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Vol. 13, Issue 6, 1977-2000, June 2002



¶ and
*Departments of Genetics and §Biochemistry,
Howard Hughes Medical Institute, Stanford University
School of Medicine, Stanford, California 94305;
Department of Biomedical Sciences, College of Medicine,
Florida State University, Tallahassee, Florida 32306; and
Department of Genetics, Lineberger Comprehensive Cancer
Center, University of North Carolina at Chapel Hill, Chapel Hill, North
Carolina 27599
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ABSTRACT |
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The genome-wide program of gene expression during the cell division cycle in a human cancer cell line (HeLa) was characterized using cDNA microarrays. Transcripts of >850 genes showed periodic variation during the cell cycle. Hierarchical clustering of the expression patterns revealed coexpressed groups of previously well-characterized genes involved in essential cell cycle processes such as DNA replication, chromosome segregation, and cell adhesion along with genes of uncharacterized function. Most of the genes whose expression had previously been reported to correlate with the proliferative state of tumors were found herein also to be periodically expressed during the HeLa cell cycle. However, some of the genes periodically expressed in the HeLa cell cycle do not have a consistent correlation with tumor proliferation. Cell cycle-regulated transcripts of genes involved in fundamental processes such as DNA replication and chromosome segregation seem to be more highly expressed in proliferative tumors simply because they contain more cycling cells. The data in this report provide a comprehensive catalog of cell cycle regulated genes that can serve as a starting point for functional discovery. The full dataset is available at http://genome-www.stanford.edu/Human-CellCycle/HeLa/.
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INTRODUCTION |
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Proper regulation of the cell division cycle is crucial to the
growth and development of all organisms; understanding this regulation
is central to the study of many diseases, most notably cancer. Many of
the diverse molecular processes needed to duplicate a cell, such as DNA
replication, monitoring of replication fidelity, and accurate
segregation of chromosomes to daughter cells, are characteristically
aberrant in cancer cells. These processes and their regulation have
been extensively investigated at the molecular level (reviewed in
Stillman, 1996
; Nurse, 2000
; Shah and Cleveland, 2000
; Hinchcliffe and
Sluder, 2001
; Wittmann et al., 2001
). Characterization of
the genome-wide transcriptional program of the cell division cycle in
mammalian cells is a critical step toward understanding the basic cell
cycle processes and their role in cancer.
The genome-wide transcriptional program during the cell cycle has been
investigated in a wide range of organisms, including budding yeast (Cho
et al., 1998
; Spellman et al., 1998
), bacteria (Laub et al., 2000
), primary human fibroblasts (Iyer
et al., 1999
; Cho et al., 2001
), mouse
fibroblasts (Ishida et al., 2001
), and human HeLa cells
(Crawford and Piwnica-Worms, 2001
). In synchronized cultures of the
budding yeast Saccharomyces cerevisiae, 800 genes were found
to be periodically expressed (Spellman et al., 1998
) and in
synchronized cultures of the bacterium Caulobacter
crescentus, 553 periodically expressed genes were identified (Laub
et al., 2000
). The human cell cycle was previously studied
first by observing the genes whose expression changes when primary
fibroblast cultures were stimulated with serum to reenter the cell
cycle from a serum-deprived, resting state. Interpretation of these
studies is complicated by the subsequent realization that the major
gene expression program observed is more than a simple cell cycle
response, but rather is combined with a prominent wound-healing
response (Iyer et al., 1999
). By modifying this approach,
Ishida et al. (2001)
identified 578 genes that were induced
upon serum stimulation and provided evidence of cell cycle regulation
based on the pattern of expression after release from a cell cycle
arrest with hydroxyurea, which blocks DNA replication. A study of the
G2 DNA damage checkpoint in HeLa cells synchronized by a double
thymidine block identified transcripts that showed differential
expression from S to G2/M phases but failed to identify a specific
transcriptional response associated with the G2 DNA damage checkpoint
(Crawford and Piwnica-Worms, 2001
). Finally, ~700 genes were
identified as cell cycle regulated in primary human fibroblasts
synchronized by a double thymidine block, which attempts to avoid the
confounding serum stimulation response altogether (Cho et
al., 2001
).
Herein, we report the extension of these results by synchronizing HeLa cells not only by double thymidine block but also by two additional methods, a thymidine-nocodazole block and a physical method, mitotic shake-off. By combining data from experiments using these three different synchronization methods in five independent experiments, we identified >850 genes that are periodically expressed during the cell cycle. Using this list of genes we were able to show that most of the genes previously associated with the proliferative state of tumors are among the genes we identified as periodically synthesized during the human cell division cycle.
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MATERIALS AND METHODS |
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Cell Culture, Synchronization, and RNA Preparation
HeLa S3 cells were plated (2 × 106
cells) in 150-mm tissue culture dishes in DMEM with 10% fetal bovine
serum and 100 U of penicillin-streptomycin (Invitrogen, Carlsbad, CA).
Cells were arrested in S phase by using a double thymidine block or in
mitosis with a thymidine-nocodazole block essentially as described
previously (Whitfield et al., 2000
) and in the supplemental
material. Poly(A) RNA was prepared from cells collected at intervals
(typically 1-2 h) by lysing cells directly on the plate with the
FastTrack 2.0 mRNA isolation kit (Invitrogen). Synchrony was monitored
by flow cytometry analysis of propidium iodide-stained cells (Stanford Shared FACS Facility, Stanford University School of Medicine, Stanford, CA).
Mitotic cells were collected every 10 min with an automated cell shaker
(Eliassen et al., 1998
), stored on ice, and plated at 2-h
intervals in fresh prewarmed media (at least 106
cells for each time point). Because only a small number of cells can be
obtained by mitotic shake-off, total RNA was prepared using ULTRASPEC
RNA isolation system (Biotecx, Houston, TX). The number of cells in S
phase at each point was determined using a 5-Bromo-2'-deoxyuridine (BrdU) Labeling and Detection Kit I (Roche Applied Science,
Indianapolis, IN).
Reference RNA was prepared from asynchronously growing HeLa cells using TRIzol (Invitrogen) and poly(A) RNA isolated by affinity chromatography on oligo-dT cellulose (Amersham Biosciences, Piscataway, NJ). Poly(A) RNA was used as a reference in all experiments except the mitotic shake-off, where total RNA was labeled.
cDNA Synthesis and Microarray Hybridization
RNA from synchronous cells was reverse transcribed into Cy5-dUTP
(Amersham Biosciences)-labeled cDNA, and reference RNA reverse transcribed into Cy3-dUTP (Amersham Biosciences)-labeled cDNA by
standard methods (Eisen and Brown, 1999
) (Details are available at
http://genome-www.stanford.edu/Human-CellCycle/HeLa/.) Total RNA
samples from cells collected in the mitotic shake-off experiment, and
total reference RNA were first amplified using a modified Eberwine
protocol before cDNA synthesis (Wang et al., 2000
) then labeled cDNA was prepared from the amplified RNA.
Spotted cDNA microarrays, containing 22,692 elements representing ~16,332 different human genes or containing 43,198 elements representing ~29,621 genes (estimated by UNIGENE clusters), were manufactured in the Stanford Microarray Facility (http://www.microarray.org). Equal amounts of Cy5- and Cy3-labeled cDNA were hybridized to spotted cDNA microarrays and scanned using a GenePix 4000A Scanner (Axon Instruments, Union City, CA). Detailed protocols are available at http://brownlab.stanford.edu/protocols.html/.
Data Processing
Data were extracted by superimposing a grid over each array
using GenePix 3.0 software (Axon Instruments). Spots of poor quality, determined by visual inspection, were removed from further analysis. Data collected for each array were stored in the Stanford Microarray Database (SMD) and are available from SMD at
http://genome-www.stanford.edu/microarray/ (Sherlock et al., 2001
).
Only features with signal intensity at least 20% above background in
both Cy5 and Cy3 channels and for which adequate quality data were
obtained for at least 80% of the samples in a given time course were
analyzed further. Data points that did not meet these criteria are
blank in the primary data tables. Log2 (Cy5/Cy3) was retrieved for each data point and used for all analysis, where (Cy5/Cy3) is the normalized ratio of the background-corrected intensities, as defined in SMD (Sherlock et al., 2001
).
Because of systematic differences between experiments (e.g., array
batch, labeling methods, and synchronization methods) each time course
was centered independently by filtering out the first, most significant
eigengene (Alter et al., 2000
), which was a dominant, constant vector. Because singular valve decomposition (SVD) requires a
full data matrix, missing data points were estimated using a k-nearest
neighbors algorithm (Troyanskaya et al., 2001
) with k = 12. These imputed values were used throughout the analysis and but were
restored to "unknown" status in the figures and left blank in the
primary data tables
(http://genome-www.stanford.edu/Human-CellCycle/HeLa/).
Identification of Periodically Expressed Transcripts
A Fourier Transform (eqs. 1-3) was applied to the data for each
clone in an experiment (Spellman et al., 1998
), and the
resulting vector (C, eq. 3) of the sine (A) and
cosine (B) coefficients was stored, where T is
the cell cycle period, t is the time after release,
is the phase offset, and ratio(t)
is the normalized Cy5/Cy3 expression ratio at time t. The
value of
was initially set to 0. The values obtained for
C were determined over a range of 40 values of T
equally spaced 1 h above and below the estimated cell cycle period
and the resulting values averaged and stored.
