Nucleic Acids Research, 2003, Vol. 31, No. 17 5157±5166
DOI: 10.1093/nar/gkg702
Simultaneous gene expression analysis of steadystate and actively translated mRNA populations from
osteosarcoma MG-63 cells in response to IL-1a via
an open expression analysis platform
Jingfang Ju*, Chunli Huang, Stacey A. Minskoff, Jane E. Mayotte, Bruce E. Taillon and
Jan F. Simons
CuraGen Corp., 555 Long Wharf Drive, New Haven, CT 06511, USA
Received April 4, 2003; Revised June 25, 2003; Accepted July 7, 2003
ABSTRACT
Pro-in¯ammatory cytokines play a key role in
various forms of metabolic bone diseases, including
osteopenia and osteoporosis. Human MG-63 cells
treated with IL-1a were used as a model system to
identify potential marker genes that are differentially
expressed. This study is designed to quantitate
gene expression of actively translated mRNAs as
compared to the steady-state mRNA population.
Both steady-state mRNAs and actively translated
mRNAs from control MG-63 cells and MG-63 cells
treated with IL-1a were isolated and converted to
cDNA. The gene expression analysis from these
samples was then quantitated with an open expression analysis platform with no requirement for
a priori knowledge of sequence information. As a
result, many differentially regulated genes were
discovered via IL-1a treatment. Some of the genes
have been described previously as playing important roles in the regulation of in¯ammation and
cell adhesion. These comparisons provided a panoramic overview of gene expression at both the
total transcript and post-transcriptional levels. In
addition, the quantitation of actively translated
mRNAs associated with polysomes also provided a
better estimation of protein expression levels. This
methodology allows for the identi®cation of genes
acutely regulated during translation. Furthermore,
the process may aid in the identi®cation of new
drug targets or biomarkers.
INTRODUCTION
It is well known that pro-in¯ammatory cytokines play a
critical role in the development of bone-related diseases. One
example is that children with in¯ammatory bowel disease are
at risk of osteopenia. The cause of osteopenia is, at least in
part, due to the overproduction of cytokines (1). Cytokines
such as IL-1 also affect bone metabolism in response to fatty
acids (2). Bone modeling/remodeling depends on close
coordinated cellular activities that are controlled by interactions with growth factors and the extracellular matrix
(ECM) (3). In fact, studies have shown that IL-1 regulates the
expression of some of the growth factors, such as FGF-2 (4).
During the past few decades, research into bone biology has
used model systems, such as human osteoblast-like cells,
derived from a variety of sources. One of the commonly used
cell lines is human osteosarcoma MG-63 cells (5±7).
However, currently there is no study to systematically analyze
the marker genes in osteoblast-like cells such as MG-63
treated with cytokines.
Current methods for quantitative measurement of gene
expression, such as chip-based array methods and real-time
quantitative PCR (RT±QPCR) analysis, have mainly focused
on measuring total cellular mRNA levels (8±10). Total mRNA
levels in a cell are predictive of the expression levels of many
proteins. However, for others the steady-state mRNA expression level does not accurately re¯ect the expression rate of the
proteins (11). Post-transcriptional regulation of gene expression occurs by several mechanisms, including pre-mRNA
splicing, mRNA transport, mRNA stability, translational
regulation and post-translational regulation. It has been
established that translational regulation plays a critical role
in many biological processes, such as in cell cycle progression
under normal and stress conditions (12,13). The central
concept of translational regulation is that gene expression may
be controlled by the ef®ciency of translation of a given mRNA
in the absence of a corresponding change in the steady-state
level of that mRNA (14). Translational regulation provides the
cell with a more precise, immediate and energy-ef®cient way
of controlling expression of a given protein. Translational
regulation can induce rapid changes in protein synthesis
without the need for transcriptional activation and subsequent
mRNA processing steps. In addition, translational control also
has the advantage of being readily reversible, providing the
cell with great ¯exibility in responding to various cytotoxic
stresses. Therefore, it is essential to know not just the levels of
individual mRNAs, but also to what extent they are being
translated into the corresponding proteins. The simultaneous
*To whom correspondence should be addressed. Tel: +1 203 974 6364; Fax: +1 203 401 3331; Email: [email protected]
Nucleic Acids Research, Vol. 31 No. 17 ã Oxford University Press 2003; all rights reserved
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Nucleic Acids Research, 2003, Vol. 31, No. 17
monitoring of steady-state cellular mRNA levels and the
translation state of all mRNAs provides not only a unique way
to quantitate gene expression level, but also provides a more
complete description of gene expression and their regulation.
