- Regular article
- Open Access
Meta-analysis reveals conserved cell cycle transcriptional network across multiple human cell types
© The Author(s). 2017
- Received: 2 August 2016
- Accepted: 19 December 2016
- Published: 5 January 2017
Cell division is central to the physiology and pathology of all eukaryotic organisms. The molecular machinery underpinning the cell cycle has been studied extensively in a number of species and core aspects of it have been found to be highly conserved. Similarly, the transcriptional changes associated with this pathway have been studied in different organisms and different cell types. In each case hundreds of genes have been reported to be regulated, however there seems to be little consensus in the genes identified across different studies. In a recent comparison of transcriptomic studies of the cell cycle in different human cell types, only 96 cell cycle genes were reported to be the same across all studies examined.
Here we perform a systematic re-examination of published human cell cycle expression data by using a network-based approach to identify groups of genes with a similar expression profile and therefore function. Two clusters in particular, containing 298 transcripts, showed patterns of expression consistent with cell cycle occurrence across the four human cell types assessed.
Our analysis shows that there is a far greater conservation of cell cycle-associated gene expression across human cell types than reported previously, which can be separated into two distinct transcriptional networks associated with the G1/S-S and G2-M phases of the cell cycle. This work also highlights the benefits of performing a re-analysis on combined datasets.
- Cell cycle
- Systems biology
- Network analysis
Cell division is a fundamental process common to all eukaryotic organisms and involves sequential duplication of the genome and daughter cell generation. These two events occur during S phase and M phase respectively, each preceded by a gap (G) phase, named G1 and G2, where cells grow in mass and prepare for the following phase. With the advent of genome-wide expression microarrays hundreds of cell cycle-regulated transcripts have been identified in yeast [1–5]. Likewise, in human cell lines several efforts have been put to define the cell cycle transcriptome. Among others, Whitfield and coworkers  synchronized HeLa cells both at G1/S transition, using a double-thymidine block, and at G2/M transition, using a thymidine-nocodazole block and identified 874 cell cycle-regulated genes. A later study on primary human foreskin fibroblasts identified 480 cell cycle-associated genes  after synchronization of fibroblasts both with a double-thymidine block and by serum deprivation. The latter synchronisation method forces cells to enter a quiescent state (G0), from which they can then re-enter proliferation as a cohort upon serum re-feeding . More recently, the cell cycle transcriptome has been further characterized in two additional studies: one reported 1249 cell cycle-associated genes employing a human keratinocyte cell line (HaCat) which although immortalized is deemed to retain a normal cell biology , whereas the second study identified 1871 periodic genes in the osteosarcoma-derived cell line (U2OS) . The studies mentioned above isolated periodic signals from the gene expression profiles, apparent when multiple cell cycle events are monitored, in order to identify cell cycle-associated genes. This is typically achieved by converting expression measurements for each gene into a wave function (Fourier transform), a method pioneered by Spellman and co-workers . However, results from independent studies showed considerable discrepancies in the identity and size of the gene lists identified, with a large portion of genes being reported by only a single study [9, 11, 12].
Works on reconciling these diverse results have been carried out in budding and fission yeasts [11, 12]. These studies concluded that the primary cause of such discrepancies are not differences in experimental procedures nor in actual biological variation but rather in the analysis of the data. To date no similar studies have been performed on data derived from human cells. Cyclebase , a publicly available web resource provides a list of 378 human cell cycle-associated genes, but these have been derived from four experiments from only one study performed in human cells (HeLa cells) . To address this gap we have re-analyzed publicly available expression data derived from four different human cell types using a correlation-based approach. This has enabled us to define conserved gene co-expression patterns associated with proliferation. Our analysis demonstrates that, as for yeast data, data interpretation is the primary reason for the discrepancies between previous results in defining a cell cycle gene set and that, contrary to what has been reported, the transcriptional network associated with the cell cycle is highly conserved across human cell types.
