Identification of novel and conserved functional and structural elements of the G1 cyclin Cln3 important for interactions with the CDK Cdc28 in Saccharomyces cerevisiae

Evidence for novel mechanisms that control cell-cycle entry and cell size

Entry into the cell cycle in late G1 phase occurs only when sufficient growth has occurred. In budding yeast, a cyclin called Cln3 is thought to link cell-cycle entry to cell growth. Cln3 accumulates during growth in early G1 phase and eventually helps trigger expression of late G1 phase cyclins that drive cell-cycle entry. All current models for cell-cycle entry assume that expression of late G1 phase cyclins is initiated at the transcriptional level. Current models also assume that the sole function of Cln3 in cell-cycle entry is to promote transcription of late G1 phase cyclins, and that Cln3 works solely in G1 phase. Here, we show that cell cycle-dependent expression of the late G1 phase cyclin Cln2 does not require any functions of the CLN2 promoter. Moreover, Cln3 can influence accumulation of Cln2 protein via posttranscriptional mechanisms. Finally, we show that Cln3 has functions in mitosis that strongly influence cell size. Together, these discoveries reveal the existence of surprising new mechanisms that challenge current models for control of cell-cycle entry and cell size.

Eukaryotic Cell Size Control and Its Relation to Biosynthesis and Senescence

The most fundamental feature of cellular form is size, which sets the scale of all cell biological processes. Growth, form, and function are all necessarily linked in cell biology, but we often do not understand the underlying molecular mechanisms nor their specific functions. Here, we review progress toward determining the molecular mechanisms that regulate cell size in yeast, animals, and plants, as well as progress toward understanding the function of cell size regulation. It has become increasingly clear that the mechanism of cell size regulation is deeply intertwined with basic mechanisms of biosynthesis, and how biosynthesis can be scaled (or not) in proportion to cell size. Finally, we highlight recent findings causally linking aberrant cell size regulation to cellular senescence and their implications for cancer therapies.

G1 cyclin-Cdk promotes cell cycle entry through localized phosphorylation of RNA polymerase II

Cell division is thought to be initiated by cyclin-dependent kinases (Cdks) inactivating key transcriptional inhibitors. In budding yeast, the G1 cyclin Cln3-Cdk1 complex is thought to directly phosphorylate the Whi5 protein, thereby releasing the transcription factor SBF and committing cells to division. We report that Whi5 is a poor substrate of Cln3-Cdk1, which instead phosphorylates the RNA polymerase II subunit Rpb1’s C-terminal domain on S5 of its heptapeptide repeats. Cln3-Cdk1 binds SBF-regulated promoters and Cln3’s function can be performed by the canonical S5 kinase Ccl1-Kin28 when synthetically recruited to SBF. Thus, we propose that Cln3-Cdk1 triggers cell division by phosphorylating Rpb1 at SBF-regulated promoters to promote transcription. Our findings blur the distinction between cell cycle and transcriptional Cdks to highlight the ancient relationship between these two processes.

Yeast Cip1 is activated by environmental stress to inhibit Cdk1-G1 cyclins via Mcm1 and Msn2/4

Upon environmental changes, proliferating cells delay cell cycle to prevent further damage accumulation. Yeast Cip1 is a Cdk1 and Cln2-associated protein. However, the function and regulation of Cip1 are still poorly understood. Here we report that Cip1 expression is co-regulated by the cell-cycle-mediated factor Mcm1 and the stress-mediated factors Msn2/4. Overexpression of Cip1 arrests cell cycle through inhibition of Cdk1–G1 cyclin complexes at G1 stage and the stress-activated protein kinase-dependent Cip1 T65, T69, and T73 phosphorylation may strengthen the Cip1and Cdk1–G1 cyclin interaction. Cip1 accumulation mainly targets Cdk1–Cln3 complex to prevent Whi5 phosphorylation and inhibit early G1 progression. Under osmotic stress, Cip1 expression triggers transient G1 delay which plays a functionally redundant role with another hyperosmolar activated CKI, Sic1. These findings indicate that Cip1 functions similarly to mammalian p21 as a stress-induced CDK inhibitor to decelerate cell cycle through G1 cyclins to cope with environmental stresses.

