An automated workflow for enhancing microbial bioprocess ... · An automated workflow for enhancing microbial bioprocess optimization on a novel microbioreactor platform Peter Rohe1*,

An automated workflow for enhancing microbialbioprocess optimization on a novelmicrobioreactor platformRohe et al.

Rohe et al. Microbial Cell Factories 2012, 11:144http://www.microbialcellfactories.com/content/11/1/144

Rohe et al. Microbial Cell Factories 2012, 11:144http://www.microbialcellfactories.com/content/11/1/144

RESEARCH Open Access

An automated workflow for enhancing microbialbioprocess optimization on a novelmicrobioreactor platformPeter Rohe1*, Deepak Venkanna1, Britta Kleine2, Roland Freudl2 and Marco Oldiges1

Abstract

Background: High-throughput methods are widely-used for strain screening effectively resulting in binaryinformation regarding high or low productivity. Nevertheless achieving quantitative and scalable parameters for fastbioprocess development is much more challenging, especially for heterologous protein production. Here, thenature of the foreign protein makes it impossible to predict the, e.g. best expression construct, secretion signalpeptide, inductor concentration, induction time, temperature and substrate feed rate in fed-batch operation toname only a few. Therefore, a high number of systematic experiments are necessary to elucidate the bestconditions for heterologous expression of each new protein of interest.

Results: To increase the throughput in bioprocess development, we used a microtiter plate based cultivationsystem (Biolector) which was fully integrated into a liquid-handling platform enclosed in laminar airflow housing.This automated cultivation platform was used for optimization of the secretory production of a cutinase fromFusarium solani pisi with Corynebacterium glutamicum. The online monitoring of biomass, dissolved oxygen and pHin each of the microtiter plate wells enables to trigger sampling or dosing events with the pipetting robot used fora reliable selection of best performing cutinase producers. In addition to this, further automated methods likemedia optimization and induction profiling were developed and validated. All biological and bioprocess parameterswere exclusively optimized at microtiter plate scale and showed perfect scalable results to 1 L and 20 L stirred tankbioreactor scale.

Conclusions: The optimization of heterologous protein expression in microbial systems currently requires extensivetesting of biological and bioprocess engineering parameters. This can be efficiently boosted by using a microtiterplate cultivation setup embedded into a liquid-handling system, providing more throughput by parallelization andautomation. Due to improved statistics by replicate cultivations, automated downstream analysis, and scalableprocess information, this setup has superior performance compared to standard microtiter plate cultivation.

Keywords: Microbioreactor, Biolector, Bioprocess development, Strain screening, Heterologous protein expression,Automation, Liquid-handling

IntroductionDetermination of quantitative process parameters foroptimizing production strains and cultivation condi-tions are key elements during bioprocess developmentfor almost every new microbial product. Especially theoptimization of heterologous protein production plays amajor role, e.g. in biocatalytic and biopharmaceutical

* Correspondence: [email protected] of Bio- and Geosciences, IBG-1: Biotechnology, SystemsBiotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, GermanyFull list of author information is available at the end of the article

© 2012 Rohe et al.; licensee BioMed Central LCommons Attribution License (http://creativecreproduction in any medium, provided the or

manufacturing processes. The protein expression itselfis influenced by many biological and bioprocessparameters, which are normally defined in early de-velopment phases and kept constant during thescale-up to production scale [1]. Influencing factorsare the choice of the expression host, the promoteras well as its induction, temperature switches, batchor fed-batch mode and different media compositions,to mention only some of them [2,3]. To optimize afermentative production process for a new protein,these parameters must be thoroughly chosen, tailored

td. This is an Open Access article distributed under the terms of the Creativeommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andiginal work is properly cited.

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Rohe et al. Microbial Cell Factories 2012, 11:144 Page 2 of 13http://www.microbialcellfactories.com/content/11/1/144

to the target protein and optimized de-novo due tothe differences in nature and characteristics of eachprotein [4-6].The challenge is that the number of possible parameter

combinations exponentially grows with every additionalparameter. To give a simple example, 2 parameters with 4possible values lead to 16 experiments, whereas for 5 para-meters in total 1024 experiments are necessary to identifythe optimal parameter setting of all combinations. Hence,on the one hand there is a strong demand for significantlyincreased cultivation throughput in early process develop-ment. On the other hand process monitoring and processcontrol should be as high as possible to allow substantialand reliable parameter optimization to ensure scalabilityof results.A process optimization in laboratory scale bioreactors

providing detailed informative process data is usuallyaccompanied by a low cultivation throughput. In a giventimeframe only a few selected process modifications canbe tested and the parameter selection process will be oftenbased on empirical knowledge or by trying to adapt astandard process regime for each new heterologous protein.This clearly illustrates that early bioprocess optimizationin laboratory scale bioreactors can only provide a limitedcontribution.An increase of cultivation throughput by at least two

orders of magnitude is achieved quite easily by cultivationin shaking flasks or microtiter plates (MTP) [7,8]. This isusually accompanied by reduced process information overtime and loss of well controlled process conditions presentin laboratory scale bioreactors. Several approaches to cir-cumvent those disadvantages were investigated in the lastyears to hit a middle ground between laboratory scalebioreactors and MTP cultivation [7,9-12] with so calledmicrobioreactors (MBR). MBR concepts can be differen-tiated in two approaches: The top-down approach is theminiaturization and parallelization of stirred tank reactorsdown to 10-100 mL gaining process information easilyscaled to laboratory scale bioreactors [13-15]. Beside this,the bottom up approach uses standard formats like MTPor shaking flasks extended with enhanced oxygen transfer[16], as well as monitoring of main process parameters[17-19]. The miniaturized stirred reactors can be handledautomatically e.g. with an integration of the vessels inliquid-handling platforms [15,20,21]. With such a setup,repeated sampling during cultivations for offline biomassand product quantification for example in an adjacentspectrophotometer as described by Knorr et al. [22] canbe carried out. In contrast to this, a sampling event fromMTP based cultivations with volumes below 2 mL isusually a total harvest of the whole reaction volume[23]. In novel MTP based cultivation systems, the on-line monitoring allows real time quantification of bio-mass and fluorescence signals [24], making sampling

