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o u g h a l a rg e p a rt o f th e m e ta b o li s m o f th e y e a s t S a c c h a ro m y c e s c e re v is ia e , a n i m p o rt a n t in d u s tr ia l ro o rg a n is m a n d e u k a ry o ti c m o d e l o rg a n is m , h a s b e e n c la ri fi e d , th e re a re a n u m b e r o f is s u e s t h a t l im it t h e p re d ic ti v e p o w e r o f m e ta b o li c m o d e ls f o r th is a n d o th e r o rg a n is m s . T h is d is s e rt a ti o n a im s m p ro v e t h e c o ll e c ti o n o f d a ta f o r m e ta b o li c m o d e ls t h ro u g h a p p li c a ti o n a n d a s s e s s m e n t o f d if fe re n t e ri m e n ta l m e th o d s , e a c h t a rg e ti n g a s p e c if ic e x p e ri m e n ta l c h a ll e n g e . s tu d y t h e i n fl u e n c e o f c h a n g in g e n v ir o n m e n ta l c o n d it io n s , o x y g e n p ro g ra m m e d f e rm e n ta ti o n w a s d . W it h t h is t e c h n iq u e , th e e ff e c t o f a d e c re a s in g o x y g e n a v a il a b il it y o n t h e g ly c e ro l m e ta b o li s m o f c h a ro m y c e s c e re v is ia e w a s d e te rm in e d i n d e ta il . M u ta n ts f o r e it h e r o n e o r b o th o f th e g e n e s f o r +-d e p e n d e n t g ly c e ro l-3 -p h o s p h a te d e h y d ro g e n a s e w e re i n v e s ti g a te d i n c o n ti n u o u s c u lt u re s u n d e r a m ic a ll y c h a n g in g , y e t p re c is e ly c o n tr o ll e d c o n d it io n s w it h l o w o x y g e n t ra n s fe r ra te s . T h e r e s u lt s w e d th a t S a c c h a ro m y c e s c e re v is ia e c o n tr o ls th e p ro d u c ti o n o f g ly c e ro l in re s p o n s e to h y p o x ic d it io n s b y re g u la ti n g th e e x p re s s io n o f s e v e ra l g e n e s . A t h ig h d e m a n d fo r N A D H re o x id a ti o n , a n g i n d u c ti o n o f th e G P D 2 g e n e e x p re s s io n w a s s e e n . T h e d y n a m ic s o f th e g e n e i n d u c ti o n a n d t h e e ro l fo rm a ti o n a t a l o w d e m a n d f o r N A D H r e o x id a ti o n p o in te d t o a n i m p o rt a n t ro le f o r th e G P D 1 p y m e . In a ∆∆∆∆ g p d 1 ∆∆∆∆ g p d 2 d o u b le -n u ll m u ta n t, th e n e c e s s a ry N A D + re g e n e ra ti o n th ro u g h g ly c e ro l d u c ti o n w a s n o l o n g e r p o s s ib le a n d t h is m u ta n t s to p p e d t o g ro w u n d e r c o n d it io n s w it h e x tr e m e l o w g e n a v a il a b il it y . e n e ti c d is tu rb a n c e o f a w e ll -d e fi n e d r e fe re n c e s tr a in i s a f re q u e n tl y a p p li e d m e th o d t o s tu d y c e ll ta b o li s m . T h ro u g h m e ta b o li c e n g in e e ri n g ( d is tu rb a n c e s i n t h e f o rm o f a im e d g e n e d e le ti o n a n d / o r re x p re s s io n a ff e c ti n g m e ta b o li s m ), t h e e x c e s s N A D H f o rm e d i n a ∆∆∆∆ g p d 1 ∆∆∆∆ g p d 2 d o u b le -n u ll m u ta n t e r a n a e ro b ic c o n d it io n s w a s u s e d to d ri v e o th e r c y to s o li c re d o x re a c ti o n s . T h is m u ta n t w a s re fo re tr a n s fo rm e d w it h a h e te ro lo g o u s g e n e c o d in g fo r N A D +-d e p e n d e n t m a n n it o l-1 -p h o s p h a te y d ro g e n a s e . T h e s tr a in w a s i n v e s ti g a te d u s in g s te p -c h a n g e b a tc h e x p e ri m e n ts i n w h ic h , d u ri n g t h e o n e n ti a l g ro w th p h a s e , th e e n v ir o n m e n t w a s c h a n g e d f ro m a e ro b ic t o a n a e ro b ic c o n d it io n s . It w a s n d t h a t th e m u ta n t p ro d u c e d t h e e x p e c te d e n d p ro d u c t m a n n it o l o n ly a ft e r th e s w it c h t o a n a e ro b ic d it io n s . H o w e v e r, a n a e ro b ic g ro w th w a s n o t re g a in e d , w h ic h w a s p ro b a b ly d u e t o a c c u m u la ti o n o f n n it o l in s id e t h e c e ll s a c c o m p a n ie d b y e x c e s s iv e s w e ll in g o f th e c e ll s . n a p p ro a c h o f m a jo r im p o rt a n c e t o r e s o lv e t h e i n o b s e rv a b il it y o f m e ta b o li c p h e n o ty p e s , a n o th e r ll e n g e i n m e ta b o li c m o d e ll in g , is t h e d e te rm in a ti o n o f th e f u ll s e t o f in tr a c e ll u la r m e ta b o li c f lu x e s . s e c a n b e r e s o lv e d w it h t h e h e lp o f 1 3C -i s o to p ic l a b e ll in g e x p e ri m e n ts . T h e a c c u ra te n e s s o f a m e th o d 3C -m e ta b o li c f lu x a n a ly s is ( 1 3C -M F A ) w a s a s s e s s e d . T h e m e th o d u s e s m a s s i s o to p o m e r d is tr ib u ti o n ) d a ta o b ta in e d b y l iq u id c h ro m a to g ra p h y -m a s s s p e c tr o m e tr y ( LC -M S ) a n a ly s is o f fr e e i n tr a c e ll u la r ta b o li te s f ro m t h e c y to s o li c c e n tr a l c a rb o n m e ta b o li s m . T h e y e a s t s p e c ie s S a c c h a ro m y c e s b u ld e ri g ro w n a n a e ro b ic a ll y i n a c h e m o s ta t c u lt u re w it h [ 1 3C 1 ]-g lu c o n o δδδδ -l a c to n e a s t h e l im it in g c a rb o n e n e rg y s u b s tr a te . U n d e r th e s e c o n d it io n s , th e r a ti o o f tw o i m p o rt a n t m e ta b o li c f lu x e s , th e p e n to s e -s p h a te p a th w a y a n d t h e g lu c o n o -l a c to n e u p ta k e f lu x , h a s b e e n r e p o rt e d t o b e f ix e d a t a v a lu e t h a t ic ta te d b y th e in tr a c e ll u la r N A D P H b a la n c e . T h e e x p e c te d fl u x ra ti o a n d th e v a lu e th a t w a s m a te d b y f it ti n g a m e ta b o li c f lu x p a tt e rn t o t h e m e a s u re d M ID d a ta s e t fr o m t h e s a m e 1 3C -l a b e li n g e ri m e n t, w e re c o m p a re d a n d th e y d if fe re d s ig n if ic a n tl y fr o m e a c h o th e r (0 .5 1 v s . 