SI Appendix

Contents

1. Cell cycle Synchronization

2. Data quality

3. The importance of the deconvolution for data analysis

4. Peak heights comparisons.

5. Detailed description of genes expressed in the various cell cycle stages.

6. Rank statistic.

7. Experimental validation of the dysregulation of cycling genes in cancer cells

8. Dysregulated cell cycle genes and cancer transformation.

9. Inter species comparison.

1. Cell-Cycle Synchronization

One of the major criticisms of mammalian cell synchronization is that the level of

synchronization is limited (1). Our novel computational approach overcomes this problem by

deconvolving the measured “noisy” expression data into single cell expression values (2). A

feature of this approach is that it allows estimation of the percentage of cells in a given cell cycle

stage at a particular time point, which can be independently verified empirically, and thus serves

as an internal validation of our computational analyses.

Another criticism of whole culture synchronization experiments concerns artifacts due to the

synchronization methods themselves (3). To circumvent this complication, we defined a gene as

cycling only if it was found to be cycling in at least two datasets obtained by distinct

synchronization methods. Importantly, gene expression profiles obtained by using the two

synchronization methods in this study were found to be in good agreement with each other (see

below).

The third major criticism of cell synchronization is that synchronization is generally assessed

according to a single measurement, such as cell size or DNA content (4). In addition to DNA

content, as measured by FACS analysis, we used time lapse cinematography to measure the time

individual cells take to reach mitosis. The good agreement between our FACS and time lapse

data (Fig. 2b) indicates that our synchronization methods indeed reflect multiple cell cycle

features.

2. Data Quality

Specificity and Sensitivity. To determine the specificity of our deconvolution method we

computed the false discovery rate (FDR) using a permutation test. To assess the sensitivity of

this list we used a prior list of known cycling genes. Combining these analyses indicates that our

“common” and “primary FF” lists have a low false positive rate while still capturing a large

fraction of known cell cycle genes.

Specificity. To compute the FDR (5) for our list we used a permutation based test (6). For this

test we have randomized all four time series datasets used in our analysis (two from Whitfield et

al. (7) and the two fibroblast datasets presented in this article). For our sets we have also applied

the deconvolution algorithm to the randomized datasets so that we follow the same preprocessing

steps. Similar to the original datasets the resulting expression profiles were scored by using

Fourier transform (8). Based on these randomization analyses, the FDR was 7% for genes

identified as “primary FF”, 4% for genes identified as ”common” and 2% for genes identified as

“HeLa”.

Sensitivity. To determine the sensitivity of our method we used a list of known cycling genes

compiled by Whitfield et al. 3. This list contains genes identified in prior work (mostly small

scale experiments) as cycling. Of the 45 genes on that list that were present on the array we have

used, 39 (87%) were included in either the ‘common’ or the ‘primary FF’ lists. This result

supports our conclusion that the fibroblast expression data are sensitive enough to detect the vast

majority of cycling human genes.

Agreement Between the Two Expression Datasets. The expression profile was monitored

twice using two synchronization methods (serum starvation and thymidine block). For each

experiment a cyclicity score was calculated and a cell cycle phase was assigned to the genes that

pass a certain threshold.

The correlation coefficient between the normalized cyclicity score of each of the two expression

datasets is 0.4, although the average correlation for random data are 0.08. Thus, the two

expression datasets are in far better agreement when compared to random datasets.

The two experiments also concur regarding the phase assignments - the table below summarizes

the agreement between the phase assignments in the two experiments.

Serum /

Thymidine

M/G1 G1/S G2 G2/M

M/G1 20 27 6 15

G1/S 16 151 12 22

G2 17 18 11 42

G2/M 28 34 9 141

As can be seen, >56% of the genes were assigned to diagonal cells (which represents only 25%

of all cells) indicating a good agreement between the two experiments. For most of nondiagonal

assignments genes were assigned in the second experiment to one of the two close cells (either

the previous or the next cell cycle phase). Due to the nature of the data hard boundaries for phase

assignments are unlikely to be realistic and so assignment to two neighboring phases is a likely

outcome.