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(1) |
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(2) |
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(3) |
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(4) |
Because each experiment does not start at exactly the same point in the
cell cycle, an offset (
, eqs. 1 and 2) was calculated for
each dataset relative to the first double thymidine arrest. The
magnitudes of the Fourier transform (D, eq. 4) for the 1000 highest scoring clones were summed using different values of
, equally spaced between 0 and 2
. The offset that gave
the highest average combined magnitude (D, eq. 4) between
the two datasets for these 1000 genes was then used. The Fourier
transform was then repeated on the remaining datasets with the
following values of T and
: Thy-Thy 2 (T = 15.5 h,
= 0.5 rad), Thy-Thy 3 (T = 15.4,
= 0.0 rad), Thy-Noc
(T = 18.5,
= 3.2 rad), and mitotic selection (T = 24.5,
= 3.5 rad). The
vectors C (eq. 3) for all five datasets were then
summed and the genes ranked according to the magnitude (D, eq. 4)
of their combined vectors. Note, the Thy-Noc and mitotic shake-off
experiments, which arrest cells in mitosis, have offsets of
approximately half a cycle (
radians) from Thy-Thy 1, which arrest
cells at G1/S.
Because the gene expression profiles of many cell cycle genes do
not precisely match sine and cosine curves, the expression profile of
each gene was correlated to an idealized vector obtained from known
genes expressed in each cell cycle phase (G1/S, S, G2, and G2/M) as
defined in Figure 2A. Using a standard Pearson correlation, each gene
received a peak correlation score defined as the highest absolute value
correlation between one of the four idealized vectors and its
expression profile (Spellman et al., 1998
). The absolute
value of the peak correlation was used to scale the magnitude of the
vector (C, eq. 2) generating a "periodicity score" for
each gene (Table 1).
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To estimate the minimum periodicity score for a cell cycle regulated
gene, the analysis described above was repeated on randomized data. The
data were randomized either within rows only, or within both rows and
columns, for each of the five datasets starting with the imputed,
SVD-centered data. The Fourier transform and correlations were
applied using the previously calculated values of T and
; the resulting vectors (C, eq. 3) were
combined for each dataset. The magnitude (D, eq. 4) of the
Fourier transform was scaled by each "gene's" peak correlation to
one of the four ideal expression profiles. This analysis, including the
data randomization, was repeated 10 times and the scores combined by
averaging the score for each of the highest scoring "genes" from
each randomization, followed by the second highest, third highest, etc.
The estimated false positive rate at a given periodicity score is the
number of genes that obtain at least that score in the randomized data. We chose a minimum periodicity score of 3.29, which gave us 1333 clones
at an initial false positive estimate of 1% when the data were
randomized in rows. Repeating this analysis 10 times gave an estimated
10 false positives (0.75%; periodicity scores of 5.18-3.33) when the
data were randomized only within rows, and two false positives (0.15%;
periodicity scores of 3.72 and 3.30) when the data were randomized in
both rows and columns.
The false positive estimate, calculated above, is likely to
underestimate the true false positive rate because it does not take
into account genes that received a high Fourier score because they
exhibited a sinusoidal pattern in only part of a time course. To filter
out genes that did not show periodic expression, autocorrelations for
each 1333 clones were calculated (eq. 5). The autocorrelation A is equal to the summation over all times t of
the product of the ratio at t multiplied by the ratio at a
time t + T, where T is the cell cycle
period determined by Fourier analysis. If the data for a gene repeats
with a period T, the autocorrelation will be high.
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(5) |
Autocorrelations were used as a filter to remove genes that showed transient expression despite receiving a high periodicity score. 199 Genes with a negative autocorrelation (a negative autocorrelation indicates the measured ratios do not repeat every cell cycle) were eliminated from the initial set of putative cell cycle-regulated genes. Autocorrelations were also calculated for data randomized in rows, whereupon few genes received negative autocorrelations in the randomized data, indicating that the negative scores are unlikely to occur by chance. The distribution of autocorrelation scores is shown in Supplemental Figure 16.
Our final list contains 1134 clones that correspond to 874 UNIGENE clusters (UNIGENE build 143, released November 9, 2001, 21 clones not found in UNIGENE, 66 map to more than one UNIGENE cluster). The data for all 1134 clones as well as the primary data are available at http://genome-www.stanford.edu/Human-CellCycle/HeLa/.
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RESULTS |
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The sensitivity with which one can detect periodic activities in
synchronized cell cultures depends almost entirely on the degree to
which cells can be synchronized. In particular, cells that fail to
begin cycling promptly upon release will contribute noise in the
analysis. We chose the well-studied epithelial cell line, HeLa S3,
derived from a cervical carcinoma (Puck et al., 1956
),
specifically because a high degree of synchrony can be achieved with
diverse methods (Knehr et al., 1995
; Whitfield et al., 2000
). HeLa cells can be synchronized so that 95% of the cells reenter the cell cycle, whereas the comparable figure for primary
cells (such as foreskin fibroblasts or mammary epithelial cells) is
usually reported to be between 60 and 80% (Tobey et al.,
1988
; Fonagy et al., 1993
; Stampfer et al.,
1993
). Three different methods were used to obtain synchronous
populations; in each case synchrony was monitored either by flow
cytometry or BrdU incorporation (Figure
1). A double thymidine block was first
used to arrest cells at the G1/S boundary (Adolph and Phelps, 1982
),
providing the best synchrony in G2 and M phases of the cell cycle
(Figure 1A). This protocol provided a somewhat less robust synchrony of
the G1-to-S transition, because cells tend to differ in the rate at
which they pass through G1. To improve our resolution in the G1-to-S
period, we used another method, in which HeLa cells were first arrested
by thymidine, released, and subsequently arrested in mitosis with the
antimicrotubule drug nocodazole (Figure 1B) (Zieve et al.,
1980
). In this case, better synchrony was obtained in G1 and S phases,
because the synchronous release was at M. The final synchronization
method involved no drugs; instead, HeLa cells at metaphase were
collected by a physical method, mitotic shake-off (Schneiderman
et al., 1972
), by using an automated cell shaker (Eliassen
et al., 1998
). More than 95% of the cells collected by
mitotic shake-off reentered the cell cycle and progressed into S phase
as monitored by incorporation of BrdU (Figure 1C). In all cases, with
the exception of the mitotic shake-off, two to three synchronous cell
cycles were obtained.
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Identification of Cell Cycle-regulated Transcripts
To identify cell cycle-regulated transcripts, RNA was isolated
from HeLa cells at intervals (typically 1-2 h) after release from a
synchronous arrest (Figure 2A). Cy5- or
Cy3-labeled cDNA was synthesized using standard protocols and
hybridized to cDNA microarrays containing either 22,692 features
representing ~16,332 different human genes (Thy-Thy 1 and Thy-Thy 2)
or 43,198 features representing ~29,621 different genes (Thy-Thy 3, Thy-Noc, and Mitotic Shake-off) as estimated by UNIGENE clusters.
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The data from each of the synchronization experiments required separate
analysis before combination into a single dataset as described in
MATERIALS AND METHODS. To identify those transcripts that were cell
cycle regulated, a periodicity score was obtained for each clone using
a Fourier transform and correlation to known cell cycle genes as
described for yeast (Spellman et al., 1998
). Representative
genes and their periodicity scores are shown in Table 1; the range was
0-58.8.
The minimum score for a gene we designated as "cell cycle-regulated" was determined by estimating the false positive rate by using randomized data as described in MATERIALS AND METHODS. We chose a threshold score of 3.29 that gave an estimated 10 false positives (0.75%; periodicity scores of 5.18-3.33) when the data were randomized only within rows (i.e., within each gene), and two false positives (0.15%; periodicity scores of 3.72-3.30) when rows and columns were randomized. Using this relatively conservative threshold, we identified 1333 clones as cell cycle regulated.
A fraction of the genes with relatively high Fourier scores varied in a quasi-sinusoidal manner during part of the time series but clearly were not periodically expressed. This quasi-sinusoidal variation resulted from variation in gene expression at the beginning of each time course, probably as a result of the synchronization procedure. Possible sources of this variation are the serum response, resulting from the addition of fresh growth media upon release of the cells from the arrest, or simply a stress response resulting from the cell cycle block. To filter out genes that did not oscillate across multiple cell cycles, each clone was assigned an autocorrelation score that describes whether the expression ratio at a given time is a good predictor for the value of the expression ratio one cell cycle later. Thus, genes with a consistent pattern of periodic expression every cell cycle have positive autocorrelation scores, whereas genes with low or negative scores do not have consistent periodic expression. Using this method, 199 of the initial 1333 clones received negative autocorrelation scores and were removed from the list, leaving 1134 clones representing 874 different genes.
To assess the quality of the criteria for a periodically expressed
gene, the false negative rate was estimated by using a list of known
cell cycle-regulated genes compiled from the literature. The known
genes were limited to those regulated at the mRNA level during a
continuous human cell cycle, as determined by traditional methods
(e.g., Northern blot, S1 nuclease assay, or RNase protection; Table
2). Many "known" cell cycle genes are
not included in this list because their regulation has been
demonstrated only during the resting-to-growing transition, not in
cells synchronized independent of serum, or because data for the
regulation of the mRNA could not be found, despite a sometimes
extensive literature on protein levels, localization, and function
during the cell cycle (e.g., CENPE; Wood et al., 1997
;
Abrieu et al., 2000
). Of the 49 known cell cycle-regulated
genes in our list, three were not measured in our analysis. Of the
remaining 46, 52.2% (24/46) are found in the top 120 scoring clones,
and 93.5% (43/46) are included in the top 850 clones, yielding a false
negative rate of 6.5%. Although most of the known genes are
represented in the top 850 clones, we believe there is still
significant information to be obtained between our 850 and 1134 clones,
which include duplicate clones of many of the known genes (e.g., the
histones and RAD51) and many genes expressed during mitosis.