Description of cell cycle studies used for the meta-analysis
Description of cell cycle studies used for the meta-analysis
Raw data availability
Cell cycle genes (Entrez ID)
Exp 1: Thy block
Exp2: serum starvation
0 h–32 h, 2 h interval
0 h–32 h, 2 h interval
Affymetrix U133A 2.0
Bar-Joseph et al. 2008 
Exp 1: Thy block
Exp 2: Thy block
Exp 3: Thy block
0 h–12 h, 2 h interval
0 h–12 h, 2 h interval
0 h–12 h, 2 h interval
Sadasivam et al. 2011
Exp 1: Thy block
Exp 2: Thy block
Exp 3:Thy block
0 h–33 h, 3 h interval
0 h–33 h, 3 h interval
0 h–33 h, 3 h interval
Diaz et al. 2013
Exp 1:Thy block
Exp 2:Thy block
Exp 3:Thy block
Exp 4:Noc block
0 h–46 h, 2 h interval
0 h–38 h, 2 h interval
0 h–46 h, 2 h interval
0 h–44 h, 2 h interval
Agilent Oligonucleotide arrays
Grant et al. 2013 
Three tests were performed to assess the array data quality from each study: 1) boxplots and histograms, to spot anomalous signal distribution and/or intensity, 2) pseudo-images of the arrays to identify spatial artifacts and 3) sample correlation matrix to identify low-correlated samples not associated with a different biology. After poor quality array removal, each sample set was normalized separately using robust multiarray averaging (RMA) normalization, a standard method for normalizing microarray data which implements background noise adjustment, quantile normalization and probe intensity summarization . Next, probe sets were annotated with Entrez gene identifiers (Entrez IDs). Ambiguous probe sets mapping to multiple gene identifiers were removed. Quality control (QC), normalization and probe sets annotation was performed in R environment using a range of Bioconductor packages. Samples for each study were further examined after normalization by principal component analysis (PCA).
Datasets were bound together using Entrez IDs as reference. The unified dataset contained 11,693 Entrez IDs and 159 samples. To adjust for different average intensities across datasets we applied ComBat , a widely used batch effect correction algorithm. Batches were manually numbered according to the study and then the algorithm was run in R environment.
Cluster analysis was performed with BioLayout Express 3D . This tool allows the conversion of a data matrix into a correlation matrix by calculating Pearson correlations between every transcript to every other transcript measurement. Following the selection of a correlation threshold value, the correlation matrix is then rendered as a weighted undirected network, where nodes represent transcripts and the edges between them the correlation coefficients. A network clustering algorithm (MCL) is implemented within the tool to identify highly connected cliques of nodes within the network that represent genes with a similar expression profile . Once the algorithm is run, clusters are color-coded and numbered according to their size in a descending order. Data was imported into BioLayout Express 3D after converting the text file into an ‘.expression’ file. Measurements were anti-logged before the calculation of correlation matrix. The correlation cutoff threshold was set to r ≥ 0.60 and signal with a coefficient of variance lower than 0.18 was removed. The MCL inflation value (MCLi) of the cluster algorithm, controlling the granularity of the clusters, was set to 1.4 and the pre-inflation value was set to 2.0. Further sub-clustering of cell cycle-related clusters was obtained with MCLi at 2.3 for cluster 4 and 4.2 for cluster 6. Minimum cluster size was set to 5. Clusters of gene expression were then visually inspected. Specifically, we searched for clusters of genes whose average expression increased with a particular phase of the cell cycle across all datasets.
where z is the z-score, x is the value of the given gene and x is the mean of the values for the given gene.
Gene ontology enrichment analysis
Enrichment analysis was performed with Database for Annotation, Visualization and Integrated Discovery (DAVID) (Version 6.8 Beta), a web-based tool for Gene Ontology enrichment analysis (http://david.abcc.ncifcrf.gov/) . Gene symbol lists were uploaded and analysed using Functional Annotation Clustering only for GO Biological Process annotation (GO_BP). Representative GO Biological Process terms selected from the top significantly enriched clusters were reported in figures. The Benjamini corrected P-values were used.
The R package “WGCNA”  was used to perform weighted gene co-expression analysis (WGCNA). Before construction of the adjacency matrix a soft threshold (β) was set by inspection of plots generated after calling the function pickSoftThreshold. The soft threshold was set to 6 as this value represented the point at which the Scale-Free Topology (SFT) Index as a function of the Soft Threshold reached saturation. Modules were generated after calling the function blockwiseModules. Arguments of this functions were kept as default. To run GO enrichments analysis the function GOenrichmentAnalysis was called returning the 10 most significant GO terms for each module. Benjamini-corrected P-values were used.