Validation of the Cycling Nature of Few Candidate Genes by RT-PCR. Semiquantitative

PCR was performed on cDNA from foreskin fibroblasts at different points along the cell cycle;

4, 8, 14, 20 and 26 h after release from serum starvation and 0, 10, 18, 26 h after release from

thymidine block. Twenty different genes were validated for cycling expression. Results are

shown for 10 of the genes (6 on cDNA from cells synchronized by thymidine block and 5 on

cDNA from cells synchronized by serum starvation). Although the other genes showed cycling

expression patterns the differences in expression were not large. This is not surprising because

this type of validation does not make any adjustments for the partial synchronization of the

primary foreskin fibroblasts. GAPDH expression was used for normalization. Two amounts of

cDNA (2 or 6 µl) were used for each point to ensure that the PCR was concentration dependent.

The top panel shows the results of these experiments, the bottom panel summarizes the results of

the top panel in a graph. Note that the level of expression was normalized to GAPDH expression

and plotted as log2 ratio of the first time point of each time series.

gapdh

fancL

arhgap11a

thymidine block

0 10 18 26 hrs

2 6 2 6 2 6 2 6

hcap-g

flj20641

fyn

c17orf41

-1.5

-1

-0.5

0

0.5

1

0 10 20 30

time (hrs)

log

(2

) ra

tio

fancl

arhgap11a

hcap-g

c17orf41

flj20641

fyn

gapdh

fancL

arhgap11a

thymidine block

0 10 18 26 hrs

2 6 2 6 2 6 2 6

hcap-g

flj20641

fyn

c17orf41

gapdh

fancL

arhgap11a

thymidine block

0 10 18 26 hrs

2 6 2 6 2 6 2 6

hcap-g

flj20641

fyn

c17orf41

-1.5

-1

-0.5

0

0.5

1

0 10 20 30

time (hrs)

log

(2

) ra

tio

fancl

arhgap11a

hcap-g

c17orf41

flj20641

fyn

Method. Total RNA was extracted from foreskin fibroblast cell cultures at different times after

release from serum starvation or thymidine block by RNeasy Mini Kit (Qiagen). Total RNA was

treated with DNaseI (Promega) and cDNA synthesized by using M-MLV reverse transcriptase

(Promega) and random primers (Promega) under conditions recommended by the manufacturer.

To normalize for the relative amount of cDNA synthesized GAPDH expression was measured.

cDNA was diluted (1:10) and used for PCRs (2 or 6 µl) in the presence of 32

P- dCTP

(Amersham Pharmacia) using appropriate primer pairs (sequences available on request). These

primers were designed to span intron-exon junctions to distinguish between cDNA and genomic

DNA. PCR amplification was calibrated to use the least number of cycles and still attain

concentration dependence (GAPDH 24 cycles; FEN1 and MCM4 31 cycles; CSPG6 32 cycles;

FYN, HCAP-G, C17ORF41, FLJ20641 and RFC3 33 cycles; ARHGAP11a 34 cycles; FANCL

35 cycles). RT-PCR fragments were separated on 6% polyacrylamide gels and exposed for

autoradiography. The level of transcription was judged by phosphor-imager analysis after

normalizing for GAPDH.

3. Importance of the Deconvolution for Data Analysis

GO Analysis Comparison. To assess the improvement gained by our deconvolution method we

have compared the GO enrichment for cycling genes in the lists of cycling genes both before and

after the application of the deconvolution method. As the figure below shows, the genes

identified by our deconvolution method were more enriched for several important categories.

These included cell cycle (a difference of 8 orders of magnitude in enrichment score) and the late

stages of the cell cycle (primarily M phase). The majority of categories were more enriched by

using the convolved dataset indicating the importance of the temporal reconstruction.