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Classification of Transcripts by Cell Cycle Phase
Figure 2 presents data for well-studied genes that showed peak
expression at specific points in the cell cycle. The times of S phase
and mitosis, as estimated from the flow cytometry and BrdU data (Figure
1), are indicated in Figure 2. Maximum expression of the mitotic
cyclins (CCNE1, CCNA2, and CCNB1) was observed in the known temporal
order and at the expected times (Pines and Hunter, 1989
; Pines and
Hunter, 1990
; Lew et al., 1991
). Cyclin E1 expression at the
G1/S boundary was accompanied, as expected, by E2F1 and the DNA
replication factors CDC6 and PCNA (Morris and Mathews, 1989
; Ohtani
et al., 1995
; Yan et al., 1998
). Many DNA
metabolism genes were expressed at the beginning of S phase; these are
represented here by RFC4, DHFR, RRM2, and RAD51. In our data, two
distinct groups of mitotic genes could be distinguished. Transcripts of
some genes, such as CCNA2, TOP2A, Cyclin F, and CDC2, peaked in G2,
whereas others, such as CCNB1, BUB1, STK15, and PLK1, peaked in
mitosis. The differences in expression of the G2 and M phase genes are
most evident in the third double thymidine arrest experiment in which
samples were taken every hour for 46 h and at the earliest times
after the thymidine-nocodazole arrest (Figure 2). We have followed our
data, rather than precedent, in recognizing that many, if not all, the
genes expressed during the physical act of mitosis continue to be
expressed into the G1 period and have thus labeled these genes M/G1.
These genes are represented in Figure 2 by RAD21, PTTG1, VEGFC, and CDKN3.
All 1134 clones identified as periodically expressed were sorted by the
point in the cell cycle when they showed peak expression as calculated
from the sine and cosine components for the Fourier transform (Figure
3A). The periodic nature of the
expression patterns is evident from the alternating red (strong
expression relative to the asynchronous reference) and green (weak
expression relative to the asynchronous reference). It is notable that
these patterns persist across multiple cell cycles in both Figures 2
and 3.
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To minimize unnecessary reassignment of genes relative to the published literature, each gene was assigned a cell cycle phase (G1/S, S, G2, G2/M, or M/G1) by their peak correlation to an idealized expression profile generated from the well-studied genes selected for Figure 2. These assignments are necessarily somewhat arbitrary because a gene assigned to G1/S can be immediately adjacent to a gene assigned to S phase. By assigning phase in this way, 211 (18.6%) of the clones were maximally expressed during the G1/S transition, and 221 (19.5%) were maximally expressed in S phase. Genes in each of these phases have a known role in replication initiation and DNA metabolism. More than 50% of the clones showed peak expression in G2 and M phases of the cell cycle; 239 clones (21.1%) peaked in G2, 273 clones (24.1%) at G2/M, and 190 clones (16.8%) at M/G1. Genes with maximal expression levels in G2 and M phases have roles in chromosome metabolism, surveillance of mitotic processes, and cell adhesion. Interestingly, relatively few genes show peak expression between M/G1 and G1/S, as judged from the distribution of arctangent values calculated from the Fourier transform (Supplemental Figure 15).
In most cases the phase assignments we made for the known genes are in agreement with the published literature (Table 2). Several genes that were previously characterized to peak in a specific cell cycle phase were assigned to an adjacent phase. Examples are CDC45L and CDKN3, both reported to peak at G1/S, whereas in our data they fell into the S phase and M/G1 groups, respectively (Table 2). The difference in assignments may represent differences in experimental systems, methods of measurement, or resolution in the different experiments.
The replication-dependent human histone mRNAs are not polyadenylated
(Marzluff, 1992
) and thus behave sporadically in all experiments except
the mitotic shake-off experiment where total RNA was labeled. Because
of this inconsistent behavior, they were incorrectly assigned a phase
of G1/S by our method. Because the data obtained from mitotic selection
and extensive literature on the cell cycle regulation of the histone
genes indicated they are expressed in S phase, the phase has been
assigned manually, rather than by correlation to the ideal vector
(indicated by an asterisk [*] in Table 2 and the supplemental data).
Overview of Periodically Expressed Gene Clusters
Hierarchical clustering of transcripts with similar expression
patterns often groups genes according to the processes in which they
participate; these processes can often be inferred from the annotations
of the known genes in each group (Eisen et al., 1998
). To
begin to identify the processes that are represented in our clusters,
we annotated the known genes in our clusters with gene ontology (GO)
terms (Ashburner et al., 2000
) for biological process from
LocusLink (Pruitt and Maglott, 2001
). GO, initially developed using the
model organisms S. cerevisiae, Drosophila
melanogaster, and the mouse Mus musculus, provides a
controlled vocabulary to describe the functions of known genes. GO
terms are divided into three different categories: 1) molecular
function, 2) biological process, and 3) cellular component. Examination
of the lists of clustered genes and their annotations shows that within
our set of 1134 clones there are clusters of genes that have similar or identical annotations (Figure 3B). In what follows, GO terms are italicized and terms we have applied provisionally herein because of
the incomplete application of GO to human genes are marked with an
asterisk (*). The DNA replication clusters include genes associated
with the following process annotations: DNA replication, expressed in G1/S (early DNA replication; e.g., the components of the
prereplication complex and ORC1); DNA metabolism (including its daughter processes DNA repair and DNA
recombination) and nucleotide metabolism, expressed in
S (late DNA replication; e.g., RAD51, DNA polymerases, and nucleotide
metabolism enzymes); and chromatin assembly/disassembly
(e.g., the histones), also expressed in S. The mitotic clusters include
mitotic chromosome segregation (the
- and
-tubulins)
expressed in G2; spindle assembly,
expressed during G2 (e.g., kinesins and centrosome
duplications genes); mitotic checkpoint and nuclear
division, expressed at G2/M (e.g., the checkpoint genes BUB1 and
CENPE); sister chromatid cohesion (e.g., PTTG1 and genes of
the cohesin complex STAG1, RAD21); and cell adhesion (e.g.,
CTNND1 and vinculin) and components of the RAS signal transduction
pathway expressed during the physical act of mitosis, M/G1. The first
point to be made about these clusters (shown in more detail in Figures
6-8 and the supplemental data, which contain the complete cluster
diagram with gene names) is that each represents a reasonably complete
and specific list of the genes necessary for the major, essential
processes that must occur every cell division cycle. The second point
to be made about these is that, in addition to genes of known function,
there are numerous genes of heretofore uncharacterized function that
are now implicated in these processes.
Tumor Proliferation Cluster
Recently, several studies of global gene expression patterns in
human tumors and tumor cell lines have been published (Perou et
al., 1999
, 2000
; Alizadeh et al., 2000
; Ross et
al., 2000
). Each of these studies identified a prominent cluster
of genes whose expression was correlated with the rate of proliferation of the tumors or cell lines under study. As we show now, half or more
of the genes that comprise this "proliferation cluster" are the
same genes that we have found to be periodically expressed in HeLa cells.
To compare explicitly the genes of the proliferation cluster with the
874 periodically expressed genes described here, we took the list of
genes in each of the proliferation clusters identified in the studies
of breast tumors (Perou et al., 2000
) and lymphoma (Alizadeh
et al., 2000
), and extracted the patterns of gene expression for each from the dataset described here. These patterns, lined up by
peak expression during the cell cycle as in Figure 3, are presented for
112 genes measured here and in breast cancer (Figure 4A) and for 96 genes measured here and in
lymphoma (Figure 4B). As the figures show, 62% (69/112) of the genes
in the breast tumor proliferation cluster and 45% (43/96) of the
lymphoma proliferation cluster and are among the 874 periodically
expressed genes we detected in synchronized HeLa cell cultures.
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The simplest interpretation of this result is that the genes whose transcript levels correlate with the proliferation rate of the tumors are expressed only in cycling cells, presumably as a consequence of regulation that allows transcription only during the appropriate stage of the cell cycle and not at any other time, including quiescence. Even if this idea is correct, it need not apply to all genes that are periodically expressed during the cell cycle. It seems quite possible that some genes that are periodically expressed in cycling cells might not appear in the proliferation clusters of tumors because they might be expressed under some circumstances in noncycling cells or strongly regulated not only by the cell cycle but also by related factors, and thus show little association with tumor proliferation. One might even anticipate that some periodically expressed genes might be regulated in ways (e.g., strong expression in differentiated nonreplicating cells) that result in reduced transcription during active cell division cycles.
To explore further the relationship between periodic expression during
the cell cycle and expression in proliferative tumors, we have
extracted the data for every clone that was both identified as
periodically expressed in this study and also measured in the breast
tumor study, regardless of its pattern there (Perou et al.,
2000
). This yielded 386 clones, of which <25% appeared in the
proliferation cluster. Hierarchical clustering of this set of 386 using
the combined data from both studies revealed, as anticipated, not only
periodically expressed genes that are highly correlated with tumor
proliferation (a more comprehensive proliferation cluster, as described
above; Figure 5B, ii and iii) but also
periodically expressed genes that show heterogeneous expression in
tumors (i.e., no obvious association with tumor proliferation; Figure
5B, v), and periodically expressed genes whose expression is apparently uncorrelated with the proliferative state of the tumors (Figure 5B, i
and iv).
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Correlation between periodic expression in the cell cycle and strong
expression in highly proliferative tumors can be accounted for simply
because there is an increase in the number of cycling cells (as
described above), but other more specific explanations can also be
proposed. Many genes that we have found to be periodically expressed
have already been implicated directly in tumorigenesis and their
expression has been reported to coincide with cellular transformation
and oncogenic potential (reviewed in Bishop, 1991
; Hunter, 1997
). Genes
of this kind are common in clusters whose expression is highly
correlated with tumor proliferation in our study, including some
expressed primarily in the G2 and M phases (Figure 5B, ii) and others
whose expression peaks in G1 and S phases (Figure 5B, iii).
