Development of a modified Respiration Activity Monitoring System for accurate and highly resolved measurement of respiration activity in shake flask fermentations
© Hansen et al.; licensee BioMed Central Ltd. 2012
Received: 2 April 2012
Accepted: 26 July 2012
Published: 1 December 2012
The Respiration Activity Monitoring System (RAMOS) is an established device to measure on-line the oxygen transfer rate (OTR), thereby, yielding relevant information about metabolic activities of microorganisms and cells during shake flask fermentations. For very fast-growing microbes, however, the RAMOS technique provides too few data points for the OTR. Thus, this current study presents a new model based evaluation method for generating much more data points to enhance the information content and the precision of OTR measurements.
In cultivations with E.coli BL21 pRSET eYFP-IL6, short diauxic and even triauxic metabolic activities were detected with much more detail compared to the conventional evaluation method. The decline of the OTR during the stop phases during oxygen limitations, which occur when the inlet and outlet valves of the RAMOS flask were closed for calibrating the oxygen sensor, were also detected. These declines reflected a reduced oxygen transfer due to the stop phases. In contrast to the conventional calculation method the new method was almost independent from the number of stop phases chosen in the experiments.
This new model based evaluation method unveils new peaks of metabolic activity which otherwise would not have been resolved by the conventional RAMOS evaluation method. The new method yields substantially more OTR data points, thereby, enhancing the information content and the precision of the OTR measurements. Furthermore, oxygen limitations can be detected by a decrease of the OTR during the stop phases.
Shake flasks are widely used in fermentations for biotechnological research and industrial process development[1, 2]. For gaining a better understanding and control of shake flask cultivations, various methods have been recently developed for online monitoring of process parameters.
Monitoring of pH-values in shake flasks has been realized both with standard autoclavable pH-probes that are immersed in the bulk liquid and with fluorescent optodes fixed at the flask wall that allow optical measurement[4–6]. Moreover, dissolved oxygen tension (DOT) can in principle be measured in shake flasks by using either Clark-type electrodes[7–10] or optical sensors based on dynamic quenching of luminescence[11–16]. These non-invasive measurement methods have proven to be more reliable, since they do not alter the hydrodynamics of the culture system due to baffling effects[17, 18].
As shown in numerous studies, almost all metabolic activities of aerobic microorganisms depend on the oxygen consumption of the culture[8, 19–21]. Therefore, online measuring techniques are also useful for determining the gas transfer rates in shake flasks. The company BlueSens GmbH (Herten, Germany) has developed a system that measures the gas transfer rates in the headspace of shake flasks down to nominal flask volumes of 500 mL.
The objective of this study is to present a new model based evaluation method for generating substantially more data points to enhance the information content and the precision of OTR measurements. First, the conventional way of generating OTR data is recalled. Then, the new evaluation method based on a complete oxygen headspace balance is developed. Microbial cultivations have been devised in order to evaluate the new method.
Results and discussion
Conventional evaluation method of the RAMOS device
Here, pO2,in is the oxygen partial pressure of the inlet flow, and is the volumetric flow into the flask during the rinsing phase, pamb is the ambient pressure and Vm is the molar volume.
Also illustrated in Figure3A is the calibration factor K as calculated by equation (A.3). This curve also shows two peaks which both occur right after the OTR peaks. These two peaks are totally unexpected, because the calibration factor K is assumed to be steady and independent from the OTR. Consequently, the assumption that the gas headspace volume at the end of the rinsing phase is in a steady state results in an inadequate calibration. Therefore, a calculation method should be developed that increases the data density of the OTR and includes a more accurate calibration.
New model based evaluation method
and describe the volumetric flows into and out of the flask, respectively, pO2,in the oxygen partial pressure of the inlet flow, K is the calibration factor of the oxygen sensor, UO2 is the oxygen sensor signal, VG and VL are the volumes of the gas headspace and the liquid medium in the flask, respectively and T is the temperature.
Here, pamb describes the ambient pressure and pH2O is the water vapor partial pressure in the gas headspace volume.
p is the regularization parameter for the smoothing spline fitting and f describes the smoothing spline function.
For solving equations (B.1) and (B.4), the calibration factor K is required. A new calibration method has been developed which also considers the dynamic behavior of the gas headspace volume instead of assuming a steady state. The end of the rinsing phase as well as the start of the subsequent stop phase of a biological experiment is utilized in the new sensor calibration method. This point occurs at 0 h in Figure2.
