Modelling the regulation of telomere length: the effects of telomerase and G-quadruplex stabilising drugs

Synthesis and Molecular Dynamic Simulation of Novel Cationic and Non-cationic Pyrimidine Derivatives as Potential G-quadruplex-ligands

BACKGROUND

Drug resistance has been a problem in cancer chemotherapy, which often causes shortterm effectiveness. Further, the literature indicates that telomere G-quadruplex could be a promising anti-cancer target.

OBJECTIVE

We synthesized and characterized two new pyrimidine derivatives as ligands for G-quadruplex DNA.

METHODS

The interaction of novel non-cationic and cationic pyrimidine derivatives (3a, b) with G-quadruplex DNA (1k8p and 3qsc) was explored by circular dichroism (CD) and ultraviolet-visible spectroscopy and polyacrylamide gel electrophoresis (PAGE) methods. The antiproliferative activity of desired compounds was evaluated by the MTT assay. Apoptosis induction was assessed by Propidium iodide (P.I.) staining and flow cytometry. Computational molecular modeling (CMM) and molecular dynamics simulation (MD) were studied on the complexes of 1k8p and 3qsc with the compounds. The van der Waals, electrostatic, polar solvation, solventaccessible surface area (SASA), and binding energies were calculated and analyzed.

RESULTS

The experimental results confirmed that both compounds 3a and 3b interacted with 1k8p and 3qsc and exerted cytotoxic and proapoptotic effects on cancer cells. The number of hydrogen bonds and the RMSD values increased in the presence of the ligands, indicating stronger binding and suggesting increased structural dynamics. The electrostatic contribution to binding energy was higher for the cationic pyrimidine 3b, indicating more negative binding energies.

CONCLUSION

Both experimental and MD results confirmed that 3b was more prone to form a complex with DNA G-quadruplex (1k8p and 3qsc), inhibit cell growth, and induce apoptosis, compared to the non-cationic pyrimidine 3a.

Telomerase reverse transcriptase downregulation by RNA interference modulates endoplasmic reticulum stress and mitochondrial energy production

Telomerase is a cancer promoting ribonucleoprotein complex and is a potential therapeutic target for cancer. In this study, the effects of telomerase downregulation on the whole cell proteome were investigated. Understanding how the effect of downregulation on the whole proteome profile will generate a greater understanding of the possible roles played by telomerase in cancer. Downregulation was achieved by RNA interference (RNAi), targeting the telomerase reverse transcriptase (TERT) subunits of telomerase. Transfection of TERT siRNA downregulates TERT gene expression and induced downregulation of telomerase activity. Investigation of the effect of silencing TERT in telomerase was further validated through proteomic analysis by performing 2-dimension electrophoresis (2DE) coupled with MALDI-TOF/TOF. 12 protein spots in HeLa cells were reported to be significantly differentially expressed with 11 of them were upregulated and 1 downregulated. Through STRING analysis, differentially expressed proteins demonstrated strong associations with endoplasmic reticulum stress marker and mitochondrial energy production marker. In conclusions, the result exhibited novel integrated proteomic response involving endoplasmic reticulum stress and mitochondrial energy production in response to the TERT downregulation in cervical cancer cells.

Mathematical modelling of telomere length dynamics

Telomeres are repetitive DNA sequences located at the ends of chromosomes. During cell division, an incomplete copy of each chromosome's DNA is made, causing telomeres to shorten on successive generations. When a threshold length is reached replication ceases and the cell becomes 'senescent'. In this paper, we consider populations of telomeres and, from discrete models, we derive partial differential equations which describe how the distribution of telomere lengths evolves over many generations. We initially consider a population of cells each containing just a single telomere. We use continuum models to compare the effects of various mechanisms of telomere shortening and rates of cell division during normal ageing. For example, the rate (or probability) of cell replication may be fixed or it may decrease as the telomeres shorten. Furthermore, the length of telomere lost on each replication may be constant, or may decrease as the telomeres shorten. Where possible, explicit solutions for the evolution of the distribution of telomere lengths are presented. In other cases, expressions for the mean of the distribution are derived. We extend the models to describe cell populations in which each cell contains a distinct subpopulation of chromosomes. As for the simpler models, constant telomere shortening leads to a linear reduction in telomere length over time, whereas length-dependent shortening results in initially rapid telomere length reduction, slowing at later times. Our analysis also reveals that constant telomere loss leads to a Gaussian (normal) distribution of telomere lengths, whereas length-dependent loss leads to a log-normal distribution. We show that stochastic models, which include a replication probability, also lead to telomere length distributions which are skewed.

