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Metabolic and engineering integrated approach for
the optimisation of recombinant fermentation
processes Gerald Striedner, Franz
Clementschitsch, Monika Cserjan-Puschmann,
Reingard Grabherr and Karl Bayer Institute of
Applied Microbiology, University of Agricultural
Sciences Vienna Muthgasse 18B, A-1190 Vienna,
Austria, bayer_at_mail.boku.ac.at
Introduction The main objectives in industrial
biopharmaceutical production are to attain high
yield in combination with required product
quality. E.coli is a widely used host for the
production of recombinant proteins. A common
strategy to increase yield is the use of strong
expression vectors, such as the T7 system. In
reality these systems are too strong and host
cell metabolism is heavily overstrained after
induction. Hence, maximum yield cannot be
attained, because recombinant protein production
can only be maintained for a short period due to
too high production rate and heavy increase of
plasmid copy number under these conditions
(Figure 1). To cope with this situation an
integrated systems approach aiming at maximal
exploitation of the cell factorys potential by
adjusting optimal ratios between biosynthesis of
(1) host cell proteins and (2) recombinant
proteins, is applied.
300
PCN
250
INDUCTION
200
BDM(g)
BDM, rhSOD, qP, PCN
150
10total rhSOD(g)
100
5qP (mg/g,h)
50
0

• Methodology
• Monitoring of host cell metabolic load due to
  overexpression of recombinant protein
• Monitoring the increase of plasmid replication
  occuring at high recombinant protein
  expression rates
• Control of plasmid replication
• Tuning the expression rate to the load limits
  of the cell factory

22
24
26
28
30
32
34
36
38
40
fermentation time (h)
Figure 1
300
1,0
0,9
250
0,8
INDUCTION
ppGpp(µmol/gBDM)
0,7
200
0,6
BDM(g)
Monitoring metabolic load Metabolic load is a
very unspecific state variable. However,
significant information can be gained from the
hierarchically organised regulatory networks,
highly involved in stress response mechanisms
(Lengeler et al., 1999). These complex regulatory
entities, acting on the highest level of
regulation, co-ordinate the activity of widely
distributed genes by formation of highly specific
signal molecules, such as guanosinetetraphosphate
(ppGpp), the key molecule of the stringent
regulatory network. The aptitude of ppGpp to
monitor metabolic load is shown in Figure 2.
BDM, qP, PCN, rhSOD
150
0,5
ppGpp
0,4
10total rhSOD(g)
100
0,3
0,2
5qP(mg/g,h)
50
0,1
0
0,0
22
24
26
28
30
32
34
36
38
40
fermentation time (h)
Figure 2

• Monitoring plasmid replication
• Monitoring of plasmid copy number (PCN) during
  recombinant protein production is important,
  because PCN determines the transcription of
  foreign DNA. Furthermore, PCN is increased
  drama-tically at high expression rates, because
  enhanced levels of uncharged tRNAs resulting
  from amino acid starvation interact with key
  molecules of plasmid replication control of ColE1
  plasmids and thereby increase metabolic burden.
  To circumvent this problem a two step strategy
  was developed
• online monitoring of plasmid copy number (PCN)
  by neural network based modelling using easily
  available online data sets (Figure 3) and
• a molecular biology based solution to make
  plasmid replication independent from expression
  rate by ori modification (Grabherr, et al.,
  2000).

120
100
INDUCTION
START FEED
80
60
PCN (real and model)
PCN model
40
20
PCN real
0
12
14
16
18
20
22
24
26
28
30
32
fermentation time (h)
Tuning of expression rate To achieve optimal
exploitation of the cell factory the rate of
recombinant protein production has to be tuned in
relation to metabolic load and plasmid copy
number. An effective approach is the
downregulation of transcription of a strong
expression vector, such as the widely used T7
system, by titration of inducer in relation to
the repressor. To determine the optimal amount of
inducer a time shifted exponential feed regime of
substrate and inducer was used in fed batch
fermentation. As shown in the simulation (Figure
4) the effect of increasing ratios of inducer to
biomass in a range from zero to maximum can be
gained in a single experiment. Overdose of
inducer is derived from significant deviations of
biomass vs. the theoretical exponential curve due
to the metabolic overload of recombinant protein
production. By the application of this
experimental setup it was found that the maximum
amount of IPTG per g BDM must not exceed 0,9
µmol. In fed batch fermentations of E.coli
HMS174(DE3)pET11ahSOD (Figure 5) using
exponential feed in combination with the
developed induction regime the expression rate of
recombinant model protein (rec. human
superoxidedismutase) can be tuned in a way that
the recombinant protein production could be
maintained during the whole fermentation process.
Thereby the capacity of host cell metabolism was
almost fully exploited, ppGpp and PCN do not
exceed the required limits. By the application of
this regime the total yield was 2,5 times
increased compared to the standard fermentation
process.
Figure 3
2,5
total IPTG(µmol)
300
IPTG related to BDM( µmol/gBDM)
2,0
1,5
IPTG related to BDM
200
BDM , total IPTG
inducer dosage into the media
1,0
feed start
100
BDM(g)
0,5
0
0,0
14
19
24
29
34
39
fermentation time (h)
Figure 4
45
300
2,5times increase of recombinant protein

• Conclusions
• Computer application made a significant
  contribution to the efficiency of process
  development by
• Enabling monitoring of complex variables by a
  neural network based modelling approach
• Determination of optimal amounts of inducer
• Application of exponential substrate feed in
  combination with control of induction

40
250
35
200ppGpp(µmol/g BDM)
30
200
BDM (g)
25
totaL rhSOD
150
ppGpp, PCN,BDM
20
PCN
15
100
10
50
5
total rhSOD (g)
0
0
20
25
30
35
40
ReferencesLengeler J.W., Drews G. and Schlegel
H.G., Biology of the Prokaryotes, (1999) Georg
Thieme Verlag StuttgartGrabherr R. , Nilsson E.
and Bayer K., Expression vectors with modified
ColE1 origin of replication for control of
plasmid copy number (EP 00121709.01222) Acknowled
gements This work was supported in part by a
grant from the Jubilaeumsfonds der
Oesterreichischen Nationalbank and by Boehringer
Ingelheim Austria with support from Austrian
Industrial Research Promotion Fund.
fermentation time (h)
Figure 5