase  1  (periplasm)  NADH   4  H+  +  NADH  +  PQper    -­‐>  NAD

+    +  3  Hper  +  PQH2  per    NADH  dehydrogenase  1  (tilacoide)  NADH   4  H+  +  NADH  +  PQtil    -­‐>  NAD

+    +  3  Htil  +  PQH2  til  Pyruvate-­‐ferredoxin  oxidoreductase   CoA  +  Pyruvate  +  2  oxidized  ferredoxin    -­‐>  H+  +  CO2  +  Acetyl-­‐CoA  +  2  

reduced  ferredoxin    Glycolate  oxidase   O2  +  Glycolate    -­‐>  H2O2  +  Glyoxylate  Malate  dehydrogenase   NAD+    +  L-­‐Malate    <=>  H+  +  NADH  +  Oxaloacetate  

         

  22  

Strategy  for  LL  pathway    The   initial   set   suggested   by   OptKnock   involves   acetate   kinase,   glycolate   oxidase,  NADH   dehydrogenases   type   1   and   type   2,   cytochrome   c   oxidase,   malate  dehydrogenase,   lactate   dehydrogenase   and   glutamate   synthase.   The   minimal  knockout   set   to   ensure   coupling   is   similar   to   the   one   obtained   for   the   NADH  pathway   (Table   2).   The   only   difference   is   the   absence   of   pyruvate-­‐ferredoxin  oxidoreductase.   Therefore,   it   is   believed   that   knockouts   have   the   same   functions.  However,  the  growth-­‐coupled  phenotype  shows  a  lower  level  of  butanol  production  (Fig.   9).   This   strategy   is   sensitive   to   NADH   consumption,   indicating   that   NADH  recycling  is  the  driving  force  behind  the  coupling.    

 Fig.   10.   Phenotypic   phase   plane   for   the   Lan   &   Liao   pathway.   In   the  modified   strain,   approximately   0.02  mmol/gDW/h  butanol  production  is  predicted  at  maximal  growth.  

 When   pyruvate-­‐ferredoxin   oxidoreductase   was   added   to   the   knockout   set,   it  resulted  in  almost  no-­‐growth.  This  suggests  that  the  model  is  sensitive  to  additional  knockouts.  Interestingly,  this  strategy  also  worked  for  the  standard  NADH  pathway  but   not   for   any   others.   This   is   consistent   with   the   assumption   that   NADH   is   the  driving  force  behind  this  strategy.  When  cofactor  specificity  is  changed  such  as  the  pathway  only  requires  NADPH,  almost  no  growth  is  observed.  Addition  of  a  reaction  recycling  NADH   to  NAD   rescues   this  phenotype   and   restores  normal   growth.  This  

