Application to Developing a Medium for Biomass Accumulation
To assess the utility of this blending-based approach, we next aimed to
identify and optimize the concentration of media components beneficial
for rapid biomass accumulation of P. pastoris in batch
cultivation. We previously described a rich defined medium (RDM)
(Matthews et al., 2017a), capable of high growth rates during biomass
accumulation. One challenge encountered with this formulation, however,
was that precipitates can form at higher pH values that require
filtering during bulk preparations. Nonetheless, this medium provided a
relevant comparison for assessing the medium realized with our new
approach due to its prior demonstrated benefits relative to complex
media. Following our modular approach, we improved biomass accumulation
by optimizing the accumulated optical density at 600 nm after 24 hours
of cultivation.
Algorithms for optimizing systems based on multiple dimensions are often
sensitive to initial conditions used (Zakharova & Minashina, 2015).
Given this potential confounding effect here, we tested first the
effects of the types of carbon source, nitrogen source, and pH set point
on biomass accumulation, using 1x YNB without amino acids or ammonium
sulfate (YNB) to satisfy minimum requirements for the concentrations of
trace elements. We conducted a full-factorial DOE using glycerol,
glucose, and fructose as carbon sources; urea and ammonium sulfate as
nitrogen sources; and potassium phosphate as a buffer with pH values of
5, 5.75, and 6.5. We selected initial concentrations of 40 g/L, 4 g/L
urea or the N-mol equivalent for ammonium sulfate, and 10 g/L potassium
phosphate, similar to values used in other media for Pichia
pastoris (Matthews et al., 2017a). A least squares regression model,
including individual, combination, and quadratic effects was fit to the
log of optical density after 24 hours, a proxy variable for the average
growth rate (R2 = 0.81). We determined that the two
most significant model terms were the type of carbon source and the
interaction of the nitrogen source with pH (Figure 2A ). We
found that cells grew significantly faster on metabolically related
sugars (glucose and fructose) than on the polyol (glycerol) commonly
used for Pichia during biomass accumulation (Figure 2B ). This
result affirms prior reports where glucose has been used for biomass
accumulation of Pichia (Guo et al., 2012; Moser et al., 2017).
The model also suggested that poor biomass accumulation during
cultivation resulted from a combination of ammonium sulfate as a source
of nitrogen with low buffer pH (Figure 2B ). This outcome may
result from the production of acidic species associated with cellular
ammonium metabolism in the batch cultivation (Villadsen, 2015).
Interestingly, the model indicated slightly greater biomass was achieved
with urea instead of ammonium sulfate. The biomass accumulation of
cultures grown with urea as a source of nitrogen were less sensitive to
reduced pH values (~5). We observed, however, that
cultivations at pH 5 showed extensive flocculation compared to those at
6.5. Given the insensitivity of urea-fed cultivations to buffer pH and
the high solubility and potential for low-cost sourcing of fructose, we
therefore chose to include fructose, urea, and a potassium phosphate
buffer with a pH of 6.5 in our initial media formulation.
With this basal formulation determined, we next screened for
concentration-dependent interactions of other key additives to the media
and then optimized concentration-dependent parameters. Following the
same approach for screening effects, we conducted a full factorial DOE
over a broad range of media component concentrations: YNB (0.5, 1, 2x),
fructose (10, 30, 50 g/L), urea (1, 4, 7 g/L), and potassium phosphate
adjusted to a pH of 6.5 (4, 10, 16 g/L). The resulting model identified
fructose as a concentration-sensitive parameter
(R2=0.73) (Figure 2D ). Terms involving the
concentration of YNB were also highly ranked, but not statistically
significant. No significant interactions between components were
identified in the model. We therefore sought to better understand the
concentration dependence of fructose and YNB independently
(Figure 2E ), over an 8-fold range of concentrations. As
expected, biomass accumulation was highly sensitive to fructose
concentration, with an optimum around 22.5 g/L of fructose. The
concentration of YNB had minimal effect on biomass accumulation; the
presence of trace elements supplied by YNB, however, was essential to
growth. Based on these results, we chose concentrations of 22.5 g/L
fructose, 1x YNB, 7 g/L urea, and 10 g/L potassium phosphate buffer. We
reasoned that although biomass accumulation was relatively insensitive
to the concentrations of YNB and urea, higher concentrations could
provide improved media depth in future applications. We named this basal
formulation DM1_dev0.
