1. Introduction:
The prevalence of diabetes in the United States has reached epidemic
levels, with more than one-quarter of those aged 65 years and older
suffer with the disease (Stailey et al., 2017 ;IDF Diabetes
Atlas). Aging is a primary risk factor for type 2 diabetes mellitus
(T2DM) (Ahima 2009 ), attributed to the concomitant increase in
insulin resistance (Ahima 2009 ;Ayan
and DeMirci 2023) and decline in pancreatic beta-cell function
(Ahima 2009 ;Ayan
and DeMirci 2023). T2DM in older adults may manifest with distinct
clinical features, such as postprandial hyperglycemia
(Taylor et al.,
2021). However, the diagnosis of T2DM in older individuals may be
complicated by the limitations of commonly used diagnostic tests,
including hemoglobin A1C and fasting plasma glucose, which may fail to
detect postprandial hyperglycemia (Korkiakangas et al., 2009 ).
Moreover, older adults with T2DM are predisposed to a heightened risk of
developing microvascular and cardiovascular complications, further
underscoring the need for effective management and monitoring of the
disease in this population
(Vaidya
et al., 2015). The therapeutic management of T2DM in older adults can
pose limitations due to comorbidities, polypharmacy that is
characterized by simultaneous use of multiple drugs, and renal or
hepatic impairment (Dardano et al., 2014 ). While oral
hypoglycemic agents are usually the preferred choice for the initial
treatment of T2DM, the progressive deterioration of pancreatic beta-cell
function may eventually require insulin therapy
(Snyder
et al., 2004). Nevertheless, administering insulin in older adults
necessitates a judicious approach as this population is particularly
susceptible to hypoglycemia and unawareness of its occurrence
(Herman
et al., 2005).
Basal-bolus therapy requires the administration of basal insulin
referred as intermediate and long-acting insulins, prandial insulin, and
correction doses as needed to replace endogenous insulin
(Garg et al.,
2010). Prandial insulin is administered to simulate the physiological
response of endogenous insulin to food intake, which involves a rapid
and vigorous first phase followed by a more prolonged second phase of
insulin secretion into the portal circulation
(Owens
et al., 2001). Recent advances in insulin therapy, such as the
development of insulin analogs, including rapid-acting insulin analogs
(RAIAs), have transformed the management of type 1 diabetes (T1DM).
RAIAs, such as insulin Aspart, Glulisine, and Lispro, offer superior
pharmacokinetic and pharmacodynamic profiles compared to regular human
insulin (RHI)
(Ashwell
et al., 2006;Hermansen
et al., 2004;Becker
et al., 2008). RAIAs may confer notable clinical benefits over RHI,
including a lower risk of hypoglycemia and improved glycemic control
(Garg et al.,
2010).
Various strategies have been employed to express recombinant insulin inE. coli , including using different expression methods, tags, and
host strains (Khalilvand et al., 2022 ). One efficient approach
involves designing insulin constructs with a protease cleavage site
using the home-made proteases to facilitate the cost-efficient
production of mature insulin (Akbarian and Yousefi, 2018 ). In
addition, host strains can be engineered to enhance insulin expression,
such as the use of Rosetta™ 2 host strains, which are BL21 derivatives
optimized for the expression of eukaryotic proteins with rare codons
(Tegel et al., 2010 ). These strains carry a compatible
chloramphenicol-resistant plasmid encoding tRNA genes for seven rare
codons (AGA, AGG, AUA, CUA, GGA, CCC, and CGG) under their native
promoters (Tegel et al., 2010 ). With IPTG induction, such strains
are ideal for high-yield insulin production from target genes cloned
into pET vectors
(Pan
and Malcolm, 2018).
During recombinant protein production, the chemical and nutritional
composition of the culture medium can significantly impact host cell
growth
(Shokri
et al., 2003). Factors such as carbon and nitrogen sources, metal
ions, and medium pH play critical roles in determining cell growth,
making it crucial to optimize the culture composition for high yields of
the target protein
(Kusuma
et al., 2019;Nikerel
et al., 2006). However, the sheer number of factors that can affect
cell growth is vast, and evaluating their effects using the
One-Factor-At-A-Time (OFAT) approach is time-consuming and
labor-intensive
(Abu
et al., 2017). Furthermore, the OFAT approach must account for the
potential dependent or independent effects and interactions between
these factors
(Abu
et al., 2017). To address these limitations, the factorial approach
offers a more comprehensive and efficient means of examining the impact
of multiple factors on cell growth
(Abu
et al., 2017). By simultaneously evaluating all levels of all factors,
this approach enables the determination of their independent effects and
interactions
(Abu
et al., 2017). The Design of Experiment (DoE) is a statistical tool
that employs fractional factorial models, such as Response Surface
Methodology (RSM), to evaluate relevant interactions among variables via
fewer experiments (Papaneophytou and Kontopidis, 2014 ;Sopyan
et al., 2022;Dentener
2002). Furthermore, the BioLector XT high-throughput microbioreactor
offers real-time monitoring of crucial cultivation parameters for
aerobes and anaerobes, such as biomass, pH, dissolved oxygen in the
liquid phase (DO), and fluorescence (Drummen;Osthege
et al., 2022). This tool provides rapid and detailed insights into
bioprocess development experiments, allowing for more efficient
optimization of the culture medium and, ultimately, higher target
protein yields (Osthege et al., 2022 ).
The objective of our study was to optimize the composition of the
culture media to enhance the biomass of Rosetta™ 2 (DE3), which
expresses a novel designer fast acting proinsulin. We employed theDesign-Expert (DoE) method, utilizing the BioLector XT system to
support our investigations. By employing these sophisticated approaches
we sought to identify a more comprehensive and efficient analysis of the
various factors affecting the growth and yield of the modified insulin
from Rosetta™ 2 (DE3); thus, we have been gain real-time insights into
crucial cultivation parameters, enabling us to identify the optimal
culture media composition to achieve maximum biomass production of the
modified insulin. Accordingly, a screening experiment was conducted to
assess the impact of various cultural components on the growth of
Rosetta™ 2 (DE3), using the Plackett-Burman Design (PBD) and
BioLector XT Microbioreactor. The optimal culture media composition was
thereafter determined through further experimental design utilizing the
BioLector XT Microbioreactor. The consequential significant factors were
subsequently optimized, employing the Response Surface Methodology (RSM)Central Composite Design (CCD) of Design Expert suite. Following
the completion of the optimization calculation, the obtained
statistically relevant results were subsequently verified through
experimental validation employing the BioLector XT Microbioreactor.