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.