Among the periodically expressed genes whose expression is
heterogeneous or uncorrelated with tumor proliferation are genes for a
variety of cell cycle processes, including genes for cell-cell adhesion
(calponin-2, smoothelin, and vinculin) (Figure 5B, i). Expression of
some of these genes, such as vinculin, in transformed cells has been
reported to decrease tumorigenecity (Rodriguez Fernandez et
al., 1992
). Another group of cell cycle-regulated genes that are
not correlated with tumor proliferation includes genes necessary for
withdrawal from the cell cycle and apoptosis (the cyclin-dependent
kinase [cdk] inhibitor CDKN2C (p18) and Caspase 3; Figure 5B, iv).
Finally, a group of G1 and S phase genes shows heterogeneous expression
in tumors (Figure 5B, v), including DHFR, E2F1, CDC6, CCNE1, CHAF1A,
TOP3A, ORC1, and BRCA1; these data suggest that the regulation of each
of these genes is more complex than simple restriction of transcription
to a particular phase of the cell division cycle.
Biological Roles of Periodically Expressed Gene Clusters
The close similarity between the set of genes whose expression is
associated with tumor proliferation and those that are periodically expressed invites a closer examination of the biological roles of the
gene clusters detected in Figure 3B, in light of the specific features
of both the cell cycle and tumor biology. In what follows, we survey
roles of the genes of known function in each of the prominent clusters.
It should be recalled, however, that each of these clusters contains,
in addition to the genes of known function, genes whose biological role
remains to be determined (expressed sequence tags; ESTs). The numbers
of such uncharacterized genes are noted in the legends to Figures
6-8 and, of course, the detailed
position of each of them can be found in the supplementary information
and on the Web site.
|
G1 and S Clusters
The "early DNA replication" cluster contains genes
expressed in late G1 phase with their expression continuing into S
phase. The GO process annotations that apply to most of these genes are DNA replication (including well-characterized genes encoding
RFC4, DNA polymerase delta 3, DDX11, and geminin), DNA packaging
(including CHAF1A, CHAF1B, and PCAF), and DNA repair
(including MSH2, FEN1, and PCNA). Many of the genes in the DNA
replication category are components of prereplicative complex and
include CDC6, ORC1L, MCM2, MCM4, MCM5, and MCM6 (Lei and Tye, 2001
);
this group of genes is consistently expressed at relatively high levels
in diverse types of tumors and has been proposed as diagnostic markers
by Laskey and coworkers (Williams et al., 1998
). The cluster
also includes genes that fall into the GO category cell cycle
control and are necessary for entry into S phase (in this group
are Cyclin E1, E2F1, CDC25A, and Cyclin E2).
The "late DNA replication" cluster (Figure 6B)
contains genes necessary for the continued synthesis of DNA, including
nucleotide metabolism (including TYMS, DHFR, RRM1, and RRM2)
and additional DNA replication genes (DNA polymerase alpha
and theta; Primase 1 and 2A). Also present are genes for DNA
repair and DNA recombination (RAD54 and RAD51). Genes
involved in DNA repair were also expressed in S phase in a study of the
cell cycle in primary human fibroblasts, and it was reported that these
genes were also induced when cells were treated with UV radiation (Cho
et al., 2001
).
Histone synthesis is required during DNA replication to package newly
synthesized chromatin and is tightly regulated during the cell
cycle (Marzluff and Pandey, 1988
; Schumperli, 1988
). The
replication-dependent histone genes are coordinately regulated and
the expression of their mRNAs is tightly restricted to S phase of the
cell cycle by both transcriptional and posttranscriptional mechanisms
(Harris et al., 1991
; Eliassen et al., 1998
). The
"histone" cluster (Figure 6C), expressed during S phase, contains
the core histones H2A, H2B, and H4 and the linker histone H1, which we have assigned to the GO category of chromatin
assembly/disassembly. These mRNAs are not polyadenylated and hence
are measured poorly and inconsistently in all experiments except
mitotic selection where total RNA was labeled. It should be noted that
the several H2A genes will cross-hybridize, as will the H2B, H4, and H1
genes, but significant cross-hybridization between the different genes (e.g., H2A and H2B) is unlikely. Histone H3 mRNA, although cell cycle-regulated, was not found in our study, possibly because of
preferential labeling of the polyadenylated, non-cell cycle-regulated variant H3.3 (Wells and Kedes, 1985
).
Two regulators of mammalian histone mRNA synthesis are present in the
G1/S cluster (Figure 6A), the stem-loop binding protein (SLBP)
necessary for histone pre-mRNA processing (Wang et al., 1996a
; Dominski et al., 1999
), and nuclear protein mapped to
the AT locus (NPAT), which was recently identified as an
activator of histone gene transcription and is hypothesized to be a
chromatin remodeling factor (Ma et al., 2000
; Zhao et
al., 2000
). The SLBP was previously shown to be cell cycle
regulated by both translational and posttranslational mechanisms; the
magnitude of the changes observed herein in the mRNA level
(approximately twofold) is consistent with Whitfield et al.
(2000)
.
Many of the genes expressed at G1/S and S phase are known E2F targets.
A study of the cell cycle and the E2F transcription factors in mouse
embryo fibroblast, using microarrays, identified both G1/S and S phase
genes, as well as genes expressed at G2 and M phases, as targets of the
E2F transcription factor (Ishida et al., 2001
). Ishida
et al. (2001)
identified the histone SLBP as induced by E2F,
suggesting that histone synthesis may be linked to the Rb/E2F pathway.
Furthermore, NPAT is a cyclin E/cdk2 substrate and its overexpression
promotes S phase entry (Zhao et al., 1998
). Although NPAT
itself has not been implicated in tumorigenesis, the role of chromatin
remodeling in cancer is becoming increasingly clear (Archer and Hodin,
1999
).
G2 and M Clusters
The mitotic genes form three distinct clusters, one of which
includes genes that encode
- and
-tubulins; the other two contain well-characterized genes that encode proteins involved in assembly and
surveillance of the mitotic spindle (Figure 3B). The remaining G2 and M
phase genes that fall outside of these core clusters also have
functions concerned with the GO processes mitosis, and its
daughter processes (e.g., mitotic chromosome segregation, spindle assembly, mitotic checkpoint, etc).
The tubulin cluster includes
- and
-tubulins (TUBA1, TUBA2,
TUBA3, TUBB, and TUBB2) as well as BUB3, which is a mitotic checkpoint
regulator (Figure 7A). (Note that there
may be cross-hybridization among the several
-tubulin genes or
between the several
-tubulin genes, but cross-hybridization between
- and
-tubulin genes is unlikely.) Coordinate regulation of
tubulin synthesis at the level of mRNA abundance is evident herein,
even though a large component of the regulation of these genes is
posttranscriptional (Cleveland, 1989
). Yeast BUB3 was isolated as a
multicopy suppressor of a conditional mutation in
-tubulin (Guenette
et al., 1995
), the coexpression of hBUB3 with the
coordinately regulated tubulins is consistent with its role of
monitoring microtubule assembly as cells enter mitosis.
|
The second G2 phase cluster contains genes predominately for
organization of the mitotic spindle, including kinesins and the TTK
kinase (human homolog of the S. cerevisiae MPS1 spindle
checkpoint gene; Figure 7B; Abrieu et al., 2001
). Five
different kinesins are present: KNSL1, KNSL2, KNSL4, KNSL5, and KNSL6.
KNSL6 is expressed at the boundary between G2 and M phase genes and in
our study has been assigned a phase of G2/M. In addition to Cyclin A2,
CDC2, CKS1, Cyclin F, and numerous other regulatory factors are found in this cluster. Also specifically expressed in G2 is ESP1 or human
Separin, which is released from PTTG1 (human homolog of the yeast
securin PDS1) during anaphase and promotes chromatid separation (Zou
et al., 1999
). Finally, it may be worth noting that Importin
alpha, also in this cluster, has recently been shown to inhibit spindle
assembly by binding a protein that promotes spindle formation, Repp86
(Xenopus TPX2) (Gruss et al., 2001
; Nachury
et al., 2001
; Wiese et al., 2001
). This
inhibition is relieved by the action of the small GTPase RAN when bound
to GTP (present in the M/G1 cluster; Figure
8), which is proposed to surround M phase
chromosomes (Carazo-Salas et al., 1999
; Hetzer et
al., 2000
), thus allowing spindle formation only in the vicinity of chromosomes. The temporal succession of the transcripts for these
two proteins suggests a possible role in regulating the mitotic
spindle.
|
The third mitotic cluster contains genes that peak at G2/M and centers
on Cyclin B2 (Figure 7C); Cyclin B1 falls just outside this cluster.
Genes that have known functions in the mitotic spindle checkpoint show
peak expression at G2/M, including BUB1, BUB1B, CDC20, and CENPE. Three
genes in this cluster (STK15, PLK1, and NEK2) have been shown to have
roles in centrosome duplication, whose improper expression is a
potential cause of tumor aneuploidy (Lengauer et al., 1998
).
It is worth noting that PLK1 and STK15 had the very highest periodicity
scores (Table 1), and thus are the most periodically synthesized mRNAs
among the cell cycle-regulated transcripts.