Figure2 also shows a section of the sensor signal curve during a RAMOS measurement and the mathematical fits including both the rinsing phase and the stop phase. Within the rinsing phase which last up to the beginning of the stop phase at 0 min, the sensor signal is described by a smoothing spline function f according to equation (B.3). During the stop phase, from 0 min to 5 min, the sensor signal strongly decreases. However, due to the lag time at the beginning of this stop phase, the initial slope of the signal curve merely changes slowly. Figure2, thus, shows both the polynomial described by equation (B.5) and the delayed signal described by equation (B.6) after the optimization procedure.
Using the same experiment, Figure3B depicts the OTR curve resulting from the new calculation method. Here, an OTR is calculated every 5 min. The OTR data from the stop phase are in good agreement with the OTR data calculated using the method of Anderlei et al.[23, 24]. The OTR data from the rinsing phase mostly concur with that of the stop phase. Only during the phase of oxygen limitation, indicated by the horizontal plateau of the OTR data at ca. 9.5 – 10.5 h[23, 24], the OTR in the stop phase is somewhat lower than that in the rinsing phase.
Up to the beginning of the oxygen limitation at 19 h both the oxygen partial pressure of the headspace and the dissolved oxygen generally decrease due to increasing respiration activity. However, during the rinsing phases of this oxygen limitation between 19 h and 21 h the oxygen partial pressure of the headspace shows a horizontal plateau of ca. 0.18 bar whereas the dissolved oxygen has very low values which are close to the KO2 value of oxygen. Both the oxygen partial pressure of the headspace and the dissolved oxygen generally increase after the oxygen limitation when the respiration activity decreases.
Up to the oxygen limitation at 19 h, both, the oxygen partial pressure of the gas headspace volume and the dissolved oxygen strongly decrease during the stop phases as can be seen by the spikes in the signal. As both signals decrease the driving force of the oxygen transfer into the medium is not affected. However, during oxygen limitation between 19 h and 21 h the dissolved oxygen in the medium approaches very low values in the range of the KM value for oxygen and cannot decrease further. Nevertheless, the oxygen partial pressure of the headspace decreases, resulting in a lower driving force for the oxygen transfer into the medium and, consequently, in a slightly lower OTR during the stop phases, as depicted for 9.5 h–10.5 h in Figure3B.
In Figure3B also the calibration factor K as calculated with equation (B.7) is shown. In contrast to the steady state calibration using equation (A.3) as shown in Figure3A, the calibration factor K calculated with equation (B.7) has a steady course as expected and does not show any peaks correlated to the OTR. However, the data for K show some fluctuations which are due to the lower accuracy of data fitting at higher sensor dynamics. Therefore, the data for K are smoothed by using a smoothing spline with a regularization parameter of p = 0.1. This value has been found to be a good trade-off between smoothing of K and revealing the effect of sensor drift.
Recombinant E. coli fermentation in mineral medium
After 9.75 h, when the glucose is fully exhausted, sorbitol is consumed as can be seen by a decline in the sorbitol concentration. This sorbitol consumption is also observed in the OTR curve via a peak beginning at 10 h. The higher the initial concentration of sorbitol, the more pronounced the peak of the OTR curve is (from Figure5A to5D). Whereas the second OTR peak reaches a maximum of 0.039 mol/L/h at an initial sorbitol concentration of 0.5 g/L (Figure5A), it attains a maximum of 0.048 mol/L/h at an initial sorbitol concentration of 1 g/L (Figure5B) and a limiting value of 0.063 mol/L/h at an initial sorbitol concentration of 1.5 g/L (Figure5C). At an initial sorbitol concentration of 2 g/L (Figure5D), the OTR peak also reaches limiting values at about 0.062 mol/L/h, but is much wider. Thus, due to the higher data density, the new evaluation method unveils the real size of these peaks.
When sorbitol is depleted, the OTR curve again decreases before rising up to form a third peak which infers that acetate is now being consumed. This third peak has the same size for all four initial sorbitol concentrations, because the acetate is formed during the phase of glucose consumption and not during the sorbitol consumption. When acetate is consumed biomass is not produced anymore. Consequently, in the historical work of Monod, who measured biomass, only diauxic growth was observed and not a triauxic metabolic activity, as observed with the newly proposed calculation method of RAMOS data.