0  

0.05  

0.1  

0.15  

0.2  

0.25  

0.3  

0.35  

0.4  

0.45  

0   0.005   0.01   0.015   0.02   0.025   0.03   0.035   0.04  

Butanol  m

mol/gDW/h  

Growth  rate  1/h  

PPP  for  LL  pathway  

initial  strain  

moditied  strain  

  23  

indicates   that   this  pathway   is   important   for  NADH  recycling  and  now  vital   for   the  cell.    OptForce  to  predict  reaction  modulations    OptForce  was  used  to  predict  reaction  modulations  to  couple  butanol  to  growth  for  the   five  different  pathways.  Gene  modulations  make  this  algorithm  more  powerful  than  OptKnock  with  fewer  interventions  needed  in  theory.  Solutions  could  be  found  for   all   pathways.   However,   many   of   these   results   were   cofactor   un-­‐sensitive  meaning   that   the   coupling   was   achieved   with   a   different   driving   force.   Adding   a  proton  sink  uncoupled  growth  to  product  in  most  of  these  strategies.  Since  proton-­‐driven  coupling  strategies  appear  unlikely,  those  designs  were  written  off.    Strategy  for  TPC  pathway    The   initial   strategy   consists   of   no  more   than   21   interventions.   Ferredoxin  NADP+  reductase   was   suggested   to   be   up-­‐regulated.   Glycolate   oxidase,   cytochrome   c  oxidase,   NADH   dehydrogenase   type   1   and   2,   transhydrogenase,   ATP   synthase,  glutamate   synthase,   glutamate   dehydrogenase,   fumarase,   malate   and   acetate  transporters   were   among   the   initial   knockout   list.   The   reduced   set   includes   14  reaction   deletions   and   one   up-­‐regulation.   This   strategy   is   sensitive   to   NADH   and  NADPH  consumption  and  ATP  production.  Among  them,  familiar  targets  (glutamate  futile  cycle,  cyclic  electron  flows)  found  in  other  strategies  re-­‐appear  and  seem  to  be  important  components  of  coupling  designs.      It   should   be   pointed   out   that   another   set   of   NADH   dehydrogenase   knockouts   is  required   here,   including   both   NADH   and   NADPH   dependent   enzymes,   consistent  with  NADH  and  NADPH  sensitivity.  ATP  synthase,  using  proton  gradient  across  the  membrane   to   generate   ATP,   is   also   suggested   as   knockout   to   hypothetically  decrease   the   ATP/NADPH   ratio.   Cyclic   electron   flows,   the   Mehler   reaction   and  hydrogenase   are   potential   competing   reactions   for   NADPH   re-­‐oxidation.   Mehler  reaction  was  also  predicted  by  CASOP  (Erdrich  et  al.,  2014)  as  a  member  of  cyclic  electron  flows.  Thus,  its  deletion  may  aim  at  lowering  ATP/NADPH  ratio  rather  than  direct   cofactor   recycling.   Restoring   both   reactions   and   swapping   cofactor  preferences  from  NADPH  to  NADH  had  no  effect  due  to  sensitivity  to  both  cofactors.    Fumarase   (FUM)  deletion   is   an   interesting   target   since   it   is   cofactor   independent.  This  knockout  offers   a   genuine   flux   redirection   instead.  To  analyze   flux   rerouting,  flux   distribution   was   compared   with   FUM+,   the   modified   strain   with   fumarase  restored.   In   the   model,   three   reactions   are   connected   to   malate:   malate  dehydrogenase   (a   knockout   encountered   earlier),   fumarase,   and   malic   enzyme  converting  malate  to  pyruvate  using  NADP+  (Fig.  12).  In  FUM+,  malic  enzyme  is  not  active   and  malate   is   entirely   converted   in   fumarate.   In   the  mutant,   knocking   out  FUM  diverts  the  flux  from  malate  back  to  pyruvate  and  to  the  acetyl-­‐CoA  pool  as  an  extension.  Additionally,  this  rerouting  produces  extra  NADPH  for  butanol.      

  24  

 Fig.   11.   Phenotypic   phase   plane   for   the   TPC   pathway.   In   the   modified   strain,   approximately   0.14  mmol/gDW/h  butanol  production  is  predicted  at  maximal  growth.  

 It  is  interesting  to  compare  this  deletion  with  malate  dehydrogenase  deletion  from  a  previous   strategy   for   the   NADH   pathway.   Even   though   these   two   knockouts   are  neighbors,   their   resulting   effects   are   quite   different.   MDH   knockout   aimed   at  removing   competing   reaction   for   NADH   for   butanol.   It   decreased   NADPH  production,  not  essential   for  butanol.   In   the  TPC  design,  NADPH  requirements  are  higher  (4  mol  vs  1mol).  For  this  reason,  producing  NADPH  at  the  expense  of  NADH  might   be   an   explanation   for   this   knockout.   This   can   be   seen   as   a   regulatory  mechanism  (similar  to  a  futile  cycle).  Phosphoenolpyruvate  (PEP)  can  be  converted  to   pyruvate   either   through   the   direct   dephosphorylation   route   producing   ATP   or  through  a  first  decarboxylation  step  to  OAA.  A  second  step  through  MDH  produces  malate  and  NADH.  Finally,  a  decarboxylation  step  to  pyruvate  produces  NADPH.   It  should  be  noted   that   a  potential   futile   cycling   involving  PEP,  malate,   oxaloacetate,  pyruvate  could  provide  a  futile  cycle  as  regulatory  mechanism.  This  is  an  example  of  how  cofactor  requirement  for  butanol  synthesis  can  lead  to  different  knockouts  in  a  specific  region.  Finally,  acetate  and  malate  transporters  could  be  replaced  by  acetate  kinase   knockout.   The   main   reason   behind   ferredoxin   NADP+   reductase  overexpression  is  production  of  extra  NADPH,  a  driving  force  in  this  design.        

0  

0.05  

0.1  

0.15  

0.2  

0.25  

0.3  

0.35  

0.4  

0.45  

0   0.005   0.01   0.015   0.02   0.025   0.03   0.035   0.04  

Butanol  m

mol/gDW/h  

Growth  rate  1/h  

PPP  for  TPC  pathway  

initial  strain  

moditied  strain  

  25  

 Fig.   12.   Overview   of   central   metabolism   reactions   around   pyruvate.   There   are   two   routes   from   PEP   to  pyruvate;  a  direct  and  an  indirect  one  through  OAA  and  malate.  PEP  carboxylase,  malate  dehydrogenase,  malic  enzyme  and  PEP  synthase  could  form  a  cycle  indirectly  converting  NADH  to  NADPH.  