GO p-values

0

5

10

15

20

25

30

35

40

45

50

0 5 10 15 20 25 30 35 40 45 50

original vlaues

co

nv

olv

ed

GO p-values

cell cycle

mitotic cell cycle

cell division

M phase

mitosis

DNA metabolism

4. Peak Height Comparisons

To correct for synchronization loss we applied a deconvolution method using a synchronization

loss model learned from FACS data. This helped us recover cyclic activity at later time points

which is masked by the synchronization loss at these points. For experiments measuring two or

more cycles, if our reconstruction was completely accurate, we would have obtained curves in

which the peak in the second cycle is exactly the same height as the first cycle.

If we over estimated the synchronization loss the second peak would be higher than the first

peak. We examined the deconvolved profiles for the thymidine experiment (the serum starvation

only measures one cycle). Based on this analysis it seems that we have indeed slightly over-

estimated the synchronization loss, but not much. For 36% of the “common” and “primary FF”

genes their peak in the first cycle was higher than their peak in the second. 64% of these genes

had a higher peak in the second cycle. These results indicate a relatively good agreement

between the first and second peaks suggesting that the parameters derived from the FACS data

agree with the expression data. Note that cyclic assignment is more dependent on the shape of

the curve (sinusoid like) than on the actual values or agreement between the peaks. Thus, this

slight bias to higher values in the second cell cycle should not influence the subset of genes

determined to be cycling.

5. Detailed Description of Genes Expressed in the Various Cell-Cycle Stages

Transcriptionaly Regulated Cell-Cycle Processes. In an attempt to map the major cell cycle

processes that are regulated at the transcriptional level we assigned each cell cycle gene to a

single stage (according to its expression profile) and to a single cellular process (according to the

literature) (Table 1).

DNA Replication Initiation. Of the 480 cell cycle genes identified, 175 showed peak expression

in the G1/S phase of the cell cycle. As expected many of the genes that had maximum expression

in G1/S phase of the cell cycle encode for components of complexes known to play a role in

replication initiation in eukaryotic cells. There was highly significant enrichment for genes in the

GO category of DNA initiation in the G1/S expressed genes. This includes subunits of the origin

recognition complex (ORC1L, ORC3L, ORC6L) and a highly conserved multiprotein complex

essential for the initiation of DNA replication (MCM2-7) and a protein associated with the MCM

complex (CDC45L). Note that MCM7 showed peak expression at a different phase of the cell

cycle, as did MCM 10, which is not part of the complex, but has been suggested to be essential

for initiation of DNA replication.

DNA Replication and Repair. Many of the cycling genes encode for proteins involved in DNA

replication and repair. We included these two subcategories together because many proteins play

a role in both processes. Proteins known to be involved in replication were significantly enriched

only in the G1/S category although proteins involved in DNA repair were enriched in both G1/S

and G2/M. Some of the proteins that are involved in the replication machinery that were found to

be cycling are: DNA polymerases (POLD3, POLE, POLE2 in G1/S and POLQ in G2/M);

replication factors (RFC3, RFC4, RPA2 in G1/S and RFC2 in G2/M, RFC5 in M/G1); DNA2L, a

DNA dependent ATPase which unwinds duplex DNA to generate template for replication;

GMNN, a factor which negatively regulates licensing factor and inhibits prereplicative complex

and both subunits of primase which synthesizes small RNA primers for Okazaki fragments

(PRIMA2 in G1/S and PRIM1 in M/G1); RNase HI which is involved in DNA replication and

participates with FEN1 in DNA repair.

DNA repair genes were highly enriched in both G1/S and G2/M. We found many of the cycling

genes to be involved in various DNA repair pathways, including the Fanconi anemia (FA)

pathway (FANCA, FANCG and FANCL; USP, BRIP1, BRCA1, BARD1, PCNA and

MRE11A); the Rad51 family of related genes known to be involved in DNA repair by

homologous recombination (RAD51AP1, RAD51C and RAD51L1); the mismatch repair

pathway (MLH3, MSH2, MSH5, MSH6 and EXO1) and three members of DCLRE1A, B, C

involved in the repair of interstrand cross-links.

