Delft University of Technology
Experimental infrastructure requirements for quantitative research on microbial
communities
Kleerebezem, Robbert; Stouten, Gerben; Koehorst, Jasper; Langenhoff, Alette; Schaap, Peter; Smidt, Hauke DOI 10.1016/j.copbio.2021.01.017 Publication date 2021 Document Version Final published version Published in
Current Opinion in Biotechnology
Citation (APA)
Kleerebezem, R., Stouten, G., Koehorst, J., Langenhoff, A., Schaap, P., & Smidt, H. (2021). Experimental infrastructure requirements for quantitative research on microbial communities. Current Opinion in
Biotechnology, 67, 158-165. https://doi.org/10.1016/j.copbio.2021.01.017 Important note
To cite this publication, please use the final published version (if applicable). Please check the document version above.
Copyright
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons. Takedown policy
Please contact us and provide details if you believe this document breaches copyrights. We will remove access to the work immediately and investigate your claim.
This work is downloaded from Delft University of Technology.
Langenhoff
c,
Peter
Schaap
band
Hauke
Smidt
dNaturalmicrobialcommunitiesarecomposedofalarge
diversityofinteractingmicroorganisms,eachwithaspecific
roleinthefunctionalpropertiesoftheecosystem.The
objectivesinmicrobialecologyresearcharerelatedto
identifying,understandingandexploringtheroleofthese
differentmicroorganisms.Becauseoftherapidlyincreasing
powerofDNAsequencingandtherapidincreaseofgenomic
data,mainattentionofmicrobialecologyresearchshiftedfrom
cultivation-orientedstudiestowardsmetagenomicstudies.
Despitetheseefforts,thedirectlinkbetweenthemolecular
propertiesandthemeasurablechangesinthefunctional
performanceoftheecosystemisoftenpoorlydocumented.
Aquantitativeunderstandingoffunctionalpropertiesinrelation
tothemolecularchangesrequireseffectiveintegration,
standardization,andparallelizationofexperiments.
High-resolutionfunctionalcharacterizationisaprerequisitefor
interpretationofchangesinmetagenomicproperties,andwill
improveourunderstandingofmicrobialcommunitiesand
facilitatetheirexplorationforhealthandcirculareconomy
relatedobjectives.
Addresses
aDelftUniversityofTechnology,DepartmentofBiotechnology,Delft,
TheNetherlands
bWageningenUniversityandResearch,LaboratoryofSystemsand
SyntheticBiology,Wageningen,TheNetherlands
c
WageningenUniversityandResearch,DepartmentofEnvironmental Technology,Wageningen,TheNetherlands
dWageningenUniversityandResearchLaboratoryofMicrobiology
Wageningen,TheNetherlands
Correspondingauthor:Kleerebezem,Robbert(r.kleerebezem@tudelft.nl)
CurrentOpinioninBiotechnology2021,67:158–165 ThisreviewcomesfromathemedissueonEnvironmental biotechnology
EditedbyRobbertKleerebezemandDianaZ.Sousa
https://doi.org/10.1016/j.copbio.2021.01.017
0958-1669/ã2021TheAuthor(s).PublishedbyElsevierLtd.Thisisan openaccessarticleundertheCCBYlicense(http://creativecommons. org/licenses/by/4.0/).
Introduction
Innaturalandman-madeenvironments,microorganisms virtually never thrive as single species. Instead, they flourishasmicrobialcommunitiesofvariouscomplexity.
Inthesemicrobialcommunitiesalldifferentspecieshave a specific role, and their combined effort results in an overallfunctionalperformancecharacterizedbythe catal-ysisofdifferentredoxreactionsandanoverallconversion ofsubstratesintoproducts.Inoursocietywemakeuseof our knowledge on these microbial communities for understandingandcontrollingoffoodfermentations,soil conditioning, host–microbe interactions, and numerous environmentalengineeringapplications[1,2,3,4].
