translatome analysis of cho cells
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7/23/2019 Translatome Analysis of Cho Cells
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Journal of Biotechnology 167 (2013) 215–224
Contents lists available at ScienceDirect
Journal of Biotechnology
journal homepage: www.elsevier .com/ locate / jb iotec
Translatome analysis of CHO cells to identify key growth genes
Franck C. Courtes a,b, Joyce Lin a, Hsueh Lee Lim a, Sze Wai Ng a, Niki S.C. Wong a,c,Geoffrey Koh a, Leah Vardy d, Miranda G.S. Yap a,b, Bernard Loo a,∗∗, Dong-Yup Lee a,b,∗
a Bioprocessing TechnologyInstitute,A*STAR (Agency for Science, Technologyand Research),20 BiopolisWay,#06-01 Centros, Singapore 138668, Singaporeb Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore117576, Singaporec AbbVie Pte Ltd., 8 Biomedical Grove, #03-01, Neuros, Singapore 138665, Singapored Institute ofMedical Biology, A*STAR(Agency for Science, Technology andResearch), 8A Biomedical Grove,#06-06 Immunos, Singapore138648, Singapore
a r t i c l e i n f o
Article history:
Received 23 March 2013
Received in revised form 14 June 2013
Accepted 10 July 2013
Available online 19 July 2013
Keywords:
Translatome
CHO cells
Polysome profiling
Cellular growth
a b s t r a c t
We report the first investigation of translational efficiency ona global scale, also known as translatome, of
a Chinese hamster ovary (CHO) DG44 cell line producing monoclonal antibodies (mAb). The translatome
data was generated via combined use of high resolution and streamlined polysome profiling technology
and proprietary Nimblegen microarrays probing for more than 13K annotated CHO-specific genes. The
distribution of ribosome loading during the exponential growth phase revealed the translational activity
corresponding to the maximal growth rate, thus allowing us to identify stably and highly translated genes
encoding heterogeneous nuclear ribonucleoproteins (Hnrnpc andHnrnpa2b1), protein regulator of cytoki-
nesis 1 (Prc1), glucose-6-phosphate dehydrogenase (G6pdh), UTP6 small subunit processome (Utp6) and
RuvB-like protein 1 (Ruvbl1) as potential key players for cellular growth. Moreover, correlation analysis
between transcriptome and translatome data sets showed that transcript level and translation efficiency
were uncoupled for 95% of investigated genes, suggesting the implication of translational control mech-
anisms such as the mTOR pathway. Thus, the current translatome analysis platform offers new insights
into gene expression in CHO cell cultures by bridging the gap between transcriptome and proteome data,
which will enable researchers of the bioprocessing field to prioritize in high-potential candidate genes
and to devise optimal strategies for cell engineering toward improving culture performance.
© 2013 Published by Elsevier B.V.
1. Introduction
Chinese hamster ovary (CHO) cells are one of the most com-
monly used mammalian host cell lines for the production of
recombinant proteins of therapeutic interest ( Jayapal et al., 2007).
In order to meet the increasing demand for such recombinant pro-
teins, consistent efforts havebeen madefor significantlyenhancing
the production capacity of CHO cell cultures (Barnes and Dickson,
2006). Some of the major improvements were achieved by empiri-
cally designed strategies (Kim et al., 2012; Lim et al., 2010; Wurm,
2004), but without clear understanding of the underlying cellular
mechanisms. The lack of such crucial understanding has hindered
the development of knowledge-based strategies to fully optimize
and control cell culture processes. To address this limitation, sev-
eral “-omics” profiling technologies have been successfully utilized
(Kuystermans et al., 2007; O’Callaghan and James, 2008; Omasa
∗ Corresponding author at: Department of Chemical and Biomolecular Engineer-
ing, National University of Singapore, 4 Engineering Drive 4, Singapore 117576,
Singapore. Tel.: +65 6516 6907; fax: +65 6779 1936.∗∗ Corresponding author.
E-mailaddresses:bernard.loo@gmail.com (B. Loo), cheld@nus.edu.sg (D.-Y. Lee).
et al., 2010; Selvarasuet al., 2012), thereby gaining a more in-depth
insight into these cellular mechanisms. For example, a targeted
transcriptomic approach was used to track the transcriptional reg-
ulation accompanying the onset of apoptosis between batch and
fed-batchcultures (Wong et al., 2006). As a result, seven core apop-
totic genes were detected as differentially expressed, including
Faim, Agl2, Fadd and Requiem. Interestingly, the transcriptome of
CHO cells treated with sodium butyrate revealed that transcripts
pertaining to apoptosis, cellcycle and protein folding (DeLeon Gatti
et al., 2007; Yee et al., 2008) as well as carbohydrate metabolism
and signal transduction pathways (Klausing et al., 2011) were dif-
ferentially regulated. The transcriptome of CHO cells undergoing
a temperature shift at 33◦C suggested that transcripts enriched
in protein trafficking and cytoskeleton reorganization were up-
regulated (Yee et al., 2009).
