Cell cycle-regulated genes and mRNA

I am a mathematician and my knowledge about biology is close to zero. I am reading a bioinformatics paper and I would like to understand a bit more about the biology task they are talking about. I cite here the first paragraph of the paper:

The identification of cell cycle-regulated genes through the cyclicity of messenger RNAs in genome-wide studies is a difficult task due to the presence of internal and external noise in microarray data. Moreover, the analysis is also complicated by the loss of synchrony occurring in cell cycle experiments, which often results in additional background noise.
[De Santis, Marianna, et al. "Combining optimization and machine learning techniques for genome-wide prediction of human cell cycle-regulated genes." Bioinformatics (2013): btt671.]

I've searched online for some explanation of this topic and I understand that cell-cycle is a set of events that cells go through for duplicating themselves. I also understand that there are some molecules that are responsible for the regulation of the cell cycle. I know that mRNA is a class of nucleic acid that is responsible for transferring the informations from the DNA inside the nucleus to the ribosomes in order begin the amino acids synthesis. Microarrays are matrices obtained by DNA sequences inside some chip.

What is the cyclicity of mRNA? How is it related to the cell cycle?

I just need a very simple explanation, without any detail, as it is not my field but I am concerned to understand the background.

It is assumed that in order for a cell to divide it must progress through a series of gradual stages. At each of these phases certain proteins have to be manufactured through mRNA and/or modified post-transcriptionally in order to serve an specific proliferative or anti-proliferative role, which overall eventually orchestrates the transit to the subsequent stage.

Conventionally the cell cycle is comprised of 3 "interphase" stages prior to execution of Mitosis itself: G1 (growth, metabolically active), S (synthesis of DNA to duplicate genetic material in preparation for its splitting into two daughter cells), and G2 (gap phase where a numerous of quality control checks are undertaken in the form of surveillance of the newly produced DNA for example to ensure viability of the progenies).

Under these premises, the protein content of a cell at an specific phase is expected to cycle or oscillate in accordance with its requirements, and this is linked to mRNA cyclicity as the most direct responsible process for modulation of protein abundance. It is then related to the cell cycle in the sense that in order to transit from the G1 phase for instance, proteins involved in DNA synthesis will need to be produced, and thus protein factors released for the transcription of those certain mRNAs to take place.

Experimentally such dissection of cell cycle-regulated genes is tricky. Extracting mRNA from pure sub-populations of cells at specific stages of cell division is challenging as the isolation of such cell sub-populations is normally through chemical enrichments and sub-optimal synchronisation procedures. The signal to noise ratio is not ideal under these circumstances.

In brief, the cell cycles through stages in order to divide and such cycling is in itself due to the cycling of certain proteins, the most renown ones are called precisely "cyclins", which are protein markers of cells division. Cyclin D, E, A, B prevail differently at different stages of the cell cycle as a result of the balanced equation of mRNA transcription and post-transcriptional protein modifications (phosphorylation events mark them for degradation for example). Overall an intertwined and well-coordinated process.

*** EDIT

For a suitable selection of some basic bibliography on the subject, robust sources covering the subject with rigour (less specific and dense than the journal articles I comment bellow) please refer to the following links:

& even a review of the latter here.


Cell cycle regulation of histone H4 gene transcription requires the oncogenic factor IRF-2

Histone genes display a peak in transcription in early S phase and are ideal models for cell cycle-regulated gene expression. We have previously shown that the transcription factor interferon regulatory factor 2 (IRF-2) can activate histone H4 gene expression. In this report we establish that a mouse histone H4 gene and its human homolog lose stringent cell cycle control in synchronized embryonic fibroblasts in which IRF-2 has been ablated. We also show that there are reduced mRNA levels of this endogenous mouse histone H4 gene in the IRF-2(-/-) cells. Strikingly, the overall mRNA level and cell cycle regulation of histone H4 transcription are restored when IRF-2 is reintroduced to these cells. IRF-2 is a negative regulator of the interferon response and has oncogenic potential, but little is known of the mechanism of these activities. Our results suggest that IRF-2 is an active player in E2F-independent cell cycle-regulated gene expression at the G1/S phase transition. IRF-2 was previously considered a passive antagonist to the tumor suppressor IRF-1 but can now join other oncogenic factors such as c-Myb and E2F1 that are predicted to mediate their transforming capabilities by actively regulating genes necessary for cell cycle progression.

Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization

We sought to create a comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle. To this end, we used DNA microarrays and samples from yeast cultures synchronized by three independent methods: α factor arrest, elutriation, and arrest of a cdc15 temperature-sensitive mutant. Using periodicity and correlation algorithms, we identified 800 genes that meet an objective minimum criterion for cell cycle regulation. In separate experiments, designed to examine the effects of inducing either the G1 cyclin Cln3p or the B-type cyclin Clb2p, we found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins. Furthermore, we analyzed our set of cell cycle-regulated genes for known and new promoter elements and show that several known elements (or variations thereof) contain information predictive of cell cycle regulation. A full description and complete data sets are available at

CDC6 mRNA fluctuates periodically in the yeast cell cycle.

Using cultures synchronized by two independent procedures, alpha-factor arrest and centrifugal elutriation, we have investigated the expression of the Saccharomyces cerevisiae CDC6 gene through the cell cycle. Our results show that the CDC6 gene is periodically expressed in the yeast cell cycle. The level of CDC6 transcripts increases in late G1, reaching a peak (approximately 10-20-fold over the initial level) at about the G1/S phase boundary. The peak of CDC6 mRNA was observed to overlap or slightly precede that of the CDC8 message, and to obviously precede that of the histone H2A message by some 25 min. Unlike histone H2A mRNA, the CDC6 mRNA as well as CDC8 mRNA were not affected by hydroxyurea treatment. These results suggest that regulation of H2A mRNA is different from that of CDC6 or CDC8. We have studied the 5'-flanking regions of CDC6 and other cell cycle-regulated genes. DNA sequence analysis of the CDC6 promoter revealed two sequences, 5'-C/GACGCGNC/G-3' and 5'-PuGNAGAAA-3' (where Pu is a purine, and N is any nucleotide), which are repeated three times each. Similar sequence elements have also been found among several cell cycle-regulated genes, including the CDC8 gene, but are not found upstream of histone genes. The possible significance of these elements is discussed.