Incongruences in previous cell cycle lists
Four previous cell cycle studies [6, 7, 9, 10] identified gene sets with periodic expression ranging from 480 cell cycle genes in fibroblasts to 1871 in U2OS cells. Grant et al. noted large differences between the gene lists (Additional file 1: Figure S1A) and phase assignation of the cell cycle genes based on their peaking times exhibited further incongruences. Three studies identified five different phases, namely: G1/S, S, G2, G2/M and M/G1 while in Bar-Joseph data S phase was not assigned. Percentage and number of genes assigned to the phases varied greatly across studies. Bar-Joseph et al. assigned 43% of the genes (221 genes) to G2/M phase while Grant et al. 21% (598 genes) to the same phase (Additional file 1: Figure S1B). Similarly, G2 phase accounted for 6% (29 genes) of the Bar-Joseph gene list and it comprised 21% (203 genes) of Peña-Diaz list. Only 18 genes were annotated as G2/M and 16 genes as G1/S consistently across all four studies while for the other phases (S, G2, M/G1), not even a single gene was identified by all studies (Additional file 1: Figure S1C). We therefore set out to perform a systematic re-analysis of the human core cell cycle transcriptome.
Data processing and generation of a clustered network graph
Clusters with G1/S-S and G2-M phase specific gene expression
Genes in clusters 4 and 6 were maximally expressed during S and M phases consistent across all cell line assessed (Fig. 1b, blue and red profiles). The expression of genes in cluster 4 was up-regulated upon release from the double-thymidine block (which synchronises cells at the G1/S transition) and raised approximately 16 h after serum-refeeding in the serum starvation experiment in fibroblasts (Fig. 1b, blue profile). This is in agreement with an increased enrichment of S phase-related genes in human fibroblasts reported previously [21–23]. Conversely, cluster 6 genes exhibited low expression upon release from the thymidine-block followed by up-regulation at around 8–12 h and after around 24 h in the starvation experiment (Fig. 1b, red profile). Gene Ontology enrichment analysis of the 155 genes in cluster 4 demonstrated a highly significant enrichment for biological processes linked with S phase including DNA replication (P = 1.58 10−55), DNA repair (P = 1.31 10−39) and G1/S transition (P = 4.3 10−26) (Fig. 1c, blue barplots). The 143 genes found in cluster 6 were instead highly enriched for mitosis-related biological processes such as mitotic cell cycle (P = 3.46 10−72), chromosome segregation (P = 1.29 10−48) and spindle organization (P = 4.47 10−22) (Fig. 1c, red barplots).
Genes in cluster 4 included several factors involved in DNA replication such as various polymerases (POLA1, POLA2, POLD1, POLD3, and POLE2), proliferating cell nuclear antigen (PCNA), cell division control protein (CDC6) and other protein complexes necessary to initiate DNA synthesis e.g. members of the DNA replication complex (GINS2-4), members of the minichromosome maintenance complex (MCM2-7 and 10), and the replication factor complex (RFCs). DNA repair and DNA damage factors known to cooperate in DNA replication were also identified including Fanconi anemia complex components (FANCE, FANCG, FANCI, and FANCL), RAD complex components (RAD51, RAD51AP1 and RAD54L) and Breast cancer type 1 susceptibility protein (BRCA1). Importantly, genes known to regulate G1/S transition including cyclins E (CCNE1 and CCNE2), M phase inducer phosphatase 1 (CDC25A) and Cell division control protein 6 homolog (CDC6) belonged to cluster 4. Genes in cluster 6 included several G2 and mitotic regulators such as mitotic checkpoint serine/threonine-protein kinase (BUB1), cyclin-dependent kinase 1 (CDK1), a master cell cycle regulator, cyclins A and the two isoforms of cyclin B (CCNA2, CCNB1, CCNB2) and M phase inducer phosphatase 2/3 (CDC25B and CDC25C). Various genes involved in kinetochore formation (CENPA, CENPE, CENPF, CENPI) and several motor proteins members of the kinesin-like proteins (KIFs) known to participate in chromosomal and spindle movements during mitosis  also belonged to this cluster. Clusters 4 and 6 together accounted for 298 genes which exhibited up-regulation associated with S phase and mitosis across all the four cell lines examined. This number is three fold higher than that previously found to be representing the core cell cycle signature across the human cell lines investigated .
