3. Discussion
E. coli is a prominent host organism for the production of
recombinant proteins, primarily owing to its advantageous
characteristics encompassing expeditious growth, facile genetic
manipulations, and commendable rates of recombinant protein synthesis
(Wang
and Li, 2014). Notably, E. coli emerged as the pioneering
expression host for the manufacture of human insulin in 1982
(Baeshen et al., 2015 ). However, the inherent propensity of
insulin expression in E. coli to aggregate culminates in the
formation of inclusion bodies (IBs) (Baeshen et al., 2016 ).
Furthermore, proinsulin conversion to mature insulin necessitates
miscellaneous enzymatic cleavage at a precisely timed interval,
manifesting a concomitant reduction in insulin yield
(Kemmler
et al., 1971). In this study, we present a novel designer insulin
variant purposefully engineered to exhibit enhanced cleavability via
trypsin. The focal objective of our investigation revolves around
optimizing the compositional makeup of the culture media, thereby
potentiating the augmentation of the modified insulin’s growth rate and
biomass yield.
Much like other natural processes, the growth rate of bacteria is
subject to the influence of numerous contributing parameters. The
identification and optimization of these factors present substantial
challenges from a financial and economic standpoint (Packiam et
al., 2020 ). Considering these challenges, the combination of
experimental BioLector micro-bioreactors and computational DoE
methodology offers a robust approach for bioprocesses’ experimental and
statistical optimization. This combinatorial approach facilitates
attaining superior outcomes while minimizing the expenditure of time and
resources (Elibol, 2004 ). Several studies have effectively
employed DoE methods to augment the yield of recombinant protein
expression in E. coli by optimizing culture media
(Sunitha
et al., 1999;Shahbazmohammadi
and Omidinia, 2017;Zare
et al., 2019;Duan
et al., 2020). The existing body of literature confines similar
investigations encompassing diverse protein types, design methodologies,
factors under evaluation, and the consequent optimization outcomes
(Sunitha
et al., 1999;Shahbazmohammadi
and Omidinia, 2017;Zare
et al., 2019;Duan
et al., 2020). Notable instances include optimizing culture media for
producing L-Asparaginase, Phytase, Streptokinase, and Reteplase inE. coli , employing DoE-based strategies that have enhanced
production yields
(Sunitha
et al., 1999;Kenari
et al., 2011;Ghoshoon
et al., 2011). A comprehensive review of the literature indicates the
presence of numerous variables capable of influencing bacterial growth
rate and biomass production
(Sunitha
et al., 1999;Shahbazmohammadi
and Omidinia, 2017;Zare
et al., 2019;Duan
et al., 2020;Kenari
et al., 2011;Ghoshoon
et al., 2011). Parameters such as the nature and concentration of
carbon and nitrogen sources, pH conditions, and the inclusion of trace
elements have received considerable attention as key factors.
Furthermore, several studies have centered around utilizing the 48-well
BioLector microbioreactor system (Osthege et al., 2022;Sparviero
et al., 2023;Flitsch
et al., 2016;Lennen
et al., 2016 ). For instance, this system has proven effective in the
real-time monitoring of lipid production and the tracking of growth inY. lipolytica(Back
et al., 2016). The literature also showcases the result
transferability and comparability assessment between the BioLector
system and fully controlled bioreactor systems operating in fed-batch
mode, specifically at moderate to high cell densities (Toeroek et
al., 2015 ). Additionally, various investigations illustrate the
efficacy of the BioLector system in screening optimal growth conditions
and engineered strains prior to scaling up (Kensy et al., 2009 ).
This robust cultivation protocol conducted on microtiter plates enables
the screening of E. coli systems under conditions closely
resembling lab-scale bioreactor cultivations (Kensy et al.,
2009 ).
In this study, the PBD was operated to investigate the effects of eleven
factors on cellular growth. These factors encompass diverse nitrogen (N)
and carbon (C) sources, salts, metal ions, pH, and the buffering system.
Among these factors, the lower concentration of LB (Luria-Bertani)
medium (30 g/L) and, in contrast to prior investigations, the
MgSO4, glucose, and glycerol levels exerted the most
pronounced effect on cellular growth. Consequently, these factors were
singled out for subsequent optimization by implementing the RSM and the
CCD. Moreover, 0.25 mM IPTG exhibited advantageous effects on cellular
growth. Model terms displaying non-significant p-values were
deliberately excluded from the culture media during the CCD experiments.