 Table  3.  Minimal  knockout  set  to  couple  growth  with  butanol  for  the  TPC  pathway  

Enzyme  name   Reaction  

Glutamate  synthase     H+  +  NADH  +  2-­‐Oxoglutarate  +  L-­‐Glutamine    -­‐>  NAD+  +  2  L-­‐Glutamate  NADH  dehydrogenase  2    (tilacoide)   H+  +  NADH  +  PQtil    -­‐>  NAD

+    +  PQH2  til    NADH  dehydrogenase  1  (tilacoide)  NADPH   4  H+  +  NADPH  +  PQtil    -­‐>  NADP

+    +  3  H+til  +  PQH2  til  

NADH  dehydrogenase  1  (tilacoide)  NADH   4  H+  +  NADH  +  PQtil    -­‐>  NAD+    +  3  H+

til  +  PQH2  til  ATP  synthase   3  ADP  +  3  Pi  +  14  H+

per  -­‐>  3  ATP  +  11  H+  +  3  H2O  

Glycolate  oxidase   O2  +  Glycolate    -­‐>  H2O2  +  Glyoxylate  Fumarase   H2O  +  Fumarate  <=>  Malate  Phosphotransacetylase   Acetyl-­‐CoA  +  Pi  <=>  CoA  +  Acetyl-­‐P  Hydrogenase   H+  +  NADPH  <=>  NADP+  +  H2  Active  CO2  transporter  facilitator  (tilacoide)   3  H+  +  H2O  +  NADPH  +  PQ  +  CO2  -­‐>  NADP

+  +  HCO3  +  3  H+  +  PQH2  

Mehler  reaction   H+  +  0.5  O2  +  NADPH  -­‐>  H2O  +  NADP+  

Cytochrome  c  oxidase  (tilacoide)   4  H+  +  2  ferrocytochrome  +  0.5  O2  til  -­‐>  2  H+  +  2  ferricytochrome  +  H2Otil  

 4  H+  +  2  plastocyanin(Cu+)  +  0.5  O2  til  -­‐>  2  H

+  +  2  plastocyanin(Cu2+)    +  H2O  til  Ferredoxin  NADP+  reductase   H+  +  NADP+  +  2  reduced  ferredoxin  <=>  NADPH  +  2  oxidized  ferredoxin        

   

  26  

This  strategy  also  works  for  all  other  pathways  including  the  NADPH  one.  But  it  did  not  work   for   the   isobutanol   pathway,  most   probably   due   another   precursor   used  (pyruvate).   This   design   is   the   first   to   show   sensitivity   to   three   factors;   NADH  consumption,   NADPH   consumption,   and  ATP   production.   The   two   last   factors   are  familiar   and   could   be   regrouped   into   the  ATP/NADPH   ratio   that   acts   as   a   driving  force.   Adding   a   reaction   that   consumes   NADPH   and   produces   ATP   mimics   an  increase  in  the  ATP/NADPH  ratio.  A  natural  question  is  how  can  we  get  two  driving  forces?  Is  one  not  sufficient  enough  or  are  these  sensitivities  related?  To  investigate  these  questions,   flux  distribution  at  maximal   growth   rate  with  an  NADH  recycling  reaction  was  analyzed.  Flux  through  the  transhydrogenase  suggests  excess  NADPH  converted   in   NADH.   Flux   distributions   were   compared   with   and   without   the  transhydrogenase   reaction   set   to   0.   This   allowed   identifying   another   regulatory  mechanism   that   can   substitute   the   transhydrogenase.   Two   glyceraldehyde-­‐3-­‐phosphate   dehydrogenases   (slr0844   &   sll1342)   form   a   cycle   that   can   consume  NADH  and  produce  NADPH  (Fig.  13).  Knocking  out  both  mechanisms  did  not  make  the   strategy   less   sensitive   to   one   of   the   two   cofactors.   This   does   not   necessarily  indicate   the   presence   of   two   independent   driving   forces.   But,   looking   at   the  transhydrogenase   direction,   ATP/NADPH   could   be   the   driving   force.   Since   the  pathway   requires   NADH,   active   conversion   of   NADPH   to   NADH   occurs   in   the  network   and   is   necessary   to   achieve   coupling.   With   hundreds   of   reaction,   it   is  always   possible   for   the   network   to   balance   NADH/NADPH   ratio   using   peripheral  reactions  rather  than  the  mechanisms  studied,  but  at  higher  metabolic  costs.      