Cell cycle-regulated genes and mRNA - Biology

Regulated gene expression is an important mechanism for controlling cell cycle progression in yeast and mammals, and genes involved in cell division-related processes often show transcriptional regulation dependent on cell cycle position. Analysis of cell cycle processes in plants has been hampered by the lack of synchronizable cell suspensions for Arabidopsis, and few cell cycle-regulated genes are known. Using a recently described synchrony system, we have analyzed RNA from sequential samples ofArabidopsis cells progressing through the cell cycle using Affymetrix Genearrays. We identify nearly 500 genes that robustly display significant fluctuation in expression, representing the first genomic analysis of cell cycle-regulated gene expression in any plant. In addition to the limited number of genes previously identified as cell cycle-regulated in plants, we also find specific patterns of regulation for genes known or suspected to be involved in signal transduction, transcriptional regulation, and hormonal regulation, including key genes of cytokinin response. Genes identified represent pathways that are cell cycle-regulated in other organisms and those involved in plant-specific processes. The range and number of cell cycle-regulated genes show the close integration of the plant cell cycle into a variety of cellular control and response pathways.


Cell culture, synchronization, and RNA preparation

HeLa and U2OS cells were passaged in a 37°C humidified incubator in DMEM with 10% fetal bovine serum and 100 U of penicillin–streptomycin following standard protocols.

U2OS cells were synchronized using a double-thymidine protocol or a thymidine–nocodazole protocol. Briefly, 3.0 × 10 5 cells were plated in 16 ml of DMEM. After 24 h of growth, thymidine was added to a final concentration of 2.5 mM. After 18 h in thymidine media cells were washed twice with prewarmed CO2-equilibrated phosphate-buffered saline (PBS) and allowed to grow for 8 h in prewarmed CO2 equilibrated DMEM. Again thymidine was added to a final concentration of 2.5 mM for another 18 h. Cells were washed twice with PBS and released into DMEM. For the thymidine–nocodazole synchronization, U2OS cells were plated (5.0 × 10 5 cells) and allowed to grow for 24 h. Thymidine (2.5 mM) was added for 18 h before cells were washed twice with prewarmed CO2-equilibrated PBS before treatment with DMEM supplemented with 100 ng/ml nocodazole for 12 h. Floating cells were collected and spun down, washed twice with prewarmed CO2-equilibrated PBS, and resuspended in prewarmed CO2-equilibrated DMEM. Nonfloating cells were washed twice with prewarmed CO2-equilibrated PBS and released into prewarmed CO2-equilibrated DMEM, and the resuspended floating cells were added back to each plate. Cells were collected every 2 h for a minimum of 36 h using RNeasy Plus Mini Kit (Qiagen, Valencia, CA). Zero-hour samples were collected while cells were still in arrest conditions.

Synchrony was monitored via fluorescence-activated cell sorting (FACS) analysis of propidium iodide–labeled cells (DartLab, Geisel School of Medicine at Dartmouth College) and FOXM1 phosphorylation state or cyclin B1 expression via Western blots (see later description). Samples were collected for Western blot analysis using SDS–PAGE sample buffer.

Reference RNA was isolated from asynchronously growing U2OS cells using an RNeasy Plus Mini Kit. The same reference was used for all hybridization experiments.

Microarray hybridization and analysis

Microarrays were run as described previously (Grant et al., 2012). Briefly, cellular RNA was amplified and Cy3 (asynchronous U2OS RNA) or Cy5 (sample) labeled using the Quick-Amp Labeling kit (Agilent Technologies, Santa Clara, CA) following the manufacturer's protocol, except that the reaction volumes were reduced by one-half. Labeled cRNA was hybridized to Agilent Whole Human Genome Oligonucleotide arrays (4 × 44k) following the manufacturer's protocol. Microarrays were scanned using a GenePix 4000B scanner (Molecular Devices, Sunnyvale, CA). Spot pixel intensities were determined using GenePix Pro 5.1 software. Poor-quality spots were identified and flagged by hand and excluded from subsequent analysis. Arrays were stored in the University of North Carolina Microarray Database (Chapel Hill, NC UMD). The full raw microarray data are available from the GEO at accession number GSE50988 (part of SuperSeries GSE52100).

Each time course was retrieved from the UMD independently from each other time course with the following conditions. Only spots with ratio of intensity over background of >1.5 were used. Genes missing >30% of their data were excluded from further analysis. Genes were normalized using Lowess normalization.

Identification of periodically expressed transcripts

Periodically expressed transcripts were identified using the same method as in Whitfield et al. (2002). Briefly, missing data were imputed using a k-nearest neighbors algorithm (Troyanskaya et al., 2001) using k = 12. Then each time course was centered by removing the first eigengene (Alter et al., 2000). Imputed data were removed from the data set as the last step of the analysis.

Rough estimates of the U2OS cell cycle were initially obtained from Western blot analysis of cell cycle–regulated phosphorylation of FOXM1 and FACS analysis for each time course. This estimate was then refined by performing a Fourier transform on each gene in each time course (Whitfield et al., 2002, Eqs. 1–3) with equally spaced values of time (every 15 min) for the estimated cell cycle length ±two hours.

An offset (φ Whitfield et al., 2002, Eqs. 1 and 2) was determined for each time course relative to the first time course. The Fourier transform was then repeated for each time course using the following values of T and φ: Thy-Thy 1 (T = 17.65, φ = 0.0), Thy-Thy 2 (T = 18.6, φ = 0.0), Thy-Thy 3 (T = 18, φ = 0.0), and Thy-Noc (T = 23.95, φ = 2.3). The vectors for each data set were then summed and genes ranked by the magnitude of their combined vectors (C). To compensate for the imperfect match to sine or cosine curves, each gene was scaled by its correlation to an idealized vector. The ideal vector for each cell cycle phase (G1/S, S, G2, G2/M, and M/G1) was defined by the average expression profiles of the indicated genes in Figure 1. Using a standard Pearson correlation, each gene received a peak correlation score, which was its largest absolute value correlation with each of the ideal vectors. This peak correlation score was then used to scale each gene's C, generating a periodicity score for each gene.