Sub-clustering of clusters 4 and 6 allows more specific cell cycle phase association
Likewise, increasing the stringency of the clustering split cluster 6 into two sub-clusters, cluster 6A and cluster 6B (Fig. 2a, right), associated with G2 and M phase respectively (Fig. 2b, bottom). Cell cycle regulators CDK1, CCNA2, CDC25B and CDC25C were found in cluster 6A (G2 cluster) and are known to intervene at the G2/M boundary . Kinetochore proteins (CENPE, CENPF, CENPI) and motor proteins (KIF11, KIF14, KIF18A, KIF18B, KIF20B, KIF22, KIF23, KIF2C, KIF5B) were included in this cluster. Cluster 6B was populated with mitotic cyclins such as: CCNB1 and CCNB2, BUB1, involved in the metaphase checkpoint  and other gene products playing a role in mitosis. Complete description of the phases assigned to genes in clusters 4 and 6 can be found in Additional file 2: Table S1. It should be emphasized however that whilst sub-division of clusters 4 and 6 may identify regions in the network that are more enriched for genes associated with particular phase of the cell cycle, the network is a continuum and these sub-divisions are relatively arbitrary.
G1-related and early growth response clusters
Cluster 2 showed a partial cell cycle-associated expression with peaks of expression coinciding to those of cluster 6 as observed in the following experiments: in experiment three in U2OS cells, in all the three experiments in HeLa cells and in the second experiment in HaCat cells (Fig. 1b, grey profile). Notably, this cluster profile also showed up-regulation at around 6 h in primary fibroblasts entering cell cycle from quiescence (starvation experiment). Genes in cluster 2 were involved in pathways indicative of an active metabolism such as: cellular metabolic process (P = 2.3 10−4), ribosome biogenesis (P = 3.8 10−2) and macromolecule modification (P = 1.4 10−2) (Fig. 1c, grey barplots). Also were found in this cluster: E2F5, a member of the E2F transcriptional factors family, which plays a role as repressor during G1 phase , the retinoblastoma protein (RB1), a main tumor suppressor which inhibits cell cycle progression during this phase by inactivating E2F1  and CDC73, another tumor suppressor which has been reported to interact with cyclin D1 . Cluster 2 also included several mitogen-activated MAP kinases (MAP2K1, MAP3K4, MAP3K7CL, MAP4K3, MAPK6) essential to deliver mitogenic stimuli signals to cell cycle regulators. Interestingly, cluster 1 and 9 (Fig. 1a) also contained G1-related genes with cluster 1 including cyclins D1 and D3 (CCND1 and CCND3), master regulators of G1 progression  while cluster 9 included CDK4, a cyclin dependent kinase which operates during G1 phase.  These clusters however failed to show expression patterns associated with cell cycle events (Additional file 1: Figure S8, green and red profiles). Cluster 3 showed a conserved sharp peak in expression in the first hours after the release of cells from blockade, with no further induction at other times (Fig. 1b, brown profile). The 128 genes in this cluster were highly enriched with pathways involving transmission of both proliferative and anti-proliferative signals (Fig. 1c, brown barplot). Accordingly, the cluster included several genes activated by mitogenic stimuli and encoding for a variety of cytoplasmatic enzymes, secreted proteins and transcription factors assigned to transduce the signal from the cell membrane to the nucleus . These included early growth response genes 2/3 (EGR2 and EGR3), fos and jun (FOSB and JUNB) which activate transcription upon dimerization  and Immediate early response gene 2/3 (IER2 and IER3).
A full list of genes included in the six clusters identified and lists of their enriched GO biological process terms can be found in Additional file 2: Table S1 and Additional file 3: Table S2, respectively.