Similarly, Lee et al. (1996) initiated the exploration of the
CHO proteome by identifying 3 proteins, CCND1, CCNE and
e2F-1, that correlated with mitogenic signals. Later, a prelim-
inary proteome map of CHO culture was established by 2-D
gel electrophoresis (Champion et al., 1999). Temperature shift
to 31 ◦C affected the quantity of 10 proteins in a CHO cell cul-
ture (Kaufmann et al., 1999) and an increase of osmotic pressure
from 300 to 450mOsmkg−1, significantly changed the level of
0168-1656/$ – seefrontmatter © 2013 Published by Elsevier B.V.
http://dx.doi.org/10.1016/j.jbiotec.2013.07.010
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216 F.C.Courtes et al. / Journal of Biotechnology 167 (2013) 215–224
glyceraldehyde-3-phosphate dehydrogenase, pyruvate kinase and
tubulin (Lee et al., 2003). The proteome of CHO cells engineered
with the apoptosis inhibitor Bcl-XL to minimize cell death high-
lighted thirty-two differentially expressed proteins enriched in
protein metabolism, transcription and cytoskeleton functional
clusters (Carlage et al., 2009). In another study, twelve differentially
expressed proteins involved in glucose metabolism and protein
translation/folding were identified through comparison between
high and low mAb producer CHO cells (Meleady et al., 2011).
With such successful applications of each level of omics pro-
filing, clearly the study on correlation between transcriptome and
proteome hasbeen a topic of greatinterestto theresearchcommu-
nity. However, only a handful attempted to combine both levels of
information (Baik et al., 2006; Doolan et al., 2010; Nissom et al.,
2006; Yee et al., 2008) and commonly concluded that there was
a general lack of correlation between mRNA and protein expres-
sion levels in CHO cells. The same conclusion was also reported
in various human cell lines and yeast (de Nobel et al., 2001; Gygi
et al., 1999; Pradet-Balade et al., 2001), implying that a change of
a transcript level may not be necessarily accompanied by a simi-
lar change (direction and amplitude) at the protein level. In fact,
it has been estimated that the concentration of only 20–40% of
the total proteins is determined by the corresponding transcript
level in mammalian cells (Cox et al., 2005; Tian et al., 2004), sug-gesting that not all mRNA are translated with the same efficiency
according to the cellular requirements (Brockmann et al., 2007).
Thus, for a more inclusive interpretation of the “-omics” landscape
in CHO cells, deeper understanding of translational regulation is
highly required. Herein, the “-omics” for investigatingtranslational
regulation is commonly referred to as the translatome.
Translatome information can be obtained via a combination
of polysome profiling and microarray technologies (Maˇ sek et al.,
2011), allowing us to cluster mRNAs with respect to ribosome
loading, and hence provide a snapshot of their translational effi-
ciency (Aravaet al., 2005). Theseclustered mRNAs can thereafter be
quantified via microarrays (global)or qRT-PCR (targeted) technolo-
gies. Recently, such translatome approach has been successfully
employed on several organisms including bacteria (Picard et al.,2012), yeast (Arava et al., 2003; Preiss et al., 2003), stem cells
(Sampath et al., 2008) and human cell lines (Thomas and Johannes,
2007) but, it is still newto CHOcells, whichare industriallyrelevant
mammalian host cells. Thus, the objective of the current study is
to establish and apply a translatomics platform to CHO cell culture
in order to elucidate their translational characteristics. We showed
the relevance of such translatome analysis as a new approach to
identify potential cell engineering targets for enhancing cellular
growth. Moreover, we explored the relationship between trans-
criptome and translatome to further understand the implication of
translational control mechanism in gene expression of this mam-
malian expression host.
2. Materials and methods
2.1. Cell line and cell culture
A stable Chinese hamster ovary (CHO) DG44 cell line express-
ing a recombinant monoclonal immunoglobulin G (IgG) against
a human rhesus-D antigen was utilized in this study (Chusainow
et al., 2009). All cultures were performed in 3 L disposable Erlen-
meyer flasks. Initial working volumes were 1L of a proprietary
protein free and chemically defined medium, supplemented with
8 mM l-glutamine (Sigma–Aldrich, St. Louis, MO), 600g mL −1
G418 (Sigma–Aldrich, St. Louis, MO), 250n M methotrexate
(Sigma–Aldrich, St. Louis, MO) and 0.1% (v/v) Pluronic® F68 (Invi-
trogen, Carlsbad,CA). Thecultures were seededat 3×
105
cell mL −1
andgrown in suspension at 37◦C,under an8% CO2 atmosphere and
shaker platforms set at 110rpm in a humidifiedincubator (Kühner,
Germany).Cell densitiesand viabilities weredetermined by thetry-
pan blue exclusion method using a Cedex automated cell counter
(Innovatis, Roche, Basel, Switzerland).
2.2. Polysome profiling and fractionation
For polysome profiling, 20×106 cells were incubated with
100g mL −1 of cycloheximide (Sigma–Aldrich, St. Louis, MO) for
10min at 37 ◦C to arrest ribosome progression on mRNAs before
harvesting the cells. The harvested cells were centrifuged at
1500rpm for 5 min at 4 ◦C and resuspended in 240L of resuspen-
sion buffer (20mM Tris–HCl pH 7.4, 20mM NaCl, 30mM MgCl2,
RNasin, 100g mL −1 Heparin, and 5g mL −1 of cycloheximide).
