Download Supplementary 1: Material and methods Determination of CHO cell

Supplementary 1: Material and methods
Determination of CHO cell composition
For the measurement of cell composition, five different CHO cell lines (CHO mAb M250-9,
M500-7, CHO K1, CHO DG44 and CHO DXB11) were used. CHO mAb M250-9, M500-7 and CHO
K1(ATCC No. CCL-61) cell lines were cultured in a mixture consisting of a 1:1 (v/v) ratio of HyQ
PF-CHO (Hyclone, UT, USA) and CD CHO (Gibco-Invitrogen, CA, USA) supplemented with 6 mM
L-glutamine (Sigma Chemical Co., MO, USA) and 0.05% (v/v) Pluronic F-68 (Invitrogen, CA,
USA). Additional 250 nM and 500 nM MTX (Sigma Chemical Co., MO, USA) were added to the
M250-9 and M500-7 cultures respectively. CHO DG44 and CHO DXB11(ATCC No. CRL-9096) cell
lines were cultured in HyQ PF-CHO (Hyclone, Logan, UT) supplemented with 4 mM L-glutamine
(Sigma Chemical Co., MO, USA), 0.1% (v/v) Pluronic F-68 (Invitrogen, CA, USA) and 1× HT
supplement (Gibco). All the cells were grown in their respective media under batch conditions.
Cell pellets (total mass ranging from 19 g to 38 g) were collected between the exponential to
stationary phase of the various cultures, when viabilities were > 90% (CHO K1, CHO DG44 and
CHO DXB11 on day 4, and CHO mAb M250-9 and M500-7 on day 7). The samples were analyzed
for 19 amino acids and 36 fatty acids; as well as moisture content using AOAC certified methods
(Pacific Lab Services, Singapore). From the moisture content, dry cell weight equivalents of the
various analytes could be calculated.
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Metabolic model development and in silico analysis
Recently available genome-scale metabolic model for mammalian cells accounts for 1494
metabolic reactions, 724 ORFs and 715 enzymes (Selvarasu et al. 2010). We expanded the
network model by adding reactions obtained from CHO cDNA annotation resulting in the
addition of 38 new reactions from various metabolic subsystems (see supplementary 1 for list
of reactions). We also examined the model for missing links and improved the network
connectivity by filling the gaps with necessary reactions. Concurrently, those reactions that
were not found in CHO were removed from the model. As a result, a metabolic model with
1540 metabolic reactions was obtained.
Once a stoichiometrically balance metabolic network is developed based on the
metabolites and reactions, the steady state metabolic flux distributions across the network can
be quantitatively predicted by resorting to constraints-based flux analysis. Often, cell growth
phenotype is maximized while satisfying thermodynamic and physico-chemical constraints
using the following linear programming (LP) model (Edwards and Palsson 1998).
N
Min/max Z   c j v j
(1)
j 1
N
s. t.
S v
j 1
ij
j
 j  vj   j
0
i = 1, 2, …, M
(2)
j = 1, 2, …, N
(3)
2
where, i refers to metabolite, j refers to reaction, Z is the desired cellular objective, vj (αj ≤ vj ≤
βj) is the flux of reaction j with bounds αj and βj, for an irreversible reaction j, αj = 0 and βj = ∞,
and for a reversible reaction αj = –∞ and βj = ∞. cj is the weight associated with the reaction
fluxes represented in objective function, and Sij is the stoichiometric coefficient of metabolite i
in reaction j. M is the number of metabolites and N is the number of reactions in the model.
When simulating the non-growth condition, maximizing cell growth cannot be chosen as
cellular objective. Therefore, we utilized least square minimization technique to reduce the sum
square error between experimentally measured fluxes and in silico simulation fluxes. This
resulted in NLP problem as described below.
N
in
Min Z   vexp
j  vj
2
(4)
j 1
where, vjexp represent experimentally measured uptake/secretion rates of metabolites and
vjin correspond to uptake/secretion flux values obtained from in silico simulation. The resulting
optimization problem can be solved with eqn. 4 as objective with eqn. 2 and eqn. 3 as
constraints using GAMS software (Brooke et al. 1998).
PCA and PLS analysis
A linear multivariate PCA technique was used to analyze the cell culture nutrients data. Initially,
experimental data were collected in a data matrix possessing the dimension [n x p] with n
number of measurements and p observations (cell density, concentration of amino acids, major
carbon sources and byproducts). A set of orthogonal eigen vectors [1 x p] was estimated from
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the data matrix based on the eigen values that indicate the total variance in the data. From the
list of eigen vectors, a small set known as principal components (key variables) can be
determined, representing vast majority of variance in the data. Subsequently, we used PLS
analysis to predict a set of dependent variables (e.g., cell growth, antibody production, lactate
and NH3 secretion) from a large set of independent variables (predictors) such as consumption
of amino acids and glucose. PLS regression generalizes and combines features from PCA and
multiple regressions (Geladi and Kowalski 1986). It finds components from independent
variables (X) that are relevant to dependant variables (Y), thus describing their common
structures. Specifically, it searches for components (called latent vectors) that perform a
simultaneous decomposition of X and Y with the inclusion of a constraint. This constraint
determines the latent vectors that can explain the varying trend of X and Y as closely as
possible. It is then followed by a regression step where the decomposition of X is used to
predict Y. All the analyses were conducted using MATLAB platform.
References
Brooke A, Kendrick D, Meeraus A, Raman R. 1998. GAMS: a user’s guide. GAMS development
corporation.
Edwards JS, Palsson BO. 1998. How will bioinformatics influence metabolic engineering?
Biotechnology and Bioengineering 58(2-3):162-169.
Geladi P, Kowalski BR. 1986. Partial least square regression: a tutorial. Anal Chim Acta 185:1-17.
Selvarasu S, Karimi IA, Ghim GH, Lee DY. 2010. Genome-scale modeling and in silico analysis of
mouse cell metabolic network. Molecular Biosystems 6(1):142-151.
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