Robustness of cell cycle control and flexible orders of signaling events

LncRNAs of Saccharomyces cerevisiae bypass the cell cycle arrest imposed by ethanol stress

Ethanol alters many subsystems of Saccharomyces cerevisiae, including the cell cycle. Two ethanol-responsive lncRNAs in yeast interact with cell cycle proteins, and here, we investigated the role of these RNAs in cell cycle. Our network dynamic modeling showed that higher and lower ethanol-tolerant strains undergo cell cycle arrest in mitosis and G1 phases, respectively, during ethanol stress. The higher population rebound of the lower ethanol-tolerant phenotype after stress relief responds to the late phase arrest. We found that the lncRNA lnc9136 of SEY6210 (a lower ethanol-tolerant strain) induces cells to skip mitosis arrest. Simulating an overexpression of lnc9136 and analyzing CRISPR–Cas9 mutants lacking this lncRNA suggest that lnc9136 induces a regular cell cycle even under ethanol stress, indirectly regulating Swe1p and Clb1/2 by binding to Gin4p and Hsl1p. Notably, lnc10883 of BY4742 (a higher ethanol-tolerant strain) does not prevent G1 arrest in this strain under ethanol stress. However, lnc19883 circumvents DNA and spindle damage checkpoints, maintaining a functional cell cycle by interacting with Mec1p or Bub1p even in the presence of DNA/spindle damage. Overall, we present the first evidence of direct roles for lncRNAs in regulating yeast cell cycle proteins, the dynamics of this system in different ethanol-tolerant phenotypes, and a new yeast cell cycle model.

Ethanol is a cell stressor in yeast that dampen ethanol production. LncRNAs are RNAs that control many cellular processes. Computational simulations allow us to study the dynamism of cell systems. Therefore, we built a computational model of the yeast cell cycle to investigate how cells respond to ethanol stress. Simulations showed that ethanol stress or spindle damage arrests the cell cycle. Furthermore, the performance of higher and lower ethanol-tolerant strains in poststress recovery growth seems to be related to the cell cycle phase in which cells are stalled. However, two lncRNAs maintain the activity of the cell cycle even in yeast cells under these stresses by repressing specific cell cycle proteins. Finally, this model facilitates analyses of the yeast cell cycle for applied or basic science purposes.

Elucidate growth control mechanisms using reconstructed spatiotemporal distributions of signaling events

A fundamental biological question is how diverse and complex signaling and patterning is controlled correctly to generate distinct tissues, organs, and body plans, but incorrectly in diseased cells and tissues. Signaling pathways important for growth control have been identified, but to identify the mechanisms their transient and context-dependent interactions encode is more difficult. Currently computational systems biology aims to infer the control mechanisms by investigating quantitative changes of gene expression and protein concentrations, but this inference is difficult in nature. We propose it is desirable to explicitly simulate events and orders of gene regulation and protein interactions, which better elucidate control mechanisms, and report a method and tool with three examples. The Drosophila wing model includes Wnt, PCP, and Hippo pathways and mechanical force, incorporates well-confirmed experimental findings, and generates novel results. The other two examples illustrate the building of three-dimensional and large-scale models. These examples support that reconstructed spatiotemporal distributions of key signaling events help elucidate growth control mechanisms. As biologists pay increasing attention to disordered signaling in diseased cells, to develop new modeling methods and tools for conducting new computational studies is important.