A G1 cell cycle regulatory kinase Cip1 has been identified in budding yeast but how this is regulated is unclear. Here the authors identify cell cycle (Mcm1) and stress-mediated (Msn 2/4) transcription factors as regulating Cip1, causing stress induced CDK inhibition and delay in cell cycle progression.

Dilution of the cell cycle inhibitor Whi5 controls budding-yeast cell size

Cell size fundamentally affects all biosynthetic processes by determining the scale of organelles and influencing surface transport^1,2. Although extensive studies have identified many mutations affecting cell size, the molecular mechanisms underlying size control have remained elusive³. In budding yeast, size control occurs in G1 phase prior to Start, the point of irreversible commitment to cell division^4,5. It was previously thought that activity of the G1 cyclin Cln3 increased with cell size to trigger Start by initiating the inhibition of the transcriptional inhibitor Whi5^6-8. However, while Cln3 concentration does modulate the rate at which cells pass Start, we found that its synthesis increases in proportion to cell size so that its total concentration is nearly constant during pre-Start G1. Rather than increasing Cln3 activity, we identify decreasing Whi5 activity — due to the dilution of Whi5 by cell growth — as a molecular mechanism through which cell size controls proliferation. Whi5 is synthesized in S/G2/M phases of the cell cycle in a largely size-independent manner. This results in smaller daughter cells being born with higher Whi5 concentrations that extend their pre-Start G1 phase. Thus, at its most fundamental level, budding yeast size control results from the differential scaling of Cln3 and Whi5 synthesis rates with cell size. More generally, our work shows that differential size-dependency of protein synthesis can provide an elegant mechanism to coordinate cellular functions with growth.

Reliable cell cycle commitment in budding yeast is ensured by signal integration

Cell fate decisions are critical for life, yet little is known about how their reliability is achieved when signals are noisy and fluctuating with time. In this study, we show that in budding yeast, the decision of cell cycle commitment (Start) is determined by the time integration of its triggering signal Cln3. We further identify the Start repressor, Whi5, as the integrator. The instantaneous kinase activity of Cln3-Cdk1 is recorded over time on the phosphorylated Whi5, and the decision is made only when phosphorylated Whi5 reaches a threshold. Cells adjust the threshold by modulating Whi5 concentration in different nutrient conditions to coordinate growth and division. Our work shows that the strategy of signal integration, which was previously found in decision-making behaviors of animals, is adopted at the cellular level to reduce noise and minimize uncertainty.

DOI:http://dx.doi.org/10.7554/eLife.03977.001

Budding yeast and other single-celled organisms can reproduce by dividing to produce two daughter cells. The timing of the cell division is critical because if the cell is still small when it divides, the resulting daughter cells may not be big enough to survive.

In budding yeast, the irreversible decision to divide—known as the ‘Start’ checkpoint—is only made once a cell reaches a certain size and is triggered by a protein called Cln3. This protein controls the activity of another protein called Whi5, which normally prevents the cell from dividing by switching off particular genes. Cln3 adds phosphate groups to Whi5 to make ‘phosphorylated Whi5’, which allows the genes involved in cell division to be switched on.

It is commonly believed that the level of Cln3 reflects the size of the cell and the nutrient conditions. Therefore, one model of cell division proposes that the cell passes the Start checkpoint when the level of Cln3 reaches a threshold value. However, levels of the Cln3 protein in cells can naturally fluctuate, and computer simulations based on this model showed that this would not produce reliable decisions on when to divide. So how do cells manage to distinguish noise from the genuine signals that indicate it is the right time to divide?

To address this question, Liu et al. studied yeast cells containing an artificial version of the gene encoding the Cln3 protein whose levels could be adjusted by adding a particular chemical. This revealed that cells with higher levels of Cln3 passed through the Start checkpoint sooner than cells that had lower levels of Cln3.