Randomized data were then used to set a cutoff value for the minimum periodicity score to be considered cell cycle regulated. The data were randomized 10 times either within rows only or in rows and columns. The full analysis pipeline was performed for each of these randomizations using the same parameters as for the unrandomized data. We chose a minimum periodicity score of 2.65, which gave us 3568 genes with an initial false-positive rate of 3.67% when randomizing by rows only. Inclusion of the Thy-Noc time course resulted in improved false-positive and false-negative rates, despite having a lower degree of synchrony than the Thy-Thy time courses (Supplemental Figure S9)

To account for genes that received a high Fourier score but did not have a sinusoidal expression pattern throughout each time course, we calculated autocorrelation scores (Whitfield et al., 2002, Eq. 5). We calculated and summed the autocorrelation scores for each time course, leaving 2878 genes. To remove any genes that had an obvious date bias from technical issues during array hybridization, we found their power spectra using the Fourier transform of each time course. The date-biased genes were then removed by projecting the power spectra onto their first two principal components and clustered by k-means (k = 2 Supplemental Figure S2). Removing these genes gave us a final data set of 2830 probes. The 2830 probes correspond to 2140 Entrez GeneIDs with 1871 unique gene identifiers.

ChIP-seq and analysis

FOXM1 ChIP-seq was carried out as previously described (Lupien et al., 2008 Grant et al., 2012) using the FOXM1 antibody sc-502 (C20 Santa Cruz Biotechnology, Santa Cruz, CA). Briefly, asynchronous HeLa cells were fixed using 1% formaldehyde before sonication to produce DNA fragment lengths of 200–600 base pairs with a Bioruptor (Diagenode, Sparta, NJ). Anti-FOXM1 was bound to a mix of Protein A and Protein G Dynabeads (Life Technologies, Grand Island, NY) before an 18-h incubation at 4°C with the fragmented DNA. Bound DNA was washed and the cross-links reversed before DNA purification with a QIAquick PCR purification kit (Qiagen). DNA concentrations were measured using Quant-iT PicoGreen (Life Technologies). Library construction and sequencing for each ChIP-seq run were carried out independently at the High Throughput Sequencing Facility at the University of North Carolina (Chapel Hill, NC) using an Illumina Genome Analyzer II. Fastq files were mapped to the human genome using Bowtie (version 0.12) using the “best” flag to constrain alignments to those with the best read quality and fewest mismatches. The first ChIP-seq run resulted in 17.1 million sequence reads (8.4 mapped sequence reads), and the second ChIP-seq run resulted in 17.0 million reads (8.4 million mapped reads human genome build Hg18). Enriched peaks were determined independently for each run using MACS, version 1.3 (run 1, mfold 32, p < 1.0 × 10 −5 run 2, mfold 25, p < 1.0 × 10 −5 Zhang et al., 2008). This resulted in 5727 peaks for the first run and 2849 peaks for the second. As a conservative estimate of FOXM1 binding, we analyzed the intersection of the sequences under the peaks that were found in both ChIP-seq runs, resulting in 2215 shared FOXM1 genomic loci. We then determined the distribution of the shared FOXM1 genomic loci using the cis-Regulatory Element Annotation System (CEAS et al., 2008 Shin et al., 2009) implemented in Cistrome ( Raw ChIP-seq data and BED files are available from GEO at accession number GSE52098 (part of SuperSeries GSE52100).

Real-time luciferase assays

U2OS cells were plated at ∼20–25% density in 30-mm dishes and allowed to grow for 24 h. After 24 h, the growth medium was replaced with assay medium (Phenol red–free L15 [Life Technologies], 10% fetal bovine serum, 1% penicillin/streptomycin, 10 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid buffer, and 0.1 mM luciferin). Cells were then transfected with equal amounts of plasmid (typically 250 ng of each plasmid) using FuGENE 6 (Life Technologies) following the manufacturer's protocol. Tissue culture dishes were sealed with glass coverslips and silicone grease and transferred to the LumiCycle (Actimetrics, Wilmette, IL) at 36°C. Data analysis was performed with LumiCycle Analysis software (Actimetrics).

Western blots

Antibodies to FoxM1 C-20 (1:500) and cyclinB1 H-433 (1:2000) were purchased from Santa Cruz Biotechnology. Anti–glyceraldehyde-3-phosphate dehydrogenase was purchased from American Research Products (Belmont, MA). Western blots were run following standard protocols.

Plasmid construction

FOXM1 expression vectors, the ACAP3/CENTB5, and the RPS6KB1 promoter constructs have been described previously (Grant et al., 2012). We obtained commercially available promoter constructs for PLK1 (S119035), CENPE (S118567), TOP2A (S118760), and RMI1 (S113323) from Switchgear Genomics (Menlo Park, CA).

The FOXM1 target promoter construct, pGL3-MCM8, was cloned based on ChIP-seq loci as determined by MACS. Primers were designed using Primer 3 (Rozen and Skaletsky, 2000) to provide an amplicon length between 800 and 1000 base pairs. DNA fragments were amplified via PCR and cloned into Zero Blunt TOPO (Life Technologies) before being subcloned into pGL3-basic (Promega, Madison, WI) using standard methods. All plasmids were verified by sequencing (Molecular Biology and Proteomics Core Facility, Dartmouth College).

Functional annotation

Functional annotation of genes was performed using DAVID (Dennis et al., 2003 Huang da et al., 2009).

Cell cycle–wide binding profiles

We investigated the distribution of transcription factor target genes in the cell cycle. First, we identified a list of 2830 cell cycle probes in U2OS cells and sorted them according to their peak expression time in the cell cycle. Then we examined the enrichment of the target genes of a given transcription factor in each sliding window of the cell cycle. We used a window size of 30° with 10° overlap between neighboring windows. We used Fisher's exact test to determine the significance of enrichment of target genes for a transcription factor in each cell cycle window.

The target genes for E2F1, E2F4, and E2F6 in HeLa cells were determined from ChIP-seq data generated by the ENCODE project (Gerstein et al., 2012). The FOXM1 target genes were determined from the ChIP-seq presented here.