1 .2 0 , p e c ti v e ly ). A t th is p o in t it is u n c le a r w h e th e r th is d is c re p a n c y p o in ts to w a rd s a n e rr o r in o u r ta b o li c m o d e l o f S a c c h a ro m y c e s b u ld e ri o r a t e rr o rs i n t h e m e a s u re d M ID d a ta f o r o n e o r s e v e ra l ta b o li te s . s a n e x a m p le o f a n a p p li c a ti o n o f th e LC -M S -b a s e d 1 3C -M F A , th e m e th o d w a s u s e d t o i n v e s ti g a te th e r c o m p li c a ti n g i s s u e i n m e ta b o li c m o d e ll in g . A lt h o u g h o ft e n m o d e ll e d t h a t w a y , a c u lt u re o f c e ll s o t a h o m o g e n e o u s c o ll e c ti o n o f c e ll s . D if fe re n c e s b e tw e e n c e ll p o p u la ti o n s w it h in a c u lt u re c a n u r d u e t o , fo r in s ta n c e , a s y n c h ro n o u s c e ll d iv is io n . T h e re fo re , th e q u e s ti o n w a s a d d re s s e d w h e th e r e rv a b le c h a n g e s in fl u x e s in th e p ri m a ry c a rb o n m e ta b o li s m o f S a c c h a ro m y c e s c e re v is ia e o c c u r e e n th e d if fe re n t p h a s e s o f th e c e ll d iv is io n c y c le . T o d e te c t s u c h c h a n g e s , a 1 3C -l a b e li n g e ri m e n t w a s p e rf o rm e d w it h a fe d -b a tc h c u lt u re in o c u la te d w it h a p a rt ia ll y -s y n c h ro n is e d c e ll u la ti o n o b ta in e d t h ro u g h c e n tr if u g a l e lu tr ia ti o n . S u c h a c u lt u re e x h ib it s d y n a m ic c h a n g e s in th e ti o n s o f c e ll s i n d if fe re n t c e ll c y c le p h a s e s o v e r ti m e . F o r fo u r ti m e p o in ts d u ri n g t h e c u lt u re t h e a s u re d M ID s w e re u s e d t o o b ta in t h e b e s t e s ti m a te s f o r th e m e ta b o li c f lu x e s . T h e o b ta in e d f lu x f it s g e s te d t h a t th e o p ti m a ll y f it te d s p li t ra ti o f o r th e p e n to s e p h o s p h a te p a th w a y c h a n g e d b y a lm o s t a r o f 2 u p a n d d o w n a ro u n d a v a lu e o f 0 .2 7 d u ri n g t h e e x p e ri m e n t. S ta ti s ti c a l a n a ly s is r e v e a le d t h a t e o f th e fi tt e d fl u x d is tr ib u ti o n s fo r d if fe re n t ti m e p o in ts w e re s ig n if ic a n tl y d if fe re n t fr o m e a c h e r, i n d ic a ti n g t h a t c e ll c y c le -d e p e n d e n t v a ri a ti o n s i n c y to s o li c m e ta b o li c f lu x e s i n d e e d o c c u r. ap pli ca tio n a nd a ss es sm en t o f e xp eri me nta l m eth od s to s tud y t he c yto so lic p rim ary m eta bo lis m o f y eaapplication and assessment of experimental methods
to study the cytosolic primary metabolism of yeast
A
pplication and assessment of experimental methods
to study the cytosolic primary metabolism of yeast
P
roefschrift
ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,
op gezag van de Rector Magnificus prof.dr.ir. J.T. Fokkema, voorzitter van het College voor Promoties,
in het openbaar te verdedigen op
donderdag 6 maart 2008 om 15.00 uur
door
R
oeland
COSTENOBLEingenieur in de bioprocestechnologie teknologie licentiat i biovetenskap
Dit proefschrift is goedgekeurd door de promotor: Prof.dr.ir. J.J. Heijnen.
samenstelling van de promotiecommissie:
Rector Magnificus voorzitter
Prof.dr.ir. J.J. Heijnen Technische Universiteit Delft, promotor
Prof. Dr.-Ing. Dr. h.c. M. Reuss Universität Stuttgart
Prof.dr.civ.ing. G. Lidén Lunds Universitet
Prof.dr. J.T. Pronk Technische Universiteit Delft
Dr.ir. D.E. Martens Wageningen Universiteit en Researchcentrum
Dr.ir. W.A. van Winden DSM N.V.
reserve lid:
Prof.dr. J.P. van Dijken Technische Universiteit Delft
The work described in chapter 2 of this dissertation was supported by the Swedish National Board for Industrial and Technical Development and the Nordic Industrial Fund.
The work described in chapter 3 of this dissertation was supported by the Chalmers University of Technology Foundation.
The work described in chapter 4 of this dissertation was supported by the Kluyver Centre for Industrial Fermentation.
The work described in chapter 5 of this dissertation was supported by the Kluyver Centre for Industrial Fermentation and the German Research Foundation.
illustration page 5: © E.H. Shepard, 1928
‘But, Eeyore,’ said Pooh, ‘was it a Joke, or an Accident? I mean —‘
‘I didn’t stop to ask, Pooh. Even at the very bottom of the river I didn’t stop to say to myself, “Is this a Hearty Joke, or is it the Merest Accident?” I just floated to the surface, and said to myself, “It’s wet.”’ Eeyore
abstract
application and assessment of experimental methods to study the cytosolic
primary metabolism of yeast
Roeland Costenoble, Department of Biotechnology, Delft University of Technology
Although a large part of the metabolism of the yeast Saccharomyces cerevisiae, an important industrial
microorganism and eukaryotic model organism, has been clarified, there are a number of issues that still limit the predictive power of metabolic models for this and other organisms. This dissertation aims to improve the collection of data for metabolic models through application and assessment of different experimental methods, each targeting a specific experimental challenge.
To study the influence of changing environmental conditions, oxygen programmed fermentation was used. With
this technique, the effect of a decreasing oxygen availability on the glycerol metabolism of Saccharomyces
cerevisiae was determined in detail. Mutants for either one or both of the genes for NAD+-dependent glycerol-3-phosphate dehydrogenase were investigated in continuous cultures under dynamically changing, yet precisely
controlled conditions with low oxygen transfer rates. The results showed that Saccharomyces cerevisiae controls
the production of glycerol in response to hypoxic conditions by regulating the expression of several genes. At high
demand for NADH reoxidation, a strong induction of the GPD2 gene expression was seen. The dynamics of the
gene induction and the glycerol formation at a low demand for NADH reoxidation pointed to an important role for
the GPD1p enzyme. In a ∆gpd1 ∆gpd2 double-null mutant, the necessary NAD+ regeneration through glycerol
production was no longer possible and this mutant stopped to grow under conditions with extreme low oxygen availability.
Genetic disturbance of a well-defined reference strain is a frequently applied method to study cell metabolism. Through metabolic engineering (disturbances in the form of aimed gene deletion and/or overexpression affecting
metabolism), the excess NADH formed in a ∆gpd1 ∆gpd2 double-null mutant under anaerobic conditions was
used to drive other cytosolic redox reactions. This mutant was therefore transformed with a heterologous gene
coding for NAD+-dependent mannitol-1-phosphate dehydrogenase. The strain was investigated using step-change
batch experiments in which, during the exponential growth phase, the environment was changed from aerobic to anaerobic conditions. It was found that the mutant produced the expected end product mannitol only after the switch to anaerobic conditions. However, anaerobic growth was not regained, which was probably due to accumulation of mannitol inside the cells accompanied by excessive swelling of the cells.
