University of Groningen
Metabolic-flux dependent regulation of microbial physiology
Litsios, Athanasios; Ortega, Álvaro D; Wit, Ernst C; Heinemann, Matthias
Published in:Current Opinion in Microbiology DOI:
10.1016/j.mib.2017.10.029
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Litsios, A., Ortega, Á. D., Wit, E. C., & Heinemann, M. (2018). Metabolic-flux dependent regulation of microbial physiology. Current Opinion in Microbiology, 42, 71-78. https://doi.org/10.1016/j.mib.2017.10.029
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Metabolic-flux
dependent
regulation
of
microbial
physiology
Athanasios
Litsios
1,3,
A´lvaro
D
Ortega
1,3,
Ernst
C
Wit
2and
Matthias
Heinemann
1Accordingtothemostprevalentnotion,changesincellular physiologyprimarilyoccurinresponsetoalteredenvironmental conditions.Yet,recentstudieshaveshownthatchangesin metabolicfluxescanalsotriggerphenotypicchangeseven whenenvironmentalconditionsareunchanged.Thissuggests thatcellshavemechanismsinplacetoassessthemagnitudeof metabolicfluxes,thatis,therateofmetabolicreactions,and usethisinformationtoregulatetheirphysiology.Inthisreview, wedescriberecentevidenceformetabolicflux-sensingand flux-dependentregulation.Furthermore,wediscusshowsuch sensingandregulationcanbemechanisticallyachievedand presentasetofnewcandidatesforflux-signalingmetabolites. Similartometabolic-fluxsensing,wearguethatcellscanalso senseproteintranslationflux.Finally,weelaborateonthe advantagesthatflux-basedregulationcanconfertocells.
Addresses
1MolecularSystemsBiology,GroningenBiomolecularSciencesand
BiotechnologyInstitute,UniversityofGroningen,Nijenborgh4,9747AG Groningen,TheNetherlands
2ProbabilityandStatistics,JohannBernoulliInstituteofMathematics
andComputerScience,UniversityofGroningen,Nijenborgh9,9747AG Groningen,TheNetherlands
Correspondingauthor:Heinemann,Matthias(m.heinemann@rug.nl)
3Sharedfirstauthors.
CurrentOpinioninMicrobiology2017,42:71–78 ThisreviewcomesfromathemedissueonCellregulation EditedbyJan-WillemVeeningandRitaTamayo
https://doi.org/10.1016/j.mib.2017.10.029
1369-5274/ã2017TheAuthors.PublishedbyElsevierLtd.Thisisan openaccessarticleundertheCCBYlicense(http://creativecommons. org/licenses/by/4.0/).
Introduction
Microorganisms are often confronted with changes in
their environment, for instance, in terms of nutrient
availability.Directassessmentoftheextracellular
condi-tions, for example through two-component systems in
bacteria [1], often leads to adaptations in response to
environmental changes. However, there is increasing
evidenceshowingthatmicrobialcellscandisplaychanges
in their phenotype, for example, in growth rate, gene
expressionandmetabolism,alsoinresponsetochangesin
intracellularmetabolicfluxes,evenwhentheextracellular
conditions are kept constant [2–5]. But does this
flux-dependentregulationhaveamajorimpactoncell
physiol-ogy?Howcancellsmechanisticallysensemetabolicfluxes,
that is, rates of enzymatic reactions andmetabolic
path-ways,andusethisinformationforregulation?Andwhyis
thisregulationadvantageoustothe cell?
Microorganisms
display
flux-dependent
phenotypes
Accumulatingevidencesuggeststhatmicrobialcellscan
displayphenotypesimposedbymetabolicfluxes,andnot
directly byextracellularconditions. Oneexample isthe
switch from respiratory to fermentative metabolism in
glucose-rich conditions. When both Escherichia coli and
yeastweregrowninthesamenutrientenvironment,but
the rate of sugar uptake was controlled by inducible
expressionof sugarpermeasesorbyusinghexose
trans-portervariantswithdifferentkinetics(Figure1a)
respec-tively, a glycolytic flux-dependence of the metabolic
mode—a respiratory or fermentative metabolism—
was found[4,6].Ameta-analysisofdatafromanumber
of studiesthat useddifferentyeast strainsgrownunder
differentconditionssuggestedthatthisswitchistriggered
whenaspecificsugaruptakerateisexceeded[7].Because
the onset of ethanol production is accompanied by a
decreaseintheoxygenuptakerate,thisstudysuggested
that this ‘overflow metabolism’is an active responseto
the level of glycolytic flux, rather than a limitation in
oxidative metabolism.
