Cytoplasmic fluidity and the cold life: proteome stability is decoupled from viability in psychrophiles

Domain: earth_science
Published: 2025-11-24
Editorial outcome: minor_revision

Abstract
Proteindiffusion,criticalforcellularmetabolism,occursinthehighlycrowdedcytoplasm.Understandinghowthisdynamicschangeswhenorganismsareadaptedtodifferentthermalnichesisafundamentalchallengeinmicrobiologyandbiophysics.InEscherichiacoli,proteindiffusionundergoesapronouncedslowdownattemperaturesnearcellulardeath,coincidingwiththeearlystagesofunfolding.Todeterminewhetherthisphenomenonisuniversal,weinvestigatedpsychrophilicandhyperthermophilicbacteria.Inbothspecies,amarkeddiffusionslowdowntakesplaceattheonsetofproteomemelting.However,whilethedynamicarrestisassociatedwiththethermaldeathpointforthehyperthermophilicproteome,thepsychrophilicproteomemaintainssubstantialmobilitywellbeyondthecellularinactivation.Thedecouplingbetweenmetabolicviabilityandproteomedynamicsandstabilitysuggeststhatthefunctionalprocessesofpsychrophilicbacteriaaretemperaturesensitive.Thisfindingechoesthebehaviorofpsychrophilicenzymes,manifestingalargetemperaturegapbetweenoptimalactivityandunfolding.Proteindiffusionisoptimizedtomaintainfunctionalfluidityattheorganism’sworkingconditions,butitstemperaturedependenceiscontrolledbytheproteomefoldedstate.Ourfindingsredefinetherelationshipbetweencytoplasmicdynamics,proteomestability,andbacterialsurvivalincoldenvironments.

Introduction
Biologicallifehasevolvedinresponsetoenvironmentalconditions,whichhavebothshapedandconstraineditsdevelopment.Amongthesefactors,temperatureplaysapivotalroleinregulatingthegrowthofdiverseorganismsonEarth.Awide range of extremophiles—organismslivingunderextremeconditions—havebeendiscoveredandstudiedinthelastcentury1.PlanococcushalocryophilusandMethanopyruskandleri,forexample,representthemostextremecasesoftemperatureadaptationknown todate, with the former growing at −15°Candremainingmetabolicallyactiveat −25°C2,whilethelatterthrivingatarecord-hightemperatureof122°C3.Theseorganismshavedrawnsignificantinterestfortheirpotentialapplicationsindiversefields,includingbiotechnologyandcancerresearch.ThewidelyusedbiotechnologicaltechniquePolymeraseChainReaction(PCR),forexample,reliesonthermostableenzymesderivedfromthermophilicandhyperthermophilicbacteriaforDNAamplification4,5.Thermostabilityalsoplaysacrucialroleincancertreatment,wherehyperthermiaisusedtoselectivelykillcancerouscellsthroughexposuretoelevatedtemperatures6.Additionally,currentresearchisexploringthepotentialforlifeonextraterrestrialhabitats,suchasMars,Venus,andicymoons,whereextremeconditionsprevail,makingextremophileskeymodelsforastrobiologyandthesearchforlifebeyondourplanet7,8.Finally,climatechangedrivestheselectionofbacterialspeciesthataremoreresistanttohightemperatures,whichcorrelateswithahigherpropensityforantibioticresistance9.

Definingthelimitsoflife’sgrowthisessentialnotonlyforpracticalapplicationsbutalsoforadvancingourunderstandingofevolutionarybiology.However,thefundamentalmechanismsunderlyingcellularthermostabilityremainlargelyelusive.Decipheringthefactorsthatdrivecelldeathathightemperaturesandidentifyingwhatenablescertainbacteriatoendureextremeheatmoreeffectivelythanothersposesmajorscientificchallenges.

Temperatureimpactsallbiomolecularcomponentsofthecell,aswellasitsbiophysicalprocesses.Hightemperaturesaffectthestructuresofproteins,DNA,RNA,andcellmembranes10,makingitdifficulttounravelthemoleculareventsthatultimatelyleadtocelldeath11–14.Amongmacromolecules,proteinsarethemostabundantyetleaststablebiomoleculesinthecell,makingtheirthermalsensitivityakeyfactorinregulatingtemperature-dependentcellularactivities15.Recently,weshowedthattheunfoldingofjustasmallfractionoftheE.coliproteomeisabletogiverisetoadynamicarrestaroundthethermalcelldeath16.Thisphenomenonisofkeybiologicalrelevance,asdiffusivedynamicsplaysafundamentalroleincellgrowthbyensuringtheproperspatialandtemporalorganizationofcytoplasmiccomponentsandregulatingsolutepartitioningduringcelldivision17.Moreover,itgovernsthemobilityofintracellularmolecules,settingtheconstraintsforessentialmolecularinteractionsandbiologicalreactions18,19.Therefore,afundamentalquestionarises:isthearrestofproteindynamicsatcelldeathauniversalphenomenon,orisitexclusiveto mesophiles like E. coli? If psychrophiles and (hyper)thermophiles exhibitsimilarordistinctdiffusiveslowdownsattheirrespectivecelldeathtemperatures,itcouldunveilkeyprinciplesunderlyingcellularthermostabilityandevolutionaryadaptation.Inthiswork,toaddressthisquestion,wecombinemoleculardynamics(MD)simulationsandquasi-elasticneutronscattering(QENS)experimentstosystematicallycomparetheproteomedynamicsofthreebacterialspeciesadaptedtodifferentthermalenvironments:thepsychrophilePsychrobacterarcticus(P.arcticus,PA),themesophileEscherichiacoli(E.coli,EC),andthehyperthermophileAquifexaeolicus(A.aeolicus,AA).

ForAA,weobserveadynamicarrestcloselymirroringthebehaviorofEC,occurringnearitscorrespondingcelldeathtemperatureof~370 K. In contrast, PA exhibits a markedly different behavior, with proteindiffusionabruptlydecliningwellaboveitslethalthresholdof~295K.Inallthreeorganisms,thearrestofproteindiffusivedynamicsisdrivenbytheunfoldingofasmallfractionoftheproteome.However,inPA,thisarrestisdecoupledfromcelldeath,whichinsteadlikelyresultsfromthelosofbiologicalactivityinkeyenzymesalreadywithinthelow-temperaturerange.

Results
Globaldiffusionofbacterialproteinsslowsdownatcritical,species-dependenttemperatures
We utilized quasi-elastic neutron scattering (QENS) experiments to investigatethediffusivedynamicsofanaveragebacterialproteinonthenanosecondtimescale. The underlying hypothesis is that the observed scattering primarily originates from the incoherent contribution of hydrogen atoms within bacterial proteins20, which constitute the predominant biomolecular species in the bacterial cytoplasm21,22 (see “Methods” section 4.2). QENS experiments were performedontheIN16BspectrometerattheInstituteLaueLangevin23 onPA,EC,andAAbacteria,acrossanextendedtemperaturerangetoincludetherespectivethrivingconditionsandtheupperthermalstabilitylimit.Theincoherentdynamicstructurefactor,S(Q,E),obtainedfromtheexperimentsandshowninFig. 1a, can be described as a combination of two distinct contributions: local dynamics, which include vibrations and local diffusivemotions withinthe interiorofproteins,andglobaldynamics,whichencompasstherotationalandtranslational diffusive motions of proteins as a whole24. All spectra werefittedusingthefollowingmodel:

S(Q,E)=R(Q,E)⊛ [A0(Q)LγG(E)+(1-A0(Q))LγG+ΓL(E)] +ISolvLSolv(E)+B (1)

wherethehalf-widthhalf-maximaγGandΓ LoftheLorentzianfunctionsarecharacteristicoftheglobalandlocalself-diffusion,respectively.ThetermA0 representstheelasticincoherentstructurefactor(EISF),ISolvistheintensityofthesolventsignal(inthiscaseD2O),Iistheintensityoftheproteinsignal,Bisthebackground,andR(Q,E)istheinstrumentalresolutionfunction.TheglobaldiffusioncoefficientsDGandthetimeconstantsofdiffusionτ Gasextractedatalltemperaturesusingthejump-diffusionmodel25(seealsoSupplementaryFigs.2,5,6) are shown in Fig. 1b,c. These values are quite similar to the ones reportedforgreenfluorescentprotein(GFP)inECintherange0.3–1.4 Å2/ns26,27. The fit of the local self-diffusion Γ L and the obtained parameters are shown in Supplementary Figs. 3, 4. For all three bacteria the temperature dependence of the experimental D G is well described by a theoretical model that captures the transition from a fluid-liketoagel-likestateoftheproteome16,28,29:

DG(T)=(1-auQENS)·(a1T+b1)+auQENS·(a2T+b2) (2)

where a u QENS is a logistic function defined as auQENS(T)=1/(1+e(-(T-Tm)/ΔT)).Inthismodel,Tm isthemidpointofthetransition, and ΔT describes the steepness of the curve at the transition.