Evaluating biomarkers to model cancer risk post cosmic ray exposure

Robust predictive models are essential to manage the risk of radiation-induced carcinogenesis. Chronic exposure to cosmic rays in the context of the complex deep space environment may place astronauts at high cancer risk. To estimate this risk, it is critical to understand how radiation-induced cellular stress impacts cell fate decisions and how this in turn alters the risk of carcinogenesis. Exposure to the heavy ion component of cosmic rays triggers a multitude of cellular changes, depending on the rate of exposure, the type of damage incurred and individual susceptibility. Heterogeneity in dose, dose rate, radiation quality, energy and particle flux contribute to the complexity of risk assessment. To unravel the impact of each of these factors, it is critical to identify sensitive biomarkers that can serve as inputs for robust modeling of individual risk of cancer or other long-term health consequences of exposure. Limitations in sensitivity of biomarkers to dose and dose rate, and the complexity of longitudinal monitoring, are some of the factors that increase uncertainties in the output from risk prediction models. Here, we critically evaluate candidate early and late biomarkers of radiation exposure and discuss their usefulness in predicting cell fate decisions. Some of the biomarkers we have reviewed include complex clustered DNA damage, persistent DNA repair foci, reactive oxygen species, chromosome aberrations and inflammation. Other biomarkers discussed, often assayed for at longer points post exposure, include mutations, chromosome aberrations, reactive oxygen species and telomere length changes. We discuss the relationship of biomarkers to different potential cell fates, including proliferation, apoptosis, senescence, and loss of stemness, which can propagate genomic instability and alter tissue composition and the underlying mRNA signatures that contribute to cell fate decisions. Our goal is to highlight factors that are important in choosing biomarkers and to evaluate the potential for biomarkers to inform models of post exposure cancer risk. Because cellular stress response pathways to space radiation and environmental carcinogens share common nodes, biomarker-driven risk models may be broadly applicable for estimating risks for other carcinogens.

Aging and computational systems biology

Aging research is undergoing a paradigm shift, which has led to new and innovative methods of exploring this complex phenomenon. The systems biology approach endeavors to understand biological systems in a holistic manner, by taking account of intrinsic interactions, while also attempting to account for the impact of external inputs, such as diet. A key technique employed in systems biology is computational modeling, which involves mathematically describing and simulating the dynamics of biological systems. Although a large number of computational models have been developed in recent years, these models have focused on various discrete components of the aging process, and to date no model has succeeded in completely representing the full scope of aging. Combining existing models or developing new models may help to address this need and in so doing could help achieve an improved understanding of the intrinsic mechanisms which underpin aging.

Stochastic simulations of normal aging and Werner's syndrome

Human cells typically consist of 23 pairs of chromosomes. Telomeres are repetitive sequences of DNA located at the ends of chromosomes. During cell replication, a number of basepairs are lost from the end of the chromosome and this shortening restricts the number of divisions that a cell can complete before it becomes senescent, or non-replicative. In this paper, we use Monte Carlo simulations to form a stochastic model of telomere shortening to investigate how telomere shortening affects normal aging. Using this model, we study various hypotheses for the way in which shortening occurs by comparing their impact on aging at the chromosome and cell levels. We consider different types of length-dependent loss and replication probabilities to describe these processes. After analyzing a simple model for a population of independent chromosomes, we simulate a population of cells in which each cell has 46 chromosomes and the shortest telomere governs the replicative potential of the cell. We generalize these simulations to Werner's syndrome, a condition in which large sections of DNA are removed during cell division and, amongst other conditions, results in rapid aging. Since the mechanisms governing the loss of additional basepairs are not known, we use our model to simulate a variety of possible forms for the rate at which additional telomeres are lost per replication and several expressions for how the probability of cell division depends on telomere length. As well as the evolution of the mean telomere length, we consider the standard deviation and the shape of the distribution. We compare our results with a variety of data from the literature, covering both experimental data and previous models. We find good agreement for the evolution of telomere length when plotted against population doubling.