Discussion
Here we have implemented a novel and broadly applicable approach for
media development that relies on rapid, automated construction of
diverse media from defined modules of components. We demonstrated the
utility of this approach by developing two new media for two phenotypes
of interest in the heterologous production of proteins by yeast, namely
biomass accumulation and secreted production. We systematically
identified and optimized the concentration of media components important
to each phenotype of interest. Importantly, defining these new
formulations of media did not require advanced analytical capabilities
and required minimal experimental time to assess more than 360 total
formulations during two to three rounds of optimization for each.
Our optimized formulations affirmed the importance of lipid-related
components for maximizing titers in Pichia pastoris cultivations.
The importance of optimizing membrane fluidity or lipid metabolism has
been well established in CHO and appears to be key to optimizing
heterologous protein secretion in P. pastoris cultivation as well
(Clincke et al., n.d.; Ritacco et al., 2018; Zhang, Wang, & Liu, 2013).
Modular media blending has four advantages over existing methods. First,
the use of common stock solutions and supplements to formulate media
reduces initial labor required for new experiments or optimizations
~15 minutes per experiment, making parallel testing of
multiple hypotheses efficient and requires less resources overall. Here,
we created 30 stock solutions, and evaluated >360 unique
media compositions, without manual preparation of individual media or
extensive blending calculations or planning. Most of these solutions
could be readily reused in future experiments to optimize for new
phenotypes of interest. Second, our method requires minimal knowledge of
the host organism a priori and could, in principle, be applied to
any measurable phenotype of interest. We anticipate that this method
could be used to optimize other phenotypes of interest, such as
glycosylation profiles. Third, our method provides certain practical
advantages, including minimal requirements for analytical
characterization and rapid identification of component interactions that
lead to solubility challenges. These traits make it possible to learn
about formulations that may lead to extensive precipitates like those
encountered with our rich defined medium formulation (Figure
4A ). Finally, modularly constructed media, such as DM2, can be
~70% pure water with low osmolarity, leaving volumetric
and osmotic space for future modifications to accommodate new or related
phenotypes of interest (Figure 4B ).
We also acknowledge certain limitations in the present study that may be
addressed in future work. First, while modular media development
identifies components key to the optimization of the phenotype of
interest, additional media optimization effort may be necessary to
translate these learning in batch cultivations to scaled-up fed-batch or
perfusion operation, where additional variables such as supplemental
feed composition and feeding schedule must also be considered. In
principle, modular media construction could be applied to
high-throughput scale-down cultivation models, such as Ambr250s. Second,
our approach for optimization relies on greedy algorithms tailored to
create a new media for a single phenotype of interest; however, given
the vast explorable media space it is possible to find a local optimum.
Further metabolic or -omic modeling techniques could be employed to
guide broader exploration of media space, co-optimize multiple
phenotypes, or facilitate biologically informed optimization, albeit
with more complex experimental and computational requirements (Matthews,
Kuo, Love, & Love, 2017b; Mohmad-Saberi et al., 2013). Third, our
current method used commercially available supplements, but in practice,
beneficial supplements could be simplified by using individual
components, to facilitate more biological inferences and aid development
of improved host-specific supplements. Finally, initial screens to
identify beneficial supplements rely on reasonable choices of initial
concentrations for screening. These currently require prior knowledge
from the literature or commercial sources; with further use in the
community of the Openblend approach, it is possible additional sharing
of knowledge could help inform further developments.
The improved speed and accessibility of in-depth media development
experiments enabled by modular media construction could help improve
expression of many classes of proteins in laboratories and discovery
centers that have not traditionally had access to such capabilities.
Since many lead candidates for new therapeutic proteins begin in small
biotech firms and academic labs, early-stage improvements in
productivity could help advance more proteins towards the clinic simply
by facilitating access to larger quantities of proteins for initial
research and non-clinical studies. In more established companies, the
ability to make rapid improvements to existing media may enable faster
product development timelines and could reduce manufacturing costs
overall. Rapid identification and optimization of sensitive media
components could also enable easier adoption of a range of industrially
relevant alternative hosts, resulting in further manufacturing
flexibility and potentially cost savings (Coleman, 2020).