Cells were then lysed for 10min on ice in 265L of lysis buffer
(1.2% (v/v) Triton X-100, 1.2% (v/v) deoxycholate). The nuclei and
cell debris were removed by centrifugation at 12,000rpm for
10min. Nucleic acid content was quantified by absorbance read-
ing at 260 nm on an aliquot of collected supernatant. Thereafter,
equal optical density (A260) unit per volume unit were loaded
onto 11.5mL linear 10–50% sucrose gradients and centrifuged at
34,000rpm for 2 h at8 ◦C in a SW41 bucket rotor (Beckman, Fuller-
ton, CA). Polysome profiles were monitored at 260nm with anEM-1 UV Monitor (BioRad, Hercules, CA). For fractionation of the
polysomes, 20 fractions (0.5mL each) were collected with a Bio-
Comp piston gradient fractionator linked to the EM-1 UV Monitor.
The fractions were incubated in 10% (v/v) SDS and 1.2% (v/v) pro-
teinase K at 42 ◦C for 30 min and stored at −80 ◦C before RNA
extraction.
2.3. RNA pooling, extraction and purification
Thirteen fractions starting from the 80S peak (Fig. 1) were
pooled in monosome (pool B) and polysome (pool A) enriched
pools according to an adaptation of the strategy previously estab-
lished by Hendrickson et al. (2009). RNAwas extractedfrom 900L
of each pool by phenol–chloroform extraction. Equal volumes of the aqueous phase containing the RNA were precipitated with 0.1
volume of 5M sodium acetate (Ambion, Austin, TX) and 1 vol-
ume of isopropanol (Sigma–Aldrich) with overnight incubation at
−80 ◦C. The RNA pellets were then washed with 75% (v/v) ethanol
(Sigma–Aldrich) and precipitated with lithium chloride (Ambion,
Austin, TX) and incubated overnight at −80 ◦C. RNA precipitates
were washed with 70% ethanol and subjected to a final round
of sodium acetate and ethanol precipitation. Finally, the precipi-
tated RNA pellets were washed in 75% ethanol and resuspended
in 30L of RNase-free water. RNA concentration was determined
using NanoDrop (Thermo Scientific, Bremen, Germany) and RNA
samples were stored at−80 ◦C until runs of microarrays.
2.4. Microarray hybridization, washing and scanning
Microarray data were generated from one biological sample per
day during the exponential growth phase, so that the data from
four time points could be obtained whilst using an affordable num-
ber of microarray chips. Importantly, the exponential phase can
represent a steady physiological state where cells are growing at
their maximum growth rate and the genes supporting growth are
likely to be steady in expression. Under this assumption, four time
points could be considered as biological replicates, offering the
advantage to provide information-richer data that cover the full
exponential growth phase. For transcriptome, total RNAs were iso-
lated from 5×106 cells using RNeasy Mini Kit (Qiagen, Valencia,
CA). For translatome samples, total RNAs were isolated and pooled
in pool A and B as described above. Fifty nanograms of total RNAs
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Table 1
List of primers pairs utilized for normalization of themicroarrays data.
# Target gene Forward (5 > 3) Reverse (5 > 3) Efficiency
1 CLPP CAGAAGGAGACTCCCACAGC AGAGCAGCCTCAATCGACAT 0.998
2 HIST1H CCAAAAGTCCACCGAGCTAC CACAGGTTGGTGTCCTCAAA 1.013
3 NFAT5 GGGTCAAACGACGAGATTGT TCCAGCTTTTGAGTTGCCTT 0.954
4 MRG2 ACACTCACACTCCCACGTCA TGCTGCTGTCTCATGGTTTC 1.013
5 PRRX1 TAAAAACGCTTCCCTCCTCA AGAGTGGGCCATTCATTCAC 0.986
6 RBMX2 CTGGAAGCAGGGCAGTAAAG TCTTCCTCCCCTCTCTGGAT 0.940
7 MAP3 K TTAAGGCAGGTGAACAAGGG CTGCTGCTGCACATAAGCTC 1.0458 GRINA CGCCATACTCTGCATCTTCA TGTGTACAGGTTCAGAGCCG 0.930
9 CRIP1 ACTCGTGCAGGACCAAGTTC GTAGCAGGGATGGTTGCAGT 1.049
10 ITGB5 CATCCAGATGACACCACAGG CATCCTTCATGGAGAGGGAA 0.923
11 TTYH2 ACAGAACACACTGAAGCCCC GGCTCACAGTATTCACGGGT 0.909
12 SLC19A1 CTTCACAATCGAGCAGGTGA CAGGATCAGGATTGGCTTGT 0.954
13 ALG3 ATAAAGGTGGTGGTTGCTCG ACCAGGCTGGCCTTAAACTT 0.913
14 PSMC3IP ATTTTGCAGACCAGGACCAG TGCATCTCAGGAGTGGTCAG 0.966
15 DOCK8 CTCATGATGGCTGGGAATCT AATTGTCCCCTGGGTAAAGG 0.969
from each sample were amplified and reverse transcribed using
TransPlex Whole Transciptome Amplification Kit (Sigma–Aldrich,
St. Louis, MO). cDNA labeling, hybridization and washing were car-
ried out as described in NimbleGen Arrays User’s Guide – Gene
Expression Arrays protocol. Labeled cDNA were synthesized using
NimbleGen One-Color DNA Labeling Kit (NimbleGen, Roche, Basel,Switzerland) from one microgram of cDNA and then purified using
salt precipitation method. Unique Sample Tracking Controls (STCs)
from NimbleGen Sample Tracking Control Kit (NimbleGen, Madi-
son, WI) was added to each labeled cDNA sample. The STCs are
to track the sample locations, after hybridization, and to monitor
Fig. 1. Schematic overview of the translatomic platform. Ribosome bound RNAs
were separatedon 10–50% sucrose gradient andthe polysome profilewas fraction-
ated in thirteen differentfractions to allow high resolution of thetranslatomedata.