In addition, analysis of translationally active mRNAs is only
one step away from direct quantitative protein analyses using
mass spectrometry and two-dimensional gel electrophoresis
analysis, but it provides considerable advantages over these
approaches in terms of cost, simplicity, sensitivity and
throughput. DNA chips have been used to study polysomeassociated mRNA expression (15,16); however, the gene
coverage is often limited (11). Therefore, there is a lack of
direct, high-throughput open platform methods to identify
genes that are potentially translationally regulated.
mRNAs that are being actively translated usually have
multiple ribosomes associated with them, forming rather large
complexes known as polysomes. Translationally inactive
mRNAs are sequestered in messenger ribonucleoprotein
(mRNP) particles or associated with a single ribosome
(monosome). This fact allows for the separation of actively
translated mRNAs from non-translated mRNAs. In one
embodiment polysomes can be separated from mRNPs and
monosomes by sucrose gradient centrifugation, which allows
distinction between well-translated and under-translated
mRNAs. Translational regulation is controlled by different
cis-acting and trans-acting elements. In most cases, the cisacting elements are located in the 5¢-UTRs and 3¢-UTRs of the
mRNA and sometimes even in the protein coding region of the
mRNA (reviewed in 17). The trans-acting elements involved
in translational regulation are RNA-binding proteins serving
as either positive or negative regulators of translation (11). In
particular, identi®cation of RNA-binding proteins involved in
translational regulation might provide an alternative strategy
for drug target discovery and therapeutic intervention in
human diseases (18).
To take full advantage of the polysomal isolation method, it
is necessary to combine it with an open, high throughput
quantitative mRNA analysis detection platform that simultaneously measures and identi®es every existing mRNA
species. In this study, both actively translated mRNAs and
steady-state mRNA were isolated and then used to prepare
samples for analysis by GeneCalling, an open, high throughput mRNA expression analysis technology (19). This is the
®rst time that an open platform expression technology has
been used for the simultaneous measurement of steady-state
and translated mRNA levels. This technology can serve as a
discovery tool to ®nd post-transcriptionally and translationally
regulated genes during a variety of treatments or conditions.
As a model system, we have used serum starved human
osteosarcoma MG-63 cells exposed to the in¯ammation
cytokine IL-1a and control MG-63 cells not subjected to
any growth factor to investigate the comprehensive gene
expression. This experimental system was chosen for the
following reasons: (i) IL-1a is a pro-in¯ammatory cytokine
known to exert biological effects on osteoblast cells; (ii)
several genes have been shown to be translationally regulated
in human T cells in response to IL-1a (20); (iii) MG-63 is a
human osteosarcoma cell line, which can be differentiated into
osteoblast-like cells or adipocytes by various treatments; (iv)
osteoblasts may participate in in¯ammatory events leading to
the loss of bone mass. Thus, the response of MG-63 cells to
IL-1a should reveal mechanisms, at the levels of transcriptional and post-transcriptional regulation, by which osteoblasts recruit lymphocytes, promote in¯ammation and
regulate hematopoiesis.
MATERIALS AND METHODS
Cell culture
Human osteosarcoma MG-63 cells were maintained in MEM
containing 10% fetal bovine serum (FBS) at 37°C and 5%
CO2. Aliquots of 3 3 106 MG-63 cells per T175 ¯ask were
serum starved in MEM medium containing 0.1% FBS for 24 h
and then treated with 10 ng/ml IL-1a for 6 h. Rabbit
polyclonal antibody against calcium-modulating cyclophilin
ligand (CAML) was provided by Dr Richard J. Bram
(Department of Pediatrics and Immunology, Mayo Clinic,
Rochester, MN). Mouse anti-b-actin monoclonal antibody
was purchased from Santa Cruz Biotechnology (Santa Cruz,
CA). Mouse monoclonal antibody against proteinphosphatase
2A (PP-2A) was purchased from Upstate Biotechnology Inc.
(Lake Placid, NY). Cycloheximide was purchased from ICN.
IL-1a was purchased from R&D Systems (Minneapolis, MN).