An approach of major importance to resolve the inobservability of metabolic phenotypes, another challenge in metabolic modelling, is the determination of the full set of intracellular metabolic fluxes. These can be resolved
with the help of 13C-isotopic labelling experiments. The accurateness of a method for 13C-metabolic flux analysis
(13C-MFA) was assessed. The method uses mass isotopomer distribution (MID) data obtained by liquid
chromatography-mass spectrometry (LC-MS) analysis of free intracellular metabolites from the cytosolic central
carbon metabolism. The yeast species Saccharomyces bulderi was grown anaerobically in a chemostat culture
with [13C
1]-glucono δ-lactone as the limiting carbon and energy substrate. Under these conditions, the ratio of
two important metabolic fluxes, the pentose-phosphate pathway and the glucono-lactone uptake flux, has been reported to be fixed at a value that is dictated by the intracellular NADPH balance. The expected flux ratio and
the value that was estimated by fitting a metabolic flux pattern to the measured MID data set from the same 13
C-labeling experiment, were compared and they differed significantly from each other (0.51 vs. 1.20, respectively). At this point it is unclear whether this discrepancy points towards an error in our metabolic model of
Saccharomyces bulderi or at errors in the measured MID data for one or several metabolites.
As an example of an application of the LC-MS-based 13C-MFA, the method was used to investigate another
complicating issue in metabolic modelling. Although often modelled that way, a culture of cells is not a homogeneous collection of cells. Differences between cell populations within a culture can occur due to, for instance, asynchronous cell division. Therefore, the question was addressed whether observable changes in
fluxes in the primary carbon metabolism of Saccharomyces cerevisiae occur between the different phases of the
cell division cycle. To detect such changes, a 13C-labeling experiment was performed with a fed-batch culture
inoculated with a partially-synchronised cell population obtained through centrifugal elutriation. Such a culture exhibits dynamic changes in the fractions of cells in different cell cycle phases over time. For four time points during the culture the measured MIDs were used to obtain the best estimates for the metabolic fluxes. The obtained flux fits suggested that the optimally fitted split ratio for the pentose phosphate pathway changed by almost a factor of 2 up and down around a value of 0.27 during the experiment. Statistical analysis revealed that some of the fitted flux distributions for different time points were significantly different from each other, indicating that cell cycle-dependent variations in cytosolic metabolic fluxes indeed occur.
samenvatting
toepassing en beoordeling van experimentele methodes om de cytosolaire
primaire stofwisseling van gist te bestuderen
Roeland Costenoble, Afdeling Biotechnologie, Technische Universiteit Delft
Hoewel een groot deel van de stofwisseling van de gist Saccharomyces cerevisiae, een belangrijk industrieel
micro-organisme en eukaryotisch modelorganisme, bekend is, zijn er een aantal kwesties die nog steeds het voorspellend vermogen van stofwisselingsmodellen voor dit en voor andere organismen beperken. Dit proefschrift poogt door toepassing en beoordeling van verschillende experimentele methodes, het verzamelen van data voor stofwisselingsmodellen te verbeteren, waarbij elke methode op een specifieke experimentele uitdaging gericht is. Zuurstofgeprogrammeerde fermentatie werd gebruikt om de invloed van veranderingen in de omgeving te bestuderen. Met deze techniek werd het effect van een dalende zuurstofbeschikbaarheid op het
glycerolmetabolisme van Saccharomyces cerevisiae tot in detail bepaald. Mutanten voor één of beide genen voor
NAD+-gekoppeld glycerol-3-fosfaatdehydrogenase werden onderzocht in continu-culturen onder alhoewel
dynamisch veranderend, toch nauwkeurig gecontroleerde omstandigheden met lage
zuurstofoverdrachtssnelheden. De resultaten toonden aan dat Saccharomyces cerevisiae de productie van
glycerol in reactie op zuurstofarme condities controleert door de expressie van verscheidene genen te regelen. Bij
een hoge behoefte aan reoxidatie van NADH werd een sterke inductie van de expressie van het gen GPD2
waargenomen. De dynamica van de geninductie en de glycerolvorming bij een lage behoefte aan reoxidatie van
NADH wezen op een belangrijke rol voor het enzym GPD1p. In een ∆gpd1 ∆gpd2 dubbel-disruptie mutant was de
benodigde regeneratie van NAD+ via productie van glycerol niet meer mogelijk en deze mutant hielt dan ook op
te groeien onder omstandigheden met extreem lage zuurstofbeschikbaarheid.
Het genetisch veranderen van een goed-gedefinieerde referentiestam is een methode die vaak wordt toegepast
om de stofwisseling van een cel te bestuderen. Het overschot aan NADH dat in een ∆gpd1 ∆gpd2
dubbel-disruptie mutant onder anaërobe omstandigheden wordt gevormd, werd met behulp van metaboliettechnologie (veranderingen in de vorm van gerichte deleties en/of overexpressie van genen die het metabolisme beïnvloeden) gebruikt om het verloop van andere cytosolaire redoxreacties te bevorderen. De dubbel-disruptie mutant werd
daarom getransformeerd met een heteroloog gen dat codeert voor NAD+-gekoppeld
mannitol-1-fosfaatdehydrogenase. De stam werd onderzocht met behulp van batchexperimenten waarin, tijdens de exponentiële groeifase, de condities in één stap veranderd werden van aërobe naar anaërobe omstandigheden. Het bleek dat de mutant het verwachte eindproduct mannitol alleen maar na de omschakeling naar anaërobe omstandigheden produceerde. Groei onder anaërobe condities werd echter niet herkregen. Dit was waarschijnlijk toe te schrijven aan accumulatie van mannitol binnen in de cellen wat overmatig zwellen van deze cellen tot gevolg had.
De bepaling van een complete verzameling van intracellulaire metabole fluxen is een belangrijke strategie om de niet-waarneembaarheid van metabole fenotypes, een volgende uitdaging bij het modelleren van het
metabolisme, te voorkomen. Deze metabole fluxen kunnen met behulp van 13C-isotopenlabelingsexperimenten
worden bepaald. De nauwkeurigheid van een methode voor 13C-metabole-fluxanalyse (13C-MFA) werd
beoordeeld. De methode maakt gebruik van data bestaande uit massa-isotopomerendistributies (MIDs) verkregen door vloeistof-chromatografische-massaspectrometrische (VC-MS) analyse van vrije intracellulaire metabolieten in
het cytosolaire, centrale koolstofmetabolisme. De gistsoort Saccharomyces bulderi werd anaëroob gekweekt in
een chemostaatcultuur met [13C
1]-glucono-δ-lacton als het limiterende koolstof- en energiesubstraat. Onder deze
omstandigheden is het bekend dat de verhouding van twee belangrijke metabole fluxen, de pentose-fosfaatroute en de opnameflux van gluconolacton, vast ligt op een waarde die bepaald wordt door de intracellulaire NADPH balans. De verwachte fluxratio en een geschatte fluxratio gebaseerd op de met VC-MS gemeten MID data van
hetzelfde 13C-labelingsexperiment, werden met elkaar vergeleken, en deze waren significant verschillend (0,51 en
1,20 respectievelijk). Op dit moment is het nog onduidelijk of deze tegenstrijdigheid op een fout in ons metabole
model van Saccharomyces bulderi duidt, of op fouten in de gemeten MIDs voor één of meerdere metabolieten.