Cell-Cycle Regulation. We divided cell cycle regulation into two subcategories; interphase and

mitosis. Genes which encode for proteins involved in cell cycle regulation were significantly

enriched in the G1/S category according to GO annotation and in the G2/M category. Not

surprisingly we found genes in this category that belong to the highly conserved cyclin family

whose members are characterized by a dramatic periodicity in protein abundance through the cell

cycle (CCNE1, CCNE2, CCNA2). In addition we found many related proteins such as catalytic

subunit of cyclin dependent protein kinase complex (CDK2, CDK4) and proteins that bind to the

catalytic subunit of cyclin dependent kinases (CKS1B, CKS2); cyclin dependent kinase

inhibitors (CDKN2C, CDKN2D, CDKN1B), activator (CDC25A) and interacting zinc finger

protein (CIZ1). Three genes which encode members of the E2F family of transcription factors

and one associated transcription factor were found to be cycling (E2F1,E2F8 and TFDP1 in G1/S

and E2F5 in G2/M). The E2F family of transcription factors plays a crucial role in the control of

cell cycle and therefore we included these E2F factors and related protein in the cell cycle

regulation category rather than RNA transcription and processing category. Three genes that

encode either Rb like or Rb binding proteins were also found to be cycling (RBBP8, RBBP6, and

RBL1).

The G2/M phase was also highly enriched for genes encoding proteins involved in regulation of

mitosis. In this category we again found several cyclins (CCNB1, CCNB2 and CCNF) that are

specifically involved in the G2/M transition. The B-type cyclins (CCNB1 and CCNB2) associate

with p34cdc2 (CDC2), which was also found to be cycling, and are essential components of the

cell cycle regulatory machinery. In addition other genes involved in regulation of mitosis were

found in this category; two members of the cdc25 phosphatase family that is required for entry to

mitosis (CDC25B and CDC25C); 2 kinases that inactivate cdc2/Cyclin B kinases and negatively

regulate cell cycle G2/M transition (WEE1 and PKMYT1), 2 M phase phosphoproteins

(MPHOSP1 and MPHOSH9), and a ubiquitin conjugating enzyme required for the destruction of

mitotic cyclins and cell cycle progression (UBE2C).

Microtubule and Spindle Formation. Another GO category that was enriched for in our list of

cycling genes was the category of microtubules and spindle formation. As expected this category

was highly enriched in G2/M phase of the cell cycle. This category includes a large group of

genes of the kinesin like protein family and an uncharacterized gene with a kinesin motor

domain (LOC146909). Members of this protein family are known to be involved in various kinds

of spindle dynamics including chromosome alignment and maintenance, centrosome separation

and establishing bipolar spindle during mitosis (KIF11, KIF14, KIF15, KIF22, KIF23, KIFC1,

KIF2C in G2/M and KIF4A and KIF18A in M/G1). Included in this category were also several

tubulin genes (TUBA1, TUBB, TUBD1) and a tubulin associated protein (TUBGCP3).

Spindle Regulation/Chromosome Condensation and Segregation. Also enriched in our list of

cycling genes with peak expression in G2/M were genes related to the process of spindle

regulation and chromosome condensation and segregation. Genes involved in spindle regulation

most notably included two kinases (BUB1 and BUB1B) involved in spindle checkpoint function

and two aurora kinases (AURKB and STK6) associated with microtubules during chromosome

movement and segregation. The category of chromosome condensation and segregation included

three centromere proteins (CENPA and CENPE in G2/M and CENPF in M/G1), genes involved

in condensation of chromosomes (BRRN1, HCAP-G, SMC421, CNAP1) and genes involved in

the process of sister chromatid cohesion (PTTG1, RAD21, CSPG6) and sister chromatid

separation (ESPL1).

Interestingly we identified many genes that are involved in RNA transcription and processing

that are cycling and have not been previously identified as such. This category was not

statistically enriched in any of the phases of the cell cycle because so many genes fall into this

category. We found in the list of genes with peak expression during G1/S enrichment for genes

encoding proteins in the metabolism category. This enrichment is significant although we were

not able to identify any single metabolic pathway that connects these genes but rather the genes

represented a wide range of metabolic enzymes and processes of the cell.