Polyribosome analysis
For preparation of cytoplasmic extracts, cells from three
175 cm2 tissue culture plates (30% con¯uency) were treated
with 100 mg/ml cycloheximide (Sigma, St Louis, MO) for
5 min at 37°C, washed with ice-cold phosphate-buffered
saline containing cycloheximide (100 mg/ml) and harvested by
trypsinization (21). Cells and homogenates were also snap
frozen in liquid nitrogen after cycloheximide treatment and
harvesting. The fresh cells were pelleted by centrifugation,
swollen for 2 min in 375 ml of low salt buffer (LSB) (20 mM
Tris pH 7.5, 10 mM NaCl and 3 mM MgCl2) containing 1 mM
dithiothreitol and 50 U recombinant RNasin (Promega, WI)
and lysed by addition of 125 ml of lysis buffer (13 LSB
containing 0.2 M sucrose and 1.2% Triton X-100) (Sigma)
followed by vortexing. The nuclei were pelleted by centrifugation in a microcentrifuge at 13 000 r.p.m. for 2 min. The
supernatant (cytoplasmic extract) was transferred to a new
1.5 ml tube on ice. Cytoplasmic extracts were carefully
layered over 0.5±1.5 M linear sucrose gradients (in LSB) and
centrifuged at 45 000 r.p.m. in a Beckman SW40 rotor for
90 min at 4°C. Gradients were fractionated using a pipette and
the absorbance at 260 nm was measured for each fraction by
UV spectrometry.
cDNA synthesis
The polysomal fractions from each sample were pooled
together and the RNAs from each sample were isolated using
Trizol Reagent (Invitrogen, CA) and reverse transcribed to
cDNA using oligo(dT) primer and SuperScript II reverse
transcriptase (Invitrogen, CA) using the protocol provided by
the manufacturer for cDNA synthesis.
GeneCalling analysis
Quantitative expression analysis (QEA) and GeneCalling
analysis were performed essentially as previously outlined
(19). These experiments were performed in triplicate with
each experiment also having triplicate samples. Furthermore,
Nucleic Acids Research, 2003, Vol. 31, No. 17
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Figure 1. Schematic diagram of polysomal sample preparation and quantitative expression analysis (GeneCalling). Details are described brie¯y in Materials
and Methods (20).
the GeneCalling chemistry was repeated three times for each
sample. The average expression level for a given gene
fragment was compared. The level of sensitivity of these
experiments was 1.5- to 2-fold with P = 0.01. Brie¯y, doublestranded cDNAs were synthesized from poly(A)+ RNA
isolated from steady-state mRNA and polysome mRNA
(Fig. 1). The cDNAs were then subdivided into 53 identical
pools with 50 pg cDNA in each well. Each pool was digested
with a distinct pair of restriction enzymes. The resulting
fragments were ligated to complementary adapters, one
labeled with biotin and the other labeled with ¯uorescein
(FAM), and ampli®ed by PCR. After af®nity puri®cation on
streptavidin columns, the fragments were loaded onto a
MegaBase 1000 96 capillary electrophoresis instrument
(MegaBase, Amersham) and separated. PCR fragments separated by size were quantitated by measuring the ¯uorescence
intensity of the 5¢-end FAM label on the PCR products. The
digestion pro®le from the MG-63 control and MG-63 cells
treated with IL-1a were compared to identify differentially
expressed gene fragments. All fragment traces were performed in triplicate. The electrophoresis pro®les were processed using a Java-based, Internet-ready software suite,
GeneScape. Linkage of a differentially expressed cDNA
fragment to a gene was made through knowledge of the
restriction enzymes used to generate the two asymmetric ends
and the length of the fragment itself.
Gene con®rmation via oligodeoxynucleotide poisoning
To con®rm the restriction fragments for a particular gene, an
unlabeled oligodeoxynucleotide designed to correspond to one
end of the restriction fragment was added in excess to the
original reaction and was re-ampli®ed for an additional 15
cycles (19). This reaction was then electrophoresed and
compared to a control reaction re-ampli®ed without the
unlabeled oligodeoxynucleotide to evaluate the selective
diminution of the peak of interest.
Western immunoblot analysis
MG-63 cells were harvested and processed as described (3).
Equal amounts of protein (100 mg) from MG-63 cells were
resolved by SDS±PAGE on 12.5% gels by the method of
Laemmli (22). Proteins were probed with rabbit anti-CAML
polyclonal antibody (1:4000 dilution), mouse anti-b-actin
monoclonal antibody (1:5000 dilution) and mouse anti-PP-2A
antibody (1:1000 dilution), followed by incubation with a
horseradish peroxidase-conjugated secondary antibody
(1:1000 dilution) (Bio-Rad, CA). Proteins were visualized
with a chemiluminescence detection system using the Super
Signal substrate (Pierce, IL).