Als voorbeeld van een toepassing werd de op VC-MSgebaseerde 13C-MFA methode gebruikt om een volgende
kwestie te bestuderen die het modelleren van het metabolisme compliceert. Hoewel vaak gemodelleerd op die manier is een celcultuur geen homogene verzameling van cellen. Zo kunnen, bijvoorbeeld, verschillen tussen celpopulaties voorkomen in een celcultuur die veroorzaakt worden door niet-synchrone celdeling. De vraag werd daarom beantwoord of tijdens de verschillende fasen van de celdelingscyclus zich waarneembare veranderingen
in de fluxen in het primaire koolstofmetabolisme van Saccharomyces cerevisiae voordoen. Om dergelijke
veranderingen waar te kunnen nemen, werd een 13C-labelingsexperiment uitgevoerd met een fed-batch cultuur
geïnoculeerd. Een dergelijke celcultuur laat in de loop van de tijd dynamische veranderingen in de fracties van cellen in de verschillende delingscyclusfasen zien. Voor vier tijdsstippen tijdens deze cultuur werden de gemeten MIDs gebruikt om de beste schattingen voor de metabole fluxen te krijgen. De verkregen fluxpatronen gaven aan dat, gedurende het experiment, de optimaal gefitte fluxratio voor de pentose-fosfaatroute met bijna een factor 2 groter of kleiner veranderde rondom een waarde van 0,27. Statistische analyse liet zien dat enkele gefitte fluxpatronen voor verschillende tijdsstippen significant verschilden en dat dus celdelingscyclus-afhankelijke variaties in cytosolaire metabole fluxen inderdaad voorkomen.
sammanfattning
tillämpning och utvärdering av experimentella metoder för att studera den
cytosolära primära ämnesomsättningen av jäst
Roeland Costenoble, Institution för Bioteknik, Delfts Tekniska Universitet
Även om en större del av jästen Saccharomyces cerevisiaes (en viktig industriell mikroorganism och eukaryot
modellorganism) ämnesomsättningen är känd, finns det ett antal problem som fortfarande begränsar förutsägelsekraften av metaboliska modeller för denna och för andra organismer. Denna avhandling siktar på att förbättra samlingen av data för metaboliska modeller genom tillämpning och utvärdering av olika experimentella metoder, där varje metod ämnar en specifik experimentell utmaning.
För att studera effekten av ändrande omgivningsbetingelser användes syrestyrd fermentation. Med denna teknik
bestämdes i detalj effekten av en minskande syretillgång på glycerolmetabolismen i Saccharomyces cerevisiae.
Mutanter för antingen en eller båda gener för NAD+-kopplat glycerol-3-fosfatdehydrogenas undersöktes i
kontinuerliga odlingar under dynamiskt förändrande, men fortfarande noga kontrollerade betingelser med låga
syreöverföringshastigheter. Resultaten visade att Saccharomyces cerevisiae kontrollerar produktionen av glycerol i
respons på betingelser som framkallar syrebrist, genom at reglera uttrycket av flera gener. Vid ett stort behov av
återoxidation av NADH observerades en stark induktion av GPD2 genuttryck. Dynamiken av geninduktionen och
glycerolbildningen vid låga behov av återoxidation av NADH pekar på en viktig roll för GPD1p enzymet. I en
∆gpd1 ∆gpd2 dubbel mutant, nödvändig återgenerering av NAD+ genom glycerolbildning var inte längre möjlig
och denna mutant slutade att växa under betingelser med extrem låg syretillgång.
Att genetiskt ändra en väldefinierad referensstam är en ofta tillämpad metod för att studera cellmetabolism. Genom metabolitteknik (ändringar i form av riktade gendeletioner och/eller överexpression som påverkar
metabolismen) användades överskottet av NADH som bildas i en ∆gpd1 ∆gpd2 dubbel mutant under anaeroba
betingelser, för att driva andra cytosolära redoxreaktioner. Denna mutant transformerades därför med en
heterolog gen som kodar för NAD+-kopplat mannitol-1-fosfatdehydrogenas. Stammen undersöktes med hjälp av
stegskiftes-satsexperiment i vilka, under den exponentiella tillväxtfasen, förhållanden ändrades från aeroba till anaeroba betingelser. Det visade sig att mutanten producerade den förväntade slutprodukten mannitol bara efter bytet till anaeroba förhållanden. Anaerob tillväxt återficks däremot inte, som troligtvis orsakades av ackumulation av mannitol inuti cellerna följd av ett starkt svällande av dessa cellerna.
Ett tillvägagångssätt av utomordentligt vikt för att lösa icke-iakttarbara metabola fenotyper, en annan utmaning inom metabolisk modellering, är bestämningen av den kompletta uppsättningen av intracellulära metaboliska
flödena. Dessa kan fås med hjälp av 13C-isotopmärkningsexperimenter. Nogrannheten av en metod för 13
C-metabolisk flödesanalys (13C-MFA) bedömdes. Metoden använder sig av massa-isotopomerdistributioner (MID)
erhållit med vätskekromatografi-massspektrometri (VK-MS) analys av fria intracellulära metaboliter från den
cytosolära centrala kolmetabolism. Jästsorten Saccharomyces bulderi odlades under anaeroba betingelser i en
kemostatkultur med [13C
1]-glukono δ-lakton som begränsande kol- och energisubstrat. Under dessa förhållanden,
har förhållandet av två viktiga metaboliska flöden, pentosfosfatrutten och inflödet av gluconolakton, rapporterats att vara fastställd på ett värde som dikteras av den intracellulära NADPH balansen. Den förväntade flödesration och värdet som uppskattades genom avpassning av en metabolisk flödesmönster med den mätta MID data sats
från samma 13C-märkningsexperiment, jämfördes och de skiljde sig signifikant åt (0,51 respektive 1,20). För
tilfället är det oklart om denna diskrepans pekar på ett fel i vår metaboliska modell av Saccharomyces bulderi
eller på fel i de mätta MID data för en eller flera metaboliter.
Som ett exempel av en tillämpning av VK-MS-baserad 13C-MFA, användes metoden för att undersöka ett annat
komplicerande fenomen inom metabolisk modellering. Även om de ofta modelleras som sådan, så är en cellkultur inte en homogen samling celler. Skillnader mellan cellpopulationer i en kultur kan förekomma på grund av, till exempel, icke-synkron celldelning. Således behandlades frågan om iaktarbara ändringar i flödena i den primära
kolmetabolism av Saccharomyces cerevisiae kan förekomma mellan de olika faserna av celldelningscykeln. För att
kunna iaktta sådana ändringar, utfördes en 13C-märkningsexperiment med en fed-batchodling ympad med en
partiell-synkroniserad cellpopulation som erhållits genom centrifugalelutriation. En sådan kultur visar dynamiska ändringar i fraktionerna av cellerna i olika cellcykelfaser i tiden. För fyra tidspunkter under odlingen, användes de mätta MID:er för att få den bästa uppskattningen för de metaboliska flödena. De erhållda flödesavpassningar antydde att det optimalt avpassade uppspaltningsförhållandet för pentosfosfatrutten ändrade sig med nästan en faktor av 2 upp och ned kring ett värde av 0,27 under experimentets gång. Statistisk analys visade att vissa av de avpassade flödesfördelningar för de olika tidspunkter skiljde sig signifikant åt, och detta tyder på att cellcykel-kopplade variationer i cytosolära metaboliska flöden sannerligen sker.