Intracellular fluxchangesunderconstantenvironmental
conditionscanalsore-shapeproteomeexpression.
Prote-ome analysescarriedoutonbacteriagrowninlactoseas
thesolecarbonsourcebutinwhichmetabolicfluxeswere
modulatedbytitratingtheexpressionofeitherthelactose
permeaseor theenzymeinvolvedinammonia
assimila-tion showed thatas muchas 50% of theproteomewas
alteredintheseconditions[8].Similarly,a
comprehen-sive fluxomics and proteomics analysis in S. cerevisiae
strainswhich weregrownin thesame environment but
had different hexose uptake capacities, found that the
expression of nearly half of 200 quantified metabolic
proteins changed in aflux-dependent manner.Proteins
whose expression correlated positively with glycolytic
flux were found to be enriched for glycolytic proteins.
Ontheotherhand,proteinswithexpressionlevels
nega-tively correlating with glycolytic fluxwere enrichedfor
proteins involved in the TCA cycle, and in pyruvate,
Apartfromdeterminingthemetabolicmodeanddictating
proteinexpression, metabolicfluxescontrolgrowth.
Glu-coseinfluxdeterminesgrowthrateinS.cerevisiae[2,4],and inE.coli[9].Inaddition,thefractionofE. colicellsthat
enterspersistence,astateofnoorslow-growth
character-ized by antibiotic tolerance, was shown to anticorrelate
withglucoseinfluxwhentheratiobetweenglucoseanda
non-metabolizable analogue was modulated [10].
Simi-larly, after a nutrient shift of E. coli from glucose to
fumarate,persistercells areformed,andthe rateof
per-sisterformationcorrelatesnegativelywithfumarateuptake
rate[11,12].Whilethe most prevalentnotion has been
thatpersistence is triggered by toxin–antitoxin systems,
recentwork demonstratedthatpreviousfindings
consid-ering toxin–antitoxin systems contained artifacts [13].
Thus, as suggested by the above-mentioned findings,
persistenceentryislikelymetabolicflux-dependent.
How
do
cells
measure
and
use
fluxes
for
regulation?
Theimportantquestionthatarisesishowcellsare
capa-bleofassessingthelevelof metabolicflux,and usethis
informationfor regulation. Changesin flux,induced by
environmentalchangesorstochasticexpressionof
trans-portersorenzymes,couldbeassessedbychangesinthe
concentrationofpathwayintermediates(Figure1a).
How-ever,theconcentrationsofmetabolitesaredeterminedby
the combination of the kinetics of the consuming and
producing reactions. Metabolite concentrations do not
necessarilychange when fluxesare altered [14],nordo
theynecessarilyscalewithflux[15].Therefore,to
accom-plishflux-sensingviatheconcentrationofcertain
metab-olites,specific kineticsoftheinvolvedenzymesand
spe-cificregulation ofthese enzymesare required,suchthat
the strict correlation (or alternatively, anti-correlation)
betweenthemetaboliteconcentrationandmetabolicflux
isanemergingbehavior.Werefertometaboliteswithsuch
abehaviorasflux-signalingmetabolites.
The glycolytic intermediate fructose-1,6-bisphosphate
(FBP)hasbeenidentifiedas aflux-signalingmetabolite
[16]. FBP levels correlate with glycolytic flux across a
broad range of microbial species and conditions
[3,7,14,17,18–20], and even in dynamic perturbations
of glycolysis [21]. It has been found recently that the
molecular system translating the glycolytic flux into
theFBPlevelencompassesallenzymesoflower
glycol-ysis including the feedforward activation of pyruvate
kinase by FBP, which ensures that FBP concentration
correlateslinearlywithglycolyticfluxoverabroadrange
offluxes[17].