We next assessed what additional media components could improve biomass
accumulation. To test over 60 different components individually would
require over 60 individual solutions. Such an approach would scale
linearly with new components; instead, we chose to screen groups of
related components, using commercially available pre-mixed supplements.
We compiled a library of 16 commercial supplements and
industrially-relevant surfactants containing more than 60 unique
components and screened their individual effect on biomass accumulation
after 24 hours. In this way, we reasoned we could efficiently identify
critical classes of components related to the phenotype of interest and
potentially deconvolve specific individual additives of interest by
inference. We used the recommended concentrations of each supplement as
supplied in product information, or critical micelle concentrations, and
prior knowledge for broad classes in yeast media to set reasonable
screening concentrations (Supporting Information). We identified five
beneficial and two detrimental supplements that significantly impacted
biomass accumulation (padj < 0.02;
1-way-ANOVA) (Figure 2F ). In general, the results suggest that
supplementation with amino acids and trace metals were beneficial for
accumulating biomass, while two surfactants, Tween 20 and CHAPS, were
detrimental. For this phenotype, the effects of vitamin and lipid
supplements were minor; supplements from either supplement category were
not significantly beneficial or detrimental to biomass accumulation. Our
earlier experiments suggest that vitamins are essential but
concentration agnostic (Figure 1E ), while lipid supplementation
provides no clear benefit for biomass accumulation.
Based on these results, we chose to test whether combinations of
supplements of amino acids and trace salts could yield synergistic
improvements in biomass accumulation. We screened pairwise combinations
of the five beneficial supplements of mixed composition and ranked the
performance of our supplementation strategies (Figure 2G ). A
combination of 1x MEM amino acids with 0.1 v/v% PTM1 salts resulted in
the highest yield of biomass, though we observed strong performance from
other combinations of amino acid and trace metal supplements. Based on
these data, we chose to add MEM amino acids and PTM1 salts in our basal
medium and optimized their concentrations (Figure 2H ).
Based on these results, we elected 0.1 v/v% PTM1 salts and 1x MEM amino
acids, in order to balance the moderate benefits and potentially high
costs of amino acids. We found, however, that the inclusion of the PTM1
salts in liter-scale preparations produced fine precipitates, which can
impede sterile transfers in use. To overcome this challenge, we screened
a broad range of PTM1 salts concentrations to identify the minimum
concentration required for improved outgrowth performance
(Figure 2I ). We found that PTM1 addition at concentrations as
low as 0.0005 v/v% led to increased biomass accumulation. We therefore
revised our PTM1 salts concentration to 0.01 v/v%, a concentration high
enough to obtain the benefits of PTM1 supplementation without inducing
precipitate formation. This formulation we named DM1.
Completing this series of optimizations with our iterative modular
approach to define a new formulation of medium, we then compared with
other common media used to grow P. pastoris . We evaluated the
performance of this new optimized medium (DM1) relative to the
unsupplemented basal medium (DM1_dev0), the rich defined medium (RDM)
we had previously developed, and a common medium 4 v/v% glycerol BMGY.
We found that DM1 yielded the highest biomass accumulation, with
significantly higher biomass accumulation relative to RDM and BMGY
(Figure 2J ). This result demonstrates the utility of our
modular strategy here for media development that yielded an improved
formulation for biomass accumulation compared to other common media with
minimal time and labor investment, and without requiring complex
analytical methods like mass spectrometry or RNA-sequencing.
Identifying media conditions important to heterologous protein
production in K. phaffii
In addition to the time and labor savings of modular media development,
our proof-of-concept experiments demonstrated that this approach creates
a flexible medium that can be rapidly adapted to new growth phenotypes,
as well as a data package that the identifies media conditions important
to the phenotype of interest. We reasoned that these additional benefits
could be particularly relevant for optimizing production of heterologous
proteins. Understanding which media components contribute most
significantly to productivity could improve culture performance and help
identify important metabolic pathways or physiological effects for
further study.