Quantifying the Landscape and Transition Paths for Proliferation-Quiescence Fate Decisions

The cell cycle, essential for biological functions, experiences delicate spatiotemporal regulation. The transition between G1 and S phase, which is called the proliferation–quiescence decision, is critical to the cell cycle. However, the stability and underlying stochastic dynamical mechanisms of the proliferation–quiescence decision have not been fully understood. To quantify the process of the proliferation–quiescence decision, we constructed its underlying landscape based on the relevant gene regulatory network. We identified three attractors on the landscape corresponding to the G0, G1, and S phases, individually, which are supported by single-cell data. By calculating the transition path, which quantifies the potential barrier, we built expression profiles in temporal order for key regulators in different transitions. We propose that the two saddle points on the landscape characterize restriction point (RP) and G1/S checkpoint, respectively, which provides quantitative and physical explanations for the mechanisms of Rb governing the RP while p21 controlling the G1/S checkpoint. We found that Emi1 inhibits the transition from G0 to G1, while Emi1 in a suitable range facilitates the transition from G1 to S. These results are partially consistent with previous studies, which also suggested new roles of Emi1 in the cell cycle. By global sensitivity analysis, we identified some critical regulatory factors influencing the proliferation–quiescence decision. Our work provides a global view of the stochasticity and dynamics in the proliferation–quiescence decision of the cell cycle.

Phosphorylation of the Ribosomal Protein RPL12/uL11 Affects Translation during Mitosis

Emerging evidence indicates that heterogeneity in ribosome composition can give rise to specialized functions. Until now, research mainly focused on differences in core ribosomal proteins and associated factors. The effect of posttranslational modifications has not been studied systematically. Analyzing ribosome heterogeneity is challenging because individual proteins can be part of different subcomplexes (40S, 60S, 80S, and polysomes). Here we develop polysome proteome profiling to obtain unbiased proteomic maps across ribosomal subcomplexes. Our method combines extensive fractionation by sucrose gradient centrifugation with quantitative mass spectrometry. The high resolution of the profiles allows us to assign proteins to specific subcomplexes. Phosphoproteomics on the fractions reveals that phosphorylation of serine 38 in RPL12/uL11, a known mitotic CDK1 substrate, is strongly depleted in polysomes. Follow-up experiments confirm that RPL12/uL11 phosphorylation regulates the translation of specific subsets of mRNAs during mitosis. Together, our results show that posttranslational modification of ribosomal proteins can regulate translation.

Quantitative Systems Biology to decipher design principles of a dynamic cell cycle network: the "Maximum Allowable mammalian Trade-Off-Weight" (MAmTOW)

Network complexity is required to lend cellular processes flexibility to respond timely to a variety of dynamic signals, while simultaneously warranting robustness to protect cellular integrity against perturbations. The cell cycle serves as a paradigm for such processes; it maintains its frequency and temporal structure (although these may differ among cell types) under the former, but accelerates under the latter. Cell cycle molecules act together in time and in different cellular compartments to execute cell type-specific programs. Strikingly, the timing at which molecular switches occur is controlled by abundance and stoichiometry of multiple proteins within complexes. However, traditional methods that investigate one effector at a time are insufficient to understand how modulation of protein complex dynamics at cell cycle transitions shapes responsiveness, yet preserving robustness. To overcome this shortcoming, we propose a multidisciplinary approach to gain a systems-level understanding of quantitative cell cycle dynamics in mammalian cells from a new perspective. By suggesting advanced experimental technologies and dedicated modeling approaches, we present innovative strategies (i) to measure absolute protein concentration in vivo, and (ii) to determine how protein dosage, e.g., altered protein abundance, and spatial (de)regulation may affect timing and robustness of phase transitions. We describe a method that we name “Maximum Allowable mammalian Trade–Off–Weight” (MAmTOW), which may be realized to determine the upper limit of gene copy numbers in mammalian cells. These aspects, not covered by current systems biology approaches, are essential requirements to generate precise computational models and identify (sub)network-centered nodes underlying a plethora of pathological conditions.

β-lapachone suppresses tumour progression by inhibiting epithelial-to-mesenchymal transition in NQO1-positive breast cancers

NQO1 is a FAD-binding protein that can form homodimers and reduce quinones to hydroquinones, and a growing body of evidence currently suggests that NQO1 is dramatically elevated in solid cancers. Here, we demonstrated that NQO1 was elevated in breast cancer and that its expression level was positively correlated with invasion and reduced disease free survival (DFS) and overall survival (OS) rates. Next, we found that β-lapachone exerted significant anti-proliferation and anti-metastasis effects in breast cancer cell lines due to its effects on NQO1 expression. Moreover, we revealed that the anti-cancer effects of β-lapachone were mediated by the inactivation of the Akt/mTOR pathway. In conclusion, these results demonstrated that NQO1 could be a useful prognostic biomarker for patients with breast cancer, and its bioactivatable drug, β-lapachone represented a promising new development and an effective strategy for indicating the progression of NQO1-positive breast cancers.