The OTRs calculated with the new evaluation method in the stop phase are slightly higher than those calculated with the method of Anderlei et al.[23, 24]. This deviation is caused by the consideration of the sensor lag time in the new calculation method (equation (B.6)).
Using the new method, it is clearly observed that during the oxygen limitation between 8.5 h and 9.5 h the data points in the stop phase (filled squares) are not in line with the data points from the rinsing phase (open squares), in contrast to the rest of the fermentation. As discussed previously, this difference is due to a reduced driving force of the oxygen transfer during the stop phases of an oxygen limitation. The oxygen concentration in the medium approaches very low values, whereas the partial pressure of oxygen in the gas headspace volume still decreases. This effect does not occur in usual Erlenmeyer flasks, where the diffusive mass transfer through the cotton plug is not interrupted. Consequently, the OTR of the stop phase, gives a slightly different OTR than appearing in a typical Erlenmeyer flask.
Recombinant E. coli fermentation in complex medium
Figure6B illustrates the OTR curves of a RAMOS cultivation of E.coli BL21 pRSET eYFP-IL6 in TB-medium. Instead of the typically chosen value of 25 min, the rinsing phase was set to a duration of 55 min. However, the data density of the OTR curve calculated with the new evaluation method is not affected by the reduced number of stop phases because the data of the rinsing phase are also considered. After the lag phase at 7 hours the OTR shows an exponential growth up to 10.5 h leading directly into a horizontal plateau which can be considered as an oxygen limitation[23, 24]. Afterwards, even two very distinct peaks can be seen between 13 h and 16 h before the OTR drops down to a level of 0.005 mol/L/h at ca. 17 h. This clearly shows that the new method is also independent from the number of stop phases selected in the experiments. This characteristic is advantageous in applications where short-term effects are expected but a high number of stop phases is not desired, e.g. for preventing further oxygen limitations due to frequent interruption of the air flow.
Gluconobacter oxydans fermentation in complex medium
Figure6C illustrates the OTR curves of an RAMOS cultivation of Gluconobacter oxydans 621 H wild type in complex medium with 40 g/L mannitol. A rinsing phase of 25 min was selected. The OTR curve calculated with the method of Anderlei et al.[23, 24] indicates an exponential growth of the culture up to 7 h of cultivation when an OTR of 0.035 mol/L/h is achieved. The next measuring point at 7.5 h also indicates an OTR of 0.035 mol/L/h before the OTR decreases again. Consequently, the time between 7 h and 7.5 h could be interpreted as a short period of oxygen limitation. Even though the OTR curve calculated with the new method basically shows the same course, the time span between 7 h and 7.5 h shows considerably more data points, and more importantly, the OTR curve with the new calculation method does not show an oxygen limitation between the two stop phases.
The newly proposed evaluation method yields substantially more OTR data points than the conventional method by Anderlei et al.[23, 24]. This new evaluation method unveils additional peaks of metabolic activity which otherwise would remain undetected by the former method. Consequently, this new technique is a sophisticated means to generate more detailed information about metabolic activities of any kind of microorganisms and cells during shake flask cultivations. Additionally, possible oxygen limitations can be detected by a decrease of the OTR during the stop phases of the RAMOS measurement.
Materials and methods
E.coli BL21 pRSET eYFP-IL6 was maintained at −80°C in Lysogeny broth (LB) medium with 100 μg/mL ampicillin. Stock solutions contained 200 g/L glycerol. Gluconobacter oxydans 621 H wild type was maintained in its cultivation medium (see below) including 80 g/L mannitol at −80°C. Stock solutions contained 150 g/L glycerol.
LB medium for maintaining E.coli consists of: 5 g/L yeast extract (powdered, Roth, Karlsruhe, Germany), 10 g/L tryptone (pancreatic digest of casein, Roth, Karlsruhe, Germany) and 10 g/L NaCl. Terrific broth (TB) medium was used for cultivating both E.coli precultures and main cultures. The medium consists of: 5 g/L glycerol, 12 g/L tryptone (pancreatic digest of casein, Roth, Karlsruhe, Germany), 24 g/L yeast extract (powdered, Roth, Karlsruhe, Germany), 12.54 g/L K2HPO4 and 2.31 g/L KH2PO4. Additionally, 0.1 g/L ampicillin was added.