Alinearincreaseinthediffusivityasafunctionofthetemperaturecanbeobservedforallthreespeciesatlowertemperatures,inagreementwiththeStokes-Einsteinlaw30.

However,weobservearankamongtheproteomes’diffusivitythatfollowsthethermalstabilitycharacteroftheorganisms.Intherange 300–320 K, the diffusion coefficient D G of the psychrophile is the highest, while that of the hyperthermophile is the lowest. Quite interestingly, protein diffusivity remains constrained across abroad temperaturerange,asitisparticularlyclearinthehyperthermophile,whichshowsvaluesofthediffusioncoefficientathightemperaturescomparabletothoseobservedinthepsychrophileandmesophileunderlower-temperatureconditions.Thissupportstheideathat,tomaintainoptimalfunctioningofdiffusion-dependentcellularprocessesatelevatedtemperatures,overallproteindiffusivityisfinelyregulated. These results build upon and extend previous findings showing that the root mean square atomic fluctuation amplitudes, which determine macromolecular flexibility, are similar among psychrophilic,mesophilic,andthermophilicorganismsattheirrespectivephysiological31ordeath32temperatures.Inthislattercase,localproteindynamicswereshowntobestronglyinfluencedbythedifferentnanoviscositiesexperiencedbytheproteins,consistentwithobservationsinproteinpowdersystems33,andtocorrelatewiththeoverallaminoacidcompositionoftheproteome. Interestingly, protein global diffusivity peaks at organism-specific temperatures–317KforPA,320KforEC,and371KforAA–withanuncertaintyinpeakpositionsofafewKelvinduetoexperimentalerrorbarsindiffusioncoefficients.Thevalueofthemaximumdiffusioncoefficientfallsintherange1.5–1.8Å2/nsforallthebacteria,whichsuggeststhatthereisacommonspecies-independentthresholdtothediffusivityoftheaveragebacterialprotein.Aftersuchamaximum,thediffusiondecreasesrapidly,abehaviorthatwerecentlyinterpretedinthecaseofE.coliasduetothegelation of the system induced by the unfolding of a small fraction of the bacterialproteome16.However,whilethemaximumdiffusivityforEC andAAoccursneartheircelldeathtemperaturesTCD,i.e.,323Kand 368K,respectively,inPA,itisnotablyhigherthanitscelldeathtemperatureof295Kbyabout22K.

Diffusionslow-downiscausedbyproteinunfolding
In order to provide a microscopic interpretation of the dynamical arrestmeasured byQENS,we performed Molecular Dynamics (MD) simulationsforvarioussystemsmodelingthecytoplasmofthePA,and AAbacteria,andwecomparedtheresultstowhatalreadyobtainedforthemodeloftheECcytoplasm16.Foreachorganism,multiplesystemsweresetupatvaryingconcentrationlevels.Foreachsystem,wecreatedaversionwhereallproteinswerefoldedandonewhereallwereunfolded.Allthesystemsweresimulatedatdifferenttemperaturesacrossthecell-deathtemperatureofthereferenceorganism.ThePA andAAsystemswereassembledbyconsideringhomologousproteinsofthoseused tomodelthe ECcytoplasminapreviouswork16.ThedetailedcompositionofthesystemsisreportedinSupplementaryTable2–5,amolecularviewisdepictedinFig.2a.

To quantify the diffusive motions of folded and unfolded proteins, the translational and rotational diffusion coefficients of the completely folded and unfolded systems have been computed and reported in Fig. 2b for the PA cytoplasm at a concentration of 299 g/L. In the figure we also report the QENS experimental values extracted from QENS data for PA (black squares). The results for all the simulated systems, PA, EC, and AA,atdifferentconcentrations,arereportedinSupplementaryFig.11.

As expected, we find a much lower diffusion coefficient upon unfoldingoftheproteins.Infact,whentheproteinsareunfolded,theyformanentangledsystemcharacterizedbyahighnumberofprotein-proteincontacts16thatcontributetoincreasingthesystemviscosityandslowingdownproteinmobility.Thefoldedandunfoldeddiffusioncoefficientscan,ateachtemperature,becombinedinordertoreproducetheexperimentaldataandbyassumingalineardependenceontheapparentfractionofunfoldedproteinsauQENSinthesystems:

DG=(1-auQENS)·DfG+auQENS·DufG (3)

wherethecoefficientauQENS istemperaturedependentandisdeducedbyfittingtheexperimentaldataasdiscussedintheprevioussession. ForthePAsystemat299g/LtheresultofthiscombinationisshownintheinsetplotofFig.2b(seealsoSupplementaryFig.12).

Itshouldbenotedthattheconcentrationlevelofthesimulatedproteinsolutionsconsiderablyaffectsthecomputedvalueofdiffusioncoefficientsforboththefoldedandunfoldedsystems.Therefore,thematchwiththeexperimentalQENSvalueswoulddependonthechosenconcentrationstate.Wehavedesignedanoptimizationproceduretocombineallthesimulationresultsanddeducetheconcentrationlevelthatallowsthediffusivityfromsimulationtobestreproducetheexperimentalresults.Infact,theapparentglobaldiffusionobtainedforeachsystematdifferentPconcentrations,DG(ci),canbecombinedusingasetofweights:DwG=∑ni=1wiDG(ci).TheweightscanthenbeoptimizedinordertomatchtheexperimentalvaluesDQENS:

DGopt=∑ni=1w*iDG(ci) (4)

withw*=argmin||DwG-DQENS||2 (5)

wherew*=[w*1,...,w*n] are the optimized weights and DQENS is the experimental global diffusivity that,for numericalpurposes, can be interpolatedattherelevanttemperatureswherethesimulativeDG(ci) are computed. The optimized curves for the three organisms are showninFig.2c.Theymatchtheexperimentaldataquitewell,initiallyshowing a thermal increase in diffusivity, followed by an abrupt slowdown.Theassociatedoptimalconcentrationsare280.5g/L,280.0 g/L and 298.7 g/L, respectively for PA, EC and AA, showing a clear differencebetweentheconcentrationofPAandECwithAA.Thisresult isconsistentwiththeviewthatthehyperthermophilicAArequireshightemperaturestoachievefunctionalproteinmobilitysotoovercomeahighercrowdedandlessfluidcytoplasmicstate32.

Partialunfoldingeffectandratioofunfoldedproteins
Toassesstheimpactoftheprogressivethermalproteinunfoldingonthediffusivedynamics,wedeployedastrategyalreadyusedforEC16. Tosystematicallysampletheunfoldedstatesoftheproteomewhenthetemperaturerises,webuiltthreeadditionalsystemsforbothPA and AA at 299 g/L, each containing different fractions of unfolded proteins(25%,50%and75%).Togetherwiththelimitingcasesof0% and 100% these configurations provide a uniformly spaced representation of the proteome as it progressively undergoes thermal melting.Theproteinsthathavebeenunfoldedwereselectedrandomly (see Supplementary Table 6–8). From the simulations of these systems,weextractedtheaveragetranslationaldiffusionoftheproteins as a function of temperature, which we report in Fig. 3a, d. By increasingtheratioofunfoldedproteins,thediffusivitydecreasesatallsulatedtemperaturesbutnotlinearly.Inparticular,forPAeventheunfoldingofjust25%oftheproteinscancauseasignificantdecreaseinthediffusion.Theslowdowncausedbytheprogressiveunfoldingisrelatedtotheincreaseininter-proteincontactsandtheshifttowarda morehighlyviscous,entangledstate.AsshowninFig.3b,c,e,f,assoon as a fraction of proteins is unfolded the probability distribution of inter-molecular contacts starts exhibiting a long power-law tail, P(c)∝c−k,andthereforealargersecondmoment,asindicatedbythestandarddeviationσofthedistributions.Theresultingentanglementcorrespondstotheformationofproteinpercolatingclustersacrossthesystem,seeSupplementaryFig.15,echoingagelationprocess34. Thisresultextendsthefindingthatproteindiffusivityundercrowdedconditionsiscontrolledbyclusteringdynamics35,36tothecasewhereunfoldingoccursandenhancesthepropensitytoclusteringbecauseofproteins’largergyrationradiiandextendedconformationsasshowninSupplementary Fig. 13. A visualization of the increase of intermolecular contacts as a function of the ratio of unfolding is presentedinFig.3g.