The recent YMC expression data (14) exhibit strong modulation of cell cycle-regulated genes in a budding yeast culture (see SI Fig. 5). On examining the temporal expression profiles of the YMC data set, we calculated (15) that the average peak-to-trough expression ratio of the 108 well known cell cycle-regulated genes (see Table 3 in SI Appendix ) is 21, compared with <9 for earlier synchronization by cdc15 or cdc28 temperature-sensitive mutants or by alpha pheromone (1, 5). Unlike the previous data (1, 3, 5), derived from rapidly growing yeast, the YMC data are collected from a continuous, highly synchronized, slowly growing yeast culture (14), which allows us to better distinguish peaks of some key cell cycle genes (Fig. 2). Furthermore, the initial synchronization of cells undergoing the YMC is achieved simply by starving the cell population after it reaches high density this approach does not rely on the use of temperature-sensitive mutants, the addition of pheromone, etc. The metabolically achieved synchrony (14) thus remains remarkably stable it has lasted for ≈100 cycles, whereas synchronization achieved by other methods deteriorates noticeably during the first three cycles (5). The stable repetition of temporal expression patterns over consecutive cycles is an essential requirement for applying our deconvolution-based, accurate timing method. Therefore, we chose to base high-resolution timing solely on the YMC data (14), whereas other data sets (1, 3, 5) were used for identification of the set of cell cycle transcriptionally regulated (CCTR) genes.

Comparison of expression patterns of key cell cycle-regulated genes in slowly growing [YMC (14), Left] vs. rapidly growing [cdc15, alpha, and cdc28 (1, 5)] yeast cultures. (Upper) G2 cyclin CLB1, mitotic transcription factor SWI5, and G1 cyclin CLN3. (Lower) Mitotic cyclin PCL9 and MCM subunit MCM3. Environment-dependent G1 phase is ≈10 times longer in YMC than found in previous studies (cdc15, alpha, and cdc28 synchronization), allowing the expression of key cell cycle genes to be timed with higher accuracy.

We observed that most genes known to be transcriptionally regulated during the cell cycle share a characteristic profile shape in the YMC data (Fig. 1). On the basis of fluorescence-activated cell sorting (FACS) analysis of DNA replication and the observation of bud appearance (14), we conclude that the observed broadness of the profile is caused not by long transcript lifetimes but by individual cells entering the cell cycle at different times. To correct for the influence of this spread on measured mRNA concentrations, we modeled the time-shift distribution of cells entering the cell cycle (see SI Appendix ). The shape of that distribution is strikingly similar to the budded cell count distribution from other cell cycle-synchronized cultures (1), suggesting that this shape is an inherent property of the cell cycle, likely caused by daughter cells needing more time than mother cells to grow big enough to divide again (16, 17).

To recover the mRNA concentration in the typical individual cell, we deconvolved the measured profile by using the common shape. The intrinsic noise in budding yeast gene expression is low (18), so we expect our estimated average individual cell expression timing to be reflective of the majority of actual single cells. We implemented a deconvolution algorithm adapted to microarray data analysis (see SI Appendix ), with regularization based on the maximum-entropy principle (19). We thus accurately determined the moment of the gene expression peak by aligning cell cycles of the whole culture by deconvolution of the observed expression profiles (see SI Appendix ). This method allows the recovery of single cell expression profiles, which in the microarray measurement are distorted because of averaging mRNA levels of imperfectly synchronized cells (Fig. 3).

Expression profiles measured for the whole culture differ considerably from single-cell mRNA profiles. Imperfect synchronization of cells results in broadening of expression profiles measured in the culture (Right), compared with the respective single-cell profiles (Left). The original single-cell expression profiles (A, C, and E) can be reconstructed from the observed profiles (B, D, and F, respectively) by using deconvolution. This method can also be applied when the single-cell expression profile is complex (C and E) rather than just one short-lived pulse (A).

Expression Peaks.

For each transcript, we calculated the deconvolved profiles (see SI Fig. 7) and identified the peaks of expression (see SI Appendix ). Deconvolution of gene expression profiles allows the discovery of secondary expression peaks, even when they are not evident in the raw data. Indeed, we find that many CCTR genes may peak twice per cycle. For example, deconvolved profiles of histone genes show that all histones except HTB2 are expressed in two distinct bursts per cycle (SI Fig. 8): the first occurring in S phase and related to DNA replication and the second in G2/M phase (the functional significance of this second wave is unknown) (Fig. 4 C). Another example is CDC28, discussed below (SI Fig. 10). Secondary peaks of CCTR genes suggest that they may function at multiple moments of the cell cycle or that only one of the observed peaks is cell cycle-related. Examples of calculated peaks are shown in Fig. 4 D and in Table 4 of SI Appendix (for the full list, see Table 6 in SI Appendix or

Transcriptional program of the yeast cell cycle. (A and B) Proteins involved in DNA replication initiation, color-coded according to timing of their expression. The prereplication complex (pre-RC) undergoes several changes before an elongation complex (EC), capable of initiating DNA synthesis, is formed (24). The order of expression (A) agrees with the order in which gene products are needed (B). In A, dotted outlines denote non-CCTR genes. Note the two groups of MCM subunits, each containing one nuclear localization signal (NLS). In B, solid outlines denote primary expression peak dashed outlines denote secondary (lower scoring) peak. The only exception to just-in-time transcription is ORC1. (C) Timing of CCTR complexes. DSE, daughter-cell-specific expression program APC act, APC activation SPB sat, spindle pole body satellite formation SPB sep, spindle pole body duplication and separation. (D) Peaks of selected CCTR genes. (E) Phases and subphases of the cell cycle. Note new prereplicative G1 (P) phase. (F) Histogram of expression peaks of CCTR genes. (G) Peaks of transcripts regulated by selected cell cycle transcription factors (11, 20, 21, 29, 30). Note the differences in expression of MBF and SBF targets. (H) Histograms of peaks of CCTR genes involved in selected cell cycle functions. Compare peaks of predicted Cdc28p targets (26) vs. peaks of CDC28 (D).

CCTR Genes.

The timing results can only be interpreted within the context of cell cycle-regulated expression if a gene is CCTR. To identify CCTR genes, we examined available whole-genome data sets in which known cell cycle-regulated genes exhibited modulation (1, 3, 5, 14). For each transcript, we constructed a probabilistic score based on the percentage of earlier proposed (1, 5) cell cycle transcriptionally regulated genes among the 100 most correlated in each data set (1, 3, 5, 14) (see SI Appendix ). This score identified a high-confidence CCTR set consisting of 694 genes and an extended set of 1,129 genes (see Table 6 in SI Appendix ). We validated these CCTR sets by using experimental transcription factor binding data (11, 20, 21), as well as transcription factor binding motifs conserved among related fungi species (22, 23). Both tests showed that our CCTR sets are more consistent with independent transcription regulation data than are sets derived from smaller groups of experiments (3, 5) (see Tables 1 and 2 in SI Appendix ). Henceforth, the term “CCTR genes” refers to the high-confidence set.

Error Estimation.