Main cultures of E.coli in mineral medium were cultivated in modified Wilms & Reuss synthetic medium (henceforth referred to as Wilms-MOPS medium)[6, 51]. This medium consists of: 20 g/L glucose, 5 g/L (NH4)2SO4, 0.5 g/L NH4Cl, 3 g/L K2HPO4, 2 g/L Na2SO4, 0.5 g/L MgSO4·7H2O, 41.85 g/L (0.2 M) 3-(N-morpholino)-propanesulfonic acid (MOPS), 0.01 g/L thiamine hydrochloride, 1 mL/L trace element solution (0.54 g/L ZnSO4·7H2O, 0.48 g/L CuSO4·5H2O, 0.3 g/L MnSO4·H2O, 0.54 g/L CoCl2·6H2O, 41.76 g/L FeCl3·6H2O, 1.98 g/L CaCl2·2H2O, 33.39 g/L Na2EDTA (Titriplex III)) and concentrations of sorbitol ranging from 0 – 2 g/L. Additionally, 0.1 g/L ampicillin was added.
Gluconobacter oxydans was cultivated in a complex medium consisting of: 5 g/L yeast extract (powdered, Roth, Karlsruhe, Germany), 1 g/L K2HPO4, 1 g/L (NH4)2SO4, 0.5 g/L glycerol, 2.5 g/L MgSO4·7H2O and 40 g/L mannitol. The pH-value was adjusted to 6 with 1 M KOH.
E. coli precultures and main cultures were cultivated in 250 ml shake flasks at a temperature of T = 30°C with a shaking diameter of d0 = 50 mm and a shaking frequency of n = 350 rpm (shaking machine LS-W, Kuehner AG, Birsfelden, Switzerland). For E. coli precultures, 20 ml of TB medium was inoculated with 200 μl of stock solution. Main cultures of E.coli in 10 mL Wilms-MOPS medium were inoculated with preculture broth from the exponential growth phase, resulting in an optical density (OD) of 0.2.
The main culture of E.coli in TB medium was cultivated in 250 ml shake flasks with a filling volume of VL = 10 mL, a shaking diameter of d0 = 50 mm, a shaking frequency of n = 300 rpm, and at a temperature of T = 30°C. This culture was inoculated with 200 μl of stock solution.
The preculture and the main culture of Gluconobacter oxydans were cultivated in 250 ml shake flasks with a filling volume of VL = 10 mL, a shaking diameter of d0 = 50 mm, a shaking frequency of n = 350 rpm, and at a temperature of T = 30°C. The preculture was inoculated with 500 μL of stock solution. The main culture was inoculated with preculture broth from the exponential growth phase resulting in an OD of 0.1.
Respiration Activity Monitoring System (RAMOS)
The respiration activity was measured in modified 250 mL Erlenmeyer flasks using a self-made RAMOS device as introduced by Anderlei et al.[23, 24]. The setup of the RAMOS device is shown in Figure1. The 8 RAMOS flasks in parallel were supplied with air using a mass flow controller (type 5850 TR, Brooks, Hatfield, PA, USA). The air flow through the flasks is controlled by using valves at the inlet and the outlet of each RAMOS flask. For the rinsing phase the RAMOS flasks were flushed for 25 min with air at a flow rate of (low flow), which corresponds to an aeration rate of 1 vvm at a filling volume of VL = 10 mL. The rinsing phase was followed by a 5 min stop phase (tstop) with no air flow through the flasks. To compensate for the drop in oxygen partial pressure during the stop phase, a higher air flow of (high flow) was applied during the first 0.9 min of the rinsing phase. This measuring cycle is regularly repeated. The oxygen partial pressure of the headspace in a RAMOS flask was measured with a MAX250 oxygen sensor from Maxtec (Salt Lake City, Utah, USA). The difference between the headspace pressure and ambient pressure was detected with a 26PCA pressure sensor from Honeywell (Morristown, NJ, USA). The RAMOS flasks ensure the same hydrodynamic conditions and headspace gas concentrations as are found in regular Erlenmeyer flasks with cotton plugs. Commercial versions of the RAMOS device are available from Kuehner AG, Birsfelden, Switzerland and Hitech Zang, Herzogenrath, Germany.
The sensor lag time τ has been determined in step change experiments. Beginning at a steady state of air in the gas headspace volume, oxygen depleted air was flushed into a RAMOS flask under well-defined conditions ( = 0.54 mL/min, pO2,in = 0.197 bar, VL = 10 mL, n = 300 rpm). By calculating the real partial pressure of oxygen in the gas headspace of the flask and then optimizing the sensor lag time τ, the model described by equation (B. 6) was fitted to the oxygen partial pressure signal measured by the sensor. This leads to a sensor lag time of τ = 0.013 h (data not shown).