obsolete for monitoring the expression of fluorescentproteins [25]. Furthermore the achieved online signalscan be used to trigger automated pipetting events likeaddition of initial cell inocula or IPTG solution withautomated robotic workstations [21].In this contribution, we setup such a smart inter-

active combination of MTP cultivation system (Biolector)and liquid-handling robotics as a versatile bioprocessoptimization platform with high cultivation throughputcalled Jülich Bioprocess Optimization System (JuBOS).The setup was customized with further hardware com-ponents, to enable basic microbial workflows includingmedia preparation, inoculation, sampling and dosingevents during cultivation. Further downstream processeslike cell separation and photometric protein analysis arepossible focusing on the optimization of recombinantprotein production.As a relevant application example, the production of

cutinase, a lipase from Fusarium solani pisi was investi-gated as model enzyme expressed in C. glutamicum, anovel expression host. This Gram-positive, facultativeanaerobic bacterium has been successfully developed asproduction organism for small metabolic intermedi-ates like glutamate and lysine at industrial scale[26,27]. Furthermore C. glutamicum possesses boththe SEC-secretion and the Twin-Arginin-Translocation(TAT) protein secretion pathways, which are known tosecrete unfolded and folded proteins, respectively. Inaddition, no detectable extracellular proteolytic activityis known for this organism [28], making it suitable forthe secretory expression of protease-sensitive proteins.Thus, current research activities deal with the potentialof C. glutamicum as producer strain for heterologousproteins [29-32]. The decision about the protein secre-tion pathway used as well as the secretion efficiencydepends strongly on the nature of the signal peptide(SP) fused to the target protein. Unfortunately, the effi-ciency of the SP is not known a-priori, since it is alsodetermined by the combination of SP and the targetprotein itself [33,34]. Computer based tools for predic-tion of efficient signal peptide sequences provide usuallyonly limited information and were proven to be incorrect[34]. Thus, experimental screening procedures with eachprotein of interest are essential to rank the SP’s regardingtheir performance for the secretory production. Withevery new combination of SP and target protein biopro-cess parameters like media components and inductionstrategy must be optimized de-novo. To meet the require-ments of such process optimizations, we describe inthis study the validation and application of a MBR sys-tem integrated into a robotic platform. In contrast toformer publications [21] one challenge is the demandfor offline protein quantification of the non-fluorescentcutinase which is met by additional hardware on the


automated platform like MTP centrifuge and MTPreader on the JuBOS.

Results and discussionSterile environment of the cultivation platformThe handling of sterilized cultivation media and solu-tions during upstream processing steps like media prep-aration and preculture handling has to be carried out ina sterile environment. To avoid airborne contaminations,the JuBOS is embedded in a laminar downflow housing,in which the air flow can be set to 0.45 m.s-1 (“operatingmode”) or to 0.2 m.s-1 (“night mode”). The operationallaminar flow conditions of this clean bench can benegatively affected by the installed hardware of thecultivation platform. To check for sterile conditions,35 LB agar plates were placed for 60 minutes in bothmodes on the whole deck (dark points in Figure 1, B)and incubated afterwards at 30°C for 18 h. As refer-ence three LB plates were positioned outside theclean bench in the lab environment in three differentheights (floor, bench, cupboard) leading to a contam-ination of 36 ± 6 CFU.h-1. In contrast, the regularoperating mode as well as the less effective nightmode showed only very low contamination risk (0-2CFU.h-1) for large working areas on the deck (Figure 1B).For the night mode a higher contamination of 5-10CFU.h-1 was observed for front right corner, wherethe MTP spectrophotometer (2) and Biolector (3) areplaced, most probably interfering the laminar flowconditions (Figure 1B, bottom). Using the regular oper-ating mode this contamination risk in that corner wasreduced to 2 CFU.h-1 (Figure 1B, top). The results indi-cate that sufficiently sterile conditions are maintained

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Figure 1 A: Front view of JuBOS integrated into a cleanbench setup.PerkinElmer); 2: MTP based cultivation system (Biolector, m2p-labs); 3: PlateCooling Adapter (Chillstation, Mécour). Colors indicate colony forming unit

for the critical working area of the JuBOS under bothoperational conditions allowing automated antibioticfree media preparation.

Biological cross contamination between pipetting eventsDuring sampling procedures and downstream processingoperations, the steel needles used for pipetting are indirect contact with cell suspension. To avoid biologicalcross contaminations, the teflon coated steel needlesneed to be cleaned and free of CFU for contaminationcritical steps like automated media preparation or dosingevents into the cultivation wells.The robotic setup uses deionized H2O as system liquid

for the piston stroke as well as for cleaning in place(CIP) protocols. The manufacturer standard protocolsinclude a flush step, were a peristaltic pump cleans theinner surface of the needle by pumping 3000 μL H2Othrough each needle into the waste. In the followingwash step also the outer surface is cleaned by pumping3000 μL H2O into flooded wells of a washing station, inwhich the steel needles are dipped in approx. 5 mmunder the liquid surface. Initial experiments with thosedefault CIP protocols to clean the steel needles, contami-nated with C. glutamicum and E. coli (as Gram-negativereference) showed insufficient reduction from approx.1010 to 106 CFU (Figure 2). To enable “sterilization inplace” (SIP) of the steel needles during an automated proto-col a three way valve connects a tank of 70% v/v EtOH withthe system tubing. After an ethanolic flush/wash-stepwithout an ethanol incubation of the tubing systemno CFU were found for E. coli and only for Gram-positiveC. glutamicum around 10 CFU were found. This contamin-ation can be completely eliminated after incubation for 5