Totransducethefluxinformation‘stored’inthe
concen-tration of a flux-signaling metabolite (e.g. FBP) into a
response,aconcentration-dependentinteractionbetween
theflux-signaling metabolite and other cellular
compo-nents is required. In fact, it is well documented that
metabolitesinteractwithandregulatemetabolicenzymes
[22],transcriptionfactors[23,24],proteinkinases[25,26],
and cis-regulatory RNA sequences (riboswitches)
(Figure 1b). Additionally, some metabolites (e.g.
ace-tyl-CoA) can have a critical role in the expression of
specificgenes becausethey areutilized as substrate for
covalentmodificationsofhistones[27,28].However,the
physiologicalrelevanceofsuchinteractionsinmostcases
isstillunclear.Mostavailableinformationstemsfromin
vitro studiesfocusing on purifiedindividual proteins or
RNAspecies[29],inpartbecausedirectperturbationof
metabolitelevelsinlivingcellswithoutoff-targeteffects
isstillimpossible.However,bysystematically
investigat-ing metabolites that affect transcriptional regulation in
vivo,Kochanowski and co-workers showed that indeed
(flux-signaling)metabolites(cyclicAMP,FBP,and
fruc-tose-1-phosphate) interacting with two major
transcrip-tionfactors(CrpandCra)areresponsibleforthemajority
ofthetranscriptionalregulationobservedacross23diverse
growthconditionsin E.coli[30].
While previously interactions between metabolites and
other cellular molecules were mostly found by
72 Cellregulation Figure1 Flux-sensing Flux-dependent regulation High flux Flux-signaling metabolite Flux-signaling metabolite Low flux [Metabolite] Cellular processes TFs Histones Riboswitches Kinases Enzymes FLUX (a) (b)
Current Opinion in Microbiology
Schematicillustrationofflux-sensingandflux-dependentregulation. (a)Kineticsofenzymesandtheirregulationissuchthatcertain intermediatesbecomeflux-signalingmetabolites,thatis,theirlevels eithercorrelate(oranticorrelate)withmetabolicflux.Atconstant nutrientconditions,differencesinmetabolicfluxescanbeachieved throughvariationsinnutrienttransporterlevels(illustratedinthe scheme),orthroughvariationsofflux-limitingenzymes.(b)Information aboutmetabolicfluxisimprintedintotheconcentrationofa flux-signalingmetabolite,whichtheninteractswithregulatoryfactorsand enzymes(greybox)tocontrolothercellularprocesses(including metabolism).Abbreviation:TFs,transcriptionfactors.
serendipity or intuition, recently, significant advances
were made towards unbiased and global identification
methods.Forinstance,Lietal.usedmassspectrometryto
identify hydrophobic metabolites bound to protein
kinases and enzymes involved in the biosynthesis of
ergosterolinyeast[31].Thisstudyrevealednovel
inter-actions among intermediates of the ergosterol pathway
and17differentenzymes(70%oftheergosterol
biosyn-thesis enzymes).Inanother study,amethodwas
devel-oped basedonlimiteddigestionofproteomes extracted
under non-denaturinginvivoconditionscoupledto
tar-getedproteomics.Thisallowed forscreeningof
confor-mational rearrangements of proteins upon metabolite
binding [32]. Besides confirming previously described
metabolite-protein interactions, the authors also
sug-gested manynovelallostericinteractions [32].
Apart from proteins, metabolites can also interact with
riboswitches—RNA elements that conditionally
regu-late geneexpression (transcriptionalregulation,in most
cases) dependingonthepresenceofasmallcompound.
Recently,Daretal.mappedthe30and50endsandglobal
transcriptlevelsindifferentmodelmicroorganismsandin
a complex microbial consortium from oral microbiota
and discovered a plethora of unknown potential
ribos-witches.Interestingly,bycomparingthelevelsof
condi-tional transcription termination after depleting lysine
fromthemediumoraddinganantibiotic,theyidentified
riboswitchesthatspecificallyrespondtoagiven
metabo-lite.Thisworkhasthepotentialtobedevelopedintoa
pipeline for high throughput screening of
metabolite-sensitiveRNAregulators [33].