contents
abstract...7
samenvatting ...9
sammanfattning...11
contents...13
preface...17
introduction...21
1.1 metabolism...21 1.1.1 organisation of metabolism...211.1.2 relevance of metabolic studies in baker’s yeast ...23
1.1.3 structure of yeast metabolism and tools for metabolic research...26
1.1.4 metabolic models ...29
1.2 major challenges in the modelling of metabolism...33
1.2.1 changing environmental conditions...33
1.2.2 cellular compartments ...34
1.2.3 heterogeneity in pure cell cultures ...35
1.2.4 unobservability of phenotypes ...35
1.3 experimental approaches for metabolic research ...37
1.3.1 oxygen programmed fermentation ...40
1.3.2 metabolic engineering ...41
1.3.3 13C-metabolic flux analysis ...42
1.4 this dissertation ...43
application of oxygen programmed fermentation to study
dynamic changes in glycerol metabolism ...45
2.1 abstract...45
2.2 introduction...45
2.3 materials and methods ...48
2.3.1 strains...48
2.3.2 medium ...49
2.3.3 experimental equipment and analytical methods ...49
2.3.4 cultivation procedures...50
2.3.5 oxygen programmed fermentation ...50
2.3.6 RQ-controlled continuous cultures...50
2.4 results...51
2.4.1 oxygen programmed fermentation ...51
2.4.2 RQ-controlled cultures...56
2.5 discussion ...57
metabolic engineering of the anaerobic redox metabolism
...63
3.1 abstract...63
3.2 introduction...63
3.3 material and methods ...66
3.3.1 strains...66
3.3.2 plasmids ...66
3.3.3 media ...67
3.3.4 experimental equipment and cultivation procedures ...67
3.3.5 analyses ...67
3.3.6 NAD+:NADH balances ...69
3.4 results...69
3.4.1 growth and metabolite production...69
3.4.2 intracellular mannitol...70
3.4.3 cell morphology ...74
3.5 discussion ...74
3.5.1 mannitol production ...74
3.5.2 NADH balances...75
3.5.3 intracellular accumulation of mannitol...77
verification of a method for LC-MS based
13C-metabolic flux
analysis by applying it to a yeast culture experiment with
restricted metabolic possibilities ...81
4.1 abstract...81
4.2 introduction...81
4.3 material and methods ...84
4.3.1 strain and precultures ...84
4.3.2 media and substrate preparation ...84
4.3.3 culture conditions ...85
4.3.4 analyses ...86
4.3.5 LC-MS analysis ...87
4.3.6 fraction of 13CO 2...87
4.3.7 elemental biomass composition...88
4.3.8 uptake and production rates ...88
4.3.9 metabolic network model for 13C-MFA ...88
4.3.10 flux fit procedure...90
4.4 results and discussion ...90
4.4.1 elemental biomass composition...90
4.4.2 uptake and production rates ...90
4.4.3 mass isotopomer distributions ...92
4.4.4 13C-metabolic flux analysis...94
application of
13C-MFA to resolve cell-cycle dependent
variations in cytosolic metabolic fluxes ...101
5.1 abstract...101
5.2 introduction...101
5.3 material and methods ...103
5.3.1 strain and media...103
5.3.2 preparation of the inoculum ...104
5.3.3 culture conditions and analyses...104
5.3.4 LC-MS analysis ...105
5.3.5 13CO 2-calibration gas ...105
5.3.6 macroscopic conversion rates ...105
5.3.7 metabolic network model and flux fitting ...107
5.4 results and discussion ...109
5.4.1 synchronisation ...109
5.4.2 macroscopic fluxes...110
5.4.3 MIDs...111
5.4.4 13C-metabolic flux analysis ...114
5.4.5 PPP split ratio and confidence intervals...119
5.4.6 cell cycle metabolism...122
5.5 conclusions...123
future directions ...127
6.1 bioreactors offer excellent experimental conditions...127
6.2 oxygen programmed fermentation ...128
6.3 metabolic engineering ...128
6.4 13C-MFA...129
6.5 cytosolic primary metabolism...130
references ...133
curriculum vitae...145
list of publications...149
preface
I have to admit it. It has been several times during these eleven years that I found myself envying PhD students who were put to work on projects that merely consisted of a sequence of small variations on a general concept; who could work with equipment or analysis
techniques that had already been installed and tested for them; who could work with equipment that always worked properly and if the machine, after a rare case of
mistreatment, would cause problems, it would not be up to them to solve these problems; PhD students who could put their experiments in the fridge at five o’clock and thaw them the next morning; who did not have to come up with their own ideas and experiments because their professor told them what they should be.
Would such a paved course of my PhD studies have made me a person and researcher different from the one I am now? I don’t know… but it would have at least yielded me a PhD degree much faster than what it took now! I guess though, it would not have made me necessarily a better researcher —or a better person for that matter.
A ‘quick and smooth’ PhD would also not have enabled me to meet and work with the majority of the people I met and worked with during these years. Some interesting, some inspiring, some motivating and some merely entertaining, many people contributed in the end to this dissertation and to me and the person I have become.
The larger part of these contributions for the work done in Delft can be ascribed to my supervision team, Wouter van Winden and Prof. Sef Heijnen. I am still gratefully indebted to them for employing me in the first place on this, from the beginning rather unusually set up, project. Their essential, accurate and patient input quickly made me acquaintance with the, as it would turn out, rather complex world of 13C-metabolic flux analysis. Their never-failing
enthusiasm (‘predator instinct’ one could call it also) to deep-dive my experimental results and, probably more important, their ability to re-appear with something fishy about them, kept me sharp and enthusiastic.
Further essential impulses came from my office-sharer Walter van Gulik, both in the form of the MNA network as well as valuable insight in how things work around the institute. The other members of the Bioprocess Technology group, Michiel, Mlawule, Fredrick, Uly, peNia, Roelco, Liang, Reza, André, Hilal, Emrah, Zhang, Lodewijk and all more temporary others, contributed all with fruitful and less fruitful discussions, expert help and by creating a nice working environment. Without our hard-working MS analysis team Cor, Angie and Jan, the BPT group would be nowhere, or at least not where it is now. Without our hard-working
fermentation technical service, Rob, Dirk, Susan and Tom, the whole Institute of Biotechnology would be nowhere, at least not where it is now.
It was a pleasure to work together with Dirk Müller and Prof. Matthias Reuss at the
University of Stuttgart. The many discussions we had on how to interpret the results of the synchronised culture are to me a schoolbook example on how cooperative and
multidisciplinary research should work.
During my time in Delft I had the pleasure to supervise a Bachelor student, Vincent Brouérius van Nidek, who developed a protocol for an anaerobic batch fermentation of
Saccharomyces bulderi. Unfortunately, practical circumstances forced me in the end to
switch to a continuous set-up of the experiments, and that is the tragic reason why his contribution, in my opinion, could not be appropriately reflected in the content of this dissertation.
Out of everything bad comes something good; when Walter and I had to leave our nice and quiet office in the ‘professor corner’ on the second floor, I ended up one floor above and got to be part of the 3rd-floor-coffee-&-chocolate-club. All ‘members’, permanent, temporal or
occasional, need to be acknowledged for shared enthusiasm, shared sorrow, shared stupid ideas and shared chocolate, with a special kiss on the cheek for María, Carol and Inés for taking the lead in these things.
During my years in Sweden I learned to appreciate the occasional enjoyment of silence – something which is not obvious for people born and raised in the Netherlands. It was
therefore with great pleasure that, if the weather allowed me to, I used the Botanical garden surrounding the institute’s building for a retreat to read an article or deliberate a new
experimental set-up. The people taking care of this garden, Bob and his team, should therefore be assured of my appreciation. My further appreciation to all the supporting staff: Sjaak; Arno and his ‘workshoppers’ ; Carla, Jenny and Miranda; Hans and Marcel; Astrid and Apilena; Herman and Jos. A special thanks to our former janitor Wim Morien, always good for a talk and a laugh.