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min proving proper pipetting conditions for C. glutamicumwithout risk of cross contamination. Consequently, forcontamination critical steps, like antibiotic free mediapreparation, the pipetting needles must be incubated for5 minutes in 70% ethanol.For other pipetting procedures which are done in later

process phases, like induction with IPTG or samplingevents, the strict necessity to avoid any carry-over ofcells from one culture to another culture is not preva-lently critical. The transfer of at least 10 C. glutamicumcells still present after quick ethanolic flushing will haveno effect in the presence of a biomass concentration of atleast 10 g.L-1, which corresponds to about 1011 CFU.L-1.This clearly shows the negligible impact of the cross-transferred cells compared to the 1010 times highercell concentration in the cultivation. Thus, it seemsto be a reasonable trade-off between sterility issuesand speed to choose the faster variant of cleaning thesteel needles if indicated. For automated media prep-aration this is no choice, since even one single con-taminant cell will spoil the experiment.

Automated media preparation with low statistical errorThe ability to perform the upstream processing in anautomated fashion under sterile conditions allows pre-paring the cultivation media for the small scale cultiva-tion directly on the robotic deck prior to cultivation. Inmost applications of parallel small scale cultivations theMTP for cultivation is filled manually instead of doingthis automatically [35,36]. Even for higher throughputthis is typically not a limiting step, since it can beachieved quickly using multi-channel pipettes or semi-automated lab dispensers if the same medium is usedfor all cultivations.

The media preparation becomes a time-critical step, ifa diverse set of media compositions has to be prepared,e.g. to investigate the influence of different media com-ponents on the production performance of the microbialsystem. In such cases, the automated media preparation ishighly advantageous, since the robotic system 1) showssuperior speed in pipetting different media compositionsfrom stock solutions and 2) provides higher reliability ofpipetted medium composition accompanied by an elec-tronic documentation of the performed pipetting events.The automated media preparation with the JuBOS was

first validated against the manually prepared medium.Therefore, the minimal medium CG XII for C. glutamicumwas prepared by the JANUS workstation from four stocksolutions containing single components ((NH4)2SO4, Urea,ZnSO4, CuSO4) in 20 fold concentration and one furtherstock solution containing all the other components ofCG XII medium in twofold concentration. The requiredvolumes of stock solutions were pipetted into the wellsof the cultivation plate (i.e. flowerplate) in fourfoldreplicates and were automatically filled up to the startvolume of 950 μL with sterile deionized water. Themanually pipetted medium was prepared from thesame stock solutions with a final volume of 42 mLand 950 μL was dispensed in four replicates into theFlowerplate. All cultivation wells were inoculated with50 μL preculture.The comparison of the growth characteristics be-

tween the manual (μ = 0.37 ± 0.01 h-1; CDWMax =11.4 ± 0.6 g.l-1) and automated (μ = 0.36 ± 0.01 h-1;CDWMax = 10.7 ± 0.3 g.l-1) media preparations showedvery good similarity (Figure 3A). The relative statisticalerror of CDWMax (3-5%) is very close to the range ofthe pipetting accuracy of the JANUS workstation [37].The slight systematic deviation of the CDWMax be-tween the two sets of replicates cannot be proven assignificant with a two sided t-test (p = 0.08). Hence,the automated media preparation could be successfullyvalidated against the standard approach of manualpreparation. This tool can now be routinely used tofacilitate the application of the cultivation platform.Medium recipes to be prepared are easily integrated bysimple upload of an excel compatible worklist into theWinPREP software of the robotic workstation enab-ling robust and convenient media preparation prior tosmall scale cultivation.To illustrate the benefit of the option for automated

media preparation, we applied this method to investigatethe effect of two nitrogen sources, namely (NH4)2SO4 andurea on the protein expression of GFP in C. glutamicum.A full factorial design approach was used with vari-able urea and ammonium sulfate concentrationresulting in 48 different media prepared in one MTPby the robotic system. The manual preparation from

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4 stock solutions and additional water like describedabove would take approx. 1.5 hour. This workload isdrastically reduced below 10 min by using the auto-mated media preparation proving a fast media prep-aration method.The C. glutamicum cells contain plasmid pEKEx2-

PhoD-GFP that harbors the gene for a GFP protein thatis fused to the signal peptide of the B. subtilis TAT-substrate PhoD. Expression of the PhoD-GFP fusion inC. glutamicum leads to TAT-dependent secretion of thefolded GFP protein into the culture supernatant [29]. Ascomparative signal for protein production, the maximalonline fluorescent signal was used and normalized withthe maximum biomass signal obtained in each cultivationto achieve a biomass-specific GFP formation (Figure 3B).The data indicate that the concentrations of the nitrogensources in standard CG XII medium (see marker) donot provide optimal conditions for GFP production.The biomass specific fluorescence is 50% higher atreduced ammonium sulfate concentrations (between 0to 5 g.L-1) and an urea concentration at 10 g.L-1, repre-sented as red area in Figure 3B.With GFP as an example for protein expression in