6. Rank Statistics

The claim about the similarity between the “common” and the “primary FF” sets regarding

normal tissues and their dissimilarity regarding cancer samples is supported by analysis of the

average expression levels of genes in a large variety of datasets (Figs. 4 and 5). To support this

conclusion we performed a different type of analysis in which genes were ranked in each dataset

according to their differential expression in proliferating versus nonproliferating tissues. Thus,

genes highly associated with proliferation will appear early on the ranked list of genes, genes

with similar expression in both tissue types will be in the middle of the ranked list and genes

with higher expression in arrested cells will appear at the end of the ranked list. For each set of

cycling genes, it is then determined whether they appear at the beginning of the ranked list of all

genes, which means they are highly associated with proliferation. This can be plotted as the

cumulative distribution of cycling genes along the ranked list of all genes on the array (SI Fig.

6). A steeply increasing curve indicates strong association with proliferation, whereas a set of

genes that are not associated with these conditions will plot a line close to the diagonal of the

plot. The significance of differences between cumulative distributions can be checked by

permutation.

We investigated four datasets [primary vs. arrested IMR90 fibroblasts (9), proliferating vs.

nonproliferating human endometrium (10), data from different sarcomas compared with normal

human tissues (11), and data from normal human lung tissue and from different types of lung

cancer (12)], for the cumulative distribution of the genes in the three cycling genes categories.

The plots in SI Fig. 6 a and b show nearly identical behavior of the ”common“ and ”primary FF“

sets, which corroborates the findings from the main manuscript with respect to the expression of

these sets in proliferating normal cells. In contrast, differences between the ”common“ and

”primary FF“ set are more pronounced in cancerous tissues (SI Fig. 6 c and d), although these

differences do not reach significance in the sarcoma dataset (see below). This, again, is in

agreement with the findings based on comparing average expression. Only the ”HeLa“ set of

genes does not show a pronounced association with proliferation, neither in normal cells nor in

cancer cells, supporting our suggestion that this set does not contain genuine cell cycle genes.

Statistical Significance. The statistical significance of the differences between the cumulative

plots in SI Fig. 6 was assessed by permutation tests and the results (P values) are shown in the

following table:

dataset distributions that are compared

common vs. primary FF common vs. HeLa primary FF vs. HeLa

arrested vs. proliferating

IMR90 fibroblasts

0.538 0.003 0.003

proliferating vs. 0.518 0.008 0.016

dataset distributions that are compared

nonproliferating

endometrium

sarcoma vs. normal

tissues

0.106 < 0.001 0.043

lung cancer

(proliferation index)

< 0.001 < 0.001 < 0.001

Note that P values <0.001 could not be exactly determined because only 1000 permutations were

performed. These P values are referred to as “< 0.001”.

Methods

We calculated cumulative distribution functions for the genes that were found to be cycling in

the three gene categories, “common”, “primary FF”, or “HeLa”. For each microarray dataset,

data were ordered according to a d statistic as described in ref. 6. Basically, this is a modified T

statistic that contains an additional term in the denumerator to make it more robust against very

small standard deviations. The number of genes found up to a certain index in the ranked list of

genes was then plotted against this index (SI Fig. 6).

Thus, the empirical distribution functions are defined by:

,

where x is the index in the ordered list of genes gi, G is the group of cycling genes to be tested,

and NG is the number of genes in that group. Card(x) is the cardinality of x.

The difference between the cumulative distributions (ES score) is given as:

,

which is similar to the statistic used in the Kolmogorov--Smirnov test for dissimilarity of

distributions. The method was inspired by the ”Gene Set Enrichment Analysis” (13-16).