According to the outcomes derived from RSM, glycerol concentration
emerged as the most influential factor, exhibiting a direct relationship
with heightened cellular proliferation. These findings concur with prior
investigations that have likewise underscored the favorable impact of
glycerol on E. coli growth, notably by fostering anaerobic
fermentation (Dharmadi et al.,2006 ). When juxtaposing the
coexistence of glycerol as a carbon source and IPTG as an inducer
(Malakar
and Venkatesh, 2012), our findings establish that the optimal glycerol
concentration, in the presence of IPTG is a mere 1% (v/v). This
observation aligns with studies evidencing a decline in cellular yield
as the glycerol concentration escalates
(Malakar
and Venkatesh, 2012). Moreover, the fitting of the Hill equation has
been validated as yielding a prototypical Monod-type expression for
growth on glycerol, both in the presence and absence of IPTG, as
corroborated by previous scholarly works
(Malakar
and Venkatesh, 2012). On the other hand, glucose and
MgSO4 have garnered recognition as pivotal factors
exerting a profound influence on E. coli growth in extant
literature (Izaki and Arima, 1965 ). However, the constructive
ramifications of limited glycerol presence on growth may also be
ascribed to the deleterious consequences of glucose, acting as a carbon
source, under conditions of meager nitrogen availability
(Bren et al.,
2016;Shiloach
et al., 1996;Michaels
et al.,1983).
The gram-negative bacterium E. coli is widely regarded as the
preferred host organism for the heterologous expression of various
recombinant proteins. This selection is primarily attributed to its
cultivation’s facile nature, the culture media’s cost-effectiveness, and
the potential for achieving high product titers
(Kopp et al., 2017).
However, using harsh induction strategies involving IPTG as an inducer
often triggers stress responses, giving rise to the phenomenon known as
”metabolic” or ”product burden.” These stress reactions are
characterized by diminished growth rates and cellular lysis during
prolonged induction. Alternative approaches have been explored to
mitigate these challenges, emphasizing ”gentle” or ”modifiable”
induction techniques employing lactose as an alternative inducer
(Kopp et al., 2017).
This approach aims to alleviate the strain exerted on the production
host. In contrast, conventional induction methods using glucose as the
primary carbon source and lactose can lead to catabolite repression
effects on lactose uptake kinetics, ultimately compromising the overall
product yield. Conversely, glycerol, an alternate carbon source, has
demonstrated promising outcomes when combined with glucose and lactose
in auto-induction systems
(Kopp et al., 2017).
Glycerol has been shown to exhibit favorable effects on recombinant
protein production, exhibiting reduced signs of catabolite repression
during co-cultivation with lactose. Furthermore, an investigation into
the mechanistic aspects of glycerol uptake in the presence of lactose as
an inducer highlighted significantly enhanced inducer uptake rates in
product-producing strains compared to non-producing strains. These
findings underscore glycerol’s practical and less disruptive nature on
cellular viability and recombinant protein productivity compared to
glucose (Kopp et al.,
2017).
Moreover, the metabolic breakdown of the carbon source results in the
accumulation of acidic by-products, mainly acetate, within the culture
medium
(Kusuma
et al., 2019;Ukkonen
et al., 2011). These acidic conditions can significantly impede cell
growth and compromise recombinant protein production
(Kusuma
et al., 2019;Ukkonen
et al., 2011). To counteract this issue, the supplementation of yeast
extract and tryptone in the culture medium serves to mitigate medium
acidification, primarily by counterbalancing the elevated ammonia levels
generated during their metabolic utilization
(Kusuma
et al., 2019;Ukkonen
et al., 2011). Among the various scenarios examined, Scenario-3
represents an optimal formulation of the culture medium, characterized
by an optimal combination of yeast (0.5 g/L), tryptone (1 g/L), and salt
(1 g/L) concentrations, in addition to a comparatively lower glucose
concentration (8.8 mM). This particular configuration may elicit
enhanced cell growth and delay entry into the death phase, thereby
signifying its suitability for promoting favorable outcomes regarding
cellular dynamics and extended cell viability.