Real-time RT±QPCR analysis
Real-time RT±QPCR analysis was performed on the experimental mRNAs to con®rm the results obtained by the
GeneCalling analysis. The PCR primers and probes
(Synthegen LLC, Houston, TX) for PP-2A, CAML and
GAPDH were as follows: PP2A-Fprimer, 5¢-GTCAAGAGCCTCTGCGAGAA-3¢; PP2A-Fprobe, FAM-5¢-TTACCGTGAACGCATCACCATTCTT-3¢-TAMRA; PP2A-Rprimer, 5¢GGGGAACTTCTTGTAGGCGAT-3¢; CAML-Fprimer, 5¢AGCTGCTCATGAACTCGGAA-3¢; CAML-Fprobe, FAM5¢-CCCTCAGCGTTCCTTCCGTTT-3¢-TAMRA; CAMLRprimer, 5¢-CACCCTGCTGGTCAGTTGTT-3¢; GAPDHFprimer, 5¢-AAAGTGGATATTGTTGCCATCA-3¢; GAPDHProbe, FAM-5¢-CCCCTTCATTGACCTCAACTACATGG-3¢TAMRA; GAPDH-Rprimer, 5¢-GGTGGAATCATATTGGAACATG-3¢.
RT±QPCR was performed on an ABI 7900HT instrument
under the following conditions: 48°C, 30 min of reverse
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Figure 2. Polysome distribution of cellular mRNAs (n = 3). Polysomal distribution of MG-63 control cells and cells treated with IL-1a for 6 h. Absorbance
pro®les at 254 nm of the collected sucrose gradients are shown. Fractions 1±7 represent free mRNA and monosomes; fractions 8±13 contain the polysomes.
transcription; 95°C, 10 min; 95°C, 15 s; 60°C, 1 min. The
reaction was performed for up to 40 cycles.
RESULTS AND DISCUSSION
In this study, we used human osteosarcoma cell line MG-63 as
a model system to study gene regulation after pro-in¯ammatory cytokine IL-1a treatment. Systematic study of gene
regulation using this model may provide new biological
understanding of the relationship between in¯ammatory
response and bone diseases such as osteopenia. It is known
that children with in¯ammatory bowel disease are at risk of
developing osteopenia. The aggressive in¯ammatory response
present in the gut during in¯ammatory bowel disease produces
many pro-in¯ammatory cytokines (2), which might further
lead to inhibition of normal mineralization, thereby contributing to osteopenia.
An open platform gene expression technology
(GeneCalling) was applied to analyze gene expression pro®les
from steady-state mRNAs and polysome-associated mRNAs
in serum starved MG-63 cells and MG-63 cells induced with
the cytokine IL-1a. The cDNA samples were then analyzed
using the GeneCalling technology described in Figure 1. To
ensure reproducibility, the GeneCalling study was performed
on triplicate samples for both control MG-63 cells and MG-63
cells treated with IL-1a. To achieve appropriate gene
coverage, typically 50±100 different restriction enzyme pairs
were used per study. The ampli®ed sample was analyzed by
capillary gel electrophoresis and each cDNA species was
represented by one or multiple fragments of precisely de®ned
size. Using a sophisticated, Web-based, database-linked
bioinformatics system, GeneScapeÔ, the relative abundance
of each fragment, and thereby the mRNA it was derived from,
were determined and gene identity was assigned to the
fragments representing genes previously known. In addition,
this analysis platform also allowed the discovery of unknown
gene products through the isolation and characterization of
novel gene fragments.
Polysomal mRNA was isolated from total cell mRNA by
sucrose density sedimentation centrifugation on 0.5±1.5 M
sucrose gradients. The optical density (OD) pro®le of sucrose
gradients is shown in Figure 2. The sucrose gradients were
loaded with either cell extracts from untreated MG-63 cells or
IL-1a treated MG-63 cells. The presence of cycloheximide, a
protein synthesis inhibitor, in the cell lysis buffer locks the
mRNA/polyribosome complex, which facilitates polysome
isolation. In each gradient the top fractions with high OD
values represent rRNAs associated with the 40S, 60S and 80S
subunits, along with free mRNAs. Fractions lower in the
gradient, with lesser ODs, contain the polysomal fractions
with actively translated mRNAs. The greater overall OD
values in the treated samples were simply due to more material
loaded onto the sucrose gradient. The difference in mRNA
amounts will not affect the ®nal GeneCalling results because
the mRNA is converted to cDNA, the cDNA samples are then
divided into equal amounts for each restriction digestion and
normalized for PCR ampli®cation and GeneCalling analysis.