To analyze whether the observed differences ES in the distribution functions were statistically

significant, the maximum difference between them was tested by permuting class labels in the

original data 1000 times. For each permuted dataset, d statistics were calculated like above, and

genes were ordered according to these statistics. The differences between cumulative distribution

functions Pi were then calculated. The empirical p value was obtained as the fraction of ES

scores obtained from permuted datasets that were equal or greater than the observed ES score for

that particular comparison of gene lists.

For the lung cancer dataset only, a proliferation index was assigned to the different samples: 0

for normal lung tissue, 1 for all lung cancer samples except small cell lung cancer, and 2 for

small cell lung cancer [which are known to posses higher percentage of proliferating cells than

the other lung cancer types (17)]. Significance Analysis of Microarrays was then performed with

a test statistic according to a quantitative response. In this case, the test statistic is given as:

,

where ri is the linear correlation coefficient for gene i over samples j

.

Here, yj refers to the quantitative response of sample j, and xij to the gene expression of gene i in

sample j. The estimated standard deviation si of gene i is given as

.

The s0 is the regularizing fudge factor, as usual in SAM (6).

Calculations were carried out in R, Version 2.3.1 with extension packages siggenes (Version

1.6.0) and samr (Version 1.20). R code is available on request.

7. Experimental Validation of the Dysregulation of Cycling Genes in Cancer Cells

To confirm our claim about the dysregulation of the periodic transcription in cancer cells we

performed two types of experiments:

(i) HeLa cells were synchronized by double thymidine block and expression levels of several

genes of the ”primary FF“ group of cycling genes were determined at six time points after

release. The expression of cyclin B1 and cyclin E1 show a cyclical pattern, as expected, whereas

the expression of 8 genes that we have identified as cycling in primary cells do not show a

cyclical pattern of expression (At each time point the data were normalized to GAPDH) .

Two of the genes were also measured in synchronized primary foreskin fibroblasts and the

comparison between the expression patterns in the two types of cells is shown.

(ii) To expand our conclusion to other cell types we looked at the expression levels of several

”primary FF“ and “common” cycling genes in another type of primary cells and in another

cancer cell line. Because of the difficulties in synchronizing cells (as discussed in the

manuscript) we arrested primary endothelial cells (HUVECs) and a fibrosarcoma cell line

(HT1080) at different stages of the cell cycle by various treatments. HUVECs were arrested in

G1 by contact inhibition (48 h), in G1/S by thymidine block (2.5 mM for 24 h) and in G2/M by

nocadozole (100 ng/ml for 24 h). The arrest of each treatment was assessed by FACS resulting in

67% in G1, 71% in G1/S and 60% in G2/M, respectively. HT1080 cells were arrested in G1 by

serum starvation (24 h), in G1/S by thymidine block (2.5 mM for 24 h) and in G2/M by

nocadozole (400 ng/ml for 20 h). The arrest of each treatment was assessed by FACS resulting in

60% in G1, 80% in G1/S and 73% in G2/M, respectively. We then compared the level of

expression of six genes that we found to be cycling in primary fibroblasts and not in HeLa cells.

We show that in the fibrosarcoma cell line the level of expression of the “primary FF” genes was

similar in all three cell cycle stages whereas the expression of genes from the “common” group

differs at the various cell cycle stages. However, in the primary endothelial cells the level of

expression of genes from both groups was different in G1/S arrested cells and G2/M arrested cells

than in G1 arrested cells (Fig. 5c and Fig. 5d).

8. Dysregulated Cell-Cycle Genes and Cancer Transformation

Extensive literature search revealed that indeed dysregulation of many genes of the “primary FF”

category is known to be involved in cancer transformation. A short summary of the existing

evidence for the involvement of some of these genes in cancer is shown below:

HOXA9. HOXA9 gene is involved in leukemic transformation. It is over expressed in a subset

of human myeloid leukemias in the form of a fusion with a subdomain of NUP98, as the result of

a reciprocal translocation between chromosomes 7 and 11 (18). HOXA9 was also found to be

up-regulated in human AML (19). Similar results were found in mice in which enforced

expression of HOXA9 immortalizes and blocks the differentiation of myeloid progenitors,

eventually leading to acute myeloid leukemia (AML) (20).