Now,followinginspirittheanalysisoftheexperimentaldata,at anytemperaturethetranslationaldiffusioncoefficientofthepartiallyunfoldedsystemscanbemodeledasalinearcombinationofthetranslationaldiffusioncoefficientsofthefoldedandunfoldedsystems,weightedbyanapparentunfoldedfractioncoefficientau,

Dt=(1-au)·Dft+au·Dutf (6)

Oursimulationresultsallowustoestablisharelationshipbetweentheapparentfractionofunfolding,au,andtherealfractionofunfoldedproteinsinthesystem,ru ∈{0.25,0.50,0.75}.Becauseoftheintrinsicnon-linear progress seen in Fig. 3a, d, we retrieve for all systems a power law relationship

au=rpu (7)

thatwerepresentinFig.3h,withexponentsp=0.305,0.142,and0.474 forPA,EC,andAA,respectively.BycomparingEquation(3),(6)and(7) weobtaintherealfractionofunfoldedproteinsasafunctionoftemperatureassociatedtothechangeofexperimentaldiffusivitydirectlyfromtheexperimentalquantityauQENS,withru=(auQENS)1/p.Asshownin Fig.4,forECandAAtheonsetoftheproteomeunfoldingislocatedafewkelvinabovetheTCD.Theseresultssuggestthatforthesemesophilicandhyperthermophilicorganismsonlyafewproteinsareactuallyunfoldedatthecelldeath16,37,38.ThepsychrobacterPA,however,behavessurprisingly,astheproteomeunfoldsmorethan20Kabove the predicted cell-death temperature. This indicates that, for this psychrophilicorganism,othermolecularprocessescausethethermalcelldeath.Aspreviouslynoted,thecomparablethermalfluctuationsof PA,EC,andAAattheirrespectivecelldeaththresholds32-quantified hereviaatomicmeansquaredisplacements-furthersupportthatlocalproteomedynamicsiscriticallyrelatedtocellularviability.

ProteinmeltingtemperaturepredictionandDSCmeasurements showthehighstabilityofP.arcticusproteome
First,wewanttoconfirmthattheunfoldingonsetoftheproteomeofPAdeducedbythetemperaturedependenceofthedynamicalpropertiesoftheproteome,reallyoccursatatemperaturecloseto320K, andthereforemorethan20Kbeyondthecelldeathoftheorganism.

DifferentialScanningCalorimetry(DSC)measurementsofthewhole cellshavebeenperformedforPAandEC.InFig.5a(top)weplotthe behaviorofthemeasuredheatcapacityCp asafunctionoftemperature.Whileforaproteinindilutesolutionthemeasuredpeakofheatfluxisattributedtothetransitionfromafoldedtoanunfoldedstate39, inthecaseofawholecell,themixtureofproteins,nucleicacids,andmembranelipidscanundergomultipletransitions12,eachcontributing totheglobalheatflux.ThesignalofDSCofawholecellistherefore expected to reflect the sum of these contributions, resulting in a broaderprofilewithmultiplepeaks.

ThecurveassociatedtoECisingoodagreementwithprevious DSC scans reported in literature,12,40,41 with a main peak present between320Kand338K.Interestingly,theonsetofthismainpeak correspondstothethermaldeathofEC(322K).Asimilarmainpeakis observedforthePAcellsinthetemperaturerange303–326K.Smaller peaksarefollowingathighertemperaturesof336Kand343K,asalso foundanddescribedbyMackeyetal.12forotherorganisms.Theorigin of the main peak is still debated, with some attributing it solely to ribosomesand rRNAdegradation12,while othersrelateitmoregenerallytothesumofindividualproteinunfoldingevents42.WhileforEC, thecomparisonbetweenthetemperaturerangeofthemainDSCpeak andthesetofmeltingtemperaturesofindividualECproteinsissupportedbyalargebodyofliterature12,37,40,41,forPA,thistaskismore difficultsinceexperimentaldataaremorescarce.Arecentstudyonthe DNA-bindingproteinParIfromPAdescribesameltingbeginningat321 K43.Similarly,Nowaketal.foundaTm of333KforanotherDNA-binding proteinofPAwithDSC44.

To provide complementary support, we exploit a machine learning-based melting temperature prediction tool, DeepSTABp45, whichallowsustodeducethemeltingtemperatureofalargesetof proteinsfromPAandEC(see“Methods”section4.7).Thismodelwas trained on millions of protein sequences, incorporating organism-specific features such as the presence of ions and chaperones, and differentiatingbetweencelllysatesandwholecellmeasurements.The predictedmeltingtemperaturesareplottedinFig.5b.Thedistribution ofthemeltingtemperaturesforthePAproteinshasat315.6Kamaximum in agreement with the DSC thermogram peak (314.7 K) and showsalongtailextendingupto351K.ThedistributionofECproteins’ meltingtemperaturesismoresymmetric,withamaximumatatemperatureof322.3K,againclosetotheoneofthemainDSCpeak(328.1 K). These results clearly support the interpretation proposed by Lepocketal.42,thussuggestingthatthebroadDSCprofileoriginates fromtheoverlapofindividualproteinunfoldingevents.Onthisbasis, we integrated the DSC curves to retrieve, after normalization, the fractionofunfoldedproteinsasafunctionofthetemperature42.This fractioncanbedirectlycomparedtotheanalogousquantityobtained bycombiningQENSexperimentswithMDsimulations.Acomparison with available DSC data was also proposed by Gosh and Dill when discussing their theoretical model describing the physical limit of proteomestabilityfordifferentorganisms46,47.Theresultsareshownin Fig.5c,dforPAandEC.Ingeneral,weobservethattheunfoldingrates obtainedfromQENS/MDareshifted~10Khigherthanthosederived fromDSC,whilemaintainingasimilarcooperativetrend.Weshould alsoemphasizethat,asdiscussedinourpreviouswork16,thechoiceof forcefieldaffectsthefinedetailsofthecurveextractedfromQENS/ MD,asshowninFig.5d,wherewecompareresultsfortheECproteome simulated with the CHARMM36m48 and AMBER a99SB-disp49 forcefields.ForEC,thediffusivityofboth,foldedandunfoldedproteome states, obtained with the AMBER a99SB-disp force field are slightlyhigherbyafactor ~1.3withrespecttothoseofCHARMM36m. Coherently, also the computed systems’ viscosity is slightly smaller withAMBERa99SB-dispvs.CHARMM36m;8vs.11mPa⋅sforthe288g/ L system. As a consequence, the rate of unfolding deduced from AMBERa99SB-dispprogressesfasterasafunctionoftemperaturethan withCHARMM36m.

Interestingly,thefractionsofunfoldedproteinscalculatedfrom thedistributionofmeltingtemperaturesareincloseagreementwith thosederivedfromDSC,especiallyinthecaseofPA.Whencomparing thefractionofunfoldedproteinswiththebacterialgrowthratecurve reproducedfrom50,51,weconfirmthatwhileinECcelldeathisassociatedwiththeonsetofproteomemelting,inPA,proteomeunfolding occursatasignificantlyhighertemperaturethanitscelldeath.

Methods
Samplepreparationforneutronscatteringexperiments
Weinvestigatedthreebacteriawithvaryingthermalstabilities:thepsychrophilicPA(TPA CD ≃295.15K)51,themesophilicEC(TEC CD ≃323.15K)47, andthehyperthermophilicAA(TAA CD ≃368.15K)74.ECneutronscattering datawasalreadyavailablefrom16.AAVF5wascultivatedat358.15Kin 2-literbottles,under1.4barsofH2/CO2 (80:20)(correspondingto68 mmolH2 perflask)andair(dissolvedoxygeninthemediumwasestimated by using the Henry equation to be in the range 0.5–0.8%) in modifiedmineralSMEmediumcontaining1g/Lofthiosulfate75.Cultures werepreparedin2LbottlesatpH7.3,withsodiumthiosulfatepresent (1g/L,SigmaAldrich)andunderaH2/CO2/O2 atmosphere(82mmolH2). PAcells(DSM17307)wereobtainedafterbeinggrownforthreedaysat 290.15withshaking(150rpm)inBactoMarinebroth(DIFCO2216,DSM Medium514),reachingafinalOD600nm of1.5.Inbothcases,thecellswere cultivated in media prepared with H2O, harvested during the late exponential growth phase by centrifugation for 20 min, and washed twice(centrifugationofthere-suspendedpelletfor18minat5400gand 280.15K)withabufferatpD8(50mMTris,150mMNaCl,5mMKCl; 40mLofbuffer/cellpellet)preparedwithD2O(99.8%atomD,AcroSeal®,Thermoscientific),asdescribedin16.ForPAculture,aprecipitate fromthegrowthmedium,whichispelletedwiththecellswhentheyare collected,iseliminatedduringthesetwowashes.ToachieveapDof8, thebufferpHwasadjustedto7.6usingHCl36.5–38%(SigmaAldrich). Finally, 1.5g and 2.3g of washed, unfrozen cells of AA and PA were obtainedandanalyzed.