Microbialcommunitiesareintrinsicallymorecomplicated toinvestigatethansinglestrainssincetheyarecomposed oftwoupto thousandsofdifferenttypesof microorgan-isms.The functional performance– thesum of all con-versionscatalysed – of a microbialecosystem therewith dependsonthecombinedactivityofallthese microorgan-isms.Thereisawiderangeoffactorsthatdeterminesthe complexityofmicrobialcommunities,butthemost com-monlydescribed include (i) catalysis of sequential con-versions(ii)thecatalysisofparallelconversions,or(iii)the existenceof (redox)gradients resultingin specificspace dependent ecological niches and associated different typesofmicroorganisms(Figure1).Additionalcomplexity arisesfrommulti-way cross-feedingonawide varietyof excretedmetabolites[5],interspeciesmicrobialfusion[6], anddynamicpropertiesofenvironmentalecosystemslike day-nightrhythms[4],orotherformsofcompetitionand cooperation.The enormousarrayof environmental con-ditionsinnaturehasfacilitatedtheevolutionofa tremen-dous microbial diversity that hasbeen found to inhabit virtuallyallecologicalnichesonearth.
Microbial ecology research aims for relating functional systemdynamicstothechangesinthemolecular proper-tiesinthesystem.Changesinmolecularpropertiesmay occuratvariouslevelsandtimescales:(i)changesinthe microbial community structure, (ii) changes in gene expression in the system as reflected in the metatran-scriptomeandmetaproteome,and(iii)directchangesin fluxes due to metabolic flux control. A wide range of experimentaltoolsiscurrentlyavailableto transfer eco-logical research questions to a laboratory experimental system,and toanalyseboththefunctional propertiesof theecosystemaswellas itsmolecularproperties.
The rapid development of experimental tools for con-ductingresearchonmicrobialcommunities givesrise to anintensediscussiononhowtoapproachresearch ques-tionsinthefieldofmicrobialecology[2,10,11,12,13].
Herewehavetakentheseconsiderationsintoaccountand analysed the current status of the different aspects of microbial ecologyresearch.Onthebasisofthis analysis we propose a strategy to overcome the limitations encountered in our currentapproach to microbial com-munityresearch.Thepotentialimpactofthisstrategyis discussed.
Microbial
ecosystems
in
the
laboratory
Researchonmicrobialcommunitiesinnaturaland full-scale engineered ecosystems is often complicated by the troublesomefunctionalcharacterization ofthe con-versions occurring. For instance, the operation of full-scale wastewater treatment plants cannot be changed with the objective to see how it affects the process performance and microbial community structure, since it may compromise the treatment performance. Simi-larly, the sampling and identification of substrate and productfluxesinhumanmicrobiomestudiesarehardto conduct, and human microbiome research therefore stronglyreliesonmolecularsystemsanalysisusingstool samples and limited insight in the actual conversions catalyzed is achieved. Overall it is in general undesir-able or impossible to expose natural microbial ecosys-tems to specific changes in environmental conditions with the objective to investigate the impact on the system.
Toovercomethelimitationsofin-vivoresearchon micro-bialecosystems,microbialcommunitiesaretransferredto thelaboratoryandinvestigatedatvariouslevelsof com-plexity, ranging from direct measurements on environ-mentalmicrobialconsortia,viamesocosms,andmicrobial enrichmentstudiesinbioreactorstoeventuallyisolation and characterization of key players from theecosystem under investigation[14–16].Dependingontheresearch question at hand, choices are made on the required degree of simplification of the ecosystem, taking into account that the experimental resolution increases at decreased systemcomplexity.
Typically,thebasictoolformimickingspecific environ-mental conditions in the laboratory is the bioreactor, ranging in complexity from simple batch bottles to high-techcontinuous bioreactorsequipped with on-line measuring facilities. In these bioreactors the functional responseofamicrobialcommunitycanbeanalysedand, when combined with appropriate molecular tools for biomass characterization, related to the molecular changesinthemicrobialcommunity.Integrationofthese experimentaldataallowsfordevelopmentofquantitative system-basedmodelsforrelatingfunctionaltomolecular changes. This integrated approach generates improved knowledgeandunderstandingofthesystemathand,and allows for identification of the impact of changes in environmentalconditionsonthesystem.Eventuallythis enables theunderstandingand predictionofchangesin thenaturalorman-madeecosystemtheexperimentaims to mimic. A schematic representation of a typical sequence of events in laboratory research on microbial communities isshownin Figure2.
