A predictive mathematical model of the DNA damage G2 checkpoint

The optimal strategy balancing risk and speed predicts DNA damage checkpoint override times

Checkpoints arrest biological processes allowing time for error correction. The phenomenon of checkpoint override (also known as checkpoint adaptation, slippage, or leakage), during cellular self-replication is biologically critical but currently lacks a quantitative, functional, or system-level understanding. To uncover fundamental laws governing error-correction systems, we derived a general theory of optimal checkpoint strategies, balancing the trade-off between risk and self-replication speed. Mathematically, the problem maps onto the optimization of an absorbing boundary for a random walk. We applied the theory to the DNA damage checkpoint (DDC) in budding yeast, an intensively researched model checkpoint. Using novel reporters for double-strand DNA breaks (DSBs), we first quantified the probability distribution of DSB repair in time including rare events and, secondly, the survival probability after override. With these inputs, the optimal theory predicted remarkably accurately override times as a function of DSB numbers, which we measured precisely for the first time. Thus, a first-principles calculation revealed undiscovered patterns underlying highly noisy override processes. Our multi-DSB measurements revise well-known past results and show that override is more general than previously thought.

Systematic expression analysis of WEE family kinases reveals the importance of PKMYT1 in breast carcinogenesis

OBJECTIVES

Many cancer cells depend on G2 checkpoint mechanism regulated by WEE family kinases to maintain genomic integrity. The PKMYT1 gene, as a member of WEE family kinases, participates in G2 checkpoint surveillance and probably links with tumorigenesis, but its role in breast cancer remains largely unclear.

MATERIALS AND METHODS

In this study, we used a set of bioinformatic tools to jointly analyse the expression of WEE family kinases and investigate the prognostic value of PKMYT1 in breast cancer.

RESULTS

The results indicated that PKMYT1 is the only frequently overexpressed member of WEE family kinases in breast cancer. KM plotter data suggests that abnormally high expression of PKMYT1 predicts poor prognosis, especially for some subtypes, such as luminal A/B and triple-negative (TNBC) types. Moreover, the up-regulation of PKMYT1 was associated with HER2-positive (HER2+), basal-like (Basal-like), TNBC statuses and increased classifications of Scarff, Bloom and Richardson (SBR). Co-expression analysis showed PKMYT1 has a strong positive correlation with Polo-like kinase 1 (PLK1), implying they may cooperate in regulating cancer cell proliferation by synchronizing rapid cell cycle with high quality of genome maintenance.

CONCLUSIONS

Collectively, this study demonstrates that overexpression of PKMYT1 is always found in breast cancer and predicts unfavourable prognosis, implicating it as an appealing therapeutic target for breast carcinoma.

WIP1 Contributes to the Adaptation of Fanconi Anemia Cells to DNA Damage as Determined by the Regulatory Network of the Fanconi Anemia and Checkpoint Recovery Pathways

DNA damage adaptation (DDA) allows the division of cells with unrepaired DNA damage. DNA repair deficient cells might take advantage of DDA to survive. The Fanconi anemia (FA) pathway repairs DNA interstrand crosslinks (ICLs), and deficiencies in this pathway cause a fraction of breast and ovarian cancers as well as FA, a chromosome instability syndrome characterized by bone marrow failure and cancer predisposition. FA cells are hypersensitive to ICLs; however, DDA might promote their survival. We present the FA-CHKREC Boolean Network Model, which explores how FA cells might use DDA. The model integrates the FA pathway with the G2 checkpoint and the checkpoint recovery (CHKREC) processes. The G2 checkpoint mediates cell-cycle arrest (CCA) and the CHKREC activates cell-cycle progression (CCP) after resolution of DNA damage. Analysis of the FA-CHKREC network indicates that CHKREC drives DDA in FA cells, ignoring the presence of unrepaired DNA damage and allowing their division. Experimental inhibition of WIP1, a CHKREC component, in FA lymphoblast and cancer cell lines prevented division of FA cells, in agreement with the prediction of the model.