The fractions were reduced to pool A (enriched in polysome) and pool B (enriched
in monosome) according to the sigmoidal-trend repartition of volumes devised by
Hendrickson et al. (2009). Translatomedefinedas the measurement of translational
efficiency of every gene was calculated as the ratio of microarray intensity obtain
for poolA and poolB.
for any cross-contamination during loading and hybridization of
target to array. The targets were hybridized to the 12×135K
CHO NimbleGen arrays (proprietary) for 16h. The arrays were
washed and scanned using Axon GenePix 4000B Scanner. Data
was extracted using NimbleScan v2.6 software. The Sample Track-
ing Report indicated that there was no cross-contamination of thesamples.
2.5. Pre-processing of microarrays data
All the chips were background corrected using NimbleScan
V2.6 software. On each chip, there were a total of 135,883 probes
covering a total of 13,514 annotated CHO genes and other con-
trol genes. The intensities of probes corresponding to same gene
were averaged and quantile normalization was then performed on
transcriptome, pool A and pool B samples separately using the R
package AffyPLM (http://svitsrv25.epfl.ch/R-doc/library/affyPLM/
html/normalize.exprSet.html). The normalized microarray inten-
sity values were validated by quantitative RT-PCR performed on a
selectionof 15 genes (Table 1) that covered the range of microarraynormalized values for transcriptome and translatome (ratio pool
A over pool B). Each qRT-PCR was performed in technical dupli-
cates in a 96-well iQ real-time PCR plate (Biorad, Hercules, CA) for
40 cycles 95◦C for 5 s and 60 ◦C for 10s. Total volume of reaction
was 10L per well. For each technical duplicate, reaction mixtures
were prepared as master mix with 8L of cDNA solution, 10L of
SsoFastTM EvaGreen® supermix (Biorad, Hercules, CA) and 2L of
500M primer pair.
2.6. Microarrays data analysis
For determining translational efficiency of each gene, intensity
value measured in pool A was divided by its counterpart intensity
value measured in pool B. A 1.5 ratio fold change was consideredas a significant differential enrichment between pool A and pool B.
For functional clustering, the online program DAVID was utilized
(Huangetal.,2009). Thisprogramenriched genes in GOterms based
on the existing functional annotation between gene identifiers
and GO terms. GO terms were themselves clustered in annota-
tion clusters that grouped together GO terms of similar biological
meaning due to sharing similar enriched gene members. Input list
of selected genes were clustered under the GOTERM BP FAT gene
ontology system. Only GO terms with a P -value lower than 0.05
were accepted as significantly enriched. In brief, P -values were cal-
culated according to a modified Fisher exactP -value that testedthe
random chance for genes to be enriched in GO terms. Thereafter,
the P -values of accepted GO terms pertaining to one annotation
cluster were averaged.
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Fig. 2. Framework of translatome analysis forthe identification of key growth genes.Left panel:Cellular growth performance of CHO cells in batch culture. The logarithmic
scale was usedto determinethe linearityrange of theexponential growth phasecharacterizedby maximum and constantgrowth rate. Rightpanel: Global translation activity
measured from polysome profiling. Only the polysome profile of day 1 is shown as an example. Monosomes (M ) and polysomes (P ) area under the curve were determined
with the trapezium rule and presented in the barchart. The ratio of P /M (blue bars×10 forpresentation purpose) was an indicator of global translation, which appeared to
be relatively stable over time. (For interpretation of thereferences to color in this figure legend, thereader is referred to theweb version of thearticle.)
3. Results and discussion
3.1. Generation of first translatome data in CHO cells
We developed a translatome platform for recombinant CHO
cells by combining high-resolution polysome profiling technol-
ogy and proprietary NimbleGenmicroarrays withstreamlined data
processing. To generate translatome data, total mRNA was sep-
arated with respect to ribosome loading density on a sucrose
gradient by centrifugal force. As such, higher density RNAs (more
ribosomes attached) were spun toward the bottom of centrifuge
tubes (Fig. 1). Thirteen different fractions from the sucrose gradi-
ent were collected ranging from the 80S monosome peak (fraction1) to the highest polysome degree (fraction 13), via polysome pro-
filing. Note thathigher number of fractions increases the resolution
of translatome data. For example, twelve fractions allow for a finer
separation of differentially translated mRNAs than four fractions
and thus a 3-fold increase in the level of information within one
sample. In this regard, thirteen fractions collected in the current
work can provide a high resolution of the translatome informa-
tion as compared to other previous translatome studies based on
two (Tebaldi et al., 2012), four (Sampath et al., 2008) and twelve
(Thomas and Johannes, 2007) fractions.
Across the fractions collected, monosomes were enriched in
mRNAs with low translation activity while polysomes represented
mRNAs that were being highly translated. As a result, each frac-
tion contained mRNA of different translation efficiencies, which
were then pooled together in two pools based on a sigmoidal trend
repartition of the fractions adapted from the pooling approach
developed by Hendrickson et al. (2009) f or human HEK293T cells.