To estimate the robustness of the timing procedure, we have investigated the extent to which the obtained peak times are affected by expected errors in the measured mRNA concentrations (see SI Appendix ). We confirmed that the timing method is very robust the median value of estimated total error for predicted peak times is 2 min (see Table 6 in SI Appendix ). Such high-accuracy timing allows annotating of each transcript to a small fraction of a cell cycle phase and reveals otherwise undetectable differences in gene expression times. The complete list of CCTR genes, together with their expression peaks and error estimates, is available online at

Phase and Subphase Assignment.

We defined time intervals corresponding to the main cell cycle phases (Fig. 4 E) by using expression peaks of known cell cycle genes (see Table 5 in SI Appendix ). The histogram of expression peaks of CCTR genes (Fig. 4 F) reveals two main waves of transcription, separated by intervals of almost no CCTR transcriptional activity, between late S and late G2 phases and in most of the G1 phase. This dramatic variation in transcriptional activity between stages of the cell cycle has not been described previously (5) (SI Fig. 6). Beyond prominent expression waves in G1/S–S and G2/M–M, the histogram in Fig. 4 F reveals a previously unidentified (1, 3, 5), distinct expression wave preceding the start of DNA replication. In YMC, this wave spans 45 min and encompasses 19% of CCTR genes. Because the majority of subunits of the prereplicative complex are expressed in this phase (Fig. 4 A), we propose to designate it the “prereplicative” or “G1 (P)” phase. Other genes expressed in G1 (P) are involved in preparation for budding (e.g., RSR1, BUD13, GIC2, RAX1, PEA2, and BNI4) and in synthesis of cell wall components (e.g., FKS1, GAS1, and GAS5). In previous studies, G1 (P) genes were perhaps incorrectly assigned to different cell cycle phases (1, 5). More than one-third had been annotated as being expressed in mitosis or M/G1 (1, 5), including all subunits of the MCM complex and the G1 cyclin CLN3 (1, 5). Our timing places expression of these genes at the beginning of the new cycle, suggesting involvement in preparation for a round of division rather than for entry into extended G1 phase, which is more consistent with their known biological function (6, 24).

Another gene expressed in G1 (P) phase is CDC28, the catalytic subunit of the main yeast cyclin-dependent kinase, which drives progress through the cell cycle (25). Our study, which classifies CDC28 as periodic, challenges the established view (3, 5, 8, 12, 25) that CDC28 is constitutively expressed. We determined that CDC28 expression peaks twice per cycle, first in G1 (P) phase and again in early mitosis (Fig. 4 C), precisely coinciding with expression waves of its predicted targets (26) (Fig. 4 H). The periodicity of CDC28 expression in YMC is strikingly clear (SI Fig. 10 P < 0.00003) it also is not an artifact of metabolic regulation, because a similar profile of CDC28 had been earlier observed under different cell cycle synchronization (1) (SI Fig. 5). The lack of earlier acceptance of CDC28 as transcriptionally regulated seems to be rather an artifact of the Fourier methods used (3, 5, 8), which, although convenient, are unable to deal appropriately with genes expressed twice per cycle (see SI Appendix ).

Our timing results have also clarified when some key cell cycle genes are expressed. For instance, on the basis of experiments with rapidly growing yeast (1, 5), CLB1, SWI5, and CLN3 have all been thought to be expressed during mitosis (1, 5), whereas our data suggest that G2 cyclin CLB1 is expressed in G2, SWI5 in G2/M, and CLN3 upon reentry to the cell cycle, in G1 (P) (see Fig. 2). Similarly, we find that the Swi5-activated cyclin, PCL9, is expressed in mitosis and MCM3 is expressed in G1 (P), and consequently their expression can be delayed by an arbitrarily long G1 phase, although, on the basis of experiments with rapidly growing yeast (1, 5), they were both believed to be expressed in M/G1 (Fig. 2). Our timing results consistently place the expression of these key genes just before the time their products are needed within the cell cycle (6, 24).

Initiation of DNA Replication.

The initiation of DNA replication occurs at the beginning of S phase and requires the prior assembly and subsequent modifications of the prereplicative complex (24), which starts in G1 (P) (Fig. 4 B). Strikingly, our timing of the expression peaks of CCTR subunits of MCM, replicative complex and elongation complex, corresponds with the exact order in which their gene products are needed (Fig. 4 A and B). The subunits of the origin of replication complex, ORC2–6, have not previously been classified as transcriptionally regulated, nor did they pass the stringent criteria of being accepted as CCTR in this study. Still, applying the deconvolution timing to their expression profiles reveals that these genes have expression peaks ≈10 min before the MCM subunits, exactly when their products are needed. This observation raises the possibility that the transcription of ORC2–6 is regulated as a function of the cell cycle, contrary to established beliefs (1, 3, 5, 8) (Fig. 4 A).

A more detailed view reveals that subunits of the MCM complex are expressed in two groups of three, separated by an ≈8-min interval, with each predicted expression group containing one MCM subunit with a nuclear localization signal (27) (Fig. 4 A). These results may provide insight into the dynamics of MCM complex assembly and transport from cytoplasm into nucleus (28).

The precision of our timing data reveals that the SBF- and MBF-activated expression programs, thought to be identically timed during the mitotic cell cycle (29), actually differ (Fig. 4 G). Unlike MBF, whose targets peak predominantly in G1/S phase, targets of SBF are also activated in G1 (P) phase and are generally characterized by a broader time distribution (Fig. 4 G). This conclusion holds, independent of whether SBF and MBF targets are defined based on evolutionary analysis of conserved binding sites in 17 related fungus species or on various experimental studies (29, 30).

Cell Cycle-Regulated Complexes.

We also timed expression of several other complexes, such as the spindle pole body (SPB) (Fig. 4 C) (see Table 5 in SI Appendix ). We observed especially tight transcriptional coregulation for complexes active in late G1 and S phase the elapsed time between expression peaks of the first and last CCTR subunits of a complex is between 5 min (RFA) and 22 min (histones) (Fig. 4 C). Transcription of many non-CCTR subunits of the cell cycle complexes also exhibits variability, allowing for timing, albeit weak. For example, only three subunits of RFC are CCTR, although expression of all five subunits occurs in the same 12-min interval (Fig. 4 C). ORC2–6 exhibit even weaker modulation, but interestingly their expression timing is nevertheless very consistent with the time in which they function (Fig. 4 A and B). The anaphase-promoting complex (APC) contains only two CCTR subunits, although the expression peaks of most of its 16 subunits appear in a time interval broadly corresponding to mitosis (Fig. 4 C). However, some APC subunits, e.g., Cdh1, seem to be only posttranscriptionally regulated (31).