   

Fig.  13.    Glyceraldehyde  3-­‐phosphate  dehydrogenase  cycle.  This  cycle  can  substitute  the  transhydrogenase  reaction.  

   Strategy  for  the  isobutanol  pathway    The   initial   list   includes   more   than   15   interventions.   The   set   was   reduced   and  reactions   including   PHB   synthesis,   succinate   dehydrogenase,   transhydrogenase  were   not   required   for   the   coupling.   The   reduced   set   incorporates   16   reactions  knockout   and   one   overexpression   (Table   4).   This   strategy   is   sensitive   to   NADH  consumption,  and  ATP  production.  Also  the  transhydrogenase  carries  no  flux  in  this  situation,  suggesting  that  NADH  is  in  excess.  This  is  confirmed  when  reversible  flux  is   allowed.   However,   when   fumarase,   succinate   dehydrogenase,   and  polyhydroxybutyrate  synthase  are  knocked  out,  the  design  is  not  sensitive  to  NADH  

  27  

anymore.  It  is  not  clear  how  butanol  synthesis  regenerates  ATP  and  can  be  coupled  to  growth.    Among   targets   not   already   discussed,   transporter   for   pyruvate   was   suggested   as  knockout.  The  most  logical  explanation  is  to  redirect  carbon  flux  to  isobutanol.  It  is  interesting   to   get   this   target   for   isobutanol,   the   only   pathway   using   pyruvate   as  direct  precursor.  Starting  from  pyruvate  may  require  a  better  rerouting  than  acetyl-­‐CoA,   which   has   no   transporter.   Pyruvate   has   been   suggested   as   an   overflow  metabolite  under  nitrogen  starvation   for  cells   lacking  glycogen  synthesis   (Gründel  et   al.,   2012).   ATP   synthase   knockout   is   consistent   with   ATP   sensitivity   and   an  ATP/NADPH   ratio   decrease.   Phosphoribulokinase   overexpression,   member   of  carbon  fixation,  was  suggested.    Increasing  carbon  fixation  may  indeed  be  beneficial  for  product  synthesis  and,  additionally,  ATP  consumed  during  this  reaction   lowers  the   ATP/NADPH   ratio.   This   strategy   also  works   for   all   other   pathways   (including  NADPH-­‐dependant  butanol)  except  for  the  LL  pathway.        

Table  4.  Minimal  knockout  set  to  couple  growth  with  isobutanol.  Enzyme  name   Reaction  

NADH  dehydrogenase  2    (tilacoide)   H+  +  NADH  +  PQtil    -­‐>  NAD+    +  PQH2  til    

NADH  dehydrogenase  1  (periplasm)   4  H+  +  NADPH  +  PQtil    -­‐>  NADP+    +  3  H+

til  +  PQH2  til  NADH  dehydrogenase  1  (tilacoide)   4  H+  +  NADH  +  PQtil    -­‐>  NAD

+    +  3  H+til  +  PQH2  til  

ATP  synthase   3  ADP  +  3  Pi  +  14  H+per  -­‐>  3  ATP  +  11  H

+  +  3  H2O  

Glycolate  oxidase   O2  +  Glycolate    -­‐>  H2O2  +  Glyoxylate  Phosphotransacetylase   Acetyl-­‐CoA  +  Pi  <=>  CoA  +  Acetyl-­‐P  Active  CO2  transporter  facilitator  (tilacoide)   3  H+  +  H2O  +  NADPH  +  PQ  +  CO2  -­‐>  NADP

+  +  HCO3  +  3  H+  +  PQH2  

Cytochrome  c  oxidase   4  H+  +  2  ferrocytochrome  +  0.5  O2  til  -­‐>  2  H+  +  2  ferricytochrome  +  H2Otil  

 4  H+  +  2  plastocyanin(Cu+)  +  0.5  O2  til  -­‐>  2  H

+  +  2  plastocyanin(Cu2+)    +  H2O  til  Cyclic  electron  flow  (FQR)   2  H+  +  PQtil  +  2  reduced  ferredoxin  -­‐>  PQH2  til  +  2  oxidized  ferredoxin  Pyruvate-­‐ferredoxin  oxidoreductase   CoA  +  Pyruvate  +  2  oxidized  ferredoxin    -­‐>  H+  +  CO2  +  Acetyl-­‐CoA  +  2  

reduced  ferredoxin  Pyruvate  transporter   Pyruvatecyt  <=>  Pyruvateext  Phosphoribulokinase   ATP  +  Ribulose-­‐5P  -­‐>  ADP  +  H+  +  Ribulose-­‐1,5-­‐biP  

     

  28  

 Fig.   14.   Phenotypic   phase   plane   for   isobutanol   production.   In   the   modified   strain,   approximately   0.3  mmol/gDW/h  isobutanol  production  is  predicted  at  maximal  growth.  This  corresponds  to  77%  of  the  maximal  theoretical  production  rate.  