Parallel shake flask cultivations
Samples were taken from E. coli cultivations in Wilms-MOPS medium in Erlenmeyer flasks in parallel to the RAMOS experiments and cultivated under the same conditions as used in the RAMOS experiments. The OD of the samples at 600 nm was determined with a Thermo Scientific Genesys 20 spectrophotometer (Waltham, MA, USA).
For determining glucose, sorbitol and acetate, the samples were centrifuged for 5 min at 14000 rpm with a Sigma 1–15 Microfuge (Osterode am Harz, Germany) and the supernatants were used for the analysis. Concentrations of glucose, sorbitol and acetate in the respective supernatants were determined using a Dionex HPLC (Dionex, Sunnyvale, USA) with an Organic Acid-Resin 300 × 8 mm (CSChromatographie, Langerwehe, Germany) and a Skodex RI-71 detector. Sulfuric acid in a concentration of 5 mM was used as solvent at a flow rate of 0.6 ml/min and a temperature of 60°C.
OTRs, the calibration factor (K) and simulation examples were calculated with MATLAB R2010b (The Math Works, MA, USA). The mathematical optimization problems were solved using the trust-region-reflective algorithm. Ordinary differential equations where solved using the trapezoidal rule.
Model used for calculating the data of Figure4:
With: Oxygen solubility LO2 = 0.0011 mol/L/bar, mass transfer coefficient kLa = 0.082 1/s, volumetric flow into the flask = 0.67 L/h, volumetric flow out of the flask = 0.7 L/h, maximal growth rate μmax = 0.26 1/h, half velocity constant of the substrate KS = 4 g/L, half velocity constant of oxygen KO2 = 8 ·10-10 mol/L, oxygen partial pressure of inlet pO2,in = 0.2095 bar, yield coefficient of the substrate YXS = 0.5 g/g, liquid volume VL = 10 mL, gas volume VG = 270 mL, gas constant R = 0.0831 L*bar/mol/K, yield coefficient of oxygen YXO2 = 41.6 g/mol, temperature T = 303.15 K, initial conditions: biomass concentration Xo = 0.5 g/L, substrate concentration S0 = 45 g/L, oxygen concentration in the liquid cO2,0 = 0.22 mmol/L, oxygen partial pressure pO2,0 = 0.2006 bar.
a, b, c: Polynomial coefficients; cO2: Dissolved oxygen in the liquid (mmol/L); cO2,0: Initial dissolved oxygen in the liquid (mmol/L); cO2*: Dissolved oxygen at the gas–liquid interface (mmol/L); f: Smoothing spline; kLa: Mass transfer coefficient (1/s); K: Calibration factor (bar/mV); KO2: half velocity constant for oxygen (mol/L); KS: Half velocity constant for the substrate (g/L); LO2: Oxygen solubility (mol/L/bar); OTR: Oxygen transfer rate (mol/L/h); p: Regularization parameter (-); pamb: Ambient pressure (bar); pH2O: Water vapor partial pressure in the headspace (bar); pO2: Oxygen partial pressure in the headspace (bar); pO2,0: Initial oxygen partial pressure in the headspace (bar); pO2,in: Oxygen partial pressure of inlet flow (bar); pO2,real: Steady state oxygen partial pressure (bar); R: Gas constant (bar L/mol/K); RQ: Respiratory quotient (-); S: Substrate concentration (g/L); S0: Initial substrate concentration (g/L); T: Temperature (K); UO2: Oxygen sensor signal (mol/L/h); VG: Gas volume (L); VL: Liquid volume (L); Vm: Molar volume (L/mol);: Volumetric flow into the flask (L/h);: Volumetric flow out of the flask (L/h); X: Biomass concentration (g/L); X0: Initial biomass concentration (g/L); YXO2: Yield coefficient for oxygen (g/mol); YXS: Yield coefficient for the substrate (g/g); μ: Growth rate (1/s); μmax: Maximal growth rate (1/s); t: Lag time of oxygen sensor (h).
We wish to thank Udo Koesfeld, Thomas Heise and René Petri for their essential technical support. Moreover, we are grateful to Mary-Joan Bluemich for the many hours we spent on editing this paper.
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