M/G1 Clusters
Three groups of genes whose expression peaks in mitosis merit a more detailed examination. We classified these genes as M/G1 because they are expressed during the physical act of mitosis and their transcripts persist into G1 phase. Some of these genes are known to have roles in cell adhesion, chromosome remodeling, and membrane trafficking as opposed to roles specifically associated with the progress of the cell division cycle.
The M/G1 cluster contains genes known to function in actin cytoskeleton
remodeling and cell adhesion (Figure 8). As a synchronous population of cells proceeds through the cell cycle, visible changes occur to the cellular morphology. The most notable of
these changes is the dramatic remodeling of cell shape that can be
observed in cell culture during mitosis, when cells detach from the
plate and surrounding cells, undergo cytokinesis, and reattach to the plate as they enter the next cell cycle (Schneiderman et
al., 1972
). A cluster of M/G1 genes is expressed during this
transition and contains genes whose products have been localized to the
structures that connect the actin cytoskeleton to the membrane or have
been implicated in the control of cell-cell contacts.
The Ras GTPase can induce perturbations in cell-cell
contacts when overexpressed in its constitutively activated form (Kinch et al., 1995
; Zhong et al., 1997
; Yamamoto
et al., 1999
). Included in the M/G1 cluster is Kirsten Ras-2
(KRAS2), regularly mutated in many malignancies, including lung
adencarcinoma, mucinous adenoma, ductal carcinoma of the pancreas, and
colon tumors (Barbacid, 1990
) (Figure 8A). Ras is believed to act
through downstream targets, including CDC42 (which appears in this
cluster; Figure 8C) as well as the Rho GTPases (which we do not detect
as cell cycle- regulated). These GTPases regulate both the assembly of
actin filaments and the assembly of the adhesion complexes. Among the M/G1 genes that have been localized to cell-cell adherens junctions are
catenin delta 1 (CTTND1, also known as p120), which regulates cadherin
clustering at adherens junctions through Rho GTPases (Anastasiadis and
Reynolds, 2001
); presenilin 1, which binds E-cadherin at cell-cell
junctions (Georgakopoulos et al., 1999
) and has also been
shown to localize to the nuclear membrane, interphase
kinetochores, and centrosomes, suggesting a function in
chromosome segregation (Li et al., 1997
); vinculin, involved
in attaching actin to the membrane (Jockusch and Isenberg, 1981
;
Wilkins and Lin, 1982
); calponin 2 (CNN2), which contains an actin
binding site and is localized to adherens junctions with vinculin
(Masuda et al., 1996
); and MLLT4 (known also as AF-6 or
afadin), which is a component of tight junctions (Yamamoto et
al., 1997
). Taken together, these results suggest that the
regulation of transcripts during the cell cycle plays a role in
orchestrating the morphological changes that occur during mitosis.
Expression of these genes during the physical act of mitosis may be
necessary to prepare the cell to reestablish contact with the
surrounding milieu.
Genes involved in chromosome architecture and remodeling are also
expressed at M/G1 (Figure 8A). These include presenilin 1, discussed
above, and parathymosin, which is localized to sites of early DNA
replication (Vareli et al., 2000
) and binds to the linker
histone H1 (Kondili et al., 1996
). The dyskeratosis gene (DKC1) is implicated in telomere maintenance, binds to H/ACA class of
snoRNPs, and has been shown to associate with the telomerase RNA that
acts as a template to add sequence repeats to the ends of chromosomes
(Mitchell et al., 1999
). Two members of the SWI/SNF complex
(SMARCB1 and SMARCD1), present in the M/G1 cluster, function in
chromatin remodeling and transcriptional regulation (Wang et al., 1996b
; Muchardt and Yaniv, 2001
). Other M/G1 genes with roles in chromosome architecture and remodeling include human securin (PTTG1), and RAD21, both genes involved in sister chromatid cohesion (Zou et al., 1999
; Hoque and Ishikawa, 2001
).
Finally, expression of CDK7 (MO15) the catalytic subunit of the
CDK-activating kinase (CAK) also peaks at M/G1 (Figure 8B). CDK7 is a
catalytic subunit of CAK, necessary for the phosphorylation and full
activity of CDK2 and CDC2 (Fisher and Morgan, 1994
). CAK activity and
CDK7 protein have been reported to be constant during the cell cycle
(Matsuoka et al., 1994
; Tassan et al., 1994
) although data on the mRNA regulation have not previously been reported.
The CAK cyclin subunit Cyclin H (Fisher and Morgan, 1994
) does not
oscillate in our dataset even though we do see oscillation of the mRNA
for the mitotic cyclins.
In summary, we identified 874 genes that show periodic expression in the cell division cycle of a human cell line. We found a relationship between genes associated with proliferation of tumors and those that are periodically expressed during the cell division cycle. Many of the genes that are periodically expressed have well-characterized functions associated with the cell division cycle, and a large number, >450 clones, are either uncharacterized ESTs or hypothetical proteins whose pattern of regulation during the cell division cycle now points to specific directions for further investigation.
| |
DISCUSSION |
|---|
|
|
|---|
We identified 874 genes that show periodic expression across the
human cell cycle in a well-studied cancer cell line (HeLa). This system
was chosen because a high degree of synchrony and low background of
noncycling cells can be obtained by multiple methods. Herein, 1134 clones (representing 874 UNIGENE clusters) passed a minimum set of
objective quantitative criteria for a periodically expressed gene.
Although both the Rb and P53 tumor suppressors are inactivated in HeLa
cells as a result of binding of the E6/E7 proteins of the human
papillomavirus (Scheffner et al., 1991
), genes involved in
basic cell cycle processes such as DNA replication, chromosome
segregation, and cell adhesion still showed periodic expression.
Each gene was assigned a cell cycle phase as described above (Figures 2 and 3). Approximately 38% of the genes identified as cell cycle regulated were assigned a phase of G1/S or S phase, whereas 45% were assigned a phase of G2 or G2/M. In mammalian cells, G2 and M phases are short (4 h of each 14-16-h cell cycle) relative to G1 and S phase, yet almost half of the periodically expressed genes peak during this interval. One potential explanation is that we were more able to detect genes expressed at G2 and M phases because three of our five synchronization methods arrest cells in S phase, which provides more robust synchrony in G2 and M phases. This possibility now seems particularly unlikely because a similar distribution is observed in the proliferation clusters, which have now been observed in a variety of tumors independently.
We recognize that our microarrays do not completely represent all expressed genes in the human genome, limiting the completeness of our survey. For example, three of the 49 genes known from the literature to be cell cycle-regulated were not represented on our arrays (see above and Table 2). Subject only to these omissions, we conclude, based on the similarities found between cells synchronized in S phase, cells synchronized in M phase, cells selected by mitotic shake-off, and the tumor proliferation clusters, that we have identified a comprehensive list of cell cycle-regulated human genes.
Comparison to Previous Surveys of Periodically Expressed Genes in Animal Cells
Genome-wide transcriptional profiles of the cell cycle in animal
cells have been reported previously from experiments carried out on
fibroblasts and epithelial cells. A study of the response of primary
human fibroblast to serum revealed not only a cell cycle response but
also an equally prominent wound-healing response (Iyer et
al., 1999
). This made clear the shortcomings of serum as a
synchronizing agent if the goal is to find periodically expressed genes. Herein, we have avoided the use of serum "starvation" entirely.
A study of the cell cycle and E2F transcription factors in mouse embryo
fibroblasts identified 578 cell cycle-regulated genes and E2F targets
(Ishida et al., 2001
). Many of the genes identified in the
mouse study were also identified as cell cycle-regulated in HeLa cells
herein. Among these are TYMS, RRM1, RRM2, Cyclin E1, MCM3, MCM7, PCNA,
TOPIIA, FEN1, RAD51, SLBP, Cyclin A2, Cyclin B1, Cyclin B2, KI-67, and
importin alpha 2. Those identified as E2F targets but not identified as
cell cycle regulated in our study include thymidine kinase 1 (TK),
CDK2, DNA ligase I, and RB. It should be noted that not all E2F targets
are expected to be cell cycle regulated in our study of HeLa cells
because some genes show regulation primarily during the resting to
growing transition rather than in a continuous cell cycle.
A study of the G2 DNA damage checkpoint in HeLa cells revealed a delay
in the expression of mitotic genes when the cells are exposed to
ionizing radiation (e.g., Cyclin B1, CKS2, TTK, STK15, CDC20, and
CENPA) (Crawford and Piwnica-Worms, 2001
). Many of these genes were
classified as G2/M in our study. It is worth pointing out that similar
results were obtained herein for G2 genes when cells are arrested in
mitosis by nocodazole; G1/S, S, and G2 genes show relatively low
expression, whereas G2/M and M/G1 genes show relatively high expression
(e.g., 0 h Thy-Noc samples in Figures 2 and 6-8). The opposite is
observed in cells arrested in S phase with thymidine; relatively high
expression of G1/S and S phase genes, and relatively lower expression
of G2, G2/M, and M/G1 genes (Figures 2 and 6-8). The exception is the replication-dependent histone mRNAs, which, although expressed during S
phase, are rapidly and coordinately degraded when DNA synthesis is
inhibited (Harris et al., 1991
). Together, these observations suggest that the gene expression program observed during a
cell cycle arrest is strongly influenced by the point in the cell cycle
where the arrest occurs; the genes normally expressed in that phase are
usually but not always, relatively overexpressed.
Another study of the cell cycle in primary human fibroblasts
synchronized by a double thymidine block, identified ~700 cell cycle-regulated genes (Cho et al., 2001
) that we have mapped
to 595 unique UNIGENE clusters. Surprisingly, only 96 genes were identified as cell cycle-regulated in both studies (these comparisons are available in the supplemental data). Of the remaining 499 genes
that were identified as cell cycle-regulated by Cho et al. (2001)
, but not in this study, only 50 were not measured on our microarrays. Of the 778 genes identified as cell cycle regulated in
this study, but not by Cho et al. (2001)
, only 109 were not measured on their microarrays. We have no ready explanation of this
difference in results, except to note that there are differences in the
cell lineage, the microarray technology, and in the analysis methods.