 Identifying  buffering  mechanisms  for  cofactor  balancing    Buffering  mechanisms  are  to  some  extent  unwanted,  as  we  want  to  create  a  cofactor  imbalance  shifting  the  flux  towards  product  synthesis.  Buffering  mechanisms  were  identified   using   cofactor   excess   conditions.   Reactions   that   create   cofactor   excess  were   added   to   the  model.   FBA  was   solved   and   flux   distributions  were   compared  with   normal   conditions.   Reactions   with   significant   changes   in   their   fluxes   were  identified   as   targets   and   potential   competing   reaction   for   cofactor   recycling.  Moreover,  since  FBA  always  aims  at  finding  optimal  distribution,  we  can  iteratively  find  such  buffering  reactions,  set  them  to  initial  flux  values  and  identify  new  ones.    A  hierarchical   order   for   regulation   can   then   be   obtained   since   the   first   reactions  predicted   by   FBA   are   the   most   efficient   mathematically   for   cofactor   recycling.  Unbalance   was   created   in   the   form   of   (1)   Reduction   of   NAD+   to   NADH,   (2)  Reduction  of  NADP+  to  NADPH,  (3)  ATP  hydrolysis  to  ADP,  and  (4)  ATP  hydrolysis  and   NADPH   production   to   account   for   ATP/NADPH   ratio   modulation.   For   each  condition,   an   arbitrary   flux   of   20  mmol/g  DW/h  was   set   for   the   reaction   creating  excess  cofators.  This  arbitrary  value  needs  to  be  high  enough  to  see  changes  but  not  too  much  to  unable  growth.  Ideally  a  value  that  leads  to  a  growth  rate  similar  to  the  ones  obtained  using  the  algorithms  would  perhaps  enable  better  comparisons.    

0  

0.05  

0.1  

0.15  

0.2  

0.25  

0.3  

0.35  

0.4  

0.45  

0   0.005   0.01   0.015   0.02   0.025   0.03   0.035   0.04  

Isobutanol  mmol/gDW/h  

Growth  rate  1/h  

PPP  for  Isobutanol  

initial  strain  

moditied  strain  

  29  

Many  of  the  regulatory  mechanisms  found  are  consistent  with  targets  predicted  by  both   OptForce   and   OptKnock.   Two   general   patterns   can   be   obtained   from   the  simulations:  one  for  (1)  NADH  balance  and  another  one  for  (2)  NADPH,  (3)  ATP,  (4)  ATP/NADPH.   This   indicates   that   ATP   production   and   NADPH   consumption   are  closely  linked  (ATP/NADPH  ratio)  and  use  a  similar  set  of  reactions.    For  NADH   regulation,   NADH   dehydrogenase   type   1   and   2   are   the   first   regulatory  mechanisms  predicted  by  FBA  to  balance  an  increased  NADH  production.  Secondary  regulating  mechanisms   involve  other  enzymes   in   the  electron   transport   chain   like  cytochrome   b6/f   complex   or   cytochrome   c   oxidase.   Interestingly,   glutamate  synthase  and  glutamate  dehydrogenase  showed  both  same  level  of  up-­‐regulation  in  what  might  be  a  futile  cycle  to  recycle  NADH  into  NAD+.  This  is  consistent  with  the  hypothesis  from  OptKnock  for  the  NADH  pathway  strategy.  Also  it  makes  sense  that  this   futile   cycle   is   listed   as   secondary   regulatory   mechanism   since   it   wastes   one  mole   ATP   per   NADH   recycled.   Other   secondary   regulating   mechanisms   involve  adenylate  kinase  consuming  ATP.  The  utility  of   this   target   for  NADH  dissipation   is  not  clearly  understood.      The   other   pattern   involves   cytochrome   b6/f   complex   or   cytochrome   c   oxidase   as  primary   regulatory   mechanism   whereas   NADH   dehydrogenase   type   1   was   here  predicted  as  secondary  mechanism  with  transhydrogenase.  Since  transhydrogenase  is  considered  as  non-­‐reversible  reaction  converting  NADPH  to  NADH,  it  makes  sense  that  it  participates  in  cofactor  balancing  in  NADPH  dissipation.                                      