For expression analysis, fractions 8±13 containing polysomes were pooled and the mRNA isolated and converted to
cDNA for expression analysis. The amount of cDNAs used for
the GeneCalling study was normalized.
Expression analysis by GeneCalling of IL-1a-treated MG63 cells versus untreated control samples yielded a total of
1709 differentially expressed gene fragments (out of a total
of 13 672 gene fragments) for polysomal analysis using a total
of 53 restriction enzyme pairs. The total mRNA samples
revealed 1581 differentially expressed gene fragments (out of
a total of 63 240 gene fragments) using the same 53 restriction
enzyme pairs (2-fold, P < 0.01). The data represent a dramatic
difference in the percentage of differentially expressed genes.
For the polysomal samples 12.5% of the monitored genes were
differentially expressed (cut-off 2-fold), whereas for the total
mRNA samples the difference was only 2.5%. The average
redundancy for gene fragments per gene is approximately 3,
which means that there are nearly 569 genes differentially
regulated via polysomal analysis and 527 genes differentially
regulated in steady-state mRNA samples. The proportionally
higher number of differentially expressed mRNAs in the
polysomal pool presumably re¯ects the exclusion of nontranslating mRNAs from this subpopulation. This is supported
by studies of quiescent cells stimulated to re-enter the cell
cycle, which produced a high percentage global increase in
protein synthesis within the ®rst few hours after activation
(23). The increase in protein synthesis is due to the recruitment
of stored mRNA to form polysomal complex. In fact, studies
have shown that as much as 80% of the under-translated
mRNAs were shifted in polysomes during the initial 6 h after
mitogenic activation (24).
Nucleic Acids Research, 2003, Vol. 31, No. 17
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Figure 3. GeneCalling analysis of cell cycle-related genes with Spot®re software. The fold changes (P < 0.01, fold changes > 2) in the mean values (n = 3)
of these genes in steady-state GeneCalling from control MG-63 cells and MG-63 cells treated with IL-1a were compared with the mean values (n = 3) of the
corresponding genes in actively translated GeneCalling analysis. Both actively translated and steady-state samples were compared. Job22012, actively
translated GeneCalling analysis; Job22075, steady-state mRNA GeneCalling analysis.
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Table 1. Translationally regulated genes in IL-1a treated MG-63 cells (partial list)
Gene
n-fold expression
Function
Ribosomal S6 kinase
Phosphatase 2A
SIM ribosomal protein S18
eIF4GII
Calcium modulating cyclophilin ligand (CAML)a,b
Phenylalanine-tRNA synthetase
Translation initiation factor 4Ba
Translation initiation factor 4AII (eIF4A-II)a
Elongation factor-1ba
SIM ribosomal protein L8
Ribosome protein S4 (RPS4X)a
Inhibitor of apoptosis protein 1
CDC42-binding protein kinase b
Diff6
CDC7
ATP synthase
Dihydrofolate reductase
Acyl-CoA oxidase
Farnesyl pyrophosphate synthetase
Ribonucleotide reductase
Macrophage in¯ammatory protein-2b (MIP2b)a
Tumor necrosis factor-inducible proteina
22
10
9
4
±4
±12
±9
±4
±2
±4
±2
20
16
9
5
±6
±6
±5
±4
±4
7
3
Cell signaling
Cell signaling
Cell signaling
Cell signaling
Cell signaling
Protein synthesis
Protein synthesis
Protein synthesis
Protein synthesis
Protein synthesis
Protein synthesis
Apoptosis
Cell cycle control
Cell cycle control
Cell cycle control
Metabolism
Metabolism
Metabolism
Metabolism
Metabolism
In¯ammation
Unknown
aThese
bThis
genes were con®rmed in a poisoning experiment.
gene was con®rmed in a western immunoblot analysis.
Instead of presenting the entire gene list from this study, one
set of genes that regulate the cell cycle is shown in Figure 3.
The differentially regulated genes identi®ed by GeneCalling
were visualized via Spot®re software (Spot®re Inc.,
Cambridge, MA). There were 116 cell cycle-related genes
that were differentially regulated. Large percentages of genes
(54%) were closely correlated at both the steady-state and
actively translated polysome levels. However, nearly 39% of
genes from the isolated polysome-associated mRNA fraction
are regulated at the active translational stage. This portion of
the differentially regulated genes would have been missed if
only steady-state transcript pro®ling had been performed.
Some of the genes discovered here are potential candidates for
involvement in translational regulation. Although the steadystate mRNA levels of the remaining genes (7%) were
differentially regulated, they had not transitioned to the
actively translated stage during the given time frame.