In the next sections we will elaborate on the recent developments in both functionaland molecular charac-terization of microbialcommunities in order to identify fundamentalshortcomingsandopportunities.
Functional
system
characterization
using
process
dynamics
and
on-line
measurements
Detailed functional characterization of microbial pro-cesses concerns the identification and quantification of redox reactions catalysed in a microbial ecosystem in relationtothedevelopmentofthebiomassconcentration andcomposition.Measurementofthesevariablesenables theidentificationofbiomassspecificfluxesinrelationto the thermodynamic driving forces. One of the major challengesintheidentificationofbiomassspecificfluxes isthedependencyof quantitativedataonthemicrobial communitystructure,whichwillbediscussedinthenext section on microbial community analysis. Here we will discuss the added value of developments in functionalStructureversusfunctioninmicrobialcommunitiesKleerebezemetal. 159
Figure1
Current Opinion in Biotechnology
Schematicrepresentationofmicrobialecosystemcomplexity,arisingfromtheactivityofdifferenttypesofmicroorganisms.Themicrobialnitrogen cyclerelatedexamplesshownare:redoxgradient(+O2/ O2)drivenautotrophicnitrogenconversionsinabiofilm(left)[7],parallelnitratereduction
todinitrogenandammonium(middle)[8],andsequentialconversionsinthetwo-stepnitrificationprocess(right)[9].Sizebarsfromlefttoright indicate100,10,and50mm.
characterizationofmicrobialcommunities:(i)on-linerate measurements,(ii)processdynamics,and(iii)uncoupling ofsolidandliquid retentiontimes:
1 On-lineoff-gas composition measurementscombined withthesupplyofaninertgas(e.g.dinitrogenorargon gas)enablestheidentificationofprocessratesateach moment in time. This approach has been used to identify different competitive strategies in pulse fed aerobicbioreactors(Figure3)[17,18].Off-gasoxygen concentration measurements enabled on-line oxygen respiration rate measurements providing a detailed insight in the process when linked to other on-line measurements such as of the off-gas carbon dioxide concentrationandacid/basedosagerateforpH-control.
IntheexampleshowninFigure3,theoxygen respira-tionratesinthreecomparablesystemswasshowntobe significantlydifferent, correspondingtoclearly differ-entecologicalstrategies[18].Itisfurthermoreevident thattheinformationdensityobtainedfromtheon-line oxygenuptakerate measurements (Figure 3b) is sig-nificantly higher compared to the off-line substrate concentrationmeasurements(Figure 3a).
Thefunctionaldynamicsofasystemduringlongterm cultivation studies can subsequently be analysed throughdefinitionof keyvariablesthatcanbe identi-fiedfromtheonlinemeasurementsforeachoperational cycle.IntheexampleshowninFigure3weusedthe(i) length of the period of substrate presence, (ii) the oxygen uptake before and after substrate depletion,
Current Opinion in Biotechnology
Typicalworkflowformicrobialcommunityresearch:(1)aresearchquestionoriginatingfromanymicrobialecosystemistranslatedtoalaboratory cultivationexperiment(2),thatallowsforidentificationoftheresponseofthemicrobialcommunitytochangesinenvironmentalconditions(3),and understandingofthemoleculardriversresponsiblefortheresponseobserved(4).
and(iii)theincreaseinoxygenuptakerateduringthe presenceofsubstrate,askeyvariablesforanalysingthe developmentofthefunctionalpropertiesofthesystem over aperiodof morethan100 generations[18]. 2 Thehighinformationdensityoftheon-linerate
mea-surements proposed in the previous paragraph only holdstrueifsomedynamicsinoperationoftheprocess are established. Continuous Stirred Tank Reactors (CSTR) operatedat aconstantdilution rateare typi-callysubstratelimited,andeventhoughtheextentof substrate limitation may vary, this will hardly be reflectedintheconversionratesobserved.Thismeans thateventhoughmajorchangesintheactual stoichio-metricandkineticcapacitiesofthemicrobial commu-nity may occur, they cannot be identified from stan-dards measurements. Hence,some form of operation dynamicsneedstobeimplementedinordertoincrease theinformationdensityofthegenerateddata.Thiscan beestablishedinpulsefedexperiments,asusedinthe experiment described in the previous paragraph, or through a periodic increase of the dilution rate, for example. Combined with on-line rate measurements the dependency of the fluxes in the process can be identifiedasafunctionoftheactualprocessconditions
during these pulses, and the long term functional propertiesofthesystemcaneffectivelybemonitored [19–21].