Quasi-elasticincoherentneutronscatteringexperiments
QENSexperimentswerecarriedoutusingtheIN16bspectrometerat the Institut Laue-Langevin (ILL) in Grenoble23 (DOI: 10.5291/ILL-DATA.8-04-960).Datawerecollectedwithanenergyresolutionof0.9 μeV,correspondingtoatimewindowrangingfrom0.3to5ns.The experiments were conducted on samples containing whole-cell organisms, as well as on a vanadium sample and an empty cell. Measurements wereperformed over various temperature ranges to encompassboththeorganisms’optimalgrowthconditionsandtheir upperthermalstabilitylimits:from276Kto324Kin6Kincrementsfor PAandfrom290Kto390Kin10KstepsforAA.

Neutrondataanalysis
QENSdatawereanalyzedusingacommonmodelforproteindiffusion from22,29.

Generally,theexperimentalscatteringsignalisobtainedbyconvolvingtheinstrumentalresolutionfunction,R(Q,E),whichisdetermined through vanadium measurements, with a theoretical model. HereR(Q,E)wasfittedbyasumofaGaussianfunctionandaconstant backgroundasshowninSupplementaryFig.1.Sinceproteinshavethe highestproportionofhydrogenatoms(andthushaveahighscattering cross-section) and constitute more than half of the dry weight in bacterial cells, they are expected to dominate the theoretical function15.Asaresult,thetheoreticalfunctioniscommonlymodeled by

Sth(Q,E,T)=SP(Q,E,T)+ϕSD2O(Q,E,T) (8)

where SP(Q, E, T) corresponds to the scattering contribution from cellular proteins, and SD2O(Q,E,T) represents the contribution from the D2O buffer surrounding the cells.The parameter ϕ denotes the fractionofD2Ointhesamples.TheD2Osignal,SD2O,canbeaccurately describedusingaLorentzianfunction29.Tosimplifythemodel,parameters for the Lorentzian function were estimated based on prior measurementsofD2OandD2O+PBSsamples,see16.Thefinalmodelof the scattering function S(Q, E) therefore takes the analytical form presented inEquation (1). Prior to analysis,data wascollected with NOMADXOP(v1.02)andMantidwasappliedtoreducedata.Thefittingprocesswasconductedintwosequentialsteps,involvinganormal fitandasimultaneousfit,asfurtherdescribedinSupplementaryNote1 and Supplementary Fig. 5, using a python code of16 (https://github.com/DanieleDiBari/NSAnalysis).Fittingofthelocalparametersofthe modelisfurthershowninSupplementaryFig.3,4.ValuesoftheEISF A0 arereportedinSupplementaryFig.7.

MDsystems
All-atomMDsimulationswereconductedinthisstudytoinvestigate thedynamicsofcytoplasmmodelsofthethreebacteria,PA,EC,and AA. All-atom simulation in explicit solvent have been successful to model crowded protein solutions and the impact of unfolding on proteindynamics16.

Startingfromasetofsystemsusedtoinvestigatethedynamicsof ECcytoplasminapreviouswork16,webuilthereequivalentsystemsfor the PA and AAorganisms by considering homologous proteins and similarconcentrations(seeSupplementaryTable2–5).

Whereavailable,the3DpdbstructurefromthePDBdatabankwas used.Iftheexperimentalstructurewasnotaccessible,anAlphaFold structurewasgeneratedandused76.Forallproteins,formulti-domain proteins,weassumedthesameoligomericstateasthecorresponding ECprotein.Beforeassemblingtheproteinsintothecytoplasmmodel, their structures were relaxed via an equilibration procedure using GROMACS77, as explained in Supplementary Note 2. The Packmol software78,79wasusedtoplacetheequilibratedproteinsintoacubic boxwithsomefinalmanualcorrectionstoavoidclashes.Thenumber ofinsertedproteinsdependsonthetargetconcentration.ASystem representationisshowninFig.2usingVMD80.Furtherdetailsonthe systemcompositionareshowninSupplementaryTable2.

MDsimulations
All simulations were performed using GROMACS versions 2019.4 2021.3and2021.577,employingtheCharmm36mforcefield48.Water wasmodeledusingtheTIP3Pmodel81.Theboxesweresubsequently hydrated,and150mMofK+andCl−ionswereadded,includingsome extra ions to neutralize the net charge of the system. The systems underwentenergyminimization,reducingtheforceconstanttobelow 100kJmol−1Å−1.Forallthesystemscontainingthefoldedproteins,a 6-step NVTprocedure lasting 11 ns using the V-rescale thermostat82 withdecreasingpositionrestraintswasappliedtotheproteinsinthe system,anda100nsNPTequilibrationwiththeBerendsenbarostat83 wassubsequentlyrun.Inparallel,withtheequilibrationofthesystems offoldedproteins,aprotocolofunfoldingwasdevisedtocreatethe samesystemswithcompletelyunfoldedproteins.Theunfoldingprocessinvolvedexposingtheproteinstoelevatedtemperatures(1200K forPAand1500KforECandAA),seeSupplementaryFig.8.Elevated-temperature MD simulations have been previously validated as an effectivemethodtoinduceproteinunfoldingonaccessibletimescales without distorting native unfolding pathways16,84,85. After this high-temperaturetreatment,acoolingprotocolwasappliedtobringthe systemsbacktothesametemperaturesasthefoldedcounterparts. Theseunfoldedsystemswerethenequilibratedusingthesameprocedureasforthefoldedproteins.Foreachorganism,partialunfolding was performed on one system. Three additional states of partial unfoldingwerealsocreated,correspondingtounfoldingfractionsru= 25, 50, 75 %, see Supplementary Table 6–8. This was achieved by applyingthesameprotocolusedforthefullyunfoldedsystems,but withtherespectivepercentagesoffoldedproteinsremainingfrozen duringtheunfoldingandcoolingsteps(seealso16).Proteinsselected forunfoldingwerechosenrandomlytomatchthedesiredunfolding rates,whichweredeterminedbasedonthefractionofhydrogenatoms ineachprotein.

A102.4nsproductionrunwasperformedintheNPTensemble using the Parrinello-Rahman barostat86, with temperatures set according to the relevant ranges for each organism. For the steps outlinedabove,acutoffof12Åwasappliedforshort-rangenon-bondedinteractions.VanderWaalsforcesweresmoothlyreducedtozero between10and12Å.Long-rangeelectrostaticinteractionswerecalculatedusingtheparticlemeshEwald(PME)method87.TheLeap-Frog algorithmwasusedtopropagateNewton’sequationsofmotion.The LINCS algorithm was applied to constrain the lengths of all protein bonds involving hydrogen88, while the SETTLE algorithm was employedtomaintaintherigidityofwatermolecules89.Thesystem configurations were recorded every 20 or 40 ps depending on the system.

MDdataanalysis
Acomparablequantityastheexperimentalapparentglobaldiffusion canbeextractedfromtheMDdata.Followingtheprocedureoutlined in16,thetranslationalandrotationaldiffusioncoefficientswereevaluatedforalltheproteinchainsforthewholetrajectoryattherelevant time window of 0.3 to 5 ns corresponding to the resolution of the experimental measurements. The translational diffusion was calculatedbyfollowingtheprotein’scenterofmassandbycalculatingthe mean-square-displacement(MSD).TheMSDwaslinearlyfittedinthe 0.3-5 ns time region. To evaluate the robustness of the data with respecttotheinitialconditions,forthePAsystemsat143g/Land299 g/L,weconstructedadifferentpackingstateoftheproteinsolutions. InSupplementaryFig.10weshowthattheobtainedtranslationaldiffusion coefficient at different temperatures is independent of the initialproteinconfigurationsinthesolution.FortheECsystemwehave alsoconsideredanextendedtrajectoryof1μsandverifiedtheconvergenceofthecalculateddiffusionconstantasafunctionoftrajectory length, see Supplementary Fig. 9. For the rotational diffusion, 1000randomunitvectorsweregeneratedintheproteinstructure.At each timestep, the protein chains were centered and rotated with respecttoareferenceinitialstructuretoestimatearotationmatrix. Therotationofeachvectorwasthenestimatedbyapplyingtherotation matrix. The rotational autocorrelation function was then calculatedusingasecond-orderLegendrepolynomial.Byfittingthe rotationalautocorrelationwithanexponentialdecayfunctioninthesame timeregime as the translational diffusion,from 0.3 to 5 ns, a decaytimeτ rwasobtained.Therotationaldiffusioncoefficientwas derivedasDr=1/(6τ r),foreachchaininallthesystemsandalltemperatures. The rotational and translational coefficients were finally corrected for PBC conditions90,91 using the viscosity calculated as explained in Supplementary Note 3 and SupplementaryTable 1. To obtain the global diffusion coefficient for comparison with the experimentsweusedtherelation

DG=∑Nn=0Bn(q)Drn(n+1)+(Dt-DG)q2N-1 (9)

with

Bn(q)=∫01ρH(r)jn(qr)dr (10)

whereρH isthe radial distribution functionofthe chain’s hydrogen atomsandj nisthesphericalBesselfunctionofordern.Tothisend,N wasfixedtoasufficientlylargenumbertoensureconvergencewithN= 550. Equation (9) was solved numerically using a minimization problem, where we set q to q = 2. To compare the results with the experimentally extracted global diffusion, D G was weighted by the hydrogenatomsofeachchain.Inthisway,foreachsystemateach temperature,onesingleDGvaluewasobtained.TheerrorofDGwas estimated as the standard error of the weighted mean, using the numberofhydrogenatomsineachchainasweights.