C. glutamicum, the CG XII medium seems to be notideally suited. In current applications C. glutamicumis mainly used for production of small metabolitesfrom primary metabolism, e.g. lysine [38], glutamate[39], valine [40] or succinate [41] and the publishedcultivation media have been optimized in the past forfast growth and amino acid production [42-44]. Butduring the last 5 years C. glutamicum gains in import-ance as a host for secretory protein production. Thepublished cultivation medium used for those proteinproduction processes [30,32] is also minimal mediaderived from CG XII [43] and differ mainly in theadded trace metal and CaCO3 concentrations. The bulknitrogen source of both, the CG XII media and the

protein secretion media is ammonium sulfate, whichshows a negative effect on the fluorescence quantity inthis study. Urea appears to be a proper alternative ni-trogen source for the production of secretory GFP.So far, a description of a systematic media optimization

for secretory protein production with C. glutamicum hasnot been investigated. Our results indicate clearly a signifi-cant influence of the media composition on the overallproduction. The optimization of the medium specificallysuited for a certain microorganism or target applicationseems to provide a clear benefit. By the use of the JuBOSsuch effects can be identified and the automation can beefficiently used for routine media optimization during bio-process development.

Precise strain characterization by triggered samplingeventsThe challenge of process related screening experimentsin MBR is to increase the experimental throughput whilemaintaining process relevant conditions and to achievescalable information about the microbial productioncharacteristics. To keep the throughput in MTP basedapproaches as high as possible, it is necessary to analyzethe characteristic strain parameters in a single cultiva-tion well per clone. A common and easy approach isend-point determination in an “over night cultivation”with a constant cultivation time, synchronous IPTGinduction at the beginning and a synchronous harvestof all cultivations wells in the stationary phase [34,45].We investigated three cutinase producer strains eachwith a different signal peptid of SEC-substrates fromB. subtilis (Bpr, YmwC, AmyE). The lipolytic activityand its statistical error are analyzed with 9 to 12 foldbiological replicates, each with different colonies asstarting material for precultures.The cultivations in the MTP were harvested com-

pletely, when the last cultivation had reached the

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Figure 4 Three harvest methods exemplarily shown with C.glutamicum producing bpr-Cutinase. A: Synchronous harvest ofall cultivation wells in the stationary phase (“over night cultivation”).B: Triggered harvest of total reaction volume 4 h after reaching4 g.L-1 CDW (BS = 100 a.u.) in early stationary phase. C: Triggeredinduction at 4 g.L-1 CDW (BS = 100 a.u.) and triggered harvest 4 hlater in early stationary phase.


stationary phase after 15 h. The growth of the threeC. glutamicum constructs showed no different phe-notypes, but a strong variability of the lag phase, aswell as differences of μMax and YX/S (Figure 4A: forclarity, only 4 growth curves each are displayed, fulldataset in Additional file 1 ), probably caused by dif-ferent vitality of the inoculated preculture suspen-sions. Those differences lead also to different timesof reaching the stationary phase resulting in meanstationary phase duration of 6 h ± 1.4 h (Table 1).The analyzed biomass specific lipolytic activity EAspec

of the supernatant has a high relative standard devi-ation up to 40% (Table 1), which leads to an uncertainstrain selection due to the high biological variation.Also other SP screening studies with B. subtilis as hostorganism resulted in an almost similar error of 25% oflipolytic activity, when 8 replicates in MTP cultivationwere sampled in the stationary phase [34]. If informa-tion solely from stationary phase is used for cell selec-tion, good producer strains might be also undetectable,if the protein is instable and degraded in the stationaryphase [46,47]. For proteins very sensitive to instabilityand degradation associated to stationary phase condi-tions the standard error is expected to increase. Evenfast and effective good producers might be not iden-tified, since the majority of the product is alreadydegraded and lost.Nevertheless, the advantages of MTP cultivations

can be used to compare different strains by synchron-izing the biomass growth with enzymatic glucose re-lease media from polysaccharides [48-50]. But up tothis time, there is no release medium optimized forC. glutamicum commercially available. A second op-portunity is to equalize the growth behavior by anadjustment of the initial cell number based on OD or

Table 1 Influence of three harvest methods on thelipolytic activity (EAspec) fused with three different signalpeptides (bpr, amyE, ywmC)

A: EAspec [kU.g-1]

“Over nightcultivation”

B: EAspec [kU.g-1]

Triggeredharvest

C: EAspec [kU.g-1]

Triggeredinduction + harvest

bpr 10.11 ± 2.98 11.84 ± 0.96 13.34 ± 1.25

ywm 3.61 ± 1.50 5.31 ± 0.60 4.40 ± 0.54

amyE 1.22 ± 0.64 1.62 ± 0.33 1.49 ± 0.24

Meanrelativeerror

41% 13% 13%

Duration ofstationaryphase

6.0 h ± 1.7 h 0.9 h ± 0.4 h 1.6 h ± 0.3 h

Error indicate standard deviation of 9 to 12 fold biological replicates(independent clones). Depending on the sampling method, the mean durationof stationary phase is different. Backscatter data of all processes are providedas supplemental data.


backscatter values, which can be carried out by auto-mated robotic systems [51]. With this adjustment theauthors achieved a synchronous growth pattern dur-ing the batch phase. When such a synchronized MTPwas sampled, all cultivations were in the same growthphase leading to a fair comparison of different culti-vations. This adjustment is reasonable, when the vital-ity of the starting cell material and the growth rate inthe main culture are similar.In our study, the precultures were inoculated with

cell material from single colonies, then cultivated tostationary phase and transferred to the main culture.Thus, both prerequisites for an adjustment of the initialcell material were not given and a strategy to circumventthe additional burden of culture synchronization would bebeneficial.To achieve reliable data from heterologously growing