The two pools were enriched in polysomes (pool A: highly trans-
lated mRNA) or monosomes (pool B: poorly translated mRNA).
Each pool was then probed on separate proprietary NimbleGen
CHO microarray chip covering 13,514 annotated CHO genes. For
eachgene,translatomedata wereprocessed as microarray intensity
ratios of polysome-enriched pool (pool A) to monosome-enriched
pool (pool B), noted as A/B. Interestingly, recent next-generation
sequencing technologies have enabled high-throughput quantifi-
cation of RNA samples, as an alternative to microarrays (Becker
et al., 2011). However, this approach still requires extensive bioin-formatic analysis to properly handle huge amount of sequence
reads ( Jacob et al., 2010; Wang et al., 2009). Thus, the more robust
and established microarrays technology available can be still used
in this study.
Processing translatome data with large number of fractions is
one of main challenges since it is not straightforward to weigh
the microarray intensities of mRNAs found in the different frac-
tions toward one translational efficiency value per gene. Preiss
et al. (2003) collected twelve fractions and pooled a selection of
them in two pools with no weightage of their respective contri-
bution to the overall translational efficiency. By doing so, they
could determine translational efficiency as the ratio of the two
pools, although the benefit of the twelve fractions resolution was
actually reduced to two fractions. The translational efficiency can
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F.C. Courtes et al. / Journal of Biotechnology 167 (2013) 215–224 219
also be assessed by graphical visualization showing the microarray
intensity of mRNAs in each fraction (Thomas and Johannes, 2007)
but this representation remains semi-quantitative and tedious for
global scale analysis. Recently, Sampath et al. (2008) suggested a
mathematical approach that could process the microarray intensi-
ties from four fractions into one numerical value per gene, which
could then conveniently be used for further computational analy-
sis. However, this mathematical approach was designed only for
four fractions and it is not readily applicable to a higher num-
ber of fractions. Unlike other translatome data processing, the
sigmoidal pooling strategy utilized in this study allocated a differ-
ent volume percentage of each fraction (Fig. 1), thereby weighing
their contribution, and ensuring the maintenance of the high-
resolution information even after reduction to the two pools (pools
A and B).This strategy allowed us to achieve high-resolution trans-
latome data via streamlined data processing. It should be noted
that in future, we need to validate whether this proposed sigmoid-
approach can reliably extract the relative translation efficiency in
each fraction. It could be achieved by comparing the quantitative
value(ratio of pool A/poolB) against the corresponding trend of the
relative intensity-distribution within the 13 fractions, of each gene
or statistically representative subset of genes, serving as a semi-
quantitative reference. Now, resultant data can be further analyzed
to identify key growth genes as potential cell engineering targetsas well as to explore translational control mechanisms in CHO cell
cultures.
3.2. Global translation activity during exponential cell growth
Cellular growth rate is known to reach its maximum during
the exponential growth phase. Based on the principle that cellular
growth is essentially driven by protein accumulation via transla-
tion throughout the G1 phase of the cell cycle, global translation
activitywas assessed by polysomeprofilingon exponentiallygrow-
ing CHO cells. Fig. 2 (left panel) displays the growth performance
of the cells; as indicated by the logarithmic-scale plot, the linear-
ity range corresponding to the exponential growth phase occurred
from day 1 till day 4 and at a maximal constant growth rate max
of 0.64day−1. Samples for translatome were collected on days 1–4
of the batch culture as well as for the transcriptome samples in
order to investigate the correlation between transcriptome and
translatome as discussed in the next section. Global translation
activity supported the constant and maximal growth rate (max)
during the exponential growth phase as shown by the distribution
of monosomes and polysomes via polysome profiling (right panel
in Fig.2). Thebar chartdepicts theareaunder thecurves ingreenfor
polysome (P ) and in red for monosome (M ) which were utilized to
calculate global translation as the ratio of polysome to monosome
via polysome profile analysis (Maˇ sek et al., 2011). Global transla-
tion was relatively constant at an average of P /M =1.6±0.1 over
the four days and corresponded to the maximum global transla-
tion value that was observed over a full-length culture (data notshown). The fact that this ratio was maximum and rather constant
was in good agreement with the expected physiological state of
exponentially growing CHO cells, where ideally there was neither
limitation of nutrients, nor accumulation of toxic molecules which
could have affected maximal growth ratemax. In summary, global
translation supported maximum growth rate by producing high
level of cellular proteins. Thus, the identity and biological function
of mRNAs specifically enriched in polysomes (highly translated)
can be further analyzed via the translatome data.