A special thanks also to Prof. Han de Winde and Prof. Jack Pronk for a continued interest in me and my research. My sincere and utmost gratitude to my current boss, Prof. Uwe Sauer for unknowingly pushing me to finish this whole escapade as swiftly and smoothly as possible.
My personal appreciation to my family; my two-and-half year return to the Netherlands did not automatically result in more frequent visits as you might have noticed. Be however assured of my continued interest and engagement in your welfare. My ‘kansloze’ friends, you are a continuing source of reflection on myself and my life. You were and are an essential part of my life abroad, and I hope our returning weekends will keep on returning.
A warm and hearty thanks to my two paranymphs Ilona and Elisabeth. It looks like I finally made it, so come on over and we’ll once more have a wine-filled diner. Just like the one we had when I, rather prematurely, promised the two of you to be my paranymphs at my, at that time still highly uncertain, defence. (Who would have thought… ;-)
Roeland Zürich, 22 October2007
introduction
1.1 metabolism
1.1.1 organisation of metabolism
Metabolism can, in correspondence to, for instance, the genome, be broken down into its
elementary building units [145]. Like the genome
consists of (in order of increasing functional size) base pares, codons, introns, genes and chromosomes, metabolism can be built up from its simplest element (see figure 1.1). The primary building blocks of metabolism are the
chemical reactions, converting one chemical
entity into the other. These chemical reactions can combine into an enzymatic reaction where multiple substrates are converted to multiple products catalyzed by an enzyme and where substrates can become products and vice versa for the same enzyme (reversibility). Multiple enzymatic reactions can form a metabolic pathway if one of the products of one of the enzymes is a substrate for another. Many substrates and metabolic products require special transport proteins to enter and leave the cell whereas also additional transport proteins are needed for transport of metabolites between organelles. All the metabolic pathways and the transport processes together form then the cell’s metabolism.
Main Entry: me·tab·o·lism
Pronunciation: m&-'ta-b&-"li-z&m
Function: noun
Etymology: International Scientific Vocabulary, from Greek metabolE change, from metaballein to change, from meta- +
ballein to throw -- more at DEVIL
1 a : the sum of the processes in the buildup
and destruction of protoplasm; specifically : the chemical changes in living cells by which energy is provided for vital processes and activities and new material is
assimilated b : the sum of the processes by which a particular substance is handled in the living body c : the sum of the metabolic activities taking place in a particular environment <the metabolism of a lake>
[source: Merriam-Webster’s Online Dictionary, www.m-w.com]
chapter 1: introduction
figure 1.1: global build-up of metabolism
Although it is thus possible to break down metabolism into smaller organisational units, the definitions are not as stringent as in the genome example mentioned above. For instance, isoenzymes (two or more enzymes that catalyze the same chemical conversion(s) but are encoded by different genes) are preferentially viewed upon as separate enzymatic reactions if one is interested in pathway regulation but as only a single reaction if the interest is in the end product. There is also no general rule of thumb that limits the size and shape of a pathway.
A metabolic pathway is defined as “any sequence of feasible and observable biochemical
reaction steps connecting a specified set of input and output metabolites” [180]. According to
this definition the metabolic pathway as depicted in figure 1.2, can consist of one, two or even three pathways depending on the definition of input and output. Following from this, a biochemical reaction can be part of several metabolic pathways at the same time. Metabolic pathways that share a common substrate, intermediate, product or function can be grouped together in higher organisational levels like, for instance, nitrogen metabolism, energy-generating metabolism or fatty acid metabolism. In case of a single-cell organism like, for
chapter 1: introduction
instance yeast, the organisation stops at the level of the whole-cell metabolism, although
intercellular signalling in cultures by way of chemical compounds has been reported [160].
For multicellular organisms where cells have differentiated into cell types, these cell types can all have their own typical metabolism. A metabolic product of one cell type could then be a substrate in the metabolism of an other cell type. An example is lactate which is produced by cells in active muscle tissue and converted to glucose by liver cells. The sum of these ‘cell type metabolisms’ constitutes then the metabolism of the higher organism. A similar reasoning holds for mixed cultures or even complete ecosystems in which products from one organism from that culture or ecosystem are the substrate for a second organism.
1.1.2 relevance of metabolic studies in baker’s yeast
Because the metabolism of any given living entity, cell or organism, is by definition a complex, intertwined network of interactions, extensive studies are needed to understand the mechanisms behind proper and improper functioning of metabolism. Metabolism is of importance in numerous, diverse fields of society, for instance, for sustainable, industrial
production processes relying on microbial conversions (white biotechnology) [141] or for
fighting malnutrition in Third-World countries through improvement of the crop quality and
quantity in agriculture (green biotechnology) in these countries [197]. Furthermore, a number
of human diseases is known to have their origin in malfunctioning of one or several steps in
the metabolism [128] (see table 1.1), and appropriate medication (red biotechnology) relies
heavily on understanding the causes of these metabolic defects. Development of techniques that enable the study of all aspects of metabolism in detail is therefore of utmost importance for all these fields.
chapter 1: introduction
table 1.1: examples of metabolic diseases and metabolism-affecting nutritional diseases diagnosed in humans [128]
disease name metabolic defects symptoms occurrence (possible) cure?
diabetes type 1
impaired uptake of glucose caused by the
inability to use the body’s own insulin
high concentrations of sugar in the blood
ca. 5% of the population in Western
countries
daily insulin dosages
galactosemia transferase deficiency galactose-1-uridyl lactose intolerance 1 to 30000 births lactose free diet
glucose galactose malabsorption
defect in glucose and galactose transport across the intestine
severe diarrhoea in infants, lactose and sucrose intolerance ca. 10% of the population (varying severity) fructose-based diets hemachromatosis
excess iron absorption due to disturbed iron
homeostasis
liver cirrhosis and cancer common recessive disorder animal models available Lesch-Nyhan syndrome impaired purine recycling due to loss
of hypoxanthine-guanine phospho-ribosyltransferase
activity
uric acid accumulation leading to self-mutilation, mental
retardation and muscle weakness
rare gene therapy
maple syrup urine disease
impaired breakdown of branched-chain
amino-acids
accumulation of 2-oxoacids in the urine,
neuro-degeneration and infant death
rare restrictive diet, gene therapy
obesity
defective signalling through the hormone
leptin between fat cells and the brain leading to absence of satiety and excessive
food intake
excessive gain of weight, heart disease,
diabetes, stroke and cancer 23% among men, 36% of women and 33% of the children in European countries mouse models available phenylketonuria accumulation of phenylalanine due to phenylalanine hydroxylase deficiency mental retardation, organ damage, unusual posture, compromised pregnancies
1 per 30000 phenylalanine free diet
porphyria disrupted heme production
sun sensitivity, abdominal pain, nausea, personality changes rare avoidance of drugs and alcohol, heme administration Refsum disease impaired lipid metabolism due to accumulation of phytanic acid (breakdown product of chlorophyll)
nerve diseases, failure of muscle coordination, vision
disorder
rare phytanic acid free diet
The foremost daily applications of baker’s yeast (see figure 1.3) and its metabolism are in
food and food preparation. Common edibles like bread, wine and beer are all formed
through the action of yeast. In the preparation of these products, yeast was selected based on the characteristics of its metabolism in a trial-and-error approach throughout many
chapter 1: introduction
figure 1.3: electron microscope picture of Saccharomyces cerevisiae
© www.genomenewsnetwork.org
centuries. The metabolism of yeast specifically enables it to quickly break down sugars and from that create carbon dioxide (important for the production of bread), and ethanol (for the wine and the beer fermentation). Although improvement of these original applications was (and still is) the primary reason to investigate yeast metabolism, by now a considerable effort is also put in widening the applications of yeast metabolism and fermentations (see table 1.2). Specifically the usage of substrates and the production of substances not naturally consumed or produced by yeast are of interest in this respect. This deviation from the traditional use of yeast in a production process can be instigated for different reasons, for instance, cost-reduction, energy-efficiency, waste-stream management, added product-value or process integration.