  30  

Discussion    Objective  function  choice    All  simulations  were  performed  under  the  assumption  that  cyanobacteria  optimize  their   growth,   which   is   true   for   most   bacteria.   Even   though   this   assumption   is  relevant   for   cyanobacteria,   this   might   not   be   its   primal   objective.   Diurnal  environment   imposes   some   flexibility   on   the   cell.   It   has   been   suggested   that  cyanobacteria   optimize   ATP   production   to   better   react   to   changes   in   the  environment  (Siurana  et  al.,  unpublished).  For  instance,  ATP  yield  optimization  has  been   shown   to   better   describe   E.   coli   flux   states   in   resting   conditions   ().   Other  groups   suggested   splitting   the   objective   function   in   two;   one   accounting   for   light  and   one   for   dark   conditions.   Biomass   is   optimized   during   the   day   and   ATP   is  maximized   during   the   night.   Nevertheless,   taking   a   good   objective   function   is  another  issue  towards  using  genome-­‐scale  model  as  close  to  reality.    Comparing  results  with  other  studies    Results   are   in   good   agreement  with   two  other   studies   using  different  models   and  algorithms.   However,   no   one   has   reported   growth-­‐coupled   strategies   for  fermentative   products   in   autotrophic   conditions.   OptORF   suggested   blocking  reactions   or   cycles   that   consume   reducing   power   in   the   form   of   NAD(P)H   for  ethanol,  acetate,  alanine,  succinate,  butanol,  and  isoprene  in  Synechococcus  sp.  PCC  7002   (Reed  et  al.,  2013).  NADH  dehydrogenase  was  also  a   target   to  be  blocked   in  their  study.  They  acknowledged  difficulties  to  couple  growth  to  chemical  production  because  the  considered  carbon-­‐limiting  conditions  (excess  light).  Thereby,  excess  of  reductants,  the  driving  force  to  growth-­‐couple  a  product,  was  inexistent.  Erdrich  et  al.   (2014)   used   the   Knoop   model   and   CASOP   to   find   strategies   for   ethanol  production   (NADPH   used).   Cyclic   electron   flows   were   the   main   targets   to   be  blocked.   The   list   includes   the   NADPH   dehydrogenase,   NADH   hydrogenase   type   2,  FQR,  cytochrome  c  oxidase,  cytochrome  oxidase  bd,  mehler  reaction.    Additionally,   the   proline   dehydrogenase   reaction   “Proline   +   PQ   =>   1-­‐Pyrroline-­‐5-­‐carboxylate   +   PQH2”   (sll1561)   is   believed   to   form   a   cyclic   electron   flow   with  pyrroline-­‐5-­‐carboxylate  reductase  catalyzing  “Pyrroline-­‐5-­‐carboxylate  +  NAD(P)H  +  H+  =>  Proline  +  NAD(P)+”  (slr0661).  In  iJN678,  proline  dehydrogenase  uses  flavine  adenine   dinucleotide   (FADH2)   as   cofactor   instead   of   plastoquinone   (PQ).   This  electron   carrier   does   not   participate   in   cyclic   electron   flows.   Nevertheless,  pyrroline-­‐5-­‐carboxylate  reductase  was  also  suggested  by  OptKnock,  even  though  its  knockout   was   not   required   to   couple   butanol   with   growth.   The   authors   also  suggested   a   similar   cycle,   catalyzed   by   two   glycerol-­‐3-­‐phosphate   dehydrogenases.  Glycerol  and  dihydroxyacetone  phosphate  NADH-­‐dependant  interconversion  forms  one  half  of  the  cycle  (slr1755).  PQ  acts  as  an  electron  carrier  to  convert  glycerol  3-­‐phosphate  to  dihydroxyacetone  phosphate  and  close  the  cycle  (sll1085).  Again,  this  last  reaction  uses  FADH2  instead  according  to  iJN678.  When  FADH2  was  swapped  for  PQ,  this  resulted  in  uncoupling  product  synthesis  with  growth.  Knocking  out  these  