3.3. Translatome: a new strategy to identify growth genes
Specific growth rate and global translation trends in theleft and
right panels of Fig. 2, respectively, were observed to be constant
Fig. 3. Workflow of data processing toward the identification of key growth genes
in CHO cells. Shortlisted genes in the translatome data (current study) and overlap
comparison with the genes previously identified by transcriptome and proteome
analysis are summarized.
during the exponential growth phase. Hence, only genes with
constant translational efficiency (relative standard deviation< 10%
over the four days) were regarded as potentially associated with
cellular growth, thereby identifying 4003 genes with annotated
functions (Fig. 3). These genes were, then, statistically analyzedbased on both average and standard deviation of their respec-
tive translational efficiencies from day 1 to day 4. The highest
and lowest translational efficiencies were A/B= 4.38±0.35 and
A/B =0.09±0.01 respectively; the overall average was 1.24. Such
distribution indicated that there were more enriched genes in
polysome fractions during the exponential growth phase and was
in goodagreement withthe observed global translation trend (right
panel in Fig. 2). Herein, we only selected highly translated genes,
which are positively correlated with the cellular growth. Amongst
the 4003 genes, 1079 (26.9%) genes were classified as highly trans-
lated based on a fold change of A/B> 1.5. Note that this moderately
stringent threshold is often used in high-throughput data analysis
(Dalman et al., 2012) and allowed for subsequent functional
enrichment analysis using the program DAVID. From the signifi-cantly enriched functional clusters (enrichmentP -value< 0.05),we
further shortlisted high-potential cellular growth candidates by
applying more stringent condition ( A/B> 2.5), as such resulting in a
total of 43 non-redundant genes listed in Table 2. Among them, 17
genes were enriched in two or more different functional clusters.
This first CHO translatome information was compared withpre-
viously reported growth-associated genes in CHO cells (Fig. 3).
It should be noted that 21 (Doolan et al., 2010) and 41 (Clarke
et al., 2012) key growth genes were identified by a comparison
of transcriptome and proteome data between fast and slow grow-
ing CHO cells while 13 by coexpression network analysis of 295
transcriptome profiles of CHO cells (Clarke et al., 2011). Inter-
estingly, among the 43 shortlisted genes by our translatomics
platform, five genes (Hnrnpc , Hnrnpa2b1, Mcm5, Pcna, Vcp) were
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220 F.C.Courtes et al. / Journal of Biotechnology 167 (2013) 215–224
Fig. 4. Correlation between transcript level and translational efficiency. (A) Genes with both stable transcript level and translational efficiency over the exponential growth
phase. (B) Correlation plot of transcriptome vs translatome. Thresholds for high and low transcript levels were defined by the average TCav plus and minus the standard
deviation ( ), respectively while a 1.5-fold change was used for the translatome data. Green and yellow regions correspond to uncoupled transcript level and translational
efficiency. Blue region marks the genes for which there is positive correlation while genes in the red region are negatively correlated. (C) Validation of microarray data via
qRT-PCR using transcriptome and translatome (ratio A/B) from day 1. Linear regression between microarrays and qRT-PCR values displayedR2 coefficients greater than 0.7,
whichwas comparableto the correlationobtainedfor translatome data measured with Affymetrixarrays (Sampath et al., 2008). (For interpretationof the references to color
in this figure legend, thereader is referred to theweb version of thearticle.)
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F.C. Courtes et al. / Journal of Biotechnology 167 (2013) 215–224 221
Table 2
Functional enrichment of genes with constant and high translational efficiency during the exponential growth phase.
Annotationcluster P -value Gene IDa Details A/B
RNA processing 1.0E−03
Utp6 UTP6, small subunit (SSU) processome component 3.36
Hnrnpc Heterogeneous nuclear ribonucleoprotein C 2.88
App Amyloid beta (A4) precursor protein Gene 2.69
Hnrnpa2b1 Heterogeneous nuclear ribonucleoprotein A2/B1 2.68
Fip1l1 FIP1 like 1 2.56
Syncrip Sy napt ot agmin bin ding, cyt oplasmicRNA int eract ing prot ein 2.54
Parn Poly(A)-specific ribonuclease ( deadenylation n uclease) 2.50
Cell cycle 1.8E−03
Prc1 Protein regulator of cytokinesis 1 Gene 3.18
Ruvbl1 RuvB-like protein 1 Gene 3.02
Vps4b Vacuolar protein sorting 4b (yeast) Gene 2.76
App Amyloid beta (A4) precursor protein Gene 2.69
Anxa11 Annexin A11 Gene 2.67
Pdcd6ip Programmed cell death 6 interacting protein Gene 2.67
Mcm5 Minichromosomemaintenance deficient5, cell divisioncycle 2.57
Chtf18 CTF18, chromosome transmission fidelity factor 18 2.54
tRNA aminoacylation 5.3E−03 Wars Tryptophanyl-tRNA synthetase 3.26
Nucleic transport of proteins 6.5E−03
Pcna Proliferating cell nuclear antigen 3.22
Snx14 Sorting nexin 14 3.09
Ipo8 Importin 8 2.81
Sec31a Sec31 homolog A 2.77
Vps4b Vacuolar protein sorting 4b 2.76
Exoc1 Exocyst complex component 1 2.74
App Amyloid beta (A4) precursor protein 2.69
Pdcd6ip Programmed cell death 6 interacting protein 2.67
Vcp Valosin containing protein 2.55
Catabolic processes 9.0E−03