Validation of clusters analysis with another unsupervised clustering technique
We analyzed the data using weighted correlation network analysis (WGCNA), an unsupervised technique that generates modules (clusters) of correlated genes after construction of an adjacency matrix. We identified (color-coded) modules after hierarchical clustering using the WGNCA package (see Methods)  (Additional file 1: Figure S9A). Reassuringly, comparisons of the genes included in the most enriched modules derived from the WGNCA analysis and genes in the clusters identified with BioLayout Express 3D showed high overlap and GO enrichments for each module (Additional file 1: Figure S9B) showed consistency of GO biological process terms, particularly for clusters 2, 3, 4, 6 (Additional file 1: Figure S9C). Moreover, we compared the overlap between the two sets of clusters/modules enriched with cell cycle genes finding 237 genes in common. Though WGCNA analysis identified many other genes included in the two modules (449) (Additional file 1: Figure S9D), the enrichment for the GO_BP term cell cycle in the two clusters found in our analysis was far more significant (Additional file 1: Figure S9E).
A network analysis of the combined data more efficiently identifies commonalities in cell cycle-related genes
Data comparison with yeast data and further human-derived datasets
As a large body of work on cell cycle transcriptomics have been performed on budding and fission yeast, we sought to compare our results with these studies. To do so, we exploited a web resource called Cyclebase (Cyclebase.org)  in which several yeast studies were re-analysed and genes were ranked according to the magnitude of their periodicity scores calculated by a statistical method that proved to give the best performance when compared to others . Ranked list of genes were downloaded from the website for the budding and the fission yeasts. The fission yeast list included 449 ranked periodic genes while the budding yeast list comprised a ranked list of the whole yeast transcriptome of which only the top 500 genes were used for comparison. Yeast orthologues to human genes were retrieved using YeastMine (http://yeastmine.yeastgenome.org) for budding yeast, and for fission yeast using PomBase (http://www.pombase.org/). The list of 298 cell cycle-associated genes identified here included 63 orthologues in budding yeast and 35 from fission yeast. When compared with the results of individual human cell cycle studies, the number of budding yeast orthologues is comparable, although in fission yeast almost a double amount of orthologues are identified by the Grant et al. study (Additional file 1: Figure S10A). Nonetheless the 96 genes overlapping in the four studies included a significantly lower number of orthologues genes compared to this list in both yeasts (Additional file 1: Figure S10A). All in all our list includes a relatively high number of orthologues which are mostly comparable with much larger gene lists and a marked higher number than those found in the 96 gene set. To further verify the quality of the 298 gene set we compared its GO enrichments for cell cycle biological process term across the four cell cycle gene lists derived from the correspondent individual studies and the set of 96 genes derived from their direct comparison (Additional file 1: Figure S10B). Gene lists from the Whitfield study were obtained both from Cyclebase and from the original study. As it can be seen, our list of genes received the highest enrichment for the GO_BP term cell cycle.
Cataloguing the genes involved in the cell cycle has proven to be a challenging task. In human, individual studies have identified highly variable lists of cell cycle-associated genes, with only 96 cell cycle genes being common to all studies performed on different cell types i.e., HeLa, primary fibroblasts, HaCat and U2OS cells . We therefore set out to perform a meta-analysis of a collated datasets to identify modules of genes co-expressing among the four cell types. Specifically, we identified two clusters containing 298 genes, associated with G1/S-S, and G2-M stages in all the cell types examined which were highly enriched for GO terms associated with early and late cell cycle progression. As circadian rhythm-associated genes are known to oscillate in expression , we looked for members of this pathway by mapping the 298 genes to Reactome pathways database . However, no genes involved in the circadian clock were identified, nor known members of this biological process were co-clustered together in the correlation network. Of the genes found in clusters 4 and 6 many have been reported by at least one of the previous four studies, however 39 genes have not been identified previously. After a literature search we found that 18 of these showed supporting evidence to be involved in cell cycle, other 13 were characterized as having non-related cell cycle functions and 8 of them were poorly characterized with no supportive literature (Additional file 5: Table S4). The fact that almost half of these genes have been shown to encode proteins associated with the cell cycle further reinforces the quality of the genes found in clusters 4 and 6. Among them were the Structural maintenance of chromosomes protein 2 (SMC2), a subunit of the condensin complex which is essential for chromosome condensation during mitosis , and Putative pituitary tumor-transforming gene 3 protein (PTTG3P), a pseudogene member of the hPTTG gene family which were found to be overexpressed in a number of human tumors . The 8 uncharacterised genes were of particular interest as they potentially represent novel cell cycle genes. Two of these, C9orf40 and DNAJC9, were confirmed to be co-expressed with known cell cycle genes as shown in diagrams generated with GeneMania  (Additional file 1: Figure S11A-B). Further, DNAJC9 was shown to physically interact with Replication protein A (RPA2), which is involved in DNA replication and repair  (Additional file 1: Figure S11B). These genes were also shown to be dysregulated in cancer: C9orf40 was reported to be dysregulated in ovarian carcinoma  whereas DNAJC9 was shown to be up-regulated in metastatic cervical cancer in cancer stem cells [40, 41].