C. glutamicum cultivations, a triggered sampling methodwas developed on the JuBOS. Depending on the onlinebiomass signal measured by Biolector, each cultivationwell was harvested independently 4 h after reaching 4g.L-1 CDW in the early stationary phase (Figure 4, B). Cellsuspensions were then stored at 4°C in deep well platesand centrifuged at 4000 g for 10 min. With this strategy,the mean duration of stationary phase was lowered to0.9 h ± 0.4 h. The mean values of EAspec achieved withbiomass triggered sampling events are in the same rangeof the mean EAspec analyzed with synchronous sampling(Table 1). However, the trigger leads to a drasticallydecreased relative error of 13% compared to 41% whichdramatically facilitates the selection of best strains. Butthis method can be still an unfair comparison, due tothe fact, that all cultivations were induced from begin-ning leading to different induction time for each singlecultivation.To investigate this effect of the induction time, a fur-

ther opportunity for synchronization of expression pat-tern was tested. A biomass triggered addition of aninductor substance (e.g. IPTG) leads to a production ofrecombinant proteins starting always at the same bio-mass concentration. This method was developed in pre-vious studies and validated with online detectablefluorescent proteins [21]. In our study we combined har-vest with this biomass triggered IPTG addition at 4 g.L-1

CDW in a way that IPTG was added at 4 g.L-1 CDWand harvesting was done 4 h later (Figure 4C). This wasindividually done for each cultivation based on the on-line biomass data fully exploiting the potential of theinteractive integration of the cultivation with the auto-mated liquid-handling operation. As it can be seen inTable 1 the lipolytic activities of the three constructsshow the same ranking like in the other two methods.Interestingly, with this high sophisticated method, a rela-tive error of 13% can be achieved, which is the same

error of the method without triggered induction butwith induction from beginning. It can be concluded, thatsynchronization of the growth related harvesting time ismore important than synchronization of induction. Reli-able data can be achieved by using the triggered sam-pling strategy with the simple synchronous induction atthe beginning of the cultivation. In our case, this methodwas developed for the screening of signal peptides,which have a significant impact for the development ofsecretory protein production [34]. Because of the flexibletrigger setpoints, this sampling method is also suitablefor any other strain characterization or the investigationof different culture conditions.

Profiling of optimal induction conditionsIn recombinant protein production processes the induc-tion in later process phases is usually beneficial to avoidinhibiting effects like toxicity or metabolic burden. Themain parameters that can be influenced in the cultiva-tion process are the concentration of the inductor andthe time point of addition. Simultaneous optimization ofboth parameters in a microtiter cultivation setup provedto be a fruitful approach during the production of fluor-escent proteins [21]. The optimal induction strategy notonly depends on the coding plasmid and its promoter,but also on the target protein itself [5]. For this reason, amethod for induction profiling, was setup analyzing ac-tivity of the cutinase enzyme, which is the heterologoustarget protein to be secreted in C. glutamicum.In this experiment, the effect of IPTG concentration

and induction time on the cutinase production wasinvestigated with the JuBOS. Secretory NprE-cutinasewas chosen to be used as model protein produced withC. glutamicum, due to improved secretion performance.At six time points, indicated by black arrows inFigure 5A, different concentrations of IPTG were addedautomatically by the liquid-handling system. This setupleads to a full factorial design for induction optimizationin one single flowerplate consisting of 48 cultivationssynchronously harvested after 14 h (red arrow,Figure 5A). Supernatant was analyzed for cutinase activ-ity, resulting in contour plot for secretory cutinase ofeach cultivation (contour plot in Figure 5B).The highest cutinase production of 13.5 ± 1.4 kU.L-1 was

observed at low IPTG concentrations (0.2 - 0.3 mmol.L-1)added in the early exponential growth phase. When usingthe IPTG concentration of 1 mmol.L-1, which is commonlyused for lac based promoters, the measured cutinase activ-ity is lowered drastically by nearly 30% from 13.5 kU.L-1 to9.2 ± 0.9 kU.L-1. Thus, the data show a clear influence ofthe induction strategy on production behavior of secretorycutinase. Consequentially using this method as an earlystage module followed by validation experiments in 1 L

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Figure 5 Induction profiling. A: CDW of a MTP culture of C. glutamicum expressing NprE- Cutinase in CG XII (20 g.L-1 Glucose). Arrows indicateinduction and sampling of 48 cultivations, dotted lines indicate standard error of 10%. B: Lipolytic activity in supernatant of sampled MTPcultures. Induction was carried out by adding 25 μL of different concentrated IPTG stock solutions leading to the indicated IPTG concentrations inthe MTP cultivations.


bioreactors is strongly recommendable in future bioprocessdevelopment for secretory target proteins.

Scalable process data achieved in MTPsReliable results for early upstream bioprocess develop-ment are normally achieved in stirred tank reactors withwell-defined environment. To characterize protein pro-duction processes in the MTP cultivation system Biolector,the obtained growth and production behavior has to bevalidated in comparison to those bioreactors with com-monly used scales like 1 L and 20 L. Here, in all scalesC. glutamicum producing recombinant cutinase fusedto the best performing signal peptide (NprE) of thisstudy was investigated. In 1 mL and 1 L scale two fur-ther SPs (YwmC, YpjP) serve as mid and low performanceproducers to compare the ranking of the SP secretion effi-ciency. Protein expression was induced at the begin-ning of the cultivation with 0.5 mM IPTG. Sequentialsamples were taken every hour, obtaining OD, CDWand lipolytic activity.In flowerplate cultivations the same preculture, tem-

perature, inoculum size and induction strategy is used.Since no pH regulation can be achieved in MTPs, themedium is buffered with MOPS to an initial pH = 7.The scale-up criterion was a DO > 20% according toRiesenberg et al. [52]. In bioreactors this was achievedby a closed-loop strategy of stirrer and gas flow rate. Inflowerplates this criterion was fulfilled at shakingfrequency n = 1200 min-1 and filling volume V = 1 mLleading to an OTRMax = 80 mmol.L-1.h-1 (informationkindly provided by m2p-labs, Aachen/D).The biomass growth in all three scales (Figure 6A)

resulted in the same YX/S = 0.6 ± 0.05 gCDW.gGlc-1. The

maximum specific growth rate of μMax = 0.41 ± 0.02 h-1

was the same in 1 mL MBR as well as in 1 L Bioreactor.Here, the online backscatter measurement in the Biolector

was suitable to obtain biomass related parameters(μMax, YX/S). For analyzing protein production para-meters like the biomass specific enzyme activity EASpec