3 Athirdimportantaspectofcurrentmicrobial commu-nitycultivationmethodsisthepossibilitytouncouple solid(biomass)and liquid retentiontimesin the pro-cess through application of a solid–liquid separating membrane in the reactor outlet. Whereasin the past solid retentionin laboratory bioreactorswas achieved throughperiodicsettlingor throughformationof bio-films, solid liquid separating membranes provide a more controlled method for solid retention in the process. Membrane bioreactors enable experiments atrelativelylow substrateandproductconcentrations and low growth rates, but well measurable biomass concentrations as often encounteredin natural envir-onments[22,23].
Microbial
community
structure
and
function
analysis
As described in the previous section, current methods enable ustoidentifyaccuratelytheoverallprocess stoi-chiometry and ratesin laboratory microbialecosystems. Thisincludestheredoxreactionscatalysedaswellasthe
StructureversusfunctioninmicrobialcommunitiesKleerebezemetal. 161
Figure3 (a) (b) (c) Cycle time (h) Acetate (mCmol) Oxygen T
ransfer Rate (mmol O2 h
-1)
Cycle time (h)
10 μm Current Opinion in Biotechnology
Off-lineacetatemeasurements(a)andon-lineOxygenTransferRate(OTR)profiles(b)inpulsefedaerobicbioreactorscontainingthreedifferent enrichmentcultures,asvisiblethroughmicroscopyimaging(c).ColoredareasoftheOTRprofilesreflectthetimeexternalsubstrate(acetate)was present.Theacetatemeasurementsshowundistinguishabletrends,whereastheOTRprofilesallowcharacterizationofadistinctmetabolic responseinthedifferentcultures.
themicrobialcommunitystructureintermsoffunctional groups of microorganisms is required. Only when the communitystructure isknownand fluxescanbe attrib-utedtospecificgroupsofmicroorganismswecanclaimto understand the system and investigate how specific experimentalvariablesaffectboththeconversionsaswell asthemicrobialcommunity.
Thecurrentlymostwidelyappliedmethodfor identify-ingthemicrobialcommunity structureisbased onhigh throughputsequenceanalysisofthe16SribosomalRNA (rRNA) gene pool that is PCR-amplifiedfrom biomass DNA. This method typically generates compositional data, that is, information on the presence of specific microbial taxa as well as their relative abundance in the community. However, such compositional data do notallow absolutequantificationofthepopulationsizes of different microbial taxa. Furthermore, a number of technical biases related to DNA-extraction efficiency, PCR specificity,variations in copy numbersof the tar-geted gene, and cell size have been identified. Conse-quently, measured community structure data can be ordersof magnitude differentfrom theactual microbial community compositionin termsoftheactual distribu-tion of protein or cell dry weight in a sample. The quantitativerepresentation of the community structure (typically in bar-charts) can bemisleading since it sug-gests insight in the actual community structure, even thoughthe biases are such thatthis cannot beclaimed [25–28]. The key value of 16S rRNA gene amplicon sequencing canbe in the determination of the relative changesintimeinmicrobialcommunitystructurewhich canbe analysed adequatelyif experimental procedures areconsequentlyapplied.