To develop a medium for improved production of a recombinant protein, we
chose to use a strain engineered to secrete a rotavirus-derived subunit
vaccine component, VP4-P[8], as a model protein. We have previously
demonstrated that this viral antigen can be expressed at high titer
under the control of the methanol-inducible pAOX1 promoter in BMMY media
(Dalvie et al., 2020). Similar to our initial approach to optimize a
medium for growing biomass, we first determined and optimized the
concentrations of the sources for carbon and nitrogen, along with the
pH. The expression of P[8] in the strain tested uses the
methanol-dependent pAOX1 promoter for inducible expression, so we
selected methanol as the initial carbon source. We then examined the
impact of the source of nitrogen and buffer pH on titer. We conducted a
full-factorial DOE using identical concentrations as those used to
create a medium for accumulating biomass. The resulting model was
visualized by ranking combinations of sources of nitrogen and buffer
(Figure 3A ). The effects showed no interaction between these
two factors. Urea was again identified as the preferred source of
nitrogen while higher pH values led to improved secreted P[8]
productivity. Unlike biomass accumulation, this pH dependence was
observed across both nitrogen sources.
We next applied the same DOE to identify important
concentration-dependent interactions that impact the production of
P[8]. Unsurprisingly, the concentration of methanol was the most
important factor, with possible minor effects from other components
(Figure 3B ). We decided to screen further a 20-fold range in
methanol concentrations using two formulations for remaining media
components—the one determined for optimal cell growth (DM1) and the
optimal base media formulation predicted by the quadratic model here (2x
YNB, 1 g/L urea, 4 g/L potassium phosphate adjusted to a pH of 6.5). We
found that production was relatively insensitive for concentrations of
methanol ranging from 1-4 v/v%, with an optimum around 2%
(Figure 3C ). We postulated that the rapid decline in
productivity observed in these milliliter-scale cultures using
concentrations >6 v/v% methanol was likely due to excess
formation of toxic metabolic byproducts such as formaldehyde and
hydrogen peroxide (Wakayama et al., 2016). Interestingly, the predicted
optimal medium from this set of studies outperformed the medium we
determined for accumulating biomass, suggesting that certain components
of the basal medium may benefit protein expression more than cellular
growth and underscores the value of optimizing media for specific
phenotypes of interest. Based on these data in total, we defined a basal
medium for production including 2x YNB, 2 v/v% methanol, 1 g/L urea,
and 4 g/L potassium phosphate buffer adjusted to a pH of 6.5
(DM2_dev0).
Next, we examined which supplements could improve the performance of
DM2_dev0. We added three chemical chaperones (TUDCA, sodium
deoxycholate monohydrate (SDM), and valproic acid) (Kuryatov, Mukherjee,
& Lindstrom, 2013; Uppala, Gani, & Ramaiah, 2017), two antioxidants
(reduced glutathione (GSH) and N-acetyl cysteine (NAC)), and the
chelator, K-ETDA, to the list of 16 supplements included in our original
screen defined for biomass accumulation. Concentrations for these
components were chosen based on product specifications, literature data,
and prior experience (Supporting Information). Many of the 22
supplements screened improved production of P[8] (Figure
3D ). The top four ranking supplements comprised surfactants or lipids,
which could modulate membrane fluidity and lipid metabolism (Butler,
Huzel, Barnab, Gray, & Bajno, 1999; Degreif, Cucu, Budin, Thiel, &
Bertl, 2019; Ritacco, Frank V; Yongqi Wu, 2018).
We then screened combinations of lipid supplements and surfactants to
identify potential synergistic effects. We ranked the individual
supplements and their combinations (Figure 3E ) according to the
measured titers of P[8]. We found that the addition of a
cholesterol-rich supplement yielded the highest secreted titers of
P[8] (~50% improvement compared with
supplement-free condition in initial screens). Interestingly, a
synthetic cholesterol supplement alone did not substantially improve
performance, suggesting the benefit results from a combination of fatty
acids and surfactant components in the supplement (Supporting
Information ). This conclusion is consistent with similar improvements
observed from other supplements, such as linoleic acid-oleic
acid-albumin (Figure 3D ).