Further details on protein radii ofgyration and protein-protein inter-molecularcontactsareprovidedinSupplementaryFig.13,14.

Meltingtemperatureprediction
TopredictthemeltingtemperaturesofvariousproteinsfromPA,EC, and AA, the open-source tool DeepSTABp was used45. The growth temperaturesusedwerethesameasthoseforthepreparationofthe experimental samples of the respective bacteria: 290.15 K for PA, 310.15 forEC, and 358.15 K for AA. Forthe predictions, the cellular environmentwasselected.AllproteinsavailableintheUniProtdatabasefortherespectivebacteriawereincluded,resultinginatotalof 4850proteinsforPA,2103proteinsforEC,and5308proteinsforAA.

DSCmeasurements
DifferentialScanningCalorimetry(DSC)measurementsofthewhole cells of PA and EC with the same samples as used in the QENS experimentswereperformedwithamulticellinstrumentfromTAat ILL,Grenoble.Theexperimentconsistedofonereferencecellfilled withthebufferasinQENS,whiletheothercellswerefilledwithbacterialsamplesandbuffer.Themachinewasheatedupfrom293.15Kto 363.15 K (the maximum possible) and then heated down again to 293.15K.TheDSCmeasurementswereallanalysedusingtheopensourcecodepyDSC92.Theweightsofthesampleswere538.8,512.3and 537.1mgwithaconcentrationof26,20and24mg/mlforPA,EC,and AA,respectively.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript presents a thorough and well-integrated study combining QENS and MD simulations to probe proteome dynamics across bacteria from distinct thermal niches. The central finding, a significant decoupling between proteome stability and cellular viability in the psychrophile P. arcticus, is novel and well-supported by the experimental and computational data.\nThe manuscript is generally clear and scientifically sound. The supplementary material is comprehensive, with details of system setup, simulation protocols, and data processing. However, a few important points would benefit from additional clarification or minor expansion to ensure reproducibility and mechanistic clarity.\nMajor Comments\n1. Simulation Independence from Initial Conditions:\nThe MD checklist (Checklist item 1d) notes that independence of results from initial configurations is not demonstrated, and indeed, I could not find evidence of independent replicas across different initial velocities or protein placements in the documents you shared. Given the crowded cytoplasm models, stochastic variability could impact diffusion and viscosity calculations. Even if full replica sets are impractical, the authors should explicitly comment on why they consider the simulations to be independent of initial conditions. e.g., strong convergence seen in RMSD (Supplementary Fig. 5), and small variations in unfolded fractions across the temperatures explored.\n2. Force Field Sensitivity:\nThe authors note slight shifts in unfolding temperatures between AMBER99SB-disp and CHARMM36m force fields (main text, p. 8). While this is discussed briefly, there’s no quantification of how this affects key observables like viscosity, diffusion coefficients, or apparent unfolding fractions. A concise comparison or a statement of how these differences fall within statistical errors would enhance confidence in the robustness of the conclusions\n3. Summary Table for MD System Setup:\nThe checklist (4a) points out the lack of a consolidated table for MD system parameters (box sizes, atom counts, water/ion content, etc.)\nWhile some information is spread across Supplementary Tables 2–7, a single summary table would be valuable for reproducibility and clarity.\n4. Data Sharing:\nThe checklist (4d) confirms that the simulation input and output files will be made available via Zenodo but are not yet uploaded. For a systems-level study of this complexity, ensuring that the data are publicly available prior to publication is essential for reproducibility and verification.\n5. Mechanistic Insight into Decoupling:\nThe authors do mention the broader context of decoupling in the main text (p. 3–4) and relate it to enzyme-specific stability trade-offs. However, a clearer articulation of how this decoupling might arise in the whole-cell context—such as roles of membrane fluidity, critical enzyme classes, or oxidative stress—would help readers connect the bulk proteome observations with cellular inactivation mechanisms.\nMinor Comments:\n1. The mathematical relationship between apparent unfolding fraction (au) from QENS and real fraction (ru) from MD (described in main text, p. 5–6) would benefit from a short schematic or equation summary to improve clarity.\n2. Minor typographical note: ensure consistent unit labeling (e.g., nm, K) in figure captions to maintain clarity.\n3. Consider adding a short note in the methods (or supporting info) that the high-temperature unfolding method is generally validated for these crowded systems (e.g., potential kinetic trapping issues in short unfolding steps).", "reviewer_2": "Thank you for the opportunity to review the manuscript entitled \"Cytoplasmic Fluidity and the Cold Life: Proteome Stability is Decoupled from Viability in Psychrophiles\". I think the authors have done good research on this topic. However, there are some comments as below:\n1) Page 2 - Introduction section - Provide the full name of the bacterial species (i.e P. arcticus and A. aeolicous), since this is the first time its been written in the manuscript.\n2) Page 4 - section C - What is the justification of choosing these percentage (25%, 50% and 75% for the unfolded protein)?\n3) Page 4 - section C - What is the protein being selected here? I think it is quite interesting if the authors selected few important proteins/enzymes that were known in maintaining its function at low temperatures (i.e heat shock protein/antifreeze proteins/fatty acid desaturases etc).\n4) Page 7 - section 2 - \".... in PA, proteome unfolding occurs at a significantly higher temperature than its cell death\". Yes, this indicates the interesting observation on the activity of some psychrophilic enzymes, where their enzyme activity are optimal near to the mesophilic temeperatures (>25 degC).\n5) Page 9 - \"These organisms’ adaptations to cold – enhanced enzyme flexibility, fluid membranes, and specialized repair systems – render them exquisitely sensitive to mild warming.\"\nHave you look the expression of some proteins related to these factors (i.e membrane fluidity, antifreeze proteins etc).\n6) Page 9 - \"In order to link the observed decoupling between metabolism and proteome stability and diffusion to single enzyme behavior, a crucial next step will be the in-vivo investigation of the temperaturedependent relationship between activity and stability, as well as the local dynamics of active sites.\" - I am in aggreement with the authors - it is very important to compare the in vivo data with the prediction data generated. Perhaps, the ribosome profiling should be performed to achieve this in the future.", "reviewer_3": "This article covers the influence of protein diffusion in the cytoplasm on temperature dependent cell viability for psychrophile, mesophile and hyperthermophile examples. This is an interesting and highly relevant field as the authors discuss. The study showcases a complementary use of experimental techniques (QENS and DSC), as well as MD simulations.\nThe paper is well written and presented, it is interesting and thorough in discussing the results presented. For the neutron scattering data it describes a well reasoned and solid approach to data analysis. The authors themselves point out that the MSD through the EISF can offer insights on protein behaviour and mention that they determined two Lorentzian widths describing the proteome behaviour. While γ is very well presented and discussed Γ is not presented in the manuscript or SI. Even if the extreme crowding makes the analysis difficult it would be interesting to see the results. Especially in light of the psychrophile behaviour not following the expected relation between proteome diffusion and cell death. While this information would not necessarily provide the explanation it would provide additional insights, which are clearly of interested given the surprising results.\nDetailed comments:\np. 2 The authors conclude, that: “This behavior suggests that life adaptation involves the evolutionary selection of protein diffusivity that remains constrained across the entire thermal niche.” Given that only two of the three systems show a drop in diffusivity around the cell death temperature this conclusion seems to be too broad. The context of the results seem to indicate that diffusion is a symptom of proteome unfolding, as the authors discuss later in the text instead.\np. 3 It would be useful if uncertainties were provided for the organism-specific maximum diffusion temperatures.\np. 4 The authors obtain the ratio of unfolding to folding needed to achieve the observed diffusive behaviour. Has it been confirmed that a higher ratio of partial unfolding to folding would not produce the same results?\n“The psychrobacter PA, however, behaves surprisingly, as the proteome unfolds more than 20 K above the predicted cell-death temperature. This indicates that, for this psychrophilic organism, other molecular processes cause the thermal cell death.” This conclusion is valid given the data shown. However, when investigating the other molecular processes Γ should be considered and if possible the MSD should also be obtained.\np. 10 The sample preparation using HCl instead of DCl introduced freely exchangeable hydrogens to the system. How were they accounted for in the data analysis? Or is the volume considered negligible?"}, "author_response": "Reviewer #1 (Remarks to the Author):\nThe manuscript presents a thorough and well-integrated study combining QENS and MD simulations to probe proteome dynamics across bacteria from distinct thermal niches. The central finding, a significant decoupling between proteome stability and cellular viability in the psychrophile P. arcticus, is novel and well-supported by the experimental and computational data.