These genes that may be translationally regulated are the
main focus of this study. Data from the two GeneCalling
analyses (total cellular mRNA and polysomal mRNA) were
compared using the GeneScapeÔ Job Array analytical
program. Sets of genes (Table 1) were identi®ed as potentially
regulated at the translational level. The genes listed in Table 1
were chosen based on the signi®cance of gene regulation
(>2-fold, P < 0.01), their roles in the in¯ammatory response
and previously known translationally regulated genes for
con®rmation of the technology. The fold difference in gene
expression was based on the FAM-labeled PCR restriction
enzyme digested fragments quantitated by ¯uorescence
intensity. For each restriction enzyme pair in each sample
set a composite trace is calculated by compiling all the
individual sample replicates. The traces of the experimental
set versus those of the controls are normalized via a
scaling algorithm. The scaled traces are then compared on a
point-by-point basis to de®ne areas of amplitude difference
that meet the minimum pre-speci®ed threshold for a signi®cant difference. Comparing the signature fragment peak
intensity versus the control sample traces generates the fold
difference.
The differentially regulated genes in Table 1 were grouped
by their cellular functions, such as translational control and
protein synthesis, cell cycle control, signal transduction and
metabolism. One example is ribosomal protein S4, which has
been described in the literature as being translationally downregulated on IL-a exposure (25). Among the con®rmed genes,
ribosomal protein S4, which was down-regulated 2-fold, is a
known example of an RNA-binding protein (26). Macrophage
in¯ammatory protein-2b was induced 7-fold by IL-a exposure
and this up-regulation was regulated at the translational level.
Macrophage in¯ammatory protein-2b is a gene involved in the
in¯ammatory response (27). Platelet endothelial cell adhesion
molecule 1 (PECAM-1), an important gene involved in
cellular adhesion (28), was also induced by IL-1a treatment.
It has been shown that PECAM-1 mediates leukocyte
traf®cking to the site of in¯ammation by facilitating their
squeezing through the borders between endothelial cells that
line post-capillary venules at that site (29). PECAM-1 could
also be a critical cell adhesion molecule that mediates binding
of osteoblast cells to the extracellular matrix in vivo.
Ribosomal S6 kinase is a gene that plays an important role
in regulating translation by controlling the biosynthesis of
translational components that make up the protein synthetic
apparatus (30). Other known translational regulated genes are
thymidylate synthase (31), p53 (32) and PP-2A (33). The
expression of PP-2A was identical in MG-63 control cells and
cells treated with IL-1a based upon the steady-state level of
mRNA expression (Fig. 4A). In contrast, the PP-2A expression level was signi®cantly up-regulated by nearly 10-fold
after IL-1a exposure based upon isolated actively translated
polysomal mRNAs (Fig. 4B). RT±QPCR analysis was
Nucleic Acids Research, 2003, Vol. 31, No. 17
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Figure 4. Up-regulation of human PP-2A expression by IL-1a treatment.
(A) Trace replication of QEA electrophoresis output for PP-2A from total
mRNA of MG-63 control cells (Set B) and cells treated with IL-1a (Set A).
(B) Trace replication of QEA electrophoresis output for PP-2A from polysomal isolated mRNA of MG-63 control cells (Set B) and cells treated with
IL-1a (Set A). Red line at peak position 307.6 indicates the gene fragment
corresponding to PP-2A.
Figure 5. (A) RT±QPCR analysis of PP-2A expression in MG-63 cells. The
average CT values for PP-2A were ®rst normalized against the housekeeping gene GAPDH and converted to a percentage for relative expression
(n = 3). (B) Western immunoblot analysis of PP-2A in MG-63 cells.
Cytosolic extracts from MG-63 cells (lane 1) and MG-63 cells treated with
IL-1a (lane 2) were prepared. PP-2A protein was detected using an anti-PP2A mouse monoclonal antibody. Filtered membranes were then reprobed
with an anti-b-actin monoclonal antibody to control for loading and
integrity of protein.