We generally find more subunits of the cell cycle-involved complexes to be CCTR than was the case in previous studies (2, 5, 8) (Fig. 4 and see SI Appendix and This difference may be explained by the increased quantity and improved accuracy of cell cycle expression data, together with our comprehensive approach to identifying CCTR genes. In addition to the examples discussed above and complexes involved in DNA replication initiation, we find more components of the septin ring of the mother-bud neck to be CCTR. Previous studies (2, 3, 5, 8) each classified only one septin (either CDC11 or CDC10) as cell cycle-regulated, whereas we classify three components of the septin ring (CDC11, CDC12, and CDC3) as CCTR. Our classification of CDC11, CDC12, and CDC3 as coregulated is independently supported by timing results (not used for classification), which places their expression peaks within an ≈6-min interval in late S phase.

Data availability

  1. Cho RJ
  2. Campbell MJ
  3. Winzeler EA
  4. Steinmetz L
  5. Conway A
  6. Wodicka L
  7. Wolfsberg TG
  8. Gabrielian AE
  9. Landsman D
  10. Lockhart DJ
  11. Davis RW
  1. Jensen LJ
  2. Kuhn M
  3. Stark M
  4. Chaffron S
  5. Creevey C
  6. Muller J
  7. Doerks T
  8. Julien P
  9. Roth A
  10. Simonovic M
  11. Bork P
  12. von Mering C
  1. Kozar K
  2. Ciemerych MA
  3. Rebel VI
  4. Shigematsu H
  5. Zagozdzon A
  6. Sicinska E
  7. Geng Y
  8. Yu Q
  9. Bhattacharya S
  10. Bronson RT
  11. Akashi K
  12. Sicinski P
  1. Lamond AI
  2. Uhlen M
  3. Horning S
  4. Makarov A
  5. Robinson CV
  6. Serrano L
  7. Hartl FU
  8. Baumeister W
  9. Werenskiold AK
  10. Andersen JS
  11. Vorm O
  12. Linial M
  13. Aebersold R
  14. Mann M
  1. Luber CA
  2. Cox J
  3. Lauterbach H
  4. Fancke B
  5. Selbach M
  6. Tschopp J
  7. Akira S
  8. Wiegand M
  9. Hochrein H
  10. O’Keeffe M
  11. Mann M
  1. Matys V
  2. Kel-Margoulis OV
  3. Fricke E
  4. Liebich I
  5. Land S
  6. Barre-Dirrie A
  7. Reuter I
  8. Chekmenev D
  9. Krull M
  10. Hornischer K
  11. Voss N
  12. Stegmaier P
  13. Lewicki-Potapov B
  14. Saxel H
  15. Kel AE
  16. Wingender E
  1. Ohta S
  2. Bukowski-Wills JC
  3. Sanchez-Pulido L
  4. Alves Fde L
  5. Wood L
  6. Chen ZA
  7. Platani M
  8. Fischer L
  9. Hudson DF
  10. Ponting CP
  11. Fukagawa T
  12. Earnshaw WC
  13. Rappsilber J
  1. Olsen JV
  2. Vermeulen M
  3. Santamaria A
  4. Kumar C
  5. Miller ML
  6. Jensen LJ
  7. Gnad F
  8. Cox J
  9. Jensen TS
  10. Nigg EA
  11. Brunak S
  12. Mann M
  1. Ramaswamy S
  2. Tamayo P
  3. Rifkin R
  4. Mukherjee S
  5. Yeang CH
  6. Angelo M
  7. Ladd C
  8. Reich M
  9. Latulippe E
  10. Mesirov JP
  11. Poggio T
  12. Gerald W
  13. Loda M
  14. Lander ES
  15. Golub TR
  1. Su AI
  2. Cooke MP
  3. Ching KA
  4. Hakak Y
  5. Walker JR
  6. Wiltshire T
  7. Orth AP
  8. Vega RG
  9. Sapinoso LM
  10. Moqrich A
  11. Patapoutian A
  12. Hampton GM
  13. Schultz PG
  14. Hogenesch JB
  1. Subramanian A
  2. Tamayo P
  3. Mootha VK
  4. Mukherjee S
  5. Ebert BL
  6. Gillette MA
  7. Paulovich A
  8. Pomeroy SL
  9. Golub TR
  10. Lander ES
  11. Mesirov JP
  1. Tian Q
  2. Stepaniants SB
  3. Mao M
  4. Weng L
  5. Feetham MC
  6. Doyle MJ
  7. Yi EC
  8. Dai H
  9. Thorsson V
  10. Eng J
  11. Goodlett D
  12. Berger JP
  13. Gunter B
  14. Linseley PS
  15. Stoughton RB
  16. Aebersold R
  17. Collins SJ
  18. Hanlon WA
  19. Hood LE
  1. Uhlen M
  2. Oksvold P
  3. Fagerberg L
  4. Lundberg E
  5. Jonasson K
  6. Forsberg M
  7. Zwahlen M
  8. Kampf C
  9. Wester K
  10. Hober S
  11. Wernerus H
  12. Björling L
  13. Ponten F
  1. Whitfield ML
  2. Sherlock G
  3. Saldanha AJ
  4. Murray JI
  5. Ball CA
  6. Alexander KE
  7. Matese JC
  8. Perou CM
  9. Hurt MM
  10. Brown PO
  11. Botstein D
  1. Zhu J
  2. Heyworth CM
  3. Glasow A
  4. Huang QH
  5. Petrie K
  6. Lanotte M
  7. Benoit G
  8. Gallagher R
  9. Waxman S
  10. Enver T
  11. Zelent A

Materials and Methods

Strains, Plasmids, Growth Conditions, and Genetic Methods

Yeast strains and plasmids are described in Table and Table. Standard yeast genetic procedures and media (Rose et al. 1990) were used, unless specified. For producing a bud3 deletion, plasmid pJC15 (Chant et al. 1995) carrying the bud3 deletion was linearized with BamHI and EcoRI and transformed into strain JC1030. Ura + transformants that exhibited the bipolar pattern were isolated.

Plasmid Construction

PJC16 (prom GAL1 -BUD3).