The observation suggests that cells add up the amount of Cln3 present over a period of time to see if this reaches the threshold needed for the Start checkpoint. This could be possible if, instead of sensing Cln3 levels directly, the cell senses the accumulation of phosphorylated Whi5. To test this idea, Liu et al. carried out additional experiments and found that the decision to pass the Start checkpoint only occurs when the amount of phosphorylated Whi5 reaches a certain threshold.

The cells are able to coordinate their growth and division under different nutrient conditions by altering the threshold of phosphorylated Whi5. When the nutrient supply is poor, more phosphorylated Whi5 needs to be accumulated to allow the cell to pass the Start checkpoint. In this way, cells adjust when they divide according to nutrient conditions. Similar strategies may be found in other signaling or decision-making systems.

DOI:http://dx.doi.org/10.7554/eLife.03977.002

Drug targets for cell cycle dysregulators in leukemogenesis: in silico docking studies

Alterations in cell cycle regulating proteins are a key characteristic in neoplastic proliferation of lymphoblast cells in patients with Acute Lymphoblastic Leukemia (ALL). The aim of our study was to investigate whether the routinely administered ALL chemotherapeutic agents would be able to bind and inhibit the key deregulated cell cycle proteins such as - Cyclins E1, D1, D3, A1 and Cyclin Dependent Kinases (CDK) 2 and 6. We used Schrödinger Glide docking protocol to dock the chemotherapeutic drugs such as Doxorubicin and Daunorubicin and others which are not very common including Clofarabine, Nelarabine and Flavopiridol, to the crystal structures of these proteins. We observed that the drugs were able to bind and interact with cyclins E1 and A1 and CDKs 2 and 6 while their docking to cyclins D1 and D3 were not successful. This binding proved favorable to interact with the G1/S cell cycle phase proteins that were examined in this study and may lead to the interruption of the growth of leukemic cells. Our observations therefore suggest that these drugs could be explored for use as inhibitors for these cell cycle proteins. Further, we have also highlighted residues which could be important in the designing of pharmacophores against these cell cycle proteins. This is the first report in understanding the mechanism of action of the drugs targeting these cell cycle proteins in leukemia through the visualization of drug-target binding and molecular docking using computational methods.

The single Cdk1-G1 cyclin of Cryptococcus neoformans is not essential for cell cycle progression, but plays important roles in the proper commitment to DNA synthesis and bud emergence in this yeast

The cell cycle pattern of the pathogenic basidiomycetous yeast Cryptococcus neoformans differs from that of the ascomycetous budding yeast Saccharomyces cerevisiae. To clarify the cell cycle control mechanisms at the molecular level, homologues of cell cycle control genes in C. neoformans were cloned and analyzed. Here, we report on the cloning and characterization of genes coding for CDK1 cyclin homologues, in particular, the C. neoformans G1 cyclin. We have identified three putative CDK1 cyclin homologues and two putative CDK5 (PHO85) cyclin homologues from the genome. Complementation tests in an S. cerevisiae G1 cyclin triple mutant confirmed that C. neoformans CLN1 is able to complement S. cerevisiae G1 cyclin deficiency, demonstrating that it is a G1 cyclin homologue. Interestingly, cells deleted of the single Cdk1-G1 cyclin were viable, demonstrating that this gene is not essential. However, it exhibited aberrant budding and cell division and a clear delay in the initiation of DNA synthesis as well as an extensive delay in budding. The fact that the mutant managed to traverse the G1 to M phase may be due to the activities of Pho85-related G1 cyclins. Also, that C. neoformans had only a single Cdk1-G1 cyclin highlighted the importance of keeping in order the commitment to the initiation of DNA synthesis first and then that of budding, as discussed.

