The Effect of Dia2 Protein Deficiency on the Cell Cycle, Cell Size, and Recruitment of Ctf4 Protein in Saccharomyces cerevisiae

Cells have evolved elaborate mechanisms to regulate DNA replication machinery and cell cycles in response to DNA damage and replication stress in order to prevent genomic instability and cancer. The E3 ubiquitin ligase SCFDia2 in S. cerevisiae is involved in the DNA replication and DNA damage stress response, but its effect on cell growth is still unclear. Here, we demonstrate that the absence of Dia2 prolongs the cell cycle by extending both S- and G2/M-phases while, at the same time, activating the S-phase checkpoint. In these conditions, Ctf4—an essential DNA replication protein and substrate of Dia2—prolongs its binding to the chromatin during the extended S- and G2/M-phases. Notably, the prolonged cell cycle when Dia2 is absent is accompanied by a marked increase in cell size. We found that while both DNA replication inhibition and an absence of Dia2 exerts effects on cell cycle duration and cell size, Dia2 deficiency leads to a much more profound increase in cell size and a substantially lesser effect on cell cycle duration compared to DNA replication inhibition. Our results suggest that the increased cell size in dia2∆ involves a complex mechanism in which the prolonged cell cycle is one of the driving forces.

Embryonic fate after somatic cell nuclear transfer in non-enucleated goldfish oocytes is determined by first cleavages and DNA methylation patterns

Reducing the variability in nuclear transfer outcome requires a better understanding of its cellular and epigenetic determinants, in order to ensure safer fish regeneration from cryobanked somatic material. In this work, clones from goldfish were obtained using cryopreserved fin cells as donor and non-enucleated oocytes as recipients. We showed that the high variability of clones survival was not correlated to spawn quality. Clones were then characterized for their first cleavages pattern in relation to their developmental fate up to hatching. The first cell cycle duration was increased in clones with abnormal first cleavage, and symmetric first two cleavages increased clone probability to reach later on 24 h- and hatching-stages. At 24 h-stage, 24% of the clones were diploids and from donor genetic origin only. However, ploidy and genetic origin did not determine clones morphological quality. DNA methylation reprogramming in the promoter region of pou2, nanog, and notail marker genes was highly variable, but clones with the nicest morphologies displayed the best DNA methylation reprogramming. To conclude, non-enucleated oocytes did allow authentic clones production. The first two cell cycles were a critical determinant of the clone ability to reach hatching-stage, and DNA methylation reprogramming significantly influenced clones morphological quality.

Effects of cell cycle variability on lineage and population measurements of messenger RNA abundance

Many models of gene expression do not explicitly incorporate a cell cycle description. Here, we derive a theory describing how messenger RNA (mRNA) fluctuations for constitutive and bursty gene expression are influenced by stochasticity in the duration of the cell cycle and the timing of DNA replication. Analytical expressions for the moments show that omitting cell cycle duration introduces an error in the predicted mean number of mRNAs that is a monotonically decreasing function of η , which is proportional to the ratio of the mean cell cycle duration and the mRNA lifetime. By contrast, the error in the variance of the mRNA distribution is highest for intermediate values of η consistent with genome-wide measurements in many organisms. Using eukaryotic cell data, we estimate the errors in the mean and variance to be at most 3% and 25%, respectively. Furthermore, we derive an accurate negative binomial mixture approximation to the mRNA distribution. This indicates that stochasticity in the cell cycle can introduce fluctuations in mRNA numbers that are similar to the effect of bursty transcription. Finally, we show that for real experimental data, disregarding cell cycle stochasticity can introduce errors in the inference of transcription rates larger than 10%.

Effects of cell cycle variability on lineage and population measurements of mRNA abundance

Many models of gene expression do not explicitly incorporate a cell cycle description. Here we derive a theory describing how mRNA fluctuations for constitutive and bursty gene expression are influenced by stochasticity in the duration of the cell cycle and the timing of DNA replication. Analytical expressions for the moments show that omitting cell cycle duration introduces an error in the predicted mean number of mRNAs that is a monotonically decreasing function of η, which is proportional to the ratio of the mean cell cycle duration and the mRNA lifetime. By contrast, the error in the variance of the mRNA distribution is highest for intermediate values of η consistent with genome-wide measurements in many organisms. Using eukaryotic cell data, we estimate the errors in the mean and variance to be at most 3% and 25%, respectively. Furthermore, we derive an accurate negative binomial mixture approximation to the mRNA distribution. This indicates that stochasticity in the cell cycle can introduce fluctuations in mRNA numbers that are similar to the effect of bursty transcription. Finally, we show that for real experimental data, disregarding cell cycle stochasticity can introduce errors in the inference of transcription rates larger than 10%.

Temporal scaling in developmental gene networks by epigenetic timing control

SummaryDuring development, progenitors follow defined temporal schedules for differentiation, to form organs and body plans with precise sizes and proportions. Across diverse contexts, these developmental schedules are encoded by autonomous timekeeping mechanisms in single cells. These autonomous timers not only operate robustly over many cell generations, but can also operate at different speeds in different species, enabling proportional scaling of temporal schedules and population sizes. By combining mathematical modeling with live-cell measurements, we elucidate the mechanism of a polycomb-based epigenetic timer, that delays activation of the T-cell commitment regulatorBcl11bto facilitate progenitor expansion. This mechanism generates activation delays that are independent of cell cycle duration, and are tunably controlled by transcription factors and epigenetic modifiers. When incorporated into regulatory gene networks, this epigenetic timer enables progenitors to set scalable temporal schedules for flexible size control. These findings illuminate how evolution may set and adjust developmental speed in multicellular organisms.

A mathematical model of viral oncology as an immuno-oncology instigator

We develop and analyse a mathematical model of tumour–immune interaction that explicitly incorporates heterogeneity in tumour cell cycle duration by using a distributed delay differential equation. We derive a necessary and sufficient condition for local stability of the cancer-free equilibrium in which the amount of tumour–immune interaction completely characterizes disease progression. Consistent with the immunoediting hypothesis, we show that decreasing tumour–immune interaction leads to tumour expansion. Finally, by simulating the mathematical model, we show that the strength of tumour–immune interaction determines the long-term success or failure of viral therapy.

A mathematical model of viral oncology as an immuno-oncology instigator

We develop and analyse a mathematical model of tumour-immune interaction that explicitly incorporates heterogeneity in tumour cell cycle duration by using a distributed delay differential equation. Our necessary and sufficient conditions for local stability of the cancer free equilibrium completely characterise the importance of tumour-immune interaction in disease progression. Consistent with the immunoediting hypothesis, we show that decreasing tumour-immune interaction leads to tumour expansion. Finally, we show that immune involvement is crucial in determining the long-term response to viral therapy.