Unlike genes involved in the core cell cycle machinery, G1 phase-associated genes did not all cluster together in one unique cluster suggesting that this cell cycle phase is less conserved across cell types. G1 phase involves cell growth, and therefore it may be more dependent on a given cell type, as cell size and metabolism is highly variable across human cell populations . Of the three clusters containing G1 phase genes (cluster 1, 2 and 9), cluster 2 included a significantly higher number of periodic genes supported by two previous studies (38 genes) compared to the other two clusters (cluster 1 = 14 genes, cluster 9 = 3 genes) (Fig. 3a) and represented a pattern of expression consistent with G1 phase. This was more pronounced in cells entering proliferation from quiescence as opposed to entering it from a previous cell division, possibly because G1 phase in actively cycling cells is shorter than in cells entering proliferation from quiescence . For instance, cyclin D1, responsible for G1 progression, is degraded when cells are not actively cycling and has to be newly synthetized upon cell cycle entry. In contrast cycling cells have enough gene product to go through forthcoming cell cycles so reducing the overall time of the cell cycle . However, G1-associated gene expression remains largely elusive. For example, an additional study specifically aimed to identify genes differentially expressed during G1 phase in cycling HeLa cells  identified 200 transcripts which however did not match any of those found in the four studies nor did they cluster together in our analyses. Further analyses focused on characterizing the G1 phase transcriptional regulation will be then of value, especially in view of its crucial role in aberrant proliferation.
Cluster 3 included early growth response genes greatly induced soon after cells were released from the synchronisation block and dropping to basal level for the rest of the experiment. This suggests that this set of genes might be essential only in triggering proliferation from a cell cycle arrest or quiescence (induced by drugs-based or serum starvation synchronisation methods, respectively) but are not needed to induce a second cell cycle in actively cycling cells. The presence of this cluster is yet another clue that the transcriptional regulation of cells entering cell cycle from quiescence compared to cycling cells is significantly different.
Finally, we note that 298 is a highly conservative estimate of conserved cell cycle genes. In collating datasets derived from different microarray platforms, thousands of genes were discarded leading to only 11,693 unique Entrez ID entries (approximately half of total number of the human genes) being shared across platforms.
These findings suggest a far more conserved transcriptional network associated with the human cell cycle than might be suggested by just comparing previous gene lists, which is in line with this system to be highly conserved across evolution. Moreover, additional biologically relevant clusters were found using such exploratory analysis, free from a priori hypothesis. The limited number of shared cell cycle genes reported previously is therefore likely to be primarily due to analysis protocols, similar to the conclusions drawn on meta-analyses on budding and fission yeasts studies [11, 12]. We also speculate that the number of conserved cell cycle genes might be even higher given the intrinsic limitations of this analysis approach.
BG and TCF are funded by an Institute Strategic Grant from the Biotechnology and Biological Sciences Research Council (BBSRC) [grant number BB/JO1446X/1]. AJ is a Chancellor’s fellow and AJ lab is supported by institute strategic funding from Biotechnology and Biological Sciences Research Council (BBSRC, BB/J004235/1).
Availability of data and material
All data generated or analysed during this study are included in this published article [and its supplementary information files].
BG performed the majority of work described here. BG, AJ, and TCF wrote and edited the manuscript. AJ and TCF supervised the project. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
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