PER2. Studies in knockout mice deficient of the mPer2 gene suggest that it is a tumor

suppressor gene. The deficiency of this gene in mice caused them to be cancer-prone suggesting

that the mPer2 gene functions in tumor suppression by regulating DNA damage-responsive

pathways (21).

ING2. ING2 (INhibitor of Growth 2) is involved in the regulation of cell cycle arrest and of

apoptosis through the regulation of the p53 pathway (22).

WHSC1. WHSC1 is thought to be involved in the progression of multiple myeloma (MM). In

15% of MM patients there is a t(4;14) (p16.3;q32) chromosomal translocation in which the

WHSC1 gene is involved (23, 24). Moreover, it has been recently reported that WHSC1

transcripts are strongly up-regulated in MM patients with the translocation (25). Appreciable

levels of WHSC1 expression in plasma cell leukemias negative for the translocation have also

been shown. These findings suggest that WHSC1 may contribute to the disease progression and

further support its putative role in the neoplastic transformation of tumors with the t(4;14) (26).

RBL1. RBL1 (p107) is a pocket protein sharing with Retinoblastoma (RB) both sequence and

function. The RB gene is deleted in many cancer types and thus it is considered a tumor

suppressor gene. Similarly, the RBL1 gene is deleted in some myeloid leukemia (27). The three

Rb-family members can inhibit cell growth, acting on the cell cycle between G0 and S phases,

primarily through binding and inactivation of transcription factors (28). Over expression of these

proteins cause cell cycle arrest (29), whereas their deletion cause cell transformation (30).

FANCL. FANCL has a crucial role in the Fanconi anemia pathway as the catalytic subunit

required for monoubiquitination of FANCD2. Fanconi anemia is a recessively inherited disease

characterized by congenital defects, bone marrow failure and cancer susceptibility. Fanconi

anemia proteins function in a DNA damage response pathway involving breast cancer

susceptibility gene products, BRCA1 and BRCA2 (31).

MRE11A. MRE11 is known to be involved in DNA double-strand break repair and participates

in exonuclease and endonuclease activities. Eight somatic mutations in seven different colorectal

cancers were found in the MRE11 gene (32).

BLM. BLM is a member of the family of RecQ helicases which maintain genomic stability by

functioning at the interface between DNA replication and DNA repair. Germ-line mutations in

BLM gives rise to a rare autosomal-recessive disorder that is associated with an elevated

incidence of cancer (33).

FYN. FYN is a member of the SRC family tyrosine kinase genes. The biological functions

reported for FYN are diverse, including both stimulatory and inhibitory effects on cellular

differentiation, proliferation and survival. A tumor suppressor role has been suggested for FYN

in neuroblastomas, possibly through induction of differentiation and cell cycle arrest (34).

BTG1. The BTG1 gene locus has been shown to be involved in a t(8;12)(q24;q22) chromosomal

translocation in a case of B-cell chronic lymphocytic leukemia (35).

DLEU2. DLEU2 was mapped to the minimally deleted region in B-CLL patients, with several

patients showing deletion borders within these genes, suggesting that DLEU2 is a tumor

suppressor gene involved in B-CLL leukemogenesis (36).

9. Interspecies Comparison

In a recent article of ours (37) we show that cyclic expression is much better conserved than

previously thought. For that article we used the HeLa expression data. To further explore how

the combined analysis of HeLa and primary cells improves the agreement we have now

computed the overlap between the conserved yeast genes and three lists of human genes: the

original Whitfield et al. HeLa list, the “common” and the “primaryFF” lists. The overlap

between the original Whitfield list and the conserved yeast list is significant (10-12

), the overlap

with the common list presented in this article is much more significant (10-17

). The overlap with

the primaryFF list is not as significant (only 2 of the ∼100 genes on that list have cycling

homologs in yeasts). Thus, the set identified in this article does improve the overlap with other

species indicating that the common set indeed contains genes of the core cell cycle machinery.

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