Since no other synergistic effects were observed in the combination
screen, we assessed the dependence of titer on the concentration of the
cholesterol-containing supplement identified (Figure 3F ).
Similar to our observations with cellular YNB used in the outgrowth
media, we found that concentrations of the supplement as low as 0.2
v/v% were beneficial for protein expression, but that production was
relatively insensitive to concentration (Figures 3F, 3G ). We
then directly compared the supplemented medium to the original
composition; the new supplemented media provided a 25% improvement in
titer (p = 0.0006, one-tailed Welch’s T test). This new formulation with
1x cholesterol supplement, which we named DM2_dev1, was the result of
one cycle of optimization using our method.
Components of the cholesterol supplement included fatty acids,
cholesterol, and cyclodextrin, which are all are known to modulate
membrane fluidity, a key parameter in vesicle trafficking (Cooper, 1978;
Degreif et al., 2019; Mahammad & Parmryd, 2015). We reasoned that the
addition of this supplement could therefore have synergistic effects
with other supplements, but did not find any further supplementation
that improved P[8] titers within our original screen (Figure
3H ). We, therefore, considered if there could be additional classes of
beneficial supplements, absent from the original screen. Previous
experiments demonstrated that P[8] productivity is highly sensitive
to methanol concentration (Figure 3C ), so we wondered whether
further modulation of central carbon metabolism could yield additional
productivity gains.
Modification of central carbon metabolism is best accomplished by
feeding cells alternative carbon sources, either entirely or as
co-feeding substrates. Four co-fed substrates have previously been shown
to be non-repressive of pAOX1: sorbitol, mannitol, trehalose, and
alanine (Inan & Meagher, 2001). These substrates can be co-utilized
with methanol without repressing the pAOX1 promoter, which controls
expression of P[8]. We hypothesized that the introduction of
supplemental carbon sources could enable further optimization of central
carbon metabolism. We screened co-fed substrates individually and in 1:1
combinations at a total concentration of 20 g/L (a concentration similar
to the optimal fructose and methanol concentrations observed in previous
carbon source optimizations) (Figure 2E,3C ). Sorbitol
co-feeding had the most beneficial effect, resulting in a
~80% increase in P[8] titer (Figure 3I ).
Mannitol supplementation was also beneficial (~70%
increase), while alanine and trehalose co-feeding were detrimental to
productivity. While co-feeding carbon sources led to increased biomass
yield during production, these differences did not account for the
improved titer, as improvements in specific productivity
(qp) of ~60% and ~45%
were also observed for the sorbitol and mannitol co-fed conditions,
respectively (Supporting Information ). Based on these data, we
chose to include sorbitol as a supplemental carbon source for further
study.
The addition of a supplemental carbon source could significantly impact
central carbon metabolism. We, therefore, wondered how the inclusion of
sorbitol might impact the optimal carbon feeding strategy. Examining
total carbon source concentrations from 20 – 70 g/L, we compared the
performance of cultures co-fed with sorbitol:methanol ratios of 3:1,
1:1, and 1:3 to a methanol-only control (Figure 3J ). All co-fed
conditions outperformed the methanol-only control, suggesting that the
presence of sorbitol is highly beneficial for producing P[8]. The
titer was relatively insensitive to sorbitol:methanol ratios and carbon
concentrations. Based on the data, we decided to use 2 v/v% methanol
and 20 g/L of sorbitol for the final sorbitol-supplemented media named
DM2.
Finally, we compared the P[8] titer obtained using DM2_dev0,
DM2_dev1, and DM2 to other common production media for P.
pastoris : BMMY and RDM. We found that DM2 led to a ~2x
improvement in P[8] titers, relative to BMMY and RDM, up to 97\(\pm\) 2 mg/L.