[kU.gCDW-1] cell suspension was analyzed offline in enzyme

activity assays. For this purpose, parallel 1 mL cultivationwells were harvested iteratively, centrifuged and analyzedwith enzyme activity assay. The measured lipolytic activ-ity shows a growth coupled cutinase secretion, just as in1 L bioreactors, both with an EASpec ≈ 1 kU.gCDW

-1

(Figure 6B). In the 20 L stirred tank reactor both,the EAspec was about 15% lower than in Biolector andthe 1 L bioreactor. Because the 20 L cultivation wasinoculated on another day with another preculture asthe 1 L and 1 mL cultivations, this difference might beattributed by the biological variation in the seed culture.In contrast to this, the 1 mL MTP cultivation processwas perfectly comparable to 1 L bioreactor. Further-more three clones each with different cutinase product-ivity based on the fused signal peptide were investigated(nprE, ywmC, ypjP). Here the three producer strainsshow the same ranking of EAspec in 1 L stirred tankreactors and 1 mL MTP cultivations (Figure 6C). Thus,Biolector is well suited for screening experiments result-ing in process relevant ranking of different strains withdifferent phenotypes. Together with the triggered sam-pling events described above, the JuBOS system cananalyze strain characteristics in higher through put e.g.during SP screening or IPTG profiling which aredirectly scalable to 1 L and 20 L stirred tank reactors.

ConclusionThe progress in MTP based microbioreactor techniquesduring the last five years has resulted in the easy integra-tion of benchtop instruments with automated liquid-handling stations. The shown validation tests of theJülich Bioprocess Optimization System (JuBOS) support

Time [h]0 2 4 6 8 10 12 14 16

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1 mL biolector 1 L bioreactor 20 L bioreactorA

B

C

Figure 6 Scale- up of C. glutamicum batch culture. Left column: Microtiter cultivation (8 replicates, cells harvested for offline analytic). Middle:1 L lab-scale bioreactor (2 replicates). Right: 20 L technical scale bioreactor (no replica). Secretory NprE- Cutinase is produced with pEKEx2 vectorand 0.5 mM IPTG induction at t = 0. Row A: CDW (black), Glucose (white dots) and lipolytic activity (grey dots) over time. B: lipolytic activity ofcutinase over CDW. Slope of regression line represents secretory specific activity EAspec during growth coupled protein expression, C: Specificactivity EAspec of cutinase expressed with different signal peptides, error bars is the confidence interval of linear regression (see row B).


the usability of such an automated platform for microbialcultivation experiments in high-throughput approach. Acomplete workflow from sterile media preparation andscalable cultivations to triggered sampling and subsequentat-line analysis of enzyme activity was established foroptimization of recombinant cutinase production. Al-though the cultivation platform was currently used inbatch mode only, it is the basis for fed-batch applica-tion in the near future employing fed-batch by use ofmicrofluidic technology [53] or slow release systemsfor substrates [49].As a key advantage, the resulting statistical error of

productivity data of non fluorescent target protein wasdrastically decreased from 41% to 13%, due to triggeredsampling events to synchronize growth pattern of biological

replicates. With these methods it is now possible not onlyto select production strains in a higher resolution but alsoto optimize reaction conditions like induction strategiesand media components based on reliable results that aredirectly scalable to stirred tank bioreactors.The throughput is drastically increased due to 48 parallel

cultivation wells of the Biolector. For example, the induc-tion profiling carried out on the JuBOS has a process timeof approx. 24 h (6 h preculture, 14 h main culture,2 h DSP) with a manual workload of approx. 2 h. Incomparison to this, 48 induction experiments in a 4parallel bioreactor system (e.g. DASGIP BioBlock) wouldhave a process time of 312 h (12 runs * [6 h setuptime & preculture, 14 h main culture, 6 h DSP &cleaning]). This 13 fold increased throughput might


seem an unfair comparison, because usually induc-tion or media optimization experiments are carriedout in shaking flasks or standard MTP techniques toincrease the throughput. Those approaches howeveroften involve cost intensive iteration steps backwardsafter failing of scale-up from bench to pilot- or pro-duction-scale. With our contribution we can closethis gap with a flexible and easy to use automatedplatform for medium throughput cultivation in theearly bioprocess development of fermentative proteinproduction processes.