Modifiedexperimentalproceduressuchastheinclusion ofinternalstandardsmaycontributetoabsolute quantifi-cationof themicrobialcommunity structure [29]. Com-binationofampliconsequencingdatawithmore quanti-tative methods like fluorescent in situ hybridization (FISH,seeFigure1),quantitativereal-timePCRorflow cytometryhasbeenproposedtoobtainmorecommunity structuredata[30–33].Itshouldbenoted,however,that also these methods are not without bias, as has been shown by comparing quantitative microbiome profiles obtainedbyqPCRandflowcytometry[34].Furthermore, currentmetagenomicand/ormetaproteomicmethodsare potentially less biased than 16S rRNA gene amplicon sequencingbased microbialcommunitystructure analy-sis,andtogetherwithmetatranscriptomicanalysis,these methodsprovideadditionalinsightsinfunctionalcapacity
Towards
the
integration
of
methods
for
effective
and
quantitative
research
on
microbial
communities
Itis evident that microbial ecology research requires a widerangeof fieldsof expertise.Nevertheless,the his-toricfocusofresearchgroupsontheindividualaspectsof microbial ecology has hampered the integration of the availabletoolsasrequiredforincreasedunderstandingof themicrobial world.Furthermore,thefinancial implica-tions of the infrastructure requirements for conducting integrated research on microbial communities suggests that effective cooperation is strictly necessary. Even thoughthe prices for sequencing dependent molecular analysisof microbialcommunities havegonedown rap-idly,samplepreparationanddataprocessingstillrequirea considerable amount of human power and computer power. Thus, in order to move microbial ecological research beyond thestate-of-the-art,major investments arerequired.
Overall we have identified three key aspects that are required to achieve more effective and quantitative researchonmicrobialcommunities:
1 Integration.Effectivelaboratoryresearchonmicrobial communitiesrequiresintegrationofthedifferentfields of expertise such as microbial physiology, molecular ecology,bioprocessengineering,biochemistry,process modelling, and bioinformatics. Depending on the actualresearchquestion,acombinationofthedifferent fieldsof research are required.In individual research groups not all these fields of expertise are available, imposingadirectneedforcollaboration.Forexample, researchersworkingondetailedmolecular characteri-sationofmicrobialcommunitiesdonothavethesame degree of expertise on functional characterisation of microbial communities in the laboratory. The same holds true vice versa. In order to focus on your own field of expertise and therelated research questions, onewouldwanttomakesurethattheotheraspectsare dealtwithinanoptimizedmannerrequiringa signifi-cantdegree of integration of research fields. In sum-mary this suggests that effective collaboration and makinguse ofeachother’s expertiseisaprerequisite forefficientresearchthatfacilitateseffectivefocuson thetopicofinterestandaneffectivesearchforanswers totheresearchquestions asked.
2 Multiplication. A second limit of current research infrastructure is the scale at which experiments can beconducted.Laboratoryscalebioreactoroperationis atimeandresourcesconsumingmethodfor character-ising microbial communities and analysis of their
responseupondifferentcultivationconditions.Online ratemeasurementsaspreviouslydiscussedcanreduce theeffortrequiredforconductinganexperiment,but typically researcherscanoperatenomorethanoneor twolaboratoryreactorsatatime.Thisimposesarange oflimitations:(i)onlyoneoperationalvariationcanbe investigated, (ii) no replicate runs are typically con-ducted, and (iii) since multiple values for a specific operationalvariable areinvestigated insequence, the startingpoint(i.e.theinoculum)ofeachexperimentis different.Therefore,inordertoinvestigatetheimpact of a specific operational variable one would want to increasethenumberofbioreactorsthatareoperatedin parallel,enablingthesamestartingpoint(inoculum)in eachbioreactor.Paralleloperationofmore(e.g.fiveto ten)bioreactors combinedwith on-linerate measure-ments enables (i)high-resolution investigation of the effectofdifferentoperationalvariablesonthesystem development,(ii)inclusionofreplicates,(iii)useofthe sameinoculum(startingpoint)inallexperiments,(iv) andinclusionofcomparativemolecularanalysisofthe developmentofmicrobialcommunitycompositionand functionalpropertiesin time.
3 Standardization. Afinal concerninthefieldof micro-bialecologyresearchisthetroublesomereproducibility ofexperimentalresultsandthelimitedaccessibilityof experimentaldata.Theseaspectscantoalargeextent be overcome by including standard protocols for (i) definition of experimental setups and protocols, (ii) data handling and storage, and (iii) adequate storage of microbialcommunitiesestablished inexperiments. Integratedstorageofboth(rawandprocesseddataof) functional system properties (including media, etc.) combined with molecular data, makes data directly availablefor inter-experimentalcomparison, post-pro-cessingof data,and(metabolic)modellingefforts.