Collectively,theinteractionofflux-signalingmetabolites
with other macromolecules can exert flux-dependent
regulation at different levels(e.g. gene expression and
enzyme activityregulation). Through therecent
devel-opmentintechniquestoidentify
metabolite–macromol-eculeinteractionswearegettingclosertoafullpictureon
how and which processesmight be regulatedin a
flux-dependentmanner.
On
the
quest
for
flux-signaling
metabolites
Although several metabolites may exert control over
cellular functions, how can we identify flux-signaling
metabolites? As mentioned, flux-signaling metabolites
(i) exhibitchangesintheirconcentration in responseto
changes in metabolic flux, and (ii) interact with other
macromoleculesinordertotranslatetheflux-information
intoacellularresponse.Withthegoaltoidentify
metab-olitesthatfulfillthesecriteria,andarethuscandidatesfor
mediatingflux-signaling,wefirstgatheredconcentration
data of glycolytic, tricarboxylic acid (TCA) cycle, and
pentose phosphate pathway (PPP) metabolites which
were generated in quantitative metabolomics
experi-ments, from seven independent studies performed on
threemicrobes (E.coli,B.subtilis,andS.aureus)[21,34–
39].To maximizethechancesto identifyflux-signaling
metabolites,thedatasetcontained30differentnutrient
regimesatsteady-stateorduringdynamicperturbations.
Weperformedastatisticalanalysisonthesedata(usinga
linearmixedeffectsmodel)toidentifythosemetabolites
whose concentrations vary most across the conditions.
Here, we found that different metabolites have largely
differentvariances(Figure2a,toprow).Themetabolites
withthe highestvarianceacrossconditionsareFBP,
cit-rate, succinate, 6-phosphogluconate (6PG),
ribulose-5-phosphate(Ru5P),andsedoheptulose-7-phosphate(S7P).
Secondly,wegatheredinformationaboutinteractionsof
thesamemetaboliteswithenzymes(https://metacyc.org/,
[40]), regulatory proteins, as wellas transcriptionaland
translational regulators (www.rcsb.org, [41]; http://
regulondb.ccg.unam.mx/,[42]).Here,wefoundthat
cer-tain metabolites had many more interactions with
enzymesandregulatoryproteinsthanothersofthesame
pathway.Particularly,citrateandalpha-ketoglutarate
(A-KG)fromtheTCAcycle,andFBP,phosphoenolpyruvate
(PEP)andpyruvatefromglycolysisstoodout(Figure2a,
central andbottomrows).
Takingthedataonthemetabolites’concentration
vari-ance and interactions with enzymes and regulators
together(Figure2b),weconfirmedFBPasa
flux-signal-ing metabolite [17]. Furthermore, we identified new
candidates for flux-signaling metabolites. For instance,
citrate,phosphoenolpyruvate(PEP)andA-KGscorehigh
on both criteria in Figure 2b, and are thus excellent
candidates, as well as succinate (SUC) and pyruvate
(PYR).Infact,itwasrecentlyshowninS.cerevisiaethat
citrateconcentration increaseswhennitrogenislimited,
andthatitsconcentrationcorrelateswellwiththedegree
ofnitrogenlimitation[14],suggestingthatcitratecould
reportonthemagnitudeofnitrogeninflux.Citratecould
exert flux-dependentregulation asan inhibitorof
pyru-vate kinase [14]; nitrogen influx would be sensed via
citrate and then lead to regulation of the flux through
glycolysis.Interestingly,a-ketoglutarate,theotherTCA
metabolitethatweidentifiedasapotentialflux-signaling
metabolite,wasalsoreportedtocoordinateglycolyticflux
withnitrogenuptake,butinE.coli[43].Suchregulatory
cross-talk between metabolicpathways (some of which
possiblymediatedinaflux-dependentmanner)seemsto
be rather common in central metabolism: metabolites
from one metabolic pathway to regulate enzymes in a
differentpathway(Figure 2c).
Unstable
proteins
as
reporters
of
translation
flux
Whileflux-signalingmetabolitescanreportonmetabolic
flux through specific pathways, important cellular
deci-sions, asfor exampletheentry tocelldivision,possibly
requirestheassessmentofthecellularmetabolicactivity
consideredas anexcellent reporterof overall metabolic activityofacell,forthefollowingreasons:(i)synthesizing
ribosomes requires most of the cellular biosynthetic
capacity [45], (ii) protein translation is by far themost
expensivebiosyntheticprocessinthecell[46],and(iii)a
highrateofproteinsynthesisalsoreflectsa
well-coordi-natedactivityofcentralmetabolism,astheproductionof
thedifferentamino acidsrequiredfor proteinsynthesis
demands a well-coordinated operation of several
path-waysincentralmetabolism[47,48].