As we have shown, 69 of the genes that were cell cycle-regulated in our
study are nevertheless associated with the proliferative state of
tumors and cell lines, whereas only 29 of the genes reported to be cell
cycle-regulated by Cho et al. (2001)
appear in the tumor
proliferation clusters. We suspect that the most significant differences may well be in the degree of cell synchrony achieved and
the percentage of cells that reentered the cell cycle after removal of
the synchronizing block. Alternatively, HeLa cells, derived from a
cervical carcinoma, may more closely resemble cancers in their cell
cycle program of gene expression and differ significantly from the
corresponding program in a normal fibroblast.
Summing up the comparisons with previous work, we believe that we have detected most of the genes expressed periodically through the HeLa cell division cycle. It will be important to compare this list of 874 genes to similarly comprehensive studies of more normal human cell types in the future.
Comparison to Yeast Cell Cycle
Comparison of the results of this study with our previous study of
the yeast cell division cycle (Spellman et al., 1998
)
produced interesting results. When the "well-characterized" (i.e.,
those that encode curated protein sequences in RefSeq) and periodically expressed human gene sequences were compared with the yeast genome sequence, ~18% (155 genes) had putative orthologs (Ball, Whitfield, and Botstein, unpublished data). Of these, only ~26% (41) were clearly cell cycle regulated in yeast (Spellman et al.,
1998
), suggesting regulation at the level of transcription is not
necessarily a conserved property. An example of a gene periodically
expressed in human cells but not in yeast is the human CDC2 kinase
(homolog of S. cerevisiae CDC28 kinase). Yeast
CDC28 is not regulated at the transcriptional level but
rather the kinase activity of CDC28 is regulated (Lee and
Nurse, 1987
). It is likely that many of the periodically expressed
genes in human cells that do not have periodically expressed
counterparts in yeast are subject to multiple layers of regulation
(e.g., phosphorylation and proteolysis) as is already known for many
well-studied cell cycle genes. Among the genes periodically expressed
in both species are many involved in basic processes such as DNA
replication, repair, and metabolism, and mitosis. However, as noted by
Spellman et al. (1998)
, many of the genes that were
periodically synthesized in yeast are not obviously involved in these
basic processes but instead are genes involved in bud emergence and bud
growth, which take place at particular points in the cell cycle because
of the particular biology of budding yeasts. Likewise, it may be that
many genes are periodically expressed in an animal cell for reasons
that do not apply to yeast. Spellman et al. (1998)
reasoned,
partly from this observation, that parsimony is likely to explain much of the observed cell cycle regulation; genes are periodically expressed
primarily because there is special need for the gene products at
particular points in the cell cycle. For human cells, the periodic
synthesis of the cytoskeleton and cell adhesion-associated genes is a
particularly interesting case in point. The overall shape of an animal
cell generally changes, and tends to lose contact with its substrate,
when it forms the mitotic spindle and undergoes cell division.
Reasoning in parallel to the yeast example, we suppose that animal
cells might periodically synthesize cytoskeletal genes and adhesion
factors because of the particular need to change shape in mitosis, and
subsequently reattach to substrate, a need not shared by the yeasts.
Cell Cycle and Cancer
Comparison of the 874 genes that we identify as cell cycle regulated in HeLa cells to studies of human breast tumors shows that almost 70% of the transcripts in the proliferation cluster are cell cycle regulated. We showed above that many genes involved in basic cell cycle processes are also more highly expressed in more proliferative tumors. However, the correlation we found may reflect only the reality that proliferative tumors contain a large proportion of actively cycling cells.
There are nevertheless periodically expressed genes (e.g., KRAS2,
PTTG1, STK15, and PLK) (Der et al., 1982
; Pei and Melmed, 1997
; Smith et al., 1997
; Zhou et al., 1998
) that
are likely to contribute more directly to tumor phenotypes. For
example, the two highest scoring genes in our study, STK15 and
Polo-like kinase (PLK), show peak expression at G2/M, are highly
expressed in proliferative tumors, and have transforming activity in
NIH3T3 cells (Holtrich et al., 1994
; Smith et
al., 1997
; Bischoff et al., 1998
; Zhou et
al., 1998
; Takai et al., 2001
). STK15 has been shown to
be amplified in human colon cancers and in cell lines derived from many
other kinds of human tumors (Bischoff et al., 1998
; Zhou et al., 1998
). High expression has also been observed in the
absence of amplification and contributes to abnormal centrosome numbers in cell lines (Zhou et al., 1998
). PLK has also been
implicated in centrosome maturation; injection of PLK into either HeLa
cells or human foreskin fibroblasts resulted in reduced centrosome size and abnormal chromatin distribution (Lane and Nigg, 1996
).
Amplification or overexpression of STK15 and/or PLK has been postulated
as a potential cause of aneuploidy in human tumors (Lengauer et
al., 1998
). The high expression of genes involved in centrosome
duplication may contribute to the chromosomal translocations and
aneuploidy found in HeLa cells.
Although there is significant overlap between the genes found in the
proliferation cluster and those that we have identified as cell
cycle-regulated, not all cell cycle-regulated genes are highly
expressed in the more proliferative tumors. Some cell cycle-regulated genes are expressed in a heterogeneous pattern with no clear
relationship to proliferation in the breast tumors studied (e.g., CDC6,
CCNE1, ORC1, and BRCA1). Some cell cycle-regulated genes are expressed at lower levels in the more proliferative tumors (calponin 2, smoothelin, vinculin, and actin filament-associated protein). Many of
the genes expressed at lower levels in tumors are genes with peak
expression at M/G1 and have roles in cell-cell adhesion and regulation
of the actin cytoskeleton. The strongly decreased expression of these
genes in tumors is often associated with increased invasion,
metastasis, and ultimately, poor prognosis (Rodriguez Fernandez
et al., 1992
; Rudiger, 1998
; Engers and Gabbert, 2000
). It
is highly likely that the regulation of these genes is responsive to
many other factors in addition to the cell cycle. Alternatively, the
relative low level of expression of some of these genes may actually be
advantageous in the proliferative tumors.
The genes identified in this study include the molecular targets of
several different classes of chemotherapeutic agents (antimetabolite targets TYMS, RRM1 and 2, and DHFR; tubulin targeted by antimicrotubule drugs, TOP1 and TOP2 inhibitors, etc.) (Ratain, 1997
). The list of
genes that are strongly expressed in proliferative tumors, especially
those previously uncharacterized, may prove to be a useful source of
additional drug targets of this kind.
In conclusion, it is important to note that we have extracted only a fraction of the information inherent in this dataset. The entire dataset is now freely available for any purpose (http://genome-www.stanford.edu/Human-CellCycle/HeLa/). We are confident that others will find it a useful source for further discoveries and insights into the cell cycle and cancer.
| |
ACKNOWLEDGMENTS |
|---|
We acknowledge members of the Botstein and Brown laboratories for helpful discussions; Max Diehn, Ash Alizadeh, and Jennifer Boldrick for assistance in the collection of time points; Orly Alter and Olga Troyanskaya for discussions of analysis methods; the Stanford Functional Genomics Core Facility for the production of microarrays; and Tim Stearns and May C. Chen for critical reading of the manuscript. M.L.W. is supported by a National Research Service Award Postdoctoral Fellowship from the National Human Genome Research Institute (HG00220-02) and by funds from the Scleroderma Research Foundation. J.I.M. is a Howard Hughes Medical Institute Predoctoral Fellow. M.M.H. is supported by a grant from the American Cancer Society, Florida Division (F99FSU-3) with prior support from the National Institutes of Health (GM-46768). This work was supported by grants from the National Cancer Institute to D.B. and P.O.B. (CA-77097 and CA-85129). P.O.B. is an Associate Investigator of the Howard Hughes Medical Institute.
| |
FOOTNOTES |
|---|
A complete data set for this article is available
at www.molbiolcell.org. Article published online ahead of print. Mol.
Biol. Cell 10.1091/mbc.02-02-0030. Article and publication date are at www.molbiolcell.org/cgi/doi/10.1091/mbc.02-02-0030.