Rnpep Arginyl aminopeptidase (aminopeptidase B) 3.29
Lgmn Legumain 3.18
Hltf Helicase-like transcription factor 2.81
Uba2 Ubiquitin-like modifier activating enzyme 2 2.67
Capn2 Calpain 2 2.60
Ube4a Ubiquitination factor E4A, UFD2 homolog 2.59
Vcp Valosin containing protein 2.55
Ctsl Cathepsin L 2.54
Fbxw8 F-box and WD-40 domain protein 8 2.50
Parn Poly(A)-specific ribonuclease ( deadenylation n uclease) 2.50
Chromosomes organization and modification 1.0E−02
Ruvbl1 RuvB-like protein 1 3.02
Actl6A Actin-like 6A 2.89
Hltf Helicase-like transcription factor 2.81
Hat1 Histone aminotransferase 1 Gene 2.77
Aspartate metabolic processes 1.2E−02 Thnsl1 Threonine synthase-like 1 3.25
Asns Asparagine synthetase 2.96
Copper ion homeostasis 1.6E−02 App Amyloid beta (A4) precursor protein 2.69
DNA metabolic processes/DNA repair 1.7E−02
Top2b Topoisomerase (DNA) II beta 3.55
Pcna Proliferating cell nuclear antigen 3.22
Ruvbl1 RuvB-like protein 1 3.02
Gtf2h4 General transcription factor II H, polypeptide 4 2.94
Ccdc47 Coiled-coil domain containing 47 2.77
Mcm5 Minich ro mo so me m aint enance deficie nt 5, celldivis ion cycle 2.57
Chtf18 CTF18, chromosome transmission fidelity factor 18 2.54
Macromolecular complex assembly 2.0E−02
Anxa5 Annexin A5 Gene 3.05
Atl3 Atlastin GTPase 3 2.91
Ipo8 Importin 8 2.81
Picalm Phosphatidylinositol b inding clathrin a ssembly p rotein 2.66
Cdh1 Cadherin-1E-Cad/CTF1E-Cad/CTF2E-Cad/CTF3 2.54
Carbohydrate metabolic processes 2.0E−02 G6pdh Glucose-6-phosphate dehydrogenase 3.44
Gbe1 Glucan (1,4-alpha-), branching enzyme 1 2.66
Cofactor metabolism 2.8E−02 G6pdh Glucose-6-phosphate dehydrogenase 3.44
Gstt2 Glutathione S-transferase, theta 2 2.86
RNA localization 3.3E−
02 Hnrnpa2b1 Heterogeneous nuclear ribonucleoprotein A2/B1 2.68Ribosome biogenesis 3.4E−02 Utp6 UTP6, s mall subunit ( SSU) process om e com pon en t, ho mo log 3.36
a Biological significance and cellular functionsfor theselectedgenes in bold areprovided in themain text.
conserved with transcriptomic and proteomic studies, despite
the differences in cell lines, culture conditions, microarrays tech-
nology and CHO annotation databases used. Of them, the geneVcp was previously reported to alter cellular growth after siRNA
functional engineering (Doolan et al., 2010) whereas two hetero-
geneous nuclear ribonucleoprotein genes (HnrnpcA/B= 2.88±0.22
andHnrnpa2b1A/B= 2.67±0.09)were identified from themostsig-
nificantly enriched annotation cluster, RNA processing, with the
lowest P -value (Table 2). Overexpressing these genes in CHO cells
could help maintain cellular homeostasis under stress conditions
(Hossain et al., 2007) and increase proliferation by binding to the
5UTRof the regulatorof gene expression c-myc and modulating its
translation(Kimet al., 2003). Inthesamecluster,thegeneUtp6 cod-
ingfor UTP6 small subunit processome component had the highest
translational efficiency ( A/B=3.36±0.25) and was also enriched in
the ribosome biogenesis annotation cluster. Overexpressing Utp6
in CHO cells would enhance cellular growth through an increase of
the number of ribosomes and an improvement of their efficiency
viaUtp6 role in the nucleolar processing of pre-18S ribosomal RNA
(Champion et al., 2008).
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Fromsecond cluster of Table2, two genesPrc1 ( A/B= 3.17±0.23)
andRuvbl1 ( A/B=3.02±0.28) coding for protein regulatorof cytoki-
nesis 1 and RuvB-like protein 1, respectively, can be also suggested
for engineering the cell cycle of CHO cells and fostering the cel-
lular growth. Protein regulator of cytokinesis 1 is crucial for the
correct splitting of the chromosomes by binding to and regulat-
ing the microtubules, thereby ensuring that chromosome number
is maintained from one generation to the next during cytokine-
sis (Subramanian et al., 2010). The gene Ruvbl1 possesses ATPase
and helicase activities and has been recently implicated in sev-
eral cellular processes including cell cycle checkpoint activation,
DNA repair and replication, snoRNP assembly, telomere regulation,
centromere stability and chromosome segregation ( Jha and Dutta,
2009; Niewiarowski et al., 2010).
Lastly, we found the gene G6pdh from two functional clus-
ters, carbohydrate metabolic processes and cofactor metabolism,
as one of highly recommended targets for cell engineering strate-
gies aimed at enhancing the metabolic efficiency of cells, toward
higher cellular growth. G6pdh codes for the rate-limiting enzyme
involved in the pentose phosphate pathway, which generates a
major source of NADPH coenzyme (Tian et al., 1998). Thus, this
source of NADPH is likely essential for exponential cellular growth
by fueling anabolicreactions suchas lipidsynthesis, which is essen-
tial for cellular-membrane growth (Stanton, 2012).In summary, as the second and last biosynthesis step (after
transcription) along the gene expression process, translation has
consequently a decisive impact onthe formationof proteins by reg-
ulating translational efficiency. Hence, the current translatomics
platformcan guide us to better understanding of thesetranslational
control mechanisms, complementing translatomic and proteomic
strategy to identify potential cell engineering targets that support
growth on the basis of the assumption that the target genes are
highly and steadily translated to meet the cellular requirement
during the exponential growth phase.