Modeling biomolecular site dynamics in immunoreceptor signaling systems

The immune system plays a central role in human health. The activities of immune cells, whether defending an organism from disease or triggering a pathological condition such as autoimmunity, are driven by the molecular machinery of cellular signaling systems. Decades of experimentation have elucidated many of the biomolecules and interactions involved in immune signaling and regulation, and recently developed technologies have led to new types of quantitative, systems-level data. To integrate such information and develop nontrivial insights into the immune system, computational modeling is needed, and it is essential for modeling methods to keep pace with experimental advances. In this chapter, we focus on the dynamic, site-specific, and context-dependent nature of interactions in immunoreceptor signaling (i.e., the biomolecular site dynamics of immunoreceptor signaling), the challenges associated with capturing these details in computational models, and how these challenges have been met through use of rule-based modeling approaches.

A predictor for predicting Escherichia coli transcriptome and the effects of gene perturbations

Background

A means to predict the effects of gene over-expression, knockouts, and environmental stimuli in silico is useful for system biologists to develop and test hypotheses. Several studies had predicted the expression of all Escherichia coli genes from sequences and reported a correlation of 0.301 between predicted and actual expression. However, these do not allow biologists to study the effects of gene perturbations on the native transcriptome.

Results

We developed a predictor to predict transcriptome-scale gene expression from a small number (n = 59) of known gene expressions using gene co-expression network, which can be used to predict the effects of over-expressions and knockdowns on E. coli transcriptome. In terms of transcriptome prediction, our results show that the correlation between predicted and actual expression value is 0.467, which is similar to the microarray intra-array variation (p-value = 0.348), suggesting that intra-array variation accounts for a substantial portion of the transcriptome prediction error. In terms of predicting the effects of gene perturbation(s), our results suggest that the expression of 83% of the genes affected by perturbation can be predicted within 40% of error and the correlation between predicted and actual expression values among the affected genes to be 0.698. With the ability to predict the effects of gene perturbations, we demonstrated that our predictor has the potential to estimate the effects of varying gene expression level on the native transcriptome.

Conclusion

We present a potential means to predict an entire transcriptome and a tool to estimate the effects of gene perturbations for E. coli, which will aid biologists in hypothesis development. This study forms the baseline for future work in using gene co-expression network for gene expression prediction.

Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and posttranslational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation).

Stathmin and microtubules regulate mitotic entry in HeLa cells by controlling activation of both Aurora kinase A and Plk1

Depletion of stathmin, a microtubule (MT) destabilizer, delays mitotic entry by ∼4 h in HeLa cells. Stathmin depletion reduced the activity of CDC25 and its upstream activators, Aurora A and Plk1. Chemical inhibition of both Aurora A and Plk1 was sufficient to delay mitotic entry by 4 h, while inhibiting either kinase alone did not cause a delay. Aurora A and Plk1 are likely regulated downstream of stathmin, because the combination of stathmin knockdown and inhibition of Aurora A and Plk1 was not additive and again delayed mitotic entry by 4 h. Aurora A localization to the centrosome required MTs, while stathmin depletion spread its localization beyond that of γ-tubulin, indicating an MT-dependent regulation of Aurora A activation. Plk1 was inhibited by excess stathmin, detected in in vitro assays and cells overexpressing stathmin-cyan fluorescent protein. Recruitment of Plk1 to the centrosome was delayed in stathmin-depleted cells, independent of MTs. It has been shown that depolymerizing MTs with nocodazole abrogates the stathmin-depletion induced cell cycle delay; in this study, depolymerization with nocodazole restored Plk1 activity to near normal levels, demonstrating that MTs also contribute to Plk1 activation. These data demonstrate that stathmin regulates mitotic entry, partially via MTs, to control localization and activation of both Aurora A and Plk1.