An EcoRI/BamHI GAL1 promoter fragment was liberated from pRS316-GAL (E. Bi, University of Pennsylvania Medical School, Philadelphia, PA). An isolate of BUD3 in YCp50, p35-1 (Chant et al. 1995), was digested with BamHI and SalI to liberate the BUD3 region. The linearized YCp50 was then digested with EcoRI. The GAL1 promoter fragment and the BUD3 fragment were then double ligated into the EcoRI/SalI YCp50 to yield BUD3 under the control of the GAL1 promoter.

PJC117 (prom MET3 -hemagglutinin [HA]-BUD3).

A 700-bp MET3 promoter region was amplified from JC1030 genomic DNA with Pfu polymerase (Stratagene) using primers: MET3promoter (prom) 1 -BamHI-5′ (5′-GCGCGCGGATCCAATACCCGTCAAGATAAGAG-3′) and MET3prom-HindIII-3′ (5′-GCGCGCAAGCTTGTTAATTATACTTTATTCTTG-3′). The MET3 promoter was ligated into pAD5 via the BamHI and HindIII sites of the primers, replacing the alcohol dehydrogenase promoter of pAD5. The BUD3 sequence was PCR amplified with Pfu polymerase from p35-1, and the fragment was ligated into MET3 promoter-containing pAD5 via SalI and SacI sites present in the vector and the primers. Primers were BUD3-SalI-5′ (5′-CTATGTCGACTATGGA-GAAAGACCTGTCGTC-3′) and BUD3-SacI-3′ (5′-GACTGAGCT-CTCCGATAATTCTCACAGG-3′).

PJC1869 (prom MET3 -BUD10-HA).

623 bp of the 5′ portion of the BUD10 coding region were amplified from pJC246 with Pfu polymerase using the primers BUD10-KpnI-5′ (5′-CCCCCCGGTACCATGACACAGCTTCAGATTT-3′) and BUD10-AgeI-3′(5′-GAAAATCCTTCAATGTCTGTAGCG-3′). pJC246 was linearized by KpnI and AgeI, which resulted in excision of the BUD10 promoter and 623 bp of the BUD10 open reading frame up to the unique AgeI site. This linearized plasmid was gel purified and ligated with the 5′ BUD10 PCR product that had been digested with KpnI and AgeI. The resulting construct (pJC255) contained BUD10-HA lacking the BUD10 promoter. BUD10-HA was excised from pJC255 by digestion with KpnI and SpeI, gel purified, and ligated into KpnI/SpeI linearized pJC1830 to yield pJC1869 carrying BUD10-HA under the MET3 promoter.

PJC256 and pJC257 (prom BUD3 -BUD10-HA).

600 bp of BUD3 upstream sequence were amplified from p35-1 with Pfu polymerase using the primers BUD3prom-EcoRI-KpnI-5′ (5′-GGGGAATTCGGTACCCCGGATCCTGTATTATATCCAGTAA-3′) and BUD3prom-EcoRI-KpnI-3′ (5′-GGGGAATTCGGTACCTGGTGAGGTGTAAATATACTCTTT-3′). The PCR product was ligated into pBluescript via the EcoRI sites of the primers. Ligation products were digested with KpnI to liberate the BUD3 promoter, which was ligated into the KpnI site of pJC255. Two resulting constructs (pJC256 and pJC257) were sequenced (Harvard Medical School, DNA Core Facility), and it was confirmed that the constructs carried BUD10 under the BUD3 promoter.

Overexpression of BUD3

The BUD3-overexpression construct (pJC16) and YCp50 (control vector) were transformed into EJY301. Ura + colonies carrying the plasmids were selected. Each transformant was grown overnight in Ura − glucose complete synthetic medium (CSM). The cultures were divided into two samples, harvested, washed twice, and resuspended in either Ura − glucose or Ura − galactose CSM for 24 h. Morphological analysis of cells was performed by counting normally dividing cells versus cells producing elongated buds. For each sample, 600 cells were scored. Visualization of Cdc3-HA in all samples was performed by immunofluorescence, as described below.

Preparation of RNA Samples from Synchronized Cell Cultures

JC1362 (containing wild-type copies of BUD3 and BUD10) in rich medium, JC2123 carrying pJC117 (prom MET3 -BUD3) in Leu − Met − CSM, JC2133 carrying pJC256 (prom BUD3 -BUD10) in Trp − CSM, and JC2133 carrying pJC1869 (prom MET3 -BUD10) in Trp − Met − CSM were grown up overnight in 500-ml cultures at 25°C. When cultures reached an optical density at 600 nm (OD600) of ∼0.15, they were harvested and resuspended in 500 ml of their respective growth media that had been prewarmed to 37°C. cdc15-2 ts –based arrest was attained by incubation of cultures at 37°C for 4.5 h. Arrest was confirmed microscopically and cells were chilled on ice for 5–10 min before resumption of growth at 25°C (0 min after release from arrest). Samples (25 ml) were harvested every 15 min (for JC1362) or 30 min (for all others), frozen in liquid nitrogen, and stored at −80°C at time points from 0–240 min after arrest. These 25-ml samples were used for subsequent RNA sample preparation. The Hot Phenol Method of total RNA preparation (Köhrer and Domdey 1991) was used. In addition, at every time point, 1 ml of culture was fixed in 4% formaldehyde for 30 min at 30°C. These samples were used to determine the budding index (the number of budded cells versus unbudded cells). For the initial portions of the synchronizations (60 min for Fig. 1, Fig. 3, and Fig. 4, and 90 min for Fig. 2 and Fig. 6) a modification of this basic method was used. Cells were scored as small budded versus other, due to the fact that cells recovering from the cdc15-2 block are delayed in completing cell separation. Cultures synchronized in identical fashion were used to correlate budding index with spindle morphology.