\nThe manuscript is generally clear and scientifically sound. The supplementary material is comprehensive, with details of system setup, simulation protocols, and data processing. However, a few important points would benefit from additional clarification or minor expansion to ensure reproducibility and mechanistic clarity.\nMajor Comments\nPoint 1. Simulation Independence from Initial Conditions:\nThe MD checklist (Checklist item 1d) notes that independence of results from initial configurations is not demonstrated, and indeed, I could not find evidence of independent replicas across different initial velocities or protein placements in the documents you shared. Given the crowded cytoplasm models, stochastic variability could impact diffusion and viscosity calculations. Even if full replica sets are impractical, the authors should explicitly comment on why they consider the simulations to be independent of initial conditions. e.g., strong convergence seen in RMSD (Supplementary Fig. 5), and small variations in unfolded fractions across the temperatures explored.\nAuthors Reply: We appreciate the reviewer’s comments, and we agree on the importance of addressing the sensitivity of those crowded systems towards different initial conditions. To consider the influence of initial states on our diffusion estimates, we have created two new configurations for System 1 (299 g/L) P. arcticus and System 4 (143 g/L) of with a different protein positioning. These were built following the same procedure with Packmol. The systems were simulated at four specific temperatures in order to monitor the temperature dependence. We computed the translational diffusion coefficients and compared the obtained results with the original data presented in the submitted version of the manuscript. We clearly show that the translational diffusion follows the same trends at both concentrations. The newly generated configurations can be considered as a long time reshuffling of the protein solution and can be viewed as an extra support of the robustness of our results. Moreover, we have checked by considering a long MD trajectory for the EC system (1μs) that the obtained results for the diffusion constants are converged. We added two new figures in SI, see Supplementary Fig. 9,10. and a short sentence in the main text (section Methods p. 12):\n“To evaluate the robustness of the data with respect to the initial conditions, for the PA systems at 143 g/L and 299 g/L we constructed a different packing state of the protein solutions. In Supplementary Fig. 9 we show that the obtained translational diffusion coefficient at different temperatures is independent of the initial protein configurations in the solution. For the EC system we have also considered an extended trajectory of 1 μs and verified the convergence of the calculated diffusion constant as a function of trajectory length, see Supplementary Fig. 10.”\nPoint 2. Force Field Sensitivity:\nThe authors note slight shifts in unfolding temperatures between AMBER99SB-disp and CHARMM36m force fields (main text, p. 8). While this is discussed briefly, there’s no quantification of how this affects key observables like viscosity, diffusion coefficients, or apparent unfolding fractions. A concise comparison or a statement of how these differences fall within statistical errors would enhance confidence in the robustness of the conclusions\nAuthors reply: We have extended the discussion on the impact of the force field. The paragraph (p. 8) reads now as follow:\n“We should also emphasize that, as discussed in our previous work [16], the choice of force field affects the fine details of the curve extracted from QENS/MD, as shown in Fig.5d, where we compare results for the EC proteome simulated with the CHARMM36m [50] and AMBER a99SB-disp [51] force fields. For EC, the diffusivity of both, folded and unfolded proteome states, obtained with the AMBER a99SB-disp force field are slightly higher by a factor ~1.3 with respect to those of CHARMM36m. Coherently, also the computed systems’ viscosity is slightly smaller with AMBER a99SB-disp vs. CHARMM36m; 8 vs. 11 mPa s for the 288 g/L system. As a consequence, the rate of unfolding deduced from AMBER a99SB-disp progresses faster as a function of temperature than with CHARMM36m.”\nPoint 3. Summary Table for MD System Setup:\nThe checklist (4a) points out the lack of a consolidated table for MD system parameters (box sizes, atom counts, water/ion content, etc.) While some information is spread across Supplementary Tables 2–7, a single summary table would be valuable for reproducibility and clarity.\nAuthors Reply: We have added a table to the Supplementary Information, Supplementary Table 2, which presents a single summary of the systems, and includes information on the box edge lengths, total number of proteins, atoms, solvent, molecules and ions. Furthermore, we would like to note that one of the systems produced during the initial stages of the work has a slightly different box size, as shown in that table. Upon review, to correctly account for this difference, we have updated our estimate on the protein concentration level of that box (see Supplementary Table 5, Supplementary Figure 10, 11). The overall results of the global diffusion presented in the main text remain unchanged. We appreciate the reviewer’s recommendation to add a summary table, which helped us identify this point.\nPoint 4. Data Sharing:\nThe checklist (4d) confirms that the simulation input and output files will be made available via Zenodo but are not yet uploaded. For a systems-level study of this complexity, ensuring that the data are publicly available prior to publication is essential for reproducibility and verification.\nAuthors Reply: We have made the MD trajectories, and associated run files, and DeepSTABp data available in the Zenodo platform (https://doi.org/10.5281/zenodo.16699803., https://doi.org/10.5281/zenodo.16600218, https://doi.org/10.5281/zenodo.16603402, https://doi.org/10.5281/zenodo.16605838 and https://doi.org/10.5281/zenodo.16610190.)\nIn compliance with ILL rules, the experimental data is currently under embargo. These data can be made available upon request. The data available section reads as follow:\n“Experimental data (raw data, metadata and processed data) obtained via public research conducted at the ILL are destined for open access after an embargo period of 3 to 5 years following the experiment, with the ILL acting as the curator during and after the embargo period. The QENS data are stored at https://doi.ill.fr/10.5291/ILL-DATA.8-04-960. The DSC data are deposited at https://doi.ill.fr/10.5291/ILL-DATA.8-03-1087. These datasets can be provided upon request.\nAll molecular dynamics starting and output configurations accross the different temperatures are available at the following Zenodo repository together with topology files: https://doi.org/10.5281/zenodo.16699803. In addition, full simulation trajectories for System 1 and System 4 of P. arcticus and System 1 of A. aeolicus are published in the following Zenodo repositories (122.5 GB): https://doi.org/10.5281/zenodo.16600218, https://doi.org/10.5281/zenodo.16603402, https://doi.org/10.5281/zenodo.16605838. Results of the melting temperature predictions with DeepSTABp are published at https://doi.org/10.5281/zenodo.16610190.”\nAdditionally, we have added a Code Availability section. The analysis of the NS is based on a previously published code available in GitHub (https://github.com/DanieleDiBari/NSAnalysis). Specific examples for the applications of the code and related Jupyter notebooks are available under request (Prof. A. Paciaroni). All the scripts for the analysis of the molecular dynamics trajectories are made available in Zenodo and GitHub (https://github.com/beatrice-caviglia/ProteomeAnalysis) with examples for users.\n“The main post-processing code of the QENS data is available at GitHub (https://github.com/DanieleDiBari/NSAnalysis). Specific Jupyter notebooks for specific applications can be obtained upon request (A. Paciaroni). Python codes and Jupyter notebooks used to analyze the MD trajectories are available at the following GitHub link (https://github.com/beatrice-caviglia/ProteomeAnalysis) and can be tested with the available MD data on Zenodo.”\nPoint 5. Mechanistic Insight into Decoupling:\nThe authors do mention the broader context of decoupling in the main text (p. 3–4) and relate it to enzyme-specific stability trade-offs. However, a clearer articulation of how this decoupling might arise in the whole-cell context—such as roles of membrane fluidity, critical enzyme classes, or oxidative stress—would help readers connect the bulk proteome observations with cellular inactivation mechanisms.\nAuthors Reply: We thank the Reviewer for seeking clarification on the decoupling between proteome diffusion slow down and cell inactivation. We now shortly discuss in the paper how the decoupling between proteome thermal stability and bacterial viability is related to cellular inactivation mechanisms. We added the following paragraph in the final Discussion and Conclusion section (p. 10):\n“[..] Therefore, in psychrophiles, several destabilizing events leading to cell death can be triggered or enhanced even when most of the proteome is still structurally intact, owing to the functional thermolability of cold-adapted enzymes. This trait stems from their highly flexible active sites, which provide significant catalytic efficiency at low temperatures but render them quite thermally sensitive compared to their mesophilic counterparts [69]. Prototypic examples include DNA ligases, esterases, and α-amylases, which display pronounced thermal lability in function despite retaining their overall tertiary structure [70-72]. The functional impairment of these enzymes disrupts critical cellular processes: DNA replication, repair, and recombination (via DNA ligases), membrane fluidity maintenance (via esterases), and carbohydrate metabolism (via α-amylases), ultimately compromising bacterial viability. In this context, the enhanced flexibility of cold-adapted enzymes would render psychrophilic bacteria extremely sensitive to mild warming.”