¶ Corresponding authors. E-mail addresses: botstein{at}genome.stanford.edu or pbrown{at}cmgm.stanford.edu.
| |
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C. Swanton, B. Nicke, M. Schuett, A. C. Eklund, C. Ng, Q. Li, T. Hardcastle, A. Lee, R. Roy, P. East, et al. Chromosomal instability determines taxane response PNAS, May 26, 2009; 106(21): 8671 - 8676. [Abstract] [Full Text] [PDF] |
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Y. Halperin, C. Linhart, I. Ulitsky, and R. Shamir Allegro: Analyzing expression and sequence in concert to discover regulatory programs Nucleic Acids Res., April 1, 2009; 37(5): 1566 - 1579. [Abstract] [Full Text] [PDF] |
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T. Komori, N. Inukai, T. Yamada, Y. Yamaguchi, and H. Handa Role of human transcription elongation factor DSIF in the suppression of senescence and apoptosis Genes Cells, March 1, 2009; 14(3): 343 - 354. [Abstract] [Full Text] [PDF] |
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T. Fujiwara, O. Misumi, K. Tashiro, Y. Yoshida, K. Nishida, F. Yagisawa, S. Imamura, M. Yoshida, T. Mori, K. Tanaka, et al. Periodic Gene Expression Patterns during the Highly Synchronized Cell Nucleus and Organelle Division Cycles in the Unicellular Red Alga Cyanidioschyzon merolae DNA Res, February 1, 2009; 16(1): 59 - 72. [Abstract] [Full Text] [PDF] |
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M. F. Reynolds, E. C. Peterson-Roth, I. A. Bespalov, T. Johnston, V. M. Gurel, H. L. Menard, and A. Zhitkovich Rapid DNA Double-Strand Breaks Resulting from Processing of Cr-DNA Cross-Links by Both MutS Dimers Cancer Res., February 1, 2009; 69(3): 1071 - 1079. [Abstract] [Full Text] [PDF] |
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M. A. White, L. Riles, and B. A. Cohen A Systematic Screen for Transcriptional Regulators of the Yeast Cell Cycle Genetics, February 1, 2009; 181(2): 435 - 446. [Abstract] [Full Text] [PDF] |
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H. Chon, A. Vassilev, M. L. DePamphilis, Y. Zhao, J. Zhang, P. M. Burgers, R. J. Crouch, and S. M. Cerritelli Contributions of the two accessory subunits, RNASEH2B and RNASEH2C, to the activity and properties of the human RNase H2 complex Nucleic Acids Res., January 1, 2009; 37(1): 96 - 110. [Abstract] [Full Text] [PDF] |
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W. Lin, J. Wu, H. Dong, D. Bouck, F.-Y. Zeng, and T. Chen Cyclin-dependent Kinase 2 Negatively Regulates Human Pregnane X Receptor-mediated CYP3A4 Gene Expression in HepG2 Liver Carcinoma Cells J. Biol. Chem., November 7, 2008; 283(45): 30650 - 30657. [Abstract] [Full Text] [PDF] |
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P. Singh, M. Yang, H. Dai, D. Yu, Q. Huang, W. Tan, K. H. Kernstine, D. Lin, and B. Shen Overexpression and Hypomethylation of Flap Endonuclease 1 Gene in Breast and Other Cancers Mol. Cancer Res., November 1, 2008; 6(11): 1710 - 1717. [Abstract] [Full Text] [PDF] |
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P. P. Provenzano, D. R. Inman, K. W. Eliceiri, H. E. Beggs, and P. J. Keely Mammary Epithelial-Specific Disruption of Focal Adhesion Kinase Retards Tumor Formation and Metastasis in a Transgenic Mouse Model of Human Breast Cancer Am. J. Pathol., November 1, 2008; 173(5): 1551 - 1565. [Abstract] [Full Text] [PDF] |
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M. Tamamori-Adachi, H. Takagi, K. Hashimoto, K. Goto, T. Hidaka, U. Koshimizu, K. Yamada, I. Goto, Y. Maejima, M. Isobe, et al. Cardiomyocyte proliferation and protection against post-myocardial infarction heart failure by cyclin D1 and Skp2 ubiquitin ligase Cardiovasc Res, November 1, 2008; 80(2): 181 - 190. [Abstract] [Full Text] [PDF] |
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O. Decaux, L. Lode, F. Magrangeas, C. Charbonnel, W. Gouraud, P. Jezequel, M. Attal, J.-L. Harousseau, P. Moreau, R. Bataille, et al. Prediction of Survival in Multiple Myeloma Based on Gene Expression Profiles Reveals Cell Cycle and Chromosomal Instability Signatures in High-Risk Patients and Hyperdiploid Signatures in Low-Risk Patients: A Study of the Intergroupe Francophone du Myelome J. Clin. Oncol., October 10, 2008; 26(29): 4798 - 4805. [Abstract] [Full Text] [PDF] |
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S. Farkash-Amar, D. Lipson, A. Polten, A. Goren, C. Helmstetter, Z. Yakhini, and I. Simon Global organization of replication time zones of the mouse genome Genome Res., October 1, 2008; 18(10): 1562 - 1570. [Abstract] [Full Text] [PDF] |
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W.-m. Zhao, J. A. Coppinger, A. Seki, X.-l. Cheng, J. R. Yates III, and G. Fang RCS1, a substrate of APC/C, controls the metaphase to anaphase transition PNAS, September 9, 2008; 105(36): 13415 - 13420. [Abstract] [Full Text] [PDF] |
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C. J. Creighton, A. Casa, Z. Lazard, S. Huang, A. Tsimelzon, S. G. Hilsenbeck, C. K. Osborne, and A. V. Lee Insulin-Like Growth Factor-I Activates Gene Transcription Programs Strongly Associated With Poor Breast Cancer Prognosis J. Clin. Oncol., September 1, 2008; 26(25): 4078 - 4085. [Abstract] [Full Text] [PDF] |
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W. S. Brooks, E. S. Helton, S. Banerjee, M. Venable, L. Johnson, T. R. Schoeb, R. A. Kesterson, and D. F. Crawford G2E3 Is a Dual Function Ubiquitin Ligase Required for Early Embryonic Development J. Biol. Chem., August 8, 2008; 283(32): 22304 - 22315. [Abstract] [Full Text] [PDF] |
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N. A. Johnson and R. D. Gascoyne Gene expression signatures in follicular lymphoma: are they ready for the clinic? Haematologica, July 1, 2008; 93(7): 982 - 987. [Full Text] [PDF] |
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C. Linhart, Y. Halperin, and R. Shamir Transcription factor and microRNA motif discovery: The Amadeus platform and a compendium of metazoan target sets Genome Res., July 1, 2008; 18(7): 1180 - 1189. [Abstract] [Full Text] [PDF] |
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A. Seki, J. A. Coppinger, C.-Y. Jang, J. R. Yates, and G. Fang Bora and the Kinase Aurora A Cooperatively Activate the Kinase Plk1 and Control Mitotic Entry Science, June 20, 2008; 320(5883): 1655 - 1658. [Abstract] [Full Text] [PDF] |
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P. Kalab and R. Heald The RanGTP gradient - a GPS for the mitotic spindle J. Cell Sci., May 15, 2008; 121(10): 1577 - 1586. [Abstract] [Full Text] [PDF] |
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C.-Y. Jang, J. Wong, J. A. Coppinger, A. Seki, J. R. Yates III, and G. Fang DDA3 recruits microtubule depolymerase Kif2a to spindle poles and controls spindle dynamics and mitotic chromosome movement J. Cell Biol., April 21, 2008; 181(2): 255 - 267. [Abstract] [Full Text] [PDF] |
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A. Seki, J. A. Coppinger, H. Du, C.-Y. Jang, J. R. Yates III, and G. Fang Plk1- and {beta}-TrCP-dependent degradation of Bora controls mitotic progression J. Cell Biol., April 3, 2008; 181(1): 65 - 78. [Abstract] [Full Text] [PDF] |
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C. A. Maxwell, J. McCarthy, and E. Turley Cell-surface and mitotic-spindle RHAMM: moonlighting or dual oncogenic functions? J. Cell Sci., April 1, 2008; 121(7): 925 - 932. [Abstract] [Full Text] [PDF] |
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M. Diehn, C. Nardini, D. S. Wang, S. McGovern, M. Jayaraman, Y. Liang, K. Aldape, S. Cha, and M. D. Kuo From the Cover: Identification of noninvasive imaging surrogates for brain tumor gene-expression modules PNAS, April 1, 2008; 105(13): 5213 - 5218. [Abstract] [Full Text] [PDF] |
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L. T. Lam, G. Wright, R. E. Davis, G. Lenz, P. Farinha, L. Dang, J. W. Chan, A. Rosenwald, R. D. Gascoyne, and L. M. Staudt Cooperative signaling through the signal transducer and activator of transcription 3 and nuclear factor-{kappa}B pathways in subtypes of diffuse large B-cell lymphoma Blood, April 1, 2008; 111(7): 3701 - 3713. [Abstract] [Full Text] [PDF] |
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S. Sinha, A. S. Adler, Y. Field, H. Y. Chang, and E. Segal Systematic functional characterization of cis-regulatory motifs in human core promoters Genome Res., March 1, 2008; 18(3): 477 - 488. [Abstract] [Full Text] [PDF] |
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Z. Yang, N. He, and Q. Zhou Brd4 Recruits P-TEFb to Chromosomes at Late Mitosis To Promote G1 Gene Expression and Cell Cycle Progression Mol. Cell. Biol., February 1, 2008; 28(3): 967 - 976. [Abstract] [Full Text] [PDF] |
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T. Shimizu, L.-L. Ho, and Z.-C. Lai The mob as tumor suppressor Gene Is Essential for Early Development and Regulates Tissue Growth in Drosophila Genetics, February 1, 2008; 178(2): 957 - 965. [Abstract] [Full Text] [PDF] |
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Z. Bar-Joseph, Z. Siegfried, M. Brandeis, B. Brors, Y. Lu, R. Eils, B. D. Dynlacht, and I. Simon Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells PNAS, January 22, 2008; 105(3): 955 - 960. [Abstract] [Full Text] [PDF] |