MethodsStrains and mediaC. glutamicum ATCC 13032 was used as host strain.GfpUV gene was ligated to phoD-SP [29]. Cutinase genefrom Fusarium solani pisi was ligated to DNA sequencesof nprE, ywmC and ypjP SEC- signal peptides [34]. Con-structs were cloned into pEKEx2 plasmid using E. coliXL1 blue [54]. Cells were transformed via electropor-ation, selected on brain heart infusion (BHI) agar plates(37 g.L-1 BHI, 91 g.L-1 sorbitol, 50 mg.L-1 kanamycin)and cultivated in BHI boullion (37 g.L-1 BHI). Cells ofexponential growth phase were harvested and incubatedin 30% v/v glycerol for 10 min at 20°C and stored in 2mL aliquots at −80°C. LB agar plates for contaminationstudies were prepared like described, but without selec-tion marker kanamycin.Precultures were performed in BHI medium. For all

main cultures minimal media CG XII was used [43]with 20 g.L-1 (NH4)2SO4, 5 g.L-1 Urea, 1 g.L-1

KH2PO4, 1 g.L-1 K2HPO4, 13.25 mg.L-1 CaCl2 × 2H2O,0.25 g.L-1 MgSO4 × 7H2O, 0.2 mg.L-1 biotin, 30 mg.L-1

protocatechuic acid, 10 mg.L-1 FeSO4 × 7H2O, 10 mg.L-1

MnSO4 ×H2O, 1 mg.L-1 ZnSO4 × 7H2O, 0.313 mg.L-1

CuSO4 × 5H2O, 0.02 mg.L-1 NiCl2 × 6H2O. The pH wasset to 7.0 with 4 M NaOH. In MTP cultivations 42 g.L-1

MOPS was used as buffer reagent. Additionally 25 mg.

L-1 kanamycin and up to 0.5 mM IPTG were added. Allchemicals were of analytical grade and supplied bySigma Aldrich.

Cultivation conditionsAll MTP cultivations were carried out in 48 wellFlowerplates™ (Art.-No: MTP-48-B, m2p-labs, Aachen/D)incubated in a Biolector device (m2p-labs, Aachen/D). Ini-tial cultivation volume was set to 1 mL per well, includingthe inoculums of 50 μL. Following cultivation conditionswere set in the incubation chamber: shaking diameter 3mm, frequency 1200 min-1, temperature 30°C and relativehumidity 80%. MTPs were sealed with gas permeablemembranes (Art.-No: F-GP-10, m2p-labs, Aachen/D) tominimize evaporation. For the given shaking diameter(3 mm) and frequency the manufacturer reported a

OTR of 0.08 mol.L-1.h-1 which is very close to valuesachieved in standard stirred tank bioreactors [55,56].Biomass, dissolved oxygen concentration and pH weremeasured online in each cultivation well with meas-urement cycle of 10 minutes. Biomass was detectedin the Biolector with backscatter measurement at 600nm and calibrated like described below. Dissolvedoxygen concentration as well as pH values were measuredby pre- calibrated optodes at the bottom of each well.Precultures for one strain were carried out in 100 mLtwo baffled shake flasks with 10 mL media. Multi-strain precultures were carried out with 200 μL mediain black 96 well microtiter plates (Greiner Bio-One,Frickenhausen/D) were used with 200 μL media, shakenwith 900 min-1 and 1.5 mm eccentricity on a Titramax100 (Biotest, Dreieich/D).For bioreactor cultivations 2 L DASGIP glass bioreac-

tors (dI = 110 mm, H = 245 mm) equipped with threesteel baffles and two six-bladed impellers (d = 45 mm)were used. Dissolved oxygen (DO) was set above 30% witha sequential feedback regulation cascade of stirrer speed(800–1500 rpm) and gas flow (60 L.h-1 and 120 L.h-1). Off-gas content of CO2 and O2 was measured with gas analysis(DASGIP, Juelich/D). The pH was set to 7 and controlledwith 4 M HCl and 4 M NaOH. Starting volume for all pro-cesses was 1 L. Addition of anti foam was controlled by afoam probe. Every hour 5 mL was drawn. Calibration of theperistaltic pumps and recording and controlling of processparameters was done with the process control softwareDASGIP Manager (DASGIP, Juelich/D).For scale- up experimentation a 30 L Chemap bioreac-

tor (dI = 260 mm, H = 560 mm) equipped with two sixbladed impellers (d = 90 mm) was used. A processcontrol system (iCCC 2000, PCS Systemtechnik GmbH,Munich/ D) enables pH control and DO control with acascade of stirrer speed (400 min-1 to 950 min-1) , gasflow rate between 850 L.h- 1 to 1800 L.h- 1 and up to 0.8bar overload pressure. Inoculation was carried out in allscales with 5% v/v vital preculture. 20 mL samples weredrawn every hour to quantify biomass, substrate andproduct concentration.

Enzyme assayThe detection of the lipolytic activity of cutinase wascarried out with the p-nitrophenyl-palmitate (pNPP)Assay according to [34,57]. The final concentration of thesubstrate was 0.8 mM. Optical density (λ = 450 nm) wasdetected in a Genios Platereader (TECAN, Crailsheim/D)every 25 second for 40 minutes with 3 seconds of shakingbefore every measurement. Lipolytic activity [kU.L-1] wasdetermined from values of OD450min-1. Samples werediluted with deionized water till values of OD.min-1 > 0.1.Samples with water instead of enzyme solution wereused as blanks. The enzymatic activity was calculated


using a molar extinction coefficient of 15 cm2.μmol-1

according to [34].

Biomass quantification and backscatter calibrationFor the determination of cell dry weight (CDW) 1 mLcell suspension was centrifuged at 13000 g for 10 min inpredryed and weighed 1.5 mL eppendorf cups. Supernatantwas discarded and after pellet resuspended in 1 mL0.9% w/v NaCl solution and centrifuged again at13000 g for 10 minutes. After drying at 80°C for 18 hthe mass difference was used to calculate CDW. For meas-urement of OD600, samples were diluted with 0.9% NaClbelow OD600 = 0.8 measured in a Pharmspec UV 1700photometer (Shimadzu, Duisburg/D).Backscatter measurement was calibrated in Flowerplates

sealed with a gas-permeable membrane (m2p-labs,Aachen/D) at V = 1000 μL, gain = 20, shaking speed1200 rpm. The backscatter calibration analysis was donewith a dilution series of C. glutamicum biomass suspensionin a range of 0 to 20 g.L-1 CDW leading to the linear regres-sion equation of CDW = 0.48*BS g.L-1.au-1 - 0.78 g.L-1

with R2 > 0.98. The calibration was confirmed withsamples of 1 L batch bioreactors, whose backscatter andCDW were measured like described above.