These three main considerationsform the basis of the research infrastructure project entitled Unlock that was grantedbytheDutchScienceFoundation(NWO)inthe springof 2020andthatwewill beimplementinginthe coming years. Unlock consists of three experimental laboratoriesandacommondataplatformforinvestigating microbialcommunitiesatdifferentlevelsofcomplexity: Themodularbioreactorplatform,forinvestigatingthe mostcomplexmicrobialcommunitiesintheir environ-ment. This lab includes specific elements like con-structed wetlands, bioelectrochemical systems, and bioreactorsthatcanbeoperatedathightemperatures andpressuresformimickingextremeconditions micro-bial ecosystemscanbeexposedto,
The parallel cultivation platform, consisting of forty identical lab-scale bioreactors equipped with stateof thearton-lineanalyticalequipmentforhigh-resolution functionalcharacterisationofmicrobialcommunitiesof
variablecomplexityupontheirexposuretoavarietyof operationalconditions.
Thebiodiscoveryplatform,consistingof microbioreac-torandmicrofluidicsunitsforscreeningand character-ising defined cocultures of microorganisms, and suspended bead–based systems for single cell geno-mics. Also a highthroughput, largely automated bio-masssampleprocessingunitwill beimplementedfor conducting awide range of molecularanalyses using standardizedapproaches.
The fair data platform, will concern an open source scalabledatastorageandprocessingfacility.Thedata platformwillbefairbydesigntomaximizedatareuse, and focuses on data ownership, intellectual property rightwhileaiming foroptimal publicdataavailability [36].
The objectiveof theUnlockresearch infrastructureisto enhancethebasiclevelofconductingintegratedresearch onmicrobialcommunities.
Conclusions
Microbialcommunityresearchdependsontheeffective integrationofdifferentfieldsofexpertise.Traditionally, research either emphasizes the development of func-tional properties of microbial communities or focuses on their molecular characterization and development. Eventhoughmostlysome degreeofintegrationof both fields ofexpertise isestablished, thetrueintegrationof state-of-the-artmethodsandexpertiseonfunctional sys-tem characterization,molecularsystem characterization, andderivativesthereofincludingprocessmodelling can-notbeachievedwithinonesingleresearchproject,norby onespecific researchgroup.
To overcomethese limitations, wepropose that micro-bial ecology research shouldaim forintegration of the different research fields, multiplication for a scale increase oftheexperimentalfacilities, and standardiza-tionofthemethodologies.Weareaimingforintegration ofexperimentalfacilitiesforbothfunctionaland molec-ularcharacterization ofmicrobialcommunitiesanddata processing and storage. Herewith the objective is the generation ofhigh qualitydata in all aspects of micro-bialecologyresearchaswellasefficientmanagementof the data generated. Integration allows researchers to focus ontheirkey researchquestionsanddeveloptheir specific field ofexpertise,beingcomforted bythe idea that all other aspects ofthe research are dealt with by specialists in the corresponding field. We have united our forces in achieving these objectives by organizing the research infrastructure entitled Unlock (www. m-unlock.nl)thatisdesignedtofacilitate ground-break-ing fundamental and applied research on microbial communities and their use in a wide range of applications.
asDelftUniversityofTechnologyandWageningenUniversityand ResearchfortheirfinancialcontributiontotheUnlockinitiative(NWO: 184.035.007).
References
and
recommended
reading
Papersofparticularinterest,publishedwithintheperiodofreview, havebeenhighlightedas:
ofspecialinterest ofoutstandinginterest
1. KleerebezemR,vanLoosdrechtMCM:Mixedculture
biotechnologyforbioenergyproduction.CurrOpinBiotechnol 2007,18:207-212.
2.
LawsonLofflerFE,CE,O’MalleyHarcombeMA,WR,MartinHatzenpichlerHG,PflegerR,BF,LindemannRaskinL,SR, VenturelliOSetal.:Commonprinciplesandbestpracticesfor engineeringmicrobiomes.NatRevMicrobiol2019,17:725-741
Review describing the state of the art analytical tools available for investigatingmicrobial communities andthat proposes bestpractice guidelinesforengineeringmicrobiomes.