Cyclin Cln3 is retained at the ER and released by the J chaperone Ydj1 in late G1 to trigger cell cycle entry

G1 cyclin Cln3 plays a key role in linking cell growth and proliferation in budding yeast. It is generally assumed that Cln3, which is present throughout G1, accumulates passively in the nucleus until a threshold is reached to trigger cell cycle entry. We show here that Cln3 is retained bound to the ER in early G1 cells. ER retention requires binding of Cln3 to the cyclin-dependent kinase Cdc28, a fraction of which also associates to the ER. Cln3 contains a chaperone-regulatory Ji domain that counteracts Ydj1, a J chaperone essential for ER release and nuclear accumulation of Cln3 in late G1. Finally, Ydj1 is limiting for release of Cln3 and timely entry into the cell cycle. As protein synthesis and ribosome assembly rates compromise chaperone availability, we hypothesize that Ydj1 transmits growth capacity information to the cell cycle for setting efficient size/ploidy ratios.

Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks

Background

A reverse engineering of gene regulatory network with large number of genes and limited number of experimental data points is a computationally challenging task. In particular, reverse engineering using linear systems is an underdetermined and ill conditioned problem, i.e. the amount of microarray data is limited and the solution is very sensitive to noise in the data. Therefore, the reverse engineering of gene regulatory networks with large number of genes and limited number of data points requires rigorous optimization algorithm.

Results

This study presents a novel algorithm for reverse engineering with linear systems. The proposed algorithm is a combination of the orthogonal least squares, second order derivative for network pruning, and Bayesian model comparison. In this study, the entire network is decomposed into a set of small networks that are defined as unit networks. The algorithm provides each unit network with P(D|H_i), which is used as confidence level. The unit network with higher P(D|H_i) has a higher confidence such that the unit network is correctly elucidated. Thus, the proposed algorithm is able to locate true positive interactions using P(D|H_i), which is a unique property of the proposed algorithm.

The algorithm is evaluated with synthetic and Saccharomyces cerevisiae expression data using the dynamic Bayesian network. With synthetic data, it is shown that the performance of the algorithm depends on the number of genes, noise level, and the number of data points. With Yeast expression data, it is shown that there is remarkable number of known physical or genetic events among all interactions elucidated by the proposed algorithm.

The performance of the algorithm is compared with Sparse Bayesian Learning algorithm using both synthetic and Saccharomyces cerevisiae expression data sets. The comparison experiments show that the algorithm produces sparser solutions with less false positives than Sparse Bayesian Learning algorithm.

Conclusion

From our evaluation experiments, we draw the conclusion as follows: 1) Simulation results show that the algorithm can be used to elucidate gene regulatory networks using limited number of experimental data points. 2) Simulation results also show that the algorithm is able to handle the problem with noisy data. 3) The experiment with Yeast expression data shows that the proposed algorithm reliably elucidates known physical or genetic events. 4) The comparison experiments show that the algorithm more efficiently performs than Sparse Bayesian Learning algorithm with noisy and limited number of data.

Ribosome biogenesis is sensed at the Start cell cycle checkpoint

In the yeast Saccharomyces cerevisiae it has long been thought that cells must reach a critical cell size, called the "setpoint," in order to allow the Start cell cycle transition. Recent evidence suggests that this setpoint is lowered when ribosome biogenesis is slowed. Here we present evidence that yeast can sense ribosome biogenesis independently of mature ribosome levels and protein synthetic capacity. Our results suggest that ribosome biogenesis directly promotes passage through Start through Whi5, the yeast functional equivalent to the human tumor suppressor Rb. When ribosome biogenesis is inhibited, a Whi5-dependent mechanism inhibits passage through Start before significant decreases in both the number of ribosomes and in overall translation capacity of the cell become evident. This delay at Start in response to decreases in ribosome biogenesis occurs independently of Cln3, the major known Whi5 antagonist. Thus ribosome biogenesis may be sensed at multiple steps in Start regulation. Ribosome biogenesis may thus both delay Start by increasing the cell size setpoint and independently may promote Start by inactivating Whi5.