performed on mRNA samples isolated from control MG-63
cells and MG-63 cells treated with IL-1a to con®rm the
GeneCalling results. The RT±QPCR results correlated well
with the GeneCalling results (Fig. 5A). To further verify that
the differential regulation of PP-2A expression in actively
translated mRNA fractions equates to a corresponding change
in protein level, western immunoblot analysis was performed
on total cellular extracts from both control MG-63 cells and
MG-63 cells exposed to IL-1a. The protein level of PP-2A
was up-regulated by nearly 8-fold after IL-1a treatment
(Fig. 5B). These results suggest that PP-2A is regulated, at
least in part, at the translational level. It has been shown that in
the mouse ®broblast cell line NIH 3T3, the catalytic subunit of
PP-2A is subject to a potent autoregulatory mechanism that
adjusts PP-2A protein to constant levels. This control is
exerted at the translational level and does not involve
regulation of transcription or RNA processing (34). Protein
phosphatase 2A is involved in MAP kinase signal transduction
pathways. It has been suggested that PP-2A plays an important
role in the response to cytokines such as IL-6 during acute
phase responses and in¯ammation (35). The data in this study
also suggest that IL-1a may regulate PP-2A at the translational level as part of the signaling event in MG-63 cells.
Another group of genes identi®ed were involved in cell
cycle control and apoptosis. Some of them are inhibitors of
apoptotic proteins, such as apoptosis protein 1; others are
cyclin G1, CDC7 and CDC42-binding protein b. Studies have
shown that the small GTPase CDC42 was activated by the
in¯ammatory cytokines TNF-a and IL-1 (36). Interestingly,
CDC42, a Rho family GTPase, has been implicated in several
signal transduction pathways, including organization of the
actin cytoskeleton, activation of the c-Jun N-terminal MAP
kinase (JNK) and stimulation of the nuclear transcription
factor kB (NFkB) (37). The ability of in¯ammatory cytokines
to activate both the JNK MAP kinase and NFkB pathways is
well established and has been proposed to account for the
majority of their biological functions. Our results show that
the regulation of CDC42-binding protein b, at least in part, is
at the translational level during IL-1a treatment in MG-63
cells. It is possible that MG-63 cells utilize this acute
translational regulation mechanism to up-regulate the
CDC42-binding protein b level in response to IL-1a exposure
to regulate the CDC42 signaling pathway.
It is also interesting to point out that inhibitor of apoptosis
protein 1 is acutely up-regulated by 20-fold at the translational
level by IL-1a treatment. It has been shown that in
Drosophila, the cell death pathway activated by the JNKmediated pathway can be blocked by Drosophila inhibitor of
apoptosis protein 1 (38). It is well understood that inhibitor of
apoptosis protein family proteins suppress cell death through
direct inhibition of caspases (39). It is possible that inhibitor of
apoptosis protein 1 blocks both caspase-dependent and
caspase-independent cell death pathways by blocking both
the caspases and JNK signaling. Perhaps it is necessary for the
up-regulation of inhibitor of apoptosis protein 1 to prevent
apoptosis by the activated JNK pathway with IL-1a in order
for MG-63 cells to differentiate to the osteoblast-like
phenotype. This result also provides the molecular basis for
identifying cis- and trans-acting elements that may regulate
inhibitor of apoptosis protein 1 expression at some level of
translational regulation.
Finally, a list of potential translationally regulated genes
was identi®ed as being involved in cellular metabolism. One
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Nucleic Acids Research, 2003, Vol. 31, No. 17
Figure 6. (A) Trace replication (n = 3) of QEA electrophoresis output for
translation initiation factor 4B from steady-state mRNA of MG-63 cells (Set
B) and cells treated with IL-1a (Set A). (B) Poisoned QEA electrophoresis
output from isolated polysome mRNA of MG-63 cells (Set B) and cells
treated with IL-1a (Set A). Red line at peak position 358.0 indicates gene
fragment corresponding to translation initiation factor 4B.
of the best examples is the dihydrofolate reductase gene,
which has been well studied as a gene controlled by
translational autoregulation (40,41). Dihydrofolate reductase
is one of the main targets for anticancer therapy. Dihydrofolate
reductase protein interacts with its own mRNA by direct
binding to the coding region of the mRNA, thereby downregulating its protein synthesis. Another known translationally
regulated target is ribonucleotide reductase, a key enzyme
involved in synthesis of dNTPs, which was shown to be
translationally regulated via eIF4E (42). The down-regulation
of these S phase enzymes could be critical for cell differentiation processes. These results, taken together, provide a
strong validation of the polysome GeneCalling technology to
discover translationally regulated genes in MG-63 cells during
cytokine IL-1a exposure.
Figure 6 shows representative QEA traces for translation
initiation factor 4B. The total mRNA expression level for
translation initiation factor 4B showed no difference based
upon steady-state mRNA GeneCalling studies (Fig. 6A).
However, the level of actively translated forms of translation
initiation factor 4B was signi®cantly down-regulated by 9-fold
in MG-63 cells treated with IL-1a compared with control MG63 cells (Fig. 6B). Translation initiation factor 4B plays a
critical role in regulating global translation initiation (43).