\nMinor Comments:\nPoint 1. The mathematical relationship between apparent unfolding fraction (au) from QENS and real fraction (ru) from MD (described in main text, p. 5–6) would benefit from a short schematic or equation summary to improve clarity.\nAuthors Reply: We now inserted explicitly two equations, eq. 6 and 7, describing i) the linearized expression of the translational di:usion involving the apparent unfolding fraction a u (cid:1) = (cid:4)1−(cid:7) (cid:9)(cid:1) (cid:10) +(cid:7) (cid:1) (cid:8)(cid:12)(cid:10) (cid:2) (cid:8) (cid:2) (cid:8) (cid:2) and ii) the relationship among the apparent and the real fraction of unfolding, (cid:7) = (cid:13) (cid:14) (cid:8) (cid:8) We then describe explicitly how to combine these equations to obtain from the experimental data the real fraction of unfolding. The added sentence reads as follow\n“[...] By comparing Eq. (3), Eq. (6) and Eq. (7) we obtain the real fraction of unfolded proteins as a function of temperature associated to the change of experimental diffusivity directly from the experimental quantity (cid:7) (cid:15)(cid:16)(cid:17)(cid:18), with (cid:13) = (cid:19)(cid:7) (cid:15)(cid:16)(cid:17)(cid:18) (cid:20) (cid:21)(cid:14) .” (cid:8) (cid:8) (cid:8)\nPoint 2. Minor typographical note: ensure consistent unit labeling (e.g., nm, K) in figure captions to maintain clarity.\nAuthors Reply: We thank the Reviewer for noticing the labeling discrepancies. We checked carefully throughout the text to ensure consistent unit labeling.\nPoint 3. Consider adding a short note in the methods (or supporting info) that the high-temperature unfolding method is generally validated for these crowded systems (e.g., potential kinetic trapping issues in short unfolding steps).\nAuthors Reply: As suggested, we have now added to section IV Methods E. MD simulations on page 11 the sentence along with appropriate references where the approach was previously used:\n“Elevated-temperature MD simulations have been previously validated as an effective method to induce protein unfolding on accessible timescales without distorting native unfolding pathways [16, 84, 85].”\nReviewer #2 (Remarks to the Author):\nThank you for the opportunity to review the manuscript entitled \"Cytoplasmic Fluidity and the Cold Life: Proteome Stability is Decoupled from Viability in Psychrophiles\". I think the authors have done good research on this topic. However, there are some comments as below:\nPoint 1. Page 2 - Introduction section - Provide the full name of the bacterial species (i.e P. arcticus and A. aeolicous), since this is the first time its been written in the manuscript.\nAuthors Reply: We thank the reviewer for noticing this point. We have adjusted the bacterial names accordingly.\nPoint 2. Page 4 - section C - What is the justification of choosing these percentage (25%, 50% and 75% for the unfolded protein)?\nAuthors Reply: We chose the percentages of unfolded proteins as 25%, 50% and 75%, to be added to the trivial ones of 0% and 100%, to reasonably and homogeneously cover the entire range of the possible unfolded proteome states along the path towards thermal melting. In the text we clarify this choice (see pg. 4):\n“To systematically sample the unfolded states of the proteome when temperature raises, we built three additional systems for both PA and AA at 299g/L, each containing different fractions of unfolded proteins (25%, 50%, and 75%). Together with the limiting cases of 0% and 100%, these configurations provide a uniformly spaced representation of the proteome as it progressively undergoes thermal melting.”\nPoint 3. Page 4 - section C - What is the protein being selected here? I think it is quite interesting if the authors selected a few important proteins/enzymes that were known in maintaining its function at low temperatures (i.e heat shock protein/antifreeze proteins/fatty acid desaturases etc).\nAuthors Reply: In “Results”, “Section B”, we specify that PA and AA systems were built using homologous proteins of our model for EC. When considering the partially unfolded state the proteins were selected randomly. Our primary focus lies in assessing the impact of protein folding state on the physical behaviour of the system—specifically, the global diffusive dynamics of proteins—rather than on their specific biological roles or adaptations, such as cold- or heat-responsiveness. In the future, it would be interesting, following the remark of the referee, to address the role of specific enzymes on the physical state of the proteome.\nPoint 4. Page 7 - section 2 - \".... in PA, proteome unfolding occurs at a significantly higher temperature than its cell death\". Yes, this indicates the interesting observation on the activity of some psychrophilic enzymes, where their enzyme activity are optimal near to the mesophilic temperatures (>25 degC).\nAuthors Reply: We thank the reviewer for acknowledging this aspect of our work.\nPoint 5. Page 9 - \"These organisms’ adaptations to cold – enhanced enzyme flexibility, fluid membranes, and specialized repair systems – render them exquisitely sensitive to mild warming.\"\nHave you look the expression of some proteins related to these factors (i.e membrane fluidity, antifreeze proteins etc).\nAuthors Reply. The proteome composition as a function of temperature is a very important aspect to consider when discussing the decoupling among proteome viability and melting. In fact, one can suppose that because of weak temperature changes, the proteome composition changes toward a non-functional folded state because of downregulation of essential enzymes. Unfortunately, as we anticipated in the conclusion, a precise proteome profiling across temperature for cold-adapted enzymes and its relation to metabolic function is still missing. Some scattered information exists. For example, in the Archaea Methanococcoides burtonii, a proteomic analysis showed the production of stress/repair proteins (in particular chaperone and oxidative stress proteins) at high temperatures (23-28°C) compared with optimal growth temperatures (1-16°C) (Williams et al, Environ Microbiol 2011). In Psychrobacter arcticus, transcriptome analysis was used to study gene expression at different temperatures (see Ref. 48). Many proteins are up- or down-regulated as a function of temperature. For example, genes involved in modifying fatty acids (membranes) are up-regulated at low temperatures (a fatty acid desaturase and cyclopropane fatty acid synthase). Changes in the expression of genes involved in the biosynthesis of peptidoglycan (cell wall) were also observed in this study. Generally, it was also shown that raising temperature causes an increased production of stress proteins and chaperones (Laura García-Descalzo et al 2022 Frontier). A clear analysis of the proteome state evolution will be the object of our future investigations. In the conclusion we added a sentence to recall this essential information pertinent for PA (see pg. 10) along with appropriate reference [48].\n“Transcriptomic analysis [48] reveals that PA bacteria over-express, at low temperature, proteins involved in fatty-acid modification and peptidoglycan biosynthesis.”\nPoint 6. Page 9 - \"In order to link the observed decoupling between metabolism and proteome stability and diffusion to single enzyme behavior, a crucial next step will be the in-vivo investigation of the temperature dependent relationship between activity and stability, as well as the local dynamics of active sites.\" - I am in aggreement with the authors - it is very important to compare the in vivo data with the prediction data generated. Perhaps, the ribosome profiling should be performed to achieve this in the future.\nAuthors Reply: We agree that the incorporation and study of ribosomes in a future work would be highly relevant, and we thank the reviewer for this comment.\nReviewer #3 (Remarks to the Author):\nThis article covers the influence of protein diffusion in the cytoplasm on temperature dependent cell viability for psychrophile, mesophile and hyperthermophile examples. This is an interesting and highly relevant field as the authors discuss. The study showcases a complementary use of experimental techniques (QENS and DSC), as well as MD simulations.\nThe paper is well written and presented, it is interesting and thorough in discussing the results presented.\nFor the neutron scattering data it describes a well reasoned and solid approach to data analysis. The authors themselves point out that the MSD through the EISF can offer insights on protein behaviour and mention that they determined two Lorentzian widths describing the proteome behaviour. While γ is very well presented and discussed Γ is not presented in the manuscript or SI. Even if the extreme crowding makes the analysis difficult it would be interesting to see the results. Especially in light of the psychrophile behaviour not following the expected relation between proteome diffusion and cell death. While this information would not necessarily provide the explanation it would provide additional insights, which are clearly of interested given the surprising results.\nAuthors Reply: We followed the suggestion of the Reviewer and showed the trend of the Lorentzian linewidths representing the local dynamics Γ , that are now reported in the Supplementary Information L together with the values of the local diffusion coefficient D and the characteristic residence time τ . We L L refer to this data in the text. We stress also that a detailed analysis of the local dynamics using EINS was previously carried out and published, see Ref. 32. In this study, performed on the same bacteria PA, EC and AA, we demonstrated that local dynamics follows a clear hierarchy progressing from cold-adapted to heat-adapted organisms. This variation in local motion may be linked to differences in the effective viscosity of the intracellular environment, shaped by the crowding characteristics of each proteome. This is now explicitly stated.