Alsohere,toassesstranslationflux,fluxistranslatedintoa
measurablequantity,andinspecificaprotein
concentra-tion(Figure3).Informationabouttranslationfluxcanbe
imprinted into the levels of a proteinif this protein is
constitutivelyexpressedandhasaveryshorthalf-life(i.e.
thereisahighproteindegradationflux).Inthiscase,the
levelofthisproteinreflectstheinstantaneoustranslation
rate.Infact,Bellandcolleaguesmeasuredthehalf-lifeof
3751proteinsin exponentiallygrowingS.cerevisiae,and
foundanumberof very unstableproteins (161 proteins
with a half-life of <4minutes) that were enriched in
proteinsinvolvedincellregulation[49].Similar
conclu-sionswerealsodrawnfromanotherproteome-widestudy,
in which it was shown that the classes of short-lived
proteinsareenrichedincellularregulators[50],although
inamorerecentstudyalsoproteinsinvolvedinribosomes
andaminoacidbiosynthesishadhighturnoverrates[51].
Throughsuch short-livedproteins, reportingtranslation
flux[52],acellcouldexert regulationonthebasisofits overallmetabolic activity(Figure 3).
Anexampleoftranslation-fluxbasedregulationinvolves
the budding yeast cyclin Cln3, which is a remarkably
74 Cellregulation Figure2 (a) Glycolysis 1.5 1.0 0.5 1.5 1.4 S7P Citrate PPP Glycolysis TC A G6P F6P 3PG Cis-Aco FUM MAL PYR A-KG PEP 6PG R5P FBP RU5P SUC X5P Isocitrate BPG DHAP SUC-CoA 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0.0 0.05 0.10
Scaled number of interactions of metabolite
0.15 0.20 0.25 0.30 0.35 1.0 0.5 0 0 1.5 1.0 0.5 0 V a riance Enzymes Regulators 20 15 10 5 0 20 20 15 10 10 5 0 8 16 16 12 12 4 4 4 3 2 1 0 0 G6P F6P FBP 3PG BPG
DHAP G3P 2PG PEP PYR Citrate Cis-Aco
Isocitrate
A-KG
SUC-CoA
SUC FUM MAL OAA
E4P Metabolite’ s variance S7P R5P X5P RU5P 6PG 6GPDL 3 2 1 0 TCA PPP (b) (C)
Current Opinion in Microbiology
Identificationofflux-signalingmetabolitesandmetabolite-mediatedregulatorycrosstalkbetweendifferentmetabolicpathways.(a)Fromtopto bottom,varianceinconcentrationofmetabolitesacrossnutrientconditions(blackdotindicateslackofdata),numberofuniquemetabolite– enzymeregulatoryinteractions,andnumberofuniquemetaboliteinteractionswithproteinsinvolvedintheregulationofgeneexpression. Abbreviations:G6P,glucose-6phosphate;F6P,fructose-6-phosphate;FBP,fructose1,6-bisphosphate;3PG,glycerate3-phosphate;BPG, 1,3-bisphosphoglycerate;DHAP,dihydroxyacetonephosphate;G3P,glyceraldehyde3phosphate;2PG,glycerate2-phosphate;PEP,phosphoenol pyruvate;PYR,pyruvate;Cis-Aco,cis-aconitate;A-KG,a-ketoglutarate;SUC-CoA,succinyl-CoA;SUC,succinate;FUM,fumarate;MAL,malate; OAA,oxaloacetate;6GPDL,6-phosphogluconolactone;6PG,6-phosphogluconate;RU5P,ribulose-5-phosphate;X5P,xylulose-5-phosphate;R5P, ribose-5-phosphate;S7P,sedoheptulose-7-phosphate;E4P,erythrose-4-phosphate.Fortheestimationofthevariancecomponents,alinear mixedeffectsmodelwasfittothemetaboliteconcentrations,wherebytheinterestcenteredonhowmuchthevariousmetaboliteconcentrations variedacrossthedifferentconditions,controllingforthestate(dynamic/steady)andthevariousstudies.Foreachmetabolite,arandomintercept wasestimatedacrosstheconditionsandthevariancecomponentassociatedwiththatrandominterceptexpressedhowmucheachmetabolite variedacrosstheconditions.Afewmetabolites(RU5P,X5P,S7P,isocitrate)wereonlymeasuredwithinonestudyandthereforedidnotneedto