Besides its traditional and new applications in the fermentation industry, the metabolism of the eukaryote yeast has become a focal point of metabolic research also for other reasons. Owing to its fundamental similarity in cellular organisation to the eukaryotic human cells (see table 1.3) and its ease of cultivation, yeast has become a frequently-used model organism for studies of human metabolic behaviour and diseases. Especially on the whole-cell level in which all organisational levels of the cell are viewed (systems biology), yeast offers a representative and easy-to-handle platform to study metabolism in vivo and in silico. For instance, Saccharomyces cerevisiae has been used as a model organism for research on the human disease adrenoleukodystrophy, a neurological disease which is caused by the lack of an enzyme activity that degrades very-long-chain fatty acids in the brain. Enzymes
product improvement main players reference
biofuel from agricultural by-products
extended ability to ferment sugars by baker’s yeast
TU Delft, Bird
Engineering, Nedalco NV [95]
biofuel from wood
increased tolerance of baker’s yeast to the harsh process conditions, facilitation of an integrated, one-step
process
Lund University, Sekab [217]
hydrocortisone
introduction of an artificial and self-sufficient biosynthetic pathway in a single Saccharomyces cerevisiae strain
Aventis Pharma [119]
artemisinin (anti-malaria drug)
semi-synthetic production with Saccharomyces cerevisiae as host, replacing the expensive extraction
from its natural source
University of California Berkeley, Bill and Melinda
Gates Foundation
[27] table 1.2: recent examples of non-traditional applications of baker’s yeast in industrial processes
chapter 1: introduction
Saccharomyces yeast humans
important similarities separate nucleus with membrane
chemo-organotrophic
glycolysis and the tricarboxylic acid cycle are major catabolic routes ATP generation possible through the electron transport chain
DNA organised in linear chromosomes
many intracellular membranes (for instance ER and Golgi)
compartmented due to the presence of organelles (among others mitochondria) 80S ribosomes with a 40S and 60S subunit
cell division through mitosis cells age
actin skeleton
spatially separated transcription and translation important differences
asymmetrical cell division symmetrical cell division
cell wall no cell wall
DNA partly organised on plasmids no plasmids present
vacuoles no vacuoles
haploid (most laboratory strains) diploid
corresponding to the enzymatic activities failing in patients were identified in Saccharomyces cerevisiae and used for elucidating the mechanisms of erroneous and inadequate fatty acid degradation (see for instance reference [174]). Also in gerontology, the study of aging, yeast
has been rewardingly used as a model organism [103].
1.1.3 structure of yeast metabolism and tools for metabolic research
In the genome of Saccharomyces cerevisiae originally 5780 open reading frames (ORFs)
were detected [62]. Currently 5794 ORFs have been located on the genome and a larger part
of those is transcribed into mRNA (the transcriptome) [173]. These ORFs were initially
estimated to code for ca. 4800 functional proteins (the proteome) [90], although currently
6163 proteins with the annotation ‘_YEAST’ (for Saccharomyces cerevisiae) are to be found
in the Swiss-Prot database [183]. Many proteins are involved in cell assemblage, gene
regulation and other ‘non-metabolic’ activities, so only a part of the total proteome has a metabolic function. To determine the cell’s complete metabolic capacity, it is necessary to screen the genome and the functions that the ORFs in it have been given, for genes with a proven or predicted function in metabolic reactions or metabolite transport.
The first reconstruction of a metabolic network for Saccharomyces cerevisiae based on its annotated genome, yielded 708 identified ORFs and their respective transcript products
responsible for reactions converting or translocating metabolites [48]. Based on their
characteristics, the 708 (hypothetical) gene products could catalyze 1035 different metabolic
table 1.3: similarities and differences between Saccharomyces yeast and humans
chapter 1: introduction
reactions due to, among others, substrate and product promiscuity of enzymes, the presence of isogenes and isoenzymes and functional enzyme subunits encoded by different ORFs. In addition to these 1035 reactions, 140 metabolic functions known to be present in Saccharomyces cerevisiae but for which the gene had not yet been identified, were added to
the metabolic network [48]. The total of 1175 metabolic reactions included in the model
involved 584 different metabolites participating in these reactions (the metabolome) [48]. In
subsequent revisions and extensions of this genome-scale stoichiometric metabolic network model, these numbers have decreased and increased somewhat depending on the
modelling approach [41] [72] [93]. *
Several approaches exist for the in vivo determination of the metabolome. These vary from the quantification of the absolute concentrations for a limited number of identified metabolites (targets), to the mere comparison of qualitative data on the presence or absence of a larger
number of unidentified metabolites (profiling) [130]. The first systematic study of differential
behaviour between a number of yeast mutant strains using the metabolite profiles of these
mutants to detect those differences, was reported by Raamsdonk and co-workers [153].
Although only accounting for six metabolites, this approach already identified differences between the mutants and led to identification of the gene function of several of the genes the mutants were deleted for. Over the last years, the commonly used analysis technique in metabolome analysis has become mass spectrometry (MS), although nuclear magnetic resonance (NMR) analysis has also been used (see reference [213] for a review). Current metabolomics techniques based on mass spectrometry analysis, allow for the identification of over one hundred of different metabolites (see for instance, references [11] or [109]).
In many metabolic studies the information content of solely the identity and extra- and intracellular concentrations of substrates, metabolites and products is not enough to create
enough clarity about the relevant biochemical processes [166]. The picture of, for instance, the
dynamics of metabolism does not become complete before conversion rates of substrate, products and eventually also metabolites have been determined (the fluxome). Quantitative conversion rates for extracellular substrates can be determined relatively easily, for instance in case of a batch process, from their time-dependent concentration profiles, but this becomes more difficult for individual intracellular reaction steps due to the complexity of the intracellular metabolic network.
* for comparison: The first published reconstructed genome-scale model of Escherichia coli, a prokaryote with a genome of
4403 genes, included 660 ORFs and 627 unique biochemical reactions [44]. A similar model for Aspergillus nidulans, a fungus
chapter 1: introduction
A frequently applied method to estimate these intracellular fluxes is metabolic flux analysis
(MFA, also called flux distribution analysis) [200]. In MFA, metabolite balances of intracellular
reactions are coupled to the conversion rates of products and substrates measured
extracellularly [180]. With the help of a mathematical representation of these coupled balances
(see figure 1.4), fluxes for each modelled reaction step can be calculated. Pathways included in the model can be described as multiple, single-step biochemical reactions or as one single reaction covering a series of sequential reactions (lumped description). Good examples of the application of MFA for yeast metabolism can be found in references [51], [132] and [165]. For an extensive discussion on MFA the reader is referred to reference [180].
In certain cases, for instance when parallel routes or cyclic pathways occur in a metabolic network, MFA cannot distinguish the relative sizes of each of the involved metabolic fluxes.
In those cases, isotopic labelling experiments with 13C-labeled carbon yield additional
constraints on the metabolic network by way of the positions of the carbon atoms in the
metabolites (13
C-MFA) (see reference [166] for a recent review). (Mass) isotopomers of
metabolites can be detected by either NMR [220] or MS [30] and several (mathematical)
approaches exist for the conversion of the measured MIDs to flux ratios and absolute fluxes
chapter 1: introduction
(see, among others references [7] and [207]). Furthermore, in order to attain separation of different metabolites prior to detection in the MS, MS is coupled to a chromatography or an electrophoresis system.