\n“In this latter case, local protein dynamics were shown to be strongly influenced by the different nano-viscosities experienced by the proteins, consistent with observations in protein powder systems [33] and to correlate with the overall amino acid composition of the proteome.\nWe also added in the text the references to the new SI figures (p. 2).\n“The fit of the local self-diffusion Γ and the obtained parameters are shown in Supplementary Fig. 3,4. “ L\nDetailed comments:\nPoint 1. p. 2 The authors conclude that: “This behavior suggests that life adaptation involves the evolutionary selection of protein diffusivity that remains constrained across the entire thermal niche.” Given that only two of the three systems show a drop in diffusivity around the cell death temperature this conclusion seems to be too broad. The context of the results seem to indicate that diffusion is a symptom of proteome unfolding, as the authors discuss later in the text instead.\nAuthors Reply. We agree with the Reviewer that our statement is too strong, therefore we rephrase as follows:\n“Quite interestingly, protein diffusivity remains constrained across a broad temperature range, as it is particularly clear in the hyperthermophile, which shows values of the diffusion coefficient at high temperatures comparable to those observed in the psychrophile and mesophile under lower-temperature conditions. “\nPoint 2. p. 3 It would be useful if uncertainties were provided for the organism-specific maximum diffusion temperatures.\nAuthors Reply: We have attempted to estimate incertitude on the value of the temperature associated to the maximal diffusivity. We have constructed two curves using the higher and lower experimental errors and fitted the resulting curves (see enclosed figure above). We therefore obtain that the maximum diffusion temperature Tmax falls in a range of a few Kelvin for all the organisms, e.g. Tmax for PA falls in the range 315.8-317.3 K, and for AA in the range 369.5-372.0 K. We have also estimated the incertitude on the midpoint transition temperature Tm, 2.8 K for PA, 4.8 K for EC and 5.4 K for AA.\nWe modified a sentence at p. 3 to report this detail:\n“Interestingly, protein global diffusivity peaks at organism-specific temperatures --317 K for PA, 320 K for EC, and 371 K for AA-- with an uncertainty in peak positions of a few Kelvin due to experimental error bars in diffusion coefficients.”\nPoint 3. p. 4 The authors obtain the ratio of unfolding to folding needed to achieve the observed diffusive behaviour. Has it been confirmed that a higher ratio of partial unfolding to folding would not produce the same results?\nOur reasoning is based on the two-state model for protein unfolding where a protein exists either in a folded or unfolded state. In an ensemble of proteins, as in our crowded solutions, this corresponds to a different fraction of folded vs unfolded proteins. Of course, in the future, it would be interesting to check if the partial unfolded states of proteins (assuming the existence of long-living intermediates) could reproduce the experimental data.\nPoint 4. “The psychrobacter PA, however, behaves surprisingly, as the proteome unfolds more than 20 K above the predicted cell-death temperature. This indicates that, for this psychrophilic organism, other molecular processes cause the thermal cell death.” This conclusion is valid given the data shown. However, when investigating the other molecular processes Γ should be considered and if possible the MSD should also be obtained.\nAuthors Reply: We thank the Reviewer for pointing out the relevance of additional molecular processes. As mentioned above, in our analysis we also examined local dynamics to determine whether relevant molecular processes could explain the anomalous behavior of psychrophilic bacteria. However, the trends in the local diffusion D and the characteristic residence time τ from the jump-diffusion model (see SI) did L L not provide clear insights into the molecular mechanisms underlying PA cell death. We reported the results on the local dynamics of the proteomes of PA, EC and AA bacteria also in our recent paper “Decoding the Role of the Global Proteome Dynamics for Cellular Thermal Stability” (Caviglia et al. J. Phys. Chem. Lett. 2024, 15, 5, 1435-1441). By using elastic incoherent neutron scattering we showed that the mean squared displacements are similar among all studied bacteria at their respective thermal cell death, about that we added the sentence (pg. 5):\n“As previously noted, the comparable thermal fluctuations of PA, EC, and AA at their respective cell death thresholds [32]—quantified here via atomic mean square displacements—further support that local proteome dynamics is critically related to cellular viability.”\nPoint 5. p. 10 The sample preparation using HCl instead of DCl introduced freely exchangeable hydrogens to the system. How were they accounted for in the data analysis? Or is the volume considered negligible?\nAuthors Reply: The effect was considered negligible. We calculated that, in 40mL of D₂O-based buffer, approximately 8.67×10²⁰ hydrogen atoms were introduced via the 12M HCl. Since only the cell pellet was used—not the entire suspension—we estimate that a maximum of 2mL of buffer remained in the sample, corresponding to about 4.33×10¹⁹ hydrogen atoms. This is negligible when compared to the approximately 4×10²¹ hydrogen atoms originating from the E. coli cells.\nDear Editor,\nThanks again for the positive evaluation of our work. Below you can find the point-by-point response to Reviewer #3:\nReviewer\nRegarding my comment about Γ_L I am pleased to see that it has now been included. However, the plots in supplementary figure 3 does at times look as if it is better described as q^2 independent. Has this been considered?\nReply: We thank the Reviewer for this insightful question. In analyzing the Γ trend, we also considered L that a Q-independent model could suitably describe the data. However, at certain temperatures the low- Q values of Γ are more accurately captured by a Jump Diffusion model, making a definitive choice of the L most appropriate model challenging. We ultimately adopted the Jump Diffusion model, as it not only provides a consistent description of our data but also enables direct comparison between the two bacterial species analyzed here and E. coli, for which this model has previously been successfully applied.\nReviewer\nRegarding point 4 of my previous comments, I am as said above happy to see Γ_L shown. Given that all the values are obtained to discuss A_0 (q), as is shown in eq(1) in the manuscript I would encourage the authors to either show it, even if only in the SI or to provide a brief comment about why it could not be determined. Given that it was determined in previous work it would be interested to see how the results from this experiment compare.\nReply: As requested by the Reviewer, we added a new figure, Supplementary Figure 7, in the SI, where we show the A (Q) for the PA and AA bacteria. In the caption we also shortly discuss the comparison of 0 these data with those from EC and we say “The trend of A (Q) for PA and AA is qualitatively similar to 0 that of EC [3], with a high-Q behaviour decreasing as a function of temperature below the cell death and increasing once the organism is no longer functional. This corresponds to a larger number of immobile hydrogen atoms, i.e. a progressive dynamic slowdown.”\nReviewer\nOn p. 10 The authors mention that they use a Tris based buffer. Was the property of Tris that the pH/pD changes depending on the temperature considered in the data interpretation/ sample preparation?\nReply: As the Reviewer noted, Tris buffer undergoes a temperature-dependent pH shift (≈0.02 pH units per °C), and our samples were prepared in a buffer adjusted at room temperature. For practical reasons, it was not feasible to continuously readjust the buffer during the temperature runs, as this would have required replacing the sample and gradually adapting the pH. This would have very likely introduced noticeable systematic errors during the measurement, due to the variability of the sample of the same bacterial cells at the different temperatures. We acknowledge this limitation and note that, although not ideal, it represents the most practical approach under the experimental conditions.\nBest regards\nAlessandro Paciaroni\nOn behalf of the corresponding authors"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The revisions are satisfactory, and all my comments have been fully addressed. I appreciate the authors’ thorough and thoughtful responses, as well as the clear improvements made to the manuscript. Thank you for your efforts.", "reviewer_2": "The authors have made substantial improvements to the manuscript following the first review process. The revised version addresses all of the concerns raised by the reviewers, with notable enhancements in the clarity of the text, the structure of the argument, and the depth of analysis.", "reviewer_3": "I would like to thank the authors for the improvements to the manuscript. The majority of my points have now been addressed.\nRegarding my comment about Γ_L I am pleased to see that it has now been included. However, the plots in supplementary figure 3 does at times look as if it is better described as q^2 independent. Has this been considered?\nRegarding point 4 of my previous comments, I am as said above happy to see Γ_L shown. Given that all the values are obtained to discuss A_0 (q), as is shown in eq(1) in the manuscript I would encourage the authors to either show it, even if only in the SI or to provide a brief comment about why it could not be determined. Given that it was determined in previous work it would be interested to see how the results from this experiment compare.\nOn p. 10 The authors mention that they use a Tris based buffer. Was the property of Tris that the pH/pD changes depending on the temperature considered in the data interpretation/ sample preparation?"}, "author_response": ""}]

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