Robotic platform for microbioreactor cultivationCore instrument of the platform is the liquid-handlingstation JANUS Integrator with a 400 mm overhangingextension (PerkinElmer, Waltham MA/USA) in two armconfiguration consisting of a robotic pipetting arm(Varispan™) equipped with 8 steel needles for liquidtransfer up to 1.2 mL and a gripper arm for MTP trans-port on the platform. Liquid-handling procedures areconducted by pumping deionized water as a systemliquid through the tube system attached to the steelneedles. The system liquid can be switched by an auto-mated 3 way valve (Bürkert, Ingelfingen/D) to 70% v/vethanol for cleaning procedures to avoid biologicalcross contamination. The MTP based cultivation systemBiolector (m2p-labs, Aachen/D) is installed on the roboticdeck in a way that the MTP plate is fully accessible, allow-ing automated transfer of liquid, i.e. dosage or sampling,as well as replacement of the complete MTP plate.The whole setup is installed in a laminar flow hood(Cleanair, Woerden/NL) with a high-efficiency-particulate-air-filter (HEPA-filter) generating sterile environment onthe robotic deck. Program automation protocols andmethods were written in the command software WinPrep(Version 4.6, PerkinElmer) to manage the required actionsof the liquid-handling robotic system. The softwareRobolector agent V.1.2 (m2p-labs, Aachen/D) organizes thecommunication between WinPrep software and the Biolec-tor. Cultivation data measured online by the Biolector(biomass, pH, DO) can be used as trigger signals to

automatically initiate pipetting events like sampling anddosing for each single cultivation well independently.The sampled cell suspension is stored in a deep well

plate (DWP) placed on a cooling rack (MeCour, GrovelandMA/USA) connected with a Cryostat Unichiller (Huber,Offenburg/D) in a temperature range beween - 10°Cand + 20°C on the robotic deck. A MTP centrifuge(IXION, Sias/CH) is also fully accessible for the roboticsystem. Supernatant was separated in DWPs via centri-fugation at 4000 g for 15 minutes.

Sterility test of steel needles1000 μL of exponential growing cell suspension (OD600 = 5)are aspirated and released again. Then different treatmentslike washing with water, EtOH and incubating with EtOHwere investigated along with positive control withoutany washing. Afterwards 50 μL of the upcoming sys-tem liquid was pipetted into 1000 μL VE- Water andCFU was determined on LB agar plates.

Additional file

Additional file 1: Table S1. Backscatter data (gain 20): Synchronizedinduction and sampling (Over-night-culture) of C.glutamicum producingcutinase with three different SP: bpr (Well 1-9), YwmC (Well 10-18), AmyE(Well 19-27). Table S2: Backscatter data (gain 20): Synchronized inductionand triggered sampling of C.glutamicum producing cutinase with threedifferent SP: bpr (Well 1-10), YwmC (Well 11-20), AmyE (Well 21-31).Table S3: Backscatter data (gain 20): Triggered Induction and triggeredsampling of C.glutamicum producing cutinase with three different SP:bpr (Well 1-12), YwmC (Well 13-24), AmyE (Well 15-36), Well 37-44: C.glutamicum with pEKEX2:Empty Vector.

Abbreviationsa.u.: Arbitrary unit; BS: Backscatter; CIP: Cleaning in place; CDW: Cell dryweight; CFU: Colony forming units; DO: Dissolved oxygen; DSP: Downstreamprocessing; DWP: Deepwell plate; EA: Enzyme activity; EtOH: Ethanol;IPTG: Isopropyl β-D-1-thiogalactopyranoside; GFP: Green fluorescent protein;MBR: Microbioreactor; MTP: Microtiter plate; OD: Optical density;SIP: Sterilizing in place; SP: Signal peptide.

Competing interestsThe authors have declared that no competing interest exists.

Authors’ contributionPR designed the presented platform, developed and validated theexperimental methods and prepared the manuscript. DV performed thescale-up studies and supported with manuscript preparation. MO initiatedthe project and is the principal investigator, supported with conception andmanuscript preparation. BK and RF constructed the secretory producerstrains, supported with data interpretation and manuscript preparation. Allauthors read and approved the final manuscript.

AcknowledgementsThe financial support from Bio.NRW, a funding initiative of the localgovernment of North Rhine-Westphalia is gratefully acknowledged (Ziel2,grant No W0805wb003d).

Author details1Institute of Bio- and Geosciences, IBG-1: Biotechnology, SystemsBiotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany.2Institute of Bio- and Geosciences, IBG-1: Biotechnology, SystemicMicrobiology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany.

http://www.biomedcentral.com/content/supplementary/1475-2859-11-144-S1.xlsx


Received: 10 September 2012 Accepted: 29 October 2012Published: 31 October 2012

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doi:10.1186/1475-2859-11-144Cite this article as: Rohe et al.: An automated workflow for enhancingmicrobial bioprocess optimization on a novel microbioreactor platform.Microbial Cell Factories 2012 11:144.

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An automated workflow for enhancing microbial bioprocess ... · An automated workflow for enhancing microbial bioprocess optimization on a novel microbioreactor platform Peter Rohe1*,