3. StrousM,SharpC:Designermicrobiomesforenvironmental, energyandhealthbiotechnology.CurrOpinMicrobiol2018, 43:117-123.
4. MooijPR,StoutenGR,vanLoosdrechtMCM,KleerebezemR: Ecology-basedselectiveenvironmentsassolutionto contaminationinmicroalgalcultivation.CurrOpinBiotechnol 2015,33:46-51.
5. DouglasAE:Themicrobialexometabolome:ecological resourceandarchitectofmicrobialcommunities.PhilosTrans RSocBBiolSci2020,375.
6. CharubinK,ModlaS,CaplanJL,PapoutsakisET:Interspecies microbialfusionandlarge-scaleexchangeofcytoplasmic proteinsandRNAinasyntrophicClostridiumcoculture.mBio 2020,11:e02030-02020.
7. WinklerMKH,KleerebezemR,KuenenJG,YangJJ,van LoosdrechtMCM:Segregationofbiomassincyclicanaerobic/ aerobicgranularsludgeallowstheenrichmentofanaerobic ammoniumoxidizingbacteriaatlowtemperatures.EnvironSci Technol2011,45:7330-7337.
8. vandenBergEM,BoleijM,KuenenJG,KleerebezemR,van LoosdrechtMCM:DNRAanddenitrificationcoexistovera broadrangeofacetate/N-NO3-ratios,inachemostat enrichmentculture.FrontMicrobiol2016,7.
9. HoekstraM:Mainstreamanammox,potential&feasibilityof autotrophicnitrogenremoval.Biotechnology.DelftUniversityof Technology;2017:135.
10. AntwisRE,GriffithsSM,HarrisonXA,Aranega-BouP,ArceA, BettridgeAS,BrailsfordFL,deMenezesA,DevaynesA, ForbesKMetal.:Fiftyimportantresearchquestionsin microbialecology.FEMSMicrobiolEcol2017,93.
11. ProsserJI:Replicateorlie.EnvironMicrobiol2010, 12:1806-1810.
12.
questionProsserJI:ofPuttingapproach.sciencePhilosbackTransintoRSocmicrobialBBiolSciecology:2020,375:9a
Probablyfor many researchers a rather confrontingand verycritical analysisofthefieldofmicrobialecologybyJimProsser,whoproposes that we should first consider and define the fundamental scientific questionsthatdriveourresearch,andonlythenconsiderwhich techni-quesarerequiredforexperimentaltesting.
13.
WidderCorderoS,OX,AllenBrownRJ,SP,PfeifferMomeniT,CurtisB,ShouTP,WYWiufetC,al.:SloanChallengesWT, in microbialecology:buildingpredictiveunderstandingof communityfunctionanddynamics.ISMEJ2016,10:2557-2568
15. ElzingaJ,vanderOostJ,deVosWM,SmidtH:Theuseof definedmicrobialcommunitiestomodelhost-microbe interactionsinthehumangut.MicrobiolMolBiolRev2019,83.
16. GrosskopfT,SoyerOS:Syntheticmicrobialcommunities.Curr OpinMicrobiol2014,18:72-77.
17. JohnsonK,KleerebezemR,vanLoosdrechtMCM:Model-based dataevaluationofpolyhydroxybutyrateproducingmixed microbialculturesinaerobicsequencingbatchandfed-batch reactors.BiotechnolBioeng2009,104:50-67.
18. StoutenGR,HogendoornC,DouwengaS,KiliasES,MuyzerG, KleerebezemR:Temperatureascompetitivestrategy determiningfactorinpulse-fedaerobicbioreactors.ISMEJ 2019,13:3112-3125.
19. KleerebezemR,vanLoosdrechtMCM:Thermodynamicand kineticcharacterizationusingprocessdynamics:acidophilic ferrousironoxidationbyLeptospirillumferrooxidans. BiotechnolBioeng2008,100:49-60.
20. Gonzalez-GilG,KleerebezemR,LettingaG:Effectsofnickeland cobaltonkineticsofmethanolconversionbymethanogenic sludgeasassessedbyon-lineCH4monitoring.ApplEnviron Microbiol1999,65:1789-1793.