1.1.4 metabolic models
It is known for yeast, but also for the metabolism of other organisms, that there is a characteristic pattern
of interconnectivity between metabolites [216]. This
so-called ‘small-world network’ enables the linkage of one metabolite to all others through only a small number of biochemical entities (a gene, a protein, a reaction, etc.). This is mainly the result of the presence of a limited number of metabolites which appear in many reactions and therefore have a high
connectivity [77] [216]. In the metabolic network model of
Saccharomyces cerevisiae mentioned in section 1.1.3, for instance, the proton is the most connected metabolite participating in no less than 229 reactions
(see table 1.4) [48]. Noteworthy in this respect are also
the enzymatic cofactors NAD(P)+/H and ATP/ADP. Because of this high interconnectivity, it is
hard to study single, isolated reactions or pathways and neglecting the influence of the rest of the metabolism on this particular reaction or pathway. On the other hand, taking into account the influence of the total metabolic activity on this single reaction or pathway is similarly complicated by the complexity and the number of the interactions. Furthermore, the high level of transcriptional, translational and allosteric regulation in yeast metabolism makes
accurate prediction of the effect a change in a certain reaction or pathway has on the rest
of the metabolism not a straightforward procedure. It should thus be clear that when engineering changes in yeast metabolism, whether it concerns endogenous characteristics or conveying new heterologous ones, besides extensive knowledge of the metabolism, also powerful tools to make accurate predictions on the effect of the engineered change are a prerequisite.
With metabolic models, mathematical representations of the metabolism, metabolic responses to changes imposed on the system can be generated and predicted in a systematic, quantitative way by simulating them with the help of a computer (in silico). The results from these simulated experiments then allow for the formulation of additional hypotheses and design of experiments to test those. These mathematical metabolic models
metabolite number of reactions in which the metabolite participates H+ 229 (out of 1175) ATP 188 ADP 146 phosphate 131 CO2 90 NADP+ 86 diphosphate 81 NADPH 78 NAD+ 78 glutamate 68 NADH 65 NH4+ 56
table 1.4: connectivity of metabolites in Saccharomyces cerevisiae (taken from reference [48])
chapter 1: introduction
are available at various levels of complexity and with different approach philosophies. Two main types of mathematical modelling approaches with broad applications in metabolic models that can be distinguished are the constraint-based and the mechanism-based
approach [178]. A third approach, the interaction-based model has its main applications in
modelling of gene regulation and protein-protein interactions [178] (see figure 1.5).
Constrained-based models, often referred to as stoichiometric models when concerning metabolic models, contain restrictions in the form of reaction stoichiometries and directions that link substrates and products in a quantitative way. Given a certain input, for instance a set of measured yields of products formed, a solution can be calculated with these models that gives the individual contributions of each step in the model to the whole metabolism. Because in general, these models are mathematically underdetermined systems (that is, there are more variables in the model than what can be determined with the input data), there will be a range of different solutions which all obey the model’s restrictions (the solution space). In case of a stoichiometric metabolic model this solution space represents all the metabolic phenotypes the cell potentially can attain. In order to come to a unique solution/phenotype, a separate objective function needs to be defined, for instance,
maximisation of cell growth (flux balance analysis (FBA) [43]). The reconstructed
genome-scale metabolic models discussed in section 1.1.3 are an example of this type of stoichiometry-based metabolic models.
figure 1.5: approaches to mathematical models used in the study of microbial metabolism, their characteristics and their typical representation of results (taken from reference [178]; © Current Opinion in Microbiology)
chapter 1: introduction
The stoichiometry of a metabolic reaction can be defined in different forms (see table 1.5), all with their own implications for the model in which they are used. The carbon-atom mapping model used in the work presented in chapters 4 and 5, although not a metabolic model in the strict sense of the words anymore, is therefore also an example of an application of a constraint-based mathematical model on metabolism. The detailed mapping of the position of the carbon atoms in the substrate(s) and the product(s) of each reaction gives constraints
with which, in combination with the 13C-labeling pattern in the overall substrate, a solution
space can be made up, much in the same way as in FBA. Only instead of flux distributions, as is the case for FBA, the solution space now consists of patterns of mass isotopomer distributions of metabolites connected to patterns of flux ratios. With a separate objective, for instance minimisation of the differences with a measured set of mass isotopomer distributions, a single solution, in this case consisting of a unique set of flux ratios, can be found. Further restrictions can be given by including, for instance, net fluxes of metabolites in and out of the network, which will yield flux patterns rather than flux ratios as final result.
stoichiometry
based on representation
metabolite identity fructose 1,6-bisphosphate
↔
dihydroxyacetone phosphate + glyceraldehyde 3-phosphate elemental composition C6H14O12P2 2 C3H7O6Pmolecular structure
carbon atom position
position in bisphosphate fructose
1,6-C1 C2 C3 C4 C5 C6 C1 0 0 1 0 0 0 C2 0 1 0 0 0 0 dihydroxyacetone phosphate C3 1 0 0 0 0 0 C1 0 0 0 1 0 0 C2 0 0 0 0 1 0 glyceraldehyde 3-phosphate C3 0 0 0 0 0 1
table 1.5: different ways to define the stoichiometry of a reaction included in a stoichiometry-based metabolic model. The reaction catalyzed by diphosphofructoaldolase is taken as an example.
chapter 1: introduction
Mechanism-based models, often referred to as kinetic models when concerning metabolism, consist of detailed descriptions of each modelled biochemical step proven to be involved in the cellular process or event under investigation. They therefore rely much stronger on detailed experimental data than constraint-based models, and henceforth crave more effort to be constructed accurately. In return, their predictive power is believed to be stronger because they yield directly detailed, quantitative predictions on cellular behaviour
[178].
A drawback of kinetic metabolic models is, besides their escalating complexity and calculation time on a system level, the fact that, so far, the biochemical and thermodynamic information these models rely on, has been obtained through experiments for each modelled step separately, instead of for the system as a whole. The information needed for detailed kinetic modelling could include, for instance, substrate, product and enzyme abundances,
substrate affinities (Km), maximum conversion rates (Vmax), turn-over rates of enzymes or, in
the case of approximative kinetics, elasticities and flux control coefficients [69]. The assessed
values of these parameters have however often been determined under conditions different from those that are actually tested with the model. For instance, many enzyme-kinetic parameters have traditionally been determined on isolated systems ‘outside’ of the cell (in vitro). The obtained parameter values may therefore be different from the actual values applying intracellularly (in vivo) and hence lead to erroneous or inaccurate predictions by the
model [178].
Inclusion of the assessed kinetic parameters in metabolic models, however, renders them in general mathematically determined as opposed to constraint-based models. This has as a result (and major advantage) that it becomes possible with a kinetic metabolic model to predict dynamic (that is, including a time aspect) features of a biological system, for instance growth rates. A stoichiometric metabolic model can, due to the lack of this dynamic aspect, only predict static behaviour, for instance, a growth yield. For further discussion on this and other aspects of metabolic models, the reader is referred to the review in reference [178]. Although usage of metabolic models greatly facilitates the study of metabolism and assists in predicting the changes in metabolic behaviour caused by environmental or physiological perturbations, these models are only as complete, and their predictive power only as accurate as the experimental data they are based upon allows them to be. The quality and quantity of these data sets is therefore of utmost importance for the creation of useful metabolic models. Special care should be taken when generating these data sets and setting