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two  cycles  rescued  the  coupling.  This  shows  the  importance  of  comparing  cofactor  preferences  between  genome-­‐scale  models.      Cofactor  buffering  mechanisms    Through   this   work,   cofactor   buffering   mechanisms   haven   proven   to   be   suitable  knockout   targets.   Getting   rid   of   these   mechanisms   permit   a   cofactor   balance  controls  through  product  synthesis.    In  E.  coli  and  Bacillus  subtilis  (B.  subtilis),   transhydrogenases   have  been   subject   to  intensive  studies  from  U.  Sauer  and  co-­‐workers  (Rühl  et  al.,  2012;  Chou  et  al.,  2015).  As   a   regulatory   reaction   that  balances  NAD(H)/NADP(H)   ratio,   it   is   of   interest   for  engineering   as   it   might   make   more   cofactor   available   for   product   synthesis  (Angermayr   et   al.,   2012).   In   iJN678,   a   reversible   transhydrogenase   converting  NADPH   to   NADH   is   present   in   the   model.   On   the   contrary,   Knoop   et   al.   (2013)  account  for  a  reversible  transhydrogenase  in  their  model.  Allowing  a  reversible  flux  through   the   transhydrogenase   uncoupled   butanol   with   growth   for   NADH  consumption   sensitive   designs.   In   other   words,   instead   of   using   the   butanol  pathway  to  recycle  NADH,  the  transhydrogenase  is  used  instead  producing  reducing  equivalents   in   the   form   of   NADPH.   NADPH   is   then   recycled   using   numerous  reactions  in  the  cell.  Transhydrogenase  should  therefore  not  be  seen  as  a  competing  reaction  for  cofactor  recycling  since  a  re-­‐oxidation  step  still  needs  to  be  carried  out.  Instead   it   allows   a   regulation   between   both   cofactors.   Nevertheless,   its   knockout  may  allow  fewer  competing  reaction  if  coupling  is  achieved  through  one  particular  cofactor  balance.   In   strategies  using  both  NADH  and  NADPH,  mechanisms   such  as  the   transhydrogenase  are   essential   to  keep  a   good   cofactor  balance  between  both  types  in  the  most  direct  way.    In  this  project,  a  couple  of  cycles  that  could  regulate  cofactor  balance  were  identified  and   could   be   added   to   a   list   of   important   targets   to   consider.   Interconversion  between  glutamine   and  glutamate  provides   a   regulatory   cycle   that   consumes  ATP  and   NADH   per   iteration.   Experimentally,   a   related   cycle   between   ana-­‐   and  catabolism   of   glutamate   (consuming   NADPH   and   producing   NADH)   has   been  identified  in  B.  subtilis  (Rühl  et  al.,  2012).  Such  cycle  exists  in  cyanobacteria,  but  did  not  seem  to  be  active  in  silico.      Another   cycle,   involves   several   reactions   around   pyruvate.   This   cycle   consumes  NADH   and   produces  NADPH   from  PEP   to   PYR,   instead   of   producing  ATP   through  pyruvate   kinase.   OptKnock   suggested   pyruvate   kinase   knockout   in   numerous  simulations,  but  was  not  essential  to  achieve  coupling.  This  cycle  can  be  modulated  to   suit   cofactor   requirements   for   pathways   of   interest.   For   instance   in   NADPH  dependant   pathways,   fumarase   deletion   was   found   to   amplify   this   cycle   and  produce   NADPH.   If   the   pathway   requires   NADH,   malate   dehydrogenase   was  suggested  for  knockout.  This  set  of  reactions  is  believed  to  play  an  important  role  in  the   cell   physiology.   Highest   flux   is   achieved   in   the   central   metabolism.   Thus,  knockouts   have   potential   high   effects   here.   In   B.   subtilis,   malic   enzyme   was  