Impact of time delays on oscillatory dynamics of interlinked positive and negative feedback loops

Interlinking a positive feedback loop (PFL) with a negative feedback loop (NFL) constitutes a typical motif in genetic networks, performing various functions in cell signaling. How time delay in feedback regulation affects the dynamics of such systems still remains unclear. Here, we investigate three systems of interlinked PFL and NFL with time delays: a synthetic genetic oscillator, a three-node circuit, and a simplified single-node model. The stability of steady states and the routes to oscillation in the single-node model are analyzed in detail. The amplitude and period of oscillations vary with a pointwise periodicity over a range of time delay. Larger-amplitude oscillations can be induced when the PFL has an appropriately long delay, in comparison with the PFL with no delay or short delay; this conclusion holds true for all the three systems. We unravel the underlying mechanism for the above effects via analytical derivation under a limiting condition. We also develop a stochastic algorithm for simulating a single reaction with two delays and show that robust oscillations can be maintained by the PFL with a properly long delay in the single-node system. This work presents an effective method for constructing robust large-amplitude oscillators and interprets why similar circuit architectures are engaged in timekeeping systems such as circadian clocks.

The spatiotemporal order of signaling events unveils the logic of development signaling

Motivation: Animals from worms and insects to birds and mammals show distinct body plans; however, the embryonic development of diverse body plans with tissues and organs within is controlled by a surprisingly few signaling pathways. It is well recognized that combinatorial use of and dynamic interactions among signaling pathways follow specific logic to control complex and accurate developmental signaling and patterning, but it remains elusive what such logic is, or even, what it looks like.

Results: We have developed a computational model for Drosophila eye development with innovated methods to reveal how interactions among multiple pathways control the dynamically generated hexagonal array of R8 cells. We obtained two novel findings. First, the coupling between the long-range inductive signals produced by the proneural Hh signaling and the short-range restrictive signals produced by the antineural Notch and EGFR signaling is essential for generating accurately spaced R8s. Second, the spatiotemporal orders of key signaling events reveal a robust pattern of lateral inhibition conducted by Ato-coordinated Notch and EGFR signaling to collectively determine R8 patterning. This pattern, stipulating the orders of signaling and comparable to the protocols of communication, may help decipher the well-appreciated but poorly defined logic of developmental signaling.

Availability and implementation: The model is available upon request.

Contact:hao.zhu@ymail.com

Supplementary information:Supplementary data are available at Bioinformatics online.

Robustness of cell cycle control and flexible orders of signaling events

The highly robust control of cell cycles in eukaryotes enables cells to undergo strictly ordered G1/S/G2/M phases and respond adaptively to regulatory signals; however the nature of the robustness remains obscure. Specifically, it is unclear whether events of signaling should be strictly ordered and whether some events are more robust than others. To quantitatively address the two questions, we have developed a novel cell cycle model upon experimental observations. It contains positive and negative E2F proteins and two Cdk inhibitors, and is parameterized, for the first time, to generate not only oscillating protein concentrations but also periodic signaling events. Events and their orders reconstructed under varied conditions indicate that proteolysis of cyclins and Cdk complexes by APC and Skp2 occurs highly robustly in a strict order, but many other events are either dispensable or can occur in flexible orders. These results suggest that strictly ordered proteolytic events are essential for irreversible cell cycle progression and the robustness of cell cycles copes with flexible orders of signaling events, and unveil a new and important dimension to the robustness of cell cycle control in particular and to biological signaling in general.