3.4. Translational control mechanisms in CHO cells
The relationship between transcriptome and translatome datawasstudied in orderto explore translationalcontrol mechanisms in
exponentially growing CHO cells. Similarly to the previous statisti-
cal analysis of translatome data, the correlation between transcript
level and translation efficiency was investigated by selecting the
genes whose transcript levels showed a stable trend over the four
days of the exponential growth phase (relative standard devi-
ation < 10% over the four days). There were 6198 genes with a
constant level of transcript across the growth phase (Fig. 4A).
The overlap of these 6198 (transcriptome) and 4003 (translatome)
genes resulted in 1353 genes that displayed constant trends for
both their transcript level and translational efficiency. Each gene
was thencharacterized according to an average value of bothtrans-
criptome (absolute value) and translational efficiency (ratio A/B)
(Fig. 4B). In parallel, microarrays data were validated by qRT-PCR (Fig. 4C).
Arguably, there was no strong correlation between transcript
level and translation efficiency as indicated by the Pearson coeffi-
cient, which was−0.057. This correlation was further characterized
by classifying genes in nine different regions based on threshold of
high and low transcriptome and translatome (Fig. 4B). High and
low transcriptome were defined as the average TCav plus or minus
one standard deviation ( ) calculated for the 1353 genes (TCav +
and TCav − ) while high and low translatome were defined upon
a 1.5-fold change. Based on these criteria, genes were classified
in coupled or uncoupled groups (Fig. 4B). Transcript level and
translation efficiency were considered as coupled if both were sig-
nificantly high or low in positive correlation (blue; supplementary
Table 1). They were instead regarded as uncoupled if (a) both were
significantly high or low but in negative correlation (red; supple-
mentary Table 2), (b) only translational efficiency was significantly
high or low (green; supplementary Table 3) and (c) only transcript
level was significantly high or low (yellow; supplementary Table
4). The overall degree of uncoupling (95% – green, yellow and red
zones) between transcript level and translational efficiency was
higher than the degree of coupling (5% – blue zone).
Of interest, genes in the blue region were significantly enriched
in cellular ion (metal/calcium) homeostasis, signal transduction,
nucleotides biosynthesis and regulation of phosphorylation. On
the other hand, the 657 genes with uncoupled transcript level
and translation efficiency (green, yellow and red regions) were
mainly enriched in protein transport, localization and complex
assembly as well as in the generation of precursor metabolites and
energy. Although this analysis was conducted based on a subset
of genes, it was highly consistent with other studies (Halbeisen
and Gerber, 2009; Preiss et al., 2003; Tebaldi et al., 2012), presum-
ably representing the characteristics of the whole CHO cell gene
expression and indicating the complex regulation of gene expres-
sion through the involvement of translational control mechanisms
(Pradet-Balade et al., 2001).
Comparison of transcriptome and translatome data could also
be a valuablestrategyto devise adequate cellengineeringstrategies
in future, by identifying potential bottleneck along the geneexpres-sion pathway. For example, the previously highlighted key growth
genes, Hnrnpc , Utp6, Pcna, Vcp andMcm5 with a high and constant
translational efficiency ( A/B> 1.5) were present in the green region
(green marker with black line; Fig. 4B) where transcripts level and
translational efficiency were uncoupled. In this case, translational
efficiency was highand thereforeunlikely to representa bottleneck
along the gene expression process. Instead, moderate transcript
level could be limiting the flow of genetic information available
for translation. We support the idea that overexpression of such
genes leading to increased level of mRNA (shift toward the blue
region) could be an appropriate strategy for cell engineering in
which the increase in mRNA level would allow for the synthesis
of more proteins with the high translational efficiency.
Interestingly, the correlation analysis also implied the involve-ment of the mTOR pathway on translation control in CHO cells.
The yellowregion of high transcript level and average translational
efficiency (ratio A/B∼1) appeared to be enriched with 20 differ-
ent ribosomal proteins (yellow marker with black line; Fig. 4B).
Ribosomal proteins are known to be 5TOP mRNA translationally
regulated by the mTOR pathway (Meyuhas, 2000) which is a mas-
ter regulator of translational activity conserved across species (Ma
and Blenis, 2009). Recent studies of the mTOR pathway in CHO
cells demonstrated the strong potential of this pathway to alter
growth and productivity (Dreesen and Fussenegger, 2011; Lee and
Lee, 2012). Therefore further investigation of the mTOR pathway is
required to understand the impact of translational control in CHO
cells.
4. Conclusions
In this study, a translatomic platform has been applied to
CHO cells cultures as a novel strategy to identify and priori-
tize high potential candidate-genes for improving cellular growth.
Future work such as performing cell engineering validation on the
potential targets and adding proteomics datato complement trans-
criptome and translatome will further enhance our understanding
of mammalian cell culture. Moreover, we expect translatome data
to foster future work related to translational efficiency in CHO
cells. For example, preferential codon usage affecting translational
efficiency (Massaer et al., 2001; Navon and Pilpel, 2011; Wang
et al., 2006) will be ideally complemented and assessed by trans-
latome data. In addition, the specific translational efficiency of
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