becontrolledforstudyandafewothermetabolites(3PG,PYR,citrate,Cis-Aco,SUC-CoA)wereonlymeasuredatoneconditionanddidnot requirecontrolforthatvariableeither.Toobtainputativeinteractionsbetweenmetabolitesandregulatoryproteins,asearchintheProteinData Bankwasperformedusingthenameofeachmetabolitetogetherwiththeword‘transcription’askeywords,andthehitsweremanuallyexamined. (b)Metabolitevarianceversusthenumberofametabolite’sinteractionswithenzymesandregulatorsrelativetothetotalnumberofinteractionsof allmetabolitesinthepathway.Toestimatetheuncertaintyinnormalizedinteractions,webootstrapped(i.e.recalculatedthesevaluesfrom reduceddatasetswhereweeachtimeleftout10%oftheinteractions)andthendeterminedthecoefficientofvariance.Markersizesreflectthe inverseofthecoefficientofvariations.Forregressionline:R2=0.69,p-value1.58e6.Metaboliteswithlargemarkerswithinthegreycirclesare strongcandidatesforflux-signalingmetabolites.(c)Circosplot[44]showingthecross-regulationbetweenpathwaysthroughmetabolite–enzyme interactions.Thefulllengthofeachideogramisproportionaltothetotalnumberofenzymesinthepathwaythatwerefoundtoberegulatedby metabolitesinoneoftheothershownpathways(10,10,and1enzymesforglycolysis,TCA,andPPPrespectively).Theribbonsindicatewhich fractionoftheseenzymes(end-pointofribbon)areregulatedbymetabolitesofanotherpathway(ribboncolor).Forexample,theyellowribbon indicatesthatapproximatelyone-thirdofthemetabolite-regulatedglycolyticenzymes,areregulatedbyTCAmetabolites.
short-livedprotein[53],andwhosesynthesisisthoughtto
depend on the translation capacity of the cell [54].
Because Cln3 is a potent activator of the cell division
program in S.cerevisiae [55],Cln3transmits information
about translation flux to the cell cycle machinery [52],
thususinginformationabouttheoverallmetabolic
activ-ityof thecelltomakeanimportant cellfatedecision.
Why
sensing
intracellular
fluxes?
Whyshouldcellsexertregulatoryactivityonthebasisof
metabolic flux, rather than, for example, solely on the
basis of extracellular nutrient concentration? Microbial
cells can growonmany differentcarbon sources (up to
180 for E. coli[56]).The simultaneousexpression of so
many sensors probing the extracellular environment
wouldpresentasignificantburdentocells.Furthermore,
signals from different sensors would need to be
‘integrated’ to ultimately lead to a coherent cellular
response.Instead,withintracellularflux-sensing,
measur-ing flux atdifferentpoints in metabolism (for instance,
where different inflowing nutrients converge) through
flux-signalingmetabolites,requiresfewersensing
mech-anisms[16].However,thisgaininexpenditurecomesat
the cost of only roughly reporting the nature of the
inflowing nutrients.In fact,it appears thatcells display
sub-optimal control of gene expression in response to
environmental conditions(reviewedin [57]),suggesting
that theyareprepared to facearangeofenvironmental
regimes,ratherthanaspecificone.Thus,wecanconsider
flux-sensing an economic way to regulate metabolism,
and as an elegant way to handle the problem of
‘integrating signals’.
Furthermore, flux-dependentregulationcould alsobea
robustwaytoregulatemetabolismandcellularprocesses.