21. StrousM,HeijnenJJ,KuenenJG,JettenMSM:Thesequencing batchreactorasapowerfultoolforthestudyofslowly growinganaerobicammonium-oxidizingmicroorganisms. ApplMicrobiolBiotechnol1998,50:589-596.
22. vanKesselM,SpethDR,AlbertsenM,NielsenPH,Opden CampHJM,KartalB,JettenMSM,LuckerS:Complete nitrificationbyasinglemicroorganism.Nature2015, 528:555-559.
23. LottiT,KleerebezernR,Abelleira-PereiraJM,AbbasB,van LoosdrechtMCM:Fasterthroughtraining:theanammoxcase. WaterRes2015,81:261-268.
24. HeijnenJJ,KleerebezemR:Bioenergeticsofmicrobialgrowth. In EncyclopediaofIndustrialBiotechnology.EditedbyFlickinger MC. JohnWiley&Sons,Inc.;2009:1-24.
25. BaileyM,ThomasA,FrancisO,StokesC,SmidtH:Thedarkside oftechnologicaladvancesinanalysisofmicrobial
ecosystems.JAnimSciBiotechnol2019,10.
26. PollockJ,GlendinningL,WisedchanwetT,WatsonM:The madnessofmicrobiome:attemptingtofindconsensus"Best Practice"for16Smicrobiomestudies.ApplEnvironMicrobiol 2018,84.
27. VandeputteD,KathagenG,D’hoeK,Vieira-SilvaS, Valles-ColomerM,SabinoJ,WangJ,TitoRY,DeCommerL,DarziY etal.:Quantitativemicrobiomeprofilinglinksgutcommunity variationtomicrobialload.Nature2017,551:507-511.
28. BrooksJP,EdwardsDJ,BlitheDL,FettweisJM,SerranoMG, ShethNU,StraussJF,BuckGA,JeffersonKK:Effectsof combinedoralcontraceptives,depotmedroxyprogesterone acetateandthelevonorgestrel-releasingintrauterinesystem onthevaginalmicrobiome.Contraception2017,95:405-413.
29. HarrisonJG,JohnCalderW,ShumanB,AlexBuerkleC:The questforabsoluteabundance:theuseofinternalstandards forDNA-basedcommunityecology.MolEcolResour2021, 21:30-43.
30. AmannR,FuchsBM:Single-cellidentificationinmicrobial communitiesbyimprovedfluorescenceinsituhybridization techniques.NatRevMicrobiol2008,6:339-348.
31. JunickeH,AbbasB,OentoroJ,vanLoosdrechtM,
KleerebezemR:Absolutequantificationofindividualbiomass concentrationsinamethanogeniccoculture.AMBExpress 2014,4.
32. PropsR,KerckhofFM,RubbensP,DeVriezeJ,SanabriaEH, WaegemanW,MonsieursP,HammesF,BoonN:Absolute quantificationofmicrobialtaxonabundances.ISMEJ2017, 11:584-587.
33. RomboutsJL,MosG,WeissbrodtDG,KleerebezemR,Van LoosdrechtMCM:Diversityandmetabolismofxyloseand glucosefermentingmicrobialcommunitiesinsequencing batchorcontinuousculturing.FEMSMicrobiolEcol2019,95.
34. GalazzoG,vanBestN,BenedikterBJ,JanssenK,BervoetsL, DriessenC,OomenM,LucchesiM,vanEijckPH,BeckerHEF etal.:Howtocountourmicrobes?Theeffectofdifferent
quantitativemicrobiomeprofilingapproaches.FrontCellInfect Microbiol2020,10.
35.
AssessingKleinerM,ThorsonspeciesE,biomassSharpCE,contributionsDongXL,LiuinD,microbialLiC,StrousM: communitiesviametaproteomics.NatCommun2017,8
Thispaperdescribesaprosperousmethodbasedonmetaproteomicsfor (quantitative) measurement of the biomass composition in terms of proteinabundance.
36. HaasR,ZelezniakA,IacovacciJ,KamradS,TownsendS, RalserM:Designingandinterpreting’multi-omic’dingof biology.CurrOpinSystBiol2017,6:37-45.