This may explain the large percentage of genes translationally
regulated via IL-1a. The results further con®rm the power of
this technology to study translational regulation.
One of the genes listed in Table 1, CAML, a gene con®rmed
by oligo poisoning, was translationally regulated in MG-63
cells treated with IL-1a. CAML was originally described as a
cyclophilin B-binding protein whose overexpression in T cells
causes a rise in intracellular calcium, thus activating transcription factors responsible for the early immune response
(44). CAML is an endoplasmic reticulum membrane bound
Figure 7. (A) RT±QPCR analysis of CAML expression in MG-63 cells. CT
values for CAML were ®rst normalized against housekeeping gene GAPDH
and converted to a percentage for relative expression. (B) Western immunoblot analysis of CAML in MG-63 cells. Cytosolic extracts from MG-63
cells (lane 2) and MG-63 cells treated with IL-1a (lane 1) were prepared.
CAML protein was detected by immunoblot analysis using an anti-CAML
polyclonal antibody. Filtered membranes were then reprobed with an anti-bactin monoclonal antibody to control for loading and integrity of protein.
protein oriented towards the cytosol (44). Holloway and Bram
recently showed that CAML functions as a regulator to control
Ca2+ storage (45). The steady-state level of CAML mRNA in
both control MG-63 cells and MG-63 cells treated with IL-1a
showed no difference. However, in the polysome, isolated,
actively translated mRNA in MG-63 cells treated with IL-1a
was down-regulated by nearly 4-fold. These results were
con®rmed by RT±QPCR analysis (Fig. 7A). The immunoblot
analysis of the CAML protein also con®rmed that the protein
level of CAML in MG-63 cells treated with IL-1a was indeed
down-regulated nearly 4-fold (Fig. 7B). This result may
provide a novel regulatory pathway for CAML expression and
an acute response to intracellular calcium level change. Recent
studies have shown that the CAML interactor (TACI) plays a
critical role in T cell activation and collagen-induced arthritis
in mice (46). In fact, it was suggested that inhibition of these
ligands might have therapeutic bene®ts for autoimmune
diseases, such as rheumatoid arthritis, which involves both B
and T cells.
By comparing the GeneCalling results from steady-state
mRNAs and isolated polysome mRNAs at any given time
point, one can identify genes that are transcriptionally
regulated, translationally regulated or both. This analysis
provides lead candidates for discovering novel regulatory
mechanisms. This approach provides a snapshot of a high
throughput, open platform for comprehensive gene expression
analysis at any single or multiple time point. In addition, one
can identify potential drug targets by targeting these key
regulatory steps of gene expression. Taken together, this study
suggests that by isolating actively translated mRNAs from
polysomes and comparing them with the steady-state mRNA
expression pro®le, one can identify translationally regulated
genes which could be missed by measuring only steady-state
total mRNA levels.
Nucleic Acids Research, 2003, Vol. 31, No. 17
CONCLUSIONS
Using MG-63 cells as a model system, actively translated
mRNAs were isolated via polysome collection and subsequently analyzed using a high throughput, open platform
expression analysis technology, GeneCalling. This technology
provides a better estimation of actual protein level than just
using steady-state mRNA expression analysis. It is one step
away from direct protein expression quantitation. In addition,
by comparing the steady-state and actively translated mRNA
levels, one can identify genes that are controlled at multiple
points in the gene expression cascade. This approach provides
an open platform, comprehensive gene regulation analysis in a
high throughput manner. As a result of this study, a number of
novel and previously known translationally regulated genes
have been identi®ed in MG-63 cells treated with the in¯ammation cytokine IL-1a. These results strongly suggest that
cytokine IL-1a mediates the cellular in¯ammation response
not only through transcriptional and post-transcriptional
regulation but also translational regulation through a number
of critical genes that responded to acute exposure to IL-1a.
The results presented here demonstrate the utility of using
Polysomal GeneCalling to identify genes regulated at the
translational level. In addition, by comparing GeneCalling
results from both total steady-state RNA samples and
polysome-derived samples, a comprehensive, panoramic
overview of gene regulation can be obtained.
ACKNOWLEDGEMENTS
The authors thank Dr Richard J. Bram for providing rabbit
anti-CAML polyclonal antibody, and Dr Michael McKenna at
CuraGen Corp. and Professor Richard Flavell and Dr Binfeng
Lu at the Department of Immunology of Yale University for
helpful comments on the manuscript.
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