Northern Blotting

Standard methods were employed (Sambrook et al. 1989) with the following modifications. 5 μl of RNA samples were mixed with 10 μl of RNA loading buffer (Ambion) and run on 1% agarose-MOPS gels containing 6% formaldehyde. Running buffer was 1× MOPS. Gels were washed five times in 0.1% diethyl pyrocarbonate–treated water then by a 45-min equilibration in 20× SSC. RNA was subjected to capillary transfer overnight onto Zeta Probe (Bio-Rad Laboratories) nylon membranes. Membranes were washed for 5 min in 6× SSC and dried at room temperature for 30 min on paper towels before baking them in a vacuum oven at 80°C for 1.5 h. Membranes were prehybridized for 1 h in UltraHyb (Ambion) at 55°C then hydridized overnight at 55°C with the appropriate radioactively labeled probes. 0.5–1.5 kb of BUD3, BUD10, LEU2, or ACT1 were PCR amplified, gel purified, and used as templates for the manufacture of probes through the use of the “Prime-a Gene” Labeling System (Promega). After hybridization, membranes were washed twice at 55°C for 15 min in 2× SSC/0.1% SDS, and then by two washes for 30 min in 0.1× SSC/0.1% SDS. Blots were exposed to a BAS-III Imaging Plate (Fuji) for appropriate times. The Imaging Plate was processed by a Fujix BAS 2000 Imager (Fuji). Images were analyzed by MacBAS V2.5 (Fuji). Blots were stripped for reprobing by washing three times for 20 min in 0.1× SSC/0.5% SDS at 95°C.

Immunofluorescence and Calcofluor Staining

JC1997 carrying pJC117 (prom MET3 -BUD3) was grown overnight in Leu − Met − CSM. JC1296 carrying pJC246 (prom BUD10 -BUD10) or pJC256 (prom BUD3 -BUD10) was grown in Trp − CSM overnight. JC1296 carrying pJC1869 (prom MET3 -BUD10) was grown in Trp − Met − CSM overnight. At an OD600 of 0.3–0.5, cells were fixed in 4% formaldehyde at 30°C for 30–60 min and washed three times in PBS. Indirect immunofluorescence was performed as described by Pringle et al. 1991. A mouse anti-HA epitope monoclonal antibody (Jackson ImmunoResearch Laboratories) or rabbit anti-Bud3p antibody (Chant et al. 1995) was used to visualize the two proteins. The secondary antibodies used were CY3-conjugated goat anti–mouse IgG and FITC-conjugated goat anti–rabbit IgG (Jackson ImmunoResearch Laboratories). Microtubule staining was performed as described in Chant et al. 1995. Budding patterns were scored by staining bud scars with Calcofluor and observing with fluorescence microscopy (Pringle 1991) 200–300 cells were scored for each set of counts. Fluorescence microscopy was performed using a Nikon Microphot SA microscope with a 63× Plan-apo objective.

Western Blot Analysis

15 OD600 units of cells were harvested and washed with distilled water. Cells were resuspended in 150 μl lysis buffer (1% SDS, 2 mM PMSF plus protease inhibitors) and lysed with glass beads by vortexing on ice for a total of 6 min. 100 μl of lysis buffer was added to the extracts that were then centrifuged at 500 g for 2 min. Lysates were decanted and stored at −80°C if they were not required immediately. The protein concentrations of the lysates were determined using Pierce Coomassie Plus protein reagent (Pierce Chemical Co.). Equal protein levels from each lysate were loaded onto an SDS-PAGE gel and immunoblotted by standard methods (Sambrook et al. 1989). Mouse anti-HA epitope monoclonal primary antibodies (Jackson ImmunoResearch Laboratories) were used at a dilution of 1:500. Secondary antibodies were goat anti–mouse antibodies conjugated to horseradish peroxidase (Sigma–Aldrich) used at a dilution of 1:2,500. Blots were developed using the ECL Western blotting detection system (Amersham Pharmacia Biotech). Autoradiographs were scanned and the protein bands were quantitated using the MacBAS V2.5 program.

Pulsed Expression Experiments

Unsynchronized Cells.

JC1296 carrying pJC1869 (prom MET3 -BUD10) was grown in repressing conditions (4 mM methionine, Trp − CSM) overnight. At an OD600 of ∼0.25, cells were spun down and washed three times in inducing medium (Trp − Met − CSM), and followed by a 30 min incubation in the same medium. Induction of BUD10 expression was terminated by the addition of 4 mM methionine. Samples were harvested and fixed in formaldehyde (as described above) at 0, 60, and 120 min after cells were removed from inducing conditions. All samples were washed three times in PBS. Samples were subjected to analysis by immunofluorescence, as described above. Typically, 100 cells were scored per sample.

Synchronized Cells.

JC2133 carrying pJC1869 (prom MET3 -BUD10) was grown up at 25°C in repressing conditions (4 mM methionine, Trp − CSM) overnight. Cell synchronization was achieved as described earlier using the cdc15-2 ts mutation. The synchronized culture was divided in two: one half for late G1 induction and the other half for S/G2 induction. All subsequent incubations were performed at 25°C. 35 min after release from cell cycle arrest, late G1 induction was performed by the following method: cells were washed three times in inducing medium (Trp − Met − CSM) and incubated 45 min in the same medium. Induction was terminated by addition of 4 mM methionine and an additional brief incubation. At 90 min after release from arrest, cell samples were taken and fixed (as performed above). Cells were washed three times in PBS and subjected to analysis by immunofluorescence. S/G2 induction was performed in identical fashion, but 110 min after release from cell cycle arrest.

Cell cycle-regulated genes and mRNA - Biology

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The yeast cell cycle analysis project's goal is to identify all genes whose mRNA levels are regulated by the cell cycle. This site complements the published information from:

Spellman et al., (1998). Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. Molecular Biology of the Cell 9, 3273-3297.

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Partial table of contents:

RNA Structure (E. Puglisi & J. Puglisi).

Transcription and Transcriptional Control: An Overview (I. Olave,et al.).

Constitutive and Alternative mRNA Splicing (S. Bernstein & D.Hodges).

mRNA Polyadenylation: Functional Implications (E. Baker).

RNA Export from the Nucleus (L. Maquat).

The Intracellular mRNA Sorting System: Postal Codes, Zip Codes,Mail Bags and Mail Boxes (O. Steward & R. Singer).

The Fundamentals of Translation Initiation (H. Huang & T.Donahue).

Mechanisms of mRNA Turnover in Eukaryotic Cells (S. Tharun & R.Parker).

Control of mRNA Decay in Plants (A. van Hoof & P. Green).

mRNA Metabolism and Cancer (M. Korth & M. Katze).

Translational Regulation in Animal Virus-Infected Cells (R.Schneider).

RNA Decay by the Interferon-Regulated 2-5A System as a Host DefenseAgainst Viruses (R. Silverman & N. Cirino).

Watch the video: Τι τρέχει τελικά με τα mRNA. DNA εμβόλια? (January 2022).