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experimentally   identified   as   a   cofactor   buffering   mechanism   alongside   a   reverse  reaction  not  present  in  cyanobacteria.    Simultaneous   activity   between   both   glyceraldehyde-­‐3-­‐phosphate   dehydrogenases  was  also  found  to  form  a  cycle   interconnecting  NADH  with  NADPH.  This  cycle  was  shown   to   be   important   for   NADPH   balancing   in   nitrogen-­‐starving  B.   subtilis.   In   a  study  where   they   study  NADPH   regulation,   Rühl   et   al.   (2012)  measured   catabolic  NADPH  production.   A   significant   part   of   this   production   could   not   be   assigned   to  measured   recycling   mechanisms,   indicating   the   presence   of   more   buffering  reactions.    ATP/NADPH  versus  NADH  as  driving  forces    Using  pathways  with  different  cofactor  requirements  enable  to  analyze  the  different  driving   forces   behind   coupling.   Overall,   driving   forces   and   pathway   requirements  correlate.  In  some  cases,  coupling  occurs  with  different  cofactors  suggesting  that  an  interconversion  mechanism  allows  meeting   cofactor   requirements   for   the   butanol  pathway.   Cofactors   are   highly   interconnected   metabolites.   This   property   and  presence  of  numerous  mechanisms  that  allows  a  balance  between  the  oxidized  and  the   reduced   form,   as   well   as   cofactor   types,   makes   it   difficult   to   understand   the  driving  forces  behind  coupling.      It  seems  two  driving  forces  exist  to  couple  growth  to  product  synthesis.  On  the  one  hand,   ATP/NADPH   ratio   has   been   already   discussed   (Erdrich   et   al.,   2014)   for  NADPH   dependent   synthesis   of   ethanol.   On   the   other   hand,   NADH   recycling   as  driving  force  in  cyanobacteria  has  not  been  reported  previously.  These  two  driving  forces  are  not  exclusive.  They  can  be  encountered   individually  or  both   for  a   same  design.   Moreover   due   to   the   presence   of   regulatory   mechanisms,   other   than   the  ones   investigated,  both  cofactor  balance  are  highly  related.  To   illustrate  this  point,  NADH   recycling  was   identified   as   a   driving   force   for   all   strategies   including   fully  NADPH-­‐dependent   pathway.   When   identified   targets   for   cofactor   balance   were  knockout,   strategy   was   still   sensitive   to   NADH.   This   could   indicate   that   NADH  recycling  is  the  driving  force,  interconnected  with  NADPH  through  mechanisms  not  identified  in  this  study.    Another   example   shows   the   complexity   of   this   close   connection   between   both  driving  forces.  In  the  first  strategy  for  the  NADH  dependent  path,  NADH  was  the  sole  driving  force  identified.  However,  when  ATP/NADPH  ratio  is  plot  for  different  fixed  growth  rates  and  butanol  production  rates,  ATP/NADPH  ratio  decreases  as  butanol  is   produced.   Even   though  NADH   is   the   driving   force,   the   strategy   correlates  with  ATP/NADPH  ratio.    Overall,   strategies   using   NADPH   as   sole   driving   force   could   not   be   found.   Often,  coupling   appeared   to   be   through   NADH   recycling.   Then   NADH   is   converted   into  NADPH  to  meet  the  pathway  needs.  One  simple  explanation  is  that  NADPH  is  listed  in   more   reactions   than   NADH   (86   versus   52   reactions).   Another   explanation   is  

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NADPH   requirement   for   carbon   fixation   in   the   Calvin   cycle.   This   could   act   as   a  competing  reaction  that  could  not  be  knockout  under  phototrophic  conditions.    Conclusion    In   conclusion,   coupling   fermentative   product   to   growth   is   difficult   in   autotrophic  conditions,  in  particular  under  carbon-­‐limited  physiology  with  light  in  excess.  In  this  work,  the  first  strategies  to  couple  butanol  to  growth  in  autotrophic  conditions  are  presented.   Strategies   aim   at   reducing   photosynthesis   robustness   by   targeting  cycling   electron   flows   in   accordance   to   similar   studies.   Additionally,   different  mechanisms  buffering  cofactor  balance  as  well  as  competing  reactions  for  cofactor  recycling   are   also   targets.   Two   driving   forces   were   identified   in   the   form   of  ATP/NADPH  ratio  and  NADH  recycling.  It  seems  that  the  latter  is  the  actual  driving  forces   in   coupling   strategies.   A   successful   strategy   aims   at   creating   a   cofactor  imbalance,   removing   cofactor   regulatory  mechanisms   so   that   product   synthesis   is  the   last   mechanism   available   to   restore   such   balance,   forcing   the   flux   towards  product  synthesis.    Future  directions    Testing   these   strategies   experimentally   or   a   combination   thereof,   is   a   genuine  extension  of   this  project.   In  particular,  MFA  could  be  used  to   further  constrain  the  model.   Metabolomics   could   help   to   identify   driving   forces   by   measuring   cofactor  concentrations   for   different   genotypes.   Transcriptomics   data   could   also   give  information   whether   certain   reactions   are   active   or   not.   To   find   alternative  strategies,  OptKnock  and  OptForce  could  be  run  again  with  different  settings  and  on  different   models   as   well.   For   time   reasons,   OptSwap   could   not   be   used   in   this  project,   but   its   use   would   be   relevant   to   study   cofactors   requirement   in   more  details.  Finally,  it  is  difficult  to  knockout  a  high  number  of  targets  at  once.  However,  with  recent  advances  in  genome  editing,  accurate  and  wide  genetic  modification  is  now  at  reach.                  

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