First, flux-sensing allows cells to determine the actual
metabolicrates.Thus,regulation canbeexertedonthe
basisofwhatisactuallyhappeninginsidecells(intermsof
metabolic activity) instead of what substrate would be
available in the extracellular environment. Maybe
becauseofthis,importantcellulardecisions,forinstance
theentryintobacterialpersistence,aremadeonthebasis
of metabolic flux [11,12].Second, flux-sensing is
inte-gratedinglobalfeedbackloops(fluxcontrolsflux)[11,16],
which allows for corrections of stochastically induced
alterationsingeneexpression,whichrecentlywasshown
to alsoaffectmetabolism[58].
Conclusion
Flux-sensingandflux-basedregulationconstitutes
possi-bly a common,previously underappreciated,
phenome-non in microorganisms. Nevertheless, even identifying
whichelementscompriseaflux-signalingsystemisafar
fromtrivialtask. Althoughquantitativemetabolomics is
nowadaysatastagewherethelevelsofmanymetabolites
can bequantitatively determined, measuringmetabolic
fluxes across differentconditionsin a trulyquantitative
mannerisstillachallenge.However,significantadvances
have recentlybeenaccomplished towardsthis direction
[59]. Moreover, thesystematicidentification of
interac-tionsbetweenmetabolitesandproteinsorRNAshasbeen
sofarrelativelylimited.Infact,therecentdevelopments
on high-throughput identification of such interactions
[31,32,33,60] suggest that the limitednumber of
cur-rently knowninteractionsisdue tomethodological
lim-itations, rather than due to their limited presence in
biological systems. Finally, in order to experimentally
prove the functioning of flux-sensing systems in living
cells,complexmetabolic perturbationsare required(i.e.
perturbing metabolicflux,perturbing metabolitelevels)
whicharetypicallyverydifficulttoachieve,oronlywith
off-target effects. Therefore, combining mathematical
modelling with elegant targeted perturbation methods,
as for example the recently developed
optogenetics-based methodfor controlling enzymeactivity [61],will
beessentialtowardselucidatingandprovingflux-sensing
andflux-dependentregulation.
Understanding how metabolic fluxes are sensed and
translatedtophysiologicalresponseswillbehighly
valu-able for metabolic engineering, as for example in the
constructioncellfactories,whichinvolvestheredirection
of metabolic fluxes for the synthesis of commercially
interesting chemicals. Also, knowingwhich metabolites
are flux-signaling will allow the construction of
biosen-sors, whereby the concentration of the flux-signaling
metabolite is translated into a measurable output, such
astheexpressionofafluorescentprotein[62].Such
flux-reporting biosensors could possibly also be used as a
research tool for screeningof drugs targetingmetabolic
diseasesandcancer.Overall,theelucidationofthe
archi-tectureandfunctionofflux-sensingsystemswillprovide
animportant,currentlymissing,perspectiveonmetabolic
regulationwith potentiallypowerfulapplications.
Figure3
Flux from different metabolic pathways
Ribosomal capacity
Regulation
Translation FLUX Degradation flux Unstable
protein
[Protein]
Current Opinion in Microbiology
Inferringglobalmetabolicactivityfromtranslationfluxviaunstable proteins.Therateoftranslationreflectsthecoordinatedactivityof centralmetabolism.Theconcentrationofconstitutivelyexpressed, highlyunstableproteins,reportstherateoftranslation(i.e.the translationflux).Suchconstitutivelyexpressed,unstableproteinscan beexploitedbycellsforregulationbasedontheiroverallmetabolic activity.
Conflict
of
interests
Theauthors declarenoconflictofinterests.
Author
contributions
ALandADOcontributedequallytothiswork.AL,ADO,
andMHconceivedthestudyandwrotemanuscript.AL
andADOcollectedandanalyzeddata.EWperformedthe
analysisonthevarianceofthemetaboliteconcentrations.
Acknowledgements
ThisworkwassupportedbytheMarieCurieInnovativeTrainingNetwork (ITN)ISOLATEundergrantagreementno.289995,theEuropeanUnion SeventhFrameworkProgramme(FP7-KBBE-2013-7-single-stage)under grantagreementno.613745(PROMYS),andtheMarieSkłodowska-Curie InnovativeTrainingNetwork(ITN)MetaRNAundergrantagreementno. 642738.WewouldliketothankSerdarO¨ zsezenforthebootstrapanalysis.
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