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L. N. Singh, L.-S. Wang, and S. Hannenhalli TREMOR a tool for retrieving transcriptional modules by incorporating motif covariance Nucleic Acids Res., December 18, 2007; 35(21): 7360 - 7371. [Abstract] [Full Text] [PDF] |
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T. Fevr, S. Robine, D. Louvard, and J. Huelsken Wnt/{beta}-Catenin Is Essential for Intestinal Homeostasis and Maintenance of Intestinal Stem Cells Mol. Cell. Biol., November 1, 2007; 27(21): 7551 - 7559. [Abstract] [Full Text] [PDF] |
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A. M. Saaf, J. M. Halbleib, X. Chen, S. T. Yuen, S. Y. Leung, W. J. Nelson, and P. O. Brown Parallels between Global Transcriptional Programs of Polarizing Caco-2 Intestinal Epithelial Cells In Vitro and Gene Expression Programs in Normal Colon and Colon Cancer Mol. Biol. Cell, November 1, 2007; 18(11): 4245 - 4260. [Abstract] [Full Text] [PDF] |
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M. Rowicka, A. Kudlicki, B. P. Tu, and Z. Otwinowski High-resolution timing of cell cycle-regulated gene expression PNAS, October 23, 2007; 104(43): 16892 - 16897. [Abstract] [Full Text] [PDF] |
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R. W. Martin, B. J. Orelli, M. Yamazoe, A. J. Minn, S. Takeda, and D. K. Bishop RAD51 Up-regulation Bypasses BRCA1 Function and Is a Common Feature of BRCA1-Deficient Breast Tumors Cancer Res., October 15, 2007; 67(20): 9658 - 9665. [Abstract] [Full Text] [PDF] |
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J. Lapointe, C. Li, C. P. Giacomini, K. Salari, S. Huang, P. Wang, M. Ferrari, T. Hernandez-Boussard, J. D. Brooks, and J. R. Pollack Genomic Profiling Reveals Alternative Genetic Pathways of Prostate Tumorigenesis Cancer Res., September 15, 2007; 67(18): 8504 - 8510. [Abstract] [Full Text] [PDF] |
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A. Seki and G. Fang CKAP2 Is a Spindle-associated Protein Degraded by APC/C-Cdh1 during Mitotic Exit J. Biol. Chem., May 18, 2007; 282(20): 15103 - 15113. [Abstract] [Full Text] [PDF] |
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A. Thalamuthu, I. Mukhopadhyay, X. Zheng, and G. C. Tseng Evaluation and comparison of gene clustering methods in microarray analysis Bioinformatics, October 1, 2006; 22(19): 2405 - 2412. [Abstract] [Full Text] [PDF] |
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K. B. Spurgers, D. L. Gold, K. R. Coombes, N. L. Bohnenstiehl, B. Mullins, R. E. Meyn, C. J. Logothetis, and T. J. McDonnell Identification of Cell Cycle Regulatory Genes as Principal Targets of p53-mediated Transcriptional Repression J. Biol. Chem., September 1, 2006; 281(35): 25134 - 25142. [Abstract] [Full Text] [PDF] |
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W.-m. Zhao, A. Seki, and G. Fang Cep55, a Microtubule-bundling Protein, Associates with Centralspindlin to Control the Midbody Integrity and Cell Abscission during Cytokinesis Mol. Biol. Cell, September 1, 2006; 17(9): 3881 - 3896. [Abstract] [Full Text] [PDF] |
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T. Pramila, W. Wu, S. Miles, W. S. Noble, and L. L. Breeden The Forkhead transcription factor Hcm1 regulates chromosome segregation genes and fills the S-phase gap in the transcriptional circuitry of the cell cycle. Genes & Dev., August 15, 2006; 20(16): 2266 - 2278. [Abstract] [Full Text] [PDF] |
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J. Wong and G. Fang HURP controls spindle dynamics to promote proper interkinetochore tension and efficient kinetochore capture J. Cell Biol., June 19, 2006; 173(6): 879 - 891. [Abstract] [Full Text] [PDF] |
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C. A. Pfrommer, W. Erl, and P. C. Weber Docosahexaenoic acid induces ciap1 mRNA and protects human endothelial cells from stress-induced apoptosis Am J Physiol Heart Circ Physiol, June 1, 2006; 290(6): H2178 - H2186. [Abstract] [Full Text] [PDF] |
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P. J.M. Hendriksen, N. F.J. Dits, K. Kokame, A. Veldhoven, W. M. van Weerden, C. H. Bangma, J. Trapman, and G. Jenster Evolution of the Androgen Receptor Pathway during Progression of Prostate Cancer. Cancer Res., May 15, 2006; 66(10): 5012 - 5020. [Abstract] [Full Text] [PDF] |
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P. Qiu, Z. J. Wang, and K. J. R. Liu Polynomial model approach for resynchronization analysis of cell-cycle gene expression data Bioinformatics, April 15, 2006; 22(8): 959 - 966. [Abstract] [Full Text] [PDF] |
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D. S. Oh, M. A. Troester, J. Usary, Z. Hu, X. He, C. Fan, J. Wu, L. A. Carey, and C. M. Perou Estrogen-Regulated Genes Predict Survival in Hormone Receptor-Positive Breast Cancers J. Clin. Oncol., April 10, 2006; 24(11): 1656 - 1664. [Abstract] [Full Text] [PDF] |
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M. Fahling, R. Mrowka, A. Steege, P. Martinka, P. B. Persson, and B. J. Thiele Heterogeneous Nuclear Ribonucleoprotein-A2/B1 Modulate Collagen Prolyl 4-Hydroxylase, {alpha} (I) mRNA Stability J. Biol. Chem., April 7, 2006; 281(14): 9279 - 9286. [Abstract] [Full Text] [PDF] |
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A. V. Tkatchenko, P. A. Walsh, T. V. Tkatchenko, S. Gustincich, and E. Raviola Form deprivation modulates retinal neurogenesis in primate experimental myopia PNAS, March 21, 2006; 103(12): 4681 - 4686. [Abstract] [Full Text] [PDF] |
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E. Ben-Chetrit, S. Bergmann, and R. Sood Mechanism of the anti-inflammatory effect of colchicine in rheumatic diseases: a possible new outlook through microarray analysis Rheumatology, March 1, 2006; 45(3): 274 - 282. [Abstract] [Full Text] [PDF] |
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S. Schmidt, J. Rainer, S. Riml, C. Ploner, S. Jesacher, C. Achmuller, E. Presul, S. Skvortsov, R. Crazzolara, M. Fiegl, et al. Identification of glucocorticoid-response genes in children with acute lymphoblastic leukemia Blood, March 1, 2006; 107(5): 2061 - 2069. [Abstract] [Full Text] [PDF] |
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P. K. Pandey, T. S. Udayakumar, X. Lin, D. Sharma, P. S. Shapiro, and J. D. Fondell Activation of TRAP/Mediator Subunit TRAP220/Med1 Is Regulated by Mitogen-Activated Protein Kinase-Dependent Phosphorylation Mol. Cell. Biol., December 15, 2005; 25(24): 10695 - 10710. [Abstract] [Full Text] [PDF] |
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J. D. Dougherty, A. D. R. Garcia, I. Nakano, M. Livingstone, B. Norris, R. Polakiewicz, E. M. Wexler, M. V. Sofroniew, H. I. Kornblum, and D. H. Geschwind PBK/TOPK, a Proliferating Neural Progenitor-Specific Mitogen-Activated Protein Kinase Kinase J. Neurosci., November 16, 2005; 25(46): 10773 - 10785. [Abstract] [Full Text] [PDF] |
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K. Willbrand, F. Radvanyi, J.-P. Nadal, J.-P. Thiery, and T. M. A. Fink Identifying genes from up-down properties of microarray expression series Bioinformatics, October 15, 2005; 21(20): 3859 - 3864. [Abstract] [Full Text] [PDF] |
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P. A. Hall, C. B. Todd, P. L. Hyland, S. S. McDade, H. Grabsch, M. Dattani, K. J. Hillan, and S.E. H. Russell The Septin-Binding Protein Anillin Is Overexpressed in Diverse Human Tumors Clin. Cancer Res., October 1, 2005; 11(19): 6780 - 6786. [Abstract] [Full Text] [PDF] |
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I. Shmulevich, S. A. Kauffman, and M. Aldana Eukaryotic cells are dynamically ordered or critical but not chaotic PNAS, September 20, 2005; 102(38): 13439 - 13444. [Abstract] [Full Text] [PDF] |
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K. J. Palmer, J. E. Konkel, and D. J. Stephens PCTAIRE protein kinases interact directly with the COPII complex and modulate secretory cargo transport J. Cell Sci., September 1, 2005; 118(17): 3839 - 3847. [Abstract] [Full Text] [PDF] |
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C.-T. R. Yu, J.-M. Hsu, Y.-C. G. Lee, A.-P. Tsou, C.-K. Chou, and C.-Y. F. Huang Phosphorylation and Stabilization of HURP by Aurora-A: Implication of HURP as a Transforming Target of Aurora-A Mol. Cell. Biol., July 15, 2005; 25(14): 5789 - 5800. [Abstract] [Full Text] [PDF] |
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Y. He, M. A. Brown, J. A. Rothnagel, N. A. Saunders, and R. Smith Roles of heterogeneous nuclear ribonucleoproteins A and B in cell proliferation J. Cell Sci., July 15, 2005; 118(14): 3173 - 3183. [Abstract] [Full Text] [PDF] |
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R. C. Osthus, B. Karim, J. E. Prescott, B. D. Smith, M. McDevitt, D. L. Huso, and C. V. Dang The Myc Target Gene JPO1/CDCA7 Is Frequently Overexpressed in Human Tumors and Has Limited Transforming Activity In vivo Cancer Res., July 1, 2005; 65(13): 5620 - 5627. [Abstract] [Full Text] [PDF] |
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G. A. Boorman, P. E. Blackshear, J. S. Parker, E. K. Lobenhofer, D. E. Malarkey, M. K. Vallant, D. K. Gerken, and R. D. Irwin Hepatic Gene Expression Changes throughout the Day in the Fischer Rat: Implications for Toxicogenomic Experiments Toxicol. Sci., July 1, 2005; 86(1): 185 - 193. [Abstract] [Full Text] [PDF] |
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