Mechanistic mathematical modelling of mercaptopurine effects on cell cycle of human acute lymphoblastic leukaemia cells

The antimetabolite mercaptopurine (MP) is widely used to treat childhood acute lymphoblastic leukaemia (ALL). To study the dynamics of MP on the cell cycle, we incubated human T-cell leukaemia cell lines (Molt-4 sensitive and resistant subline and P12 resistant) with 10 μM MP and measured total cell count, cell cycle distribution, percent viable, percent apoptotic, and percent dead cells serially over 72 h. We developed a mathematical model of the cell cycle dynamics after treatment with MP and used it to show that the Molt-4 sensitive controls had a significantly higher rate of cells entering apoptosis (2.7-fold, P<0.00001) relative to the resistant cell lines. Additionally, when treated with MP, the sensitive cell line showed a significant increase in the rate at which cells enter apoptosis compared to its controls (2.4-fold, P<0.00001). Of note, the resistant cell lines had a higher rate of antimetabolite incorporation into the DNA of viable cells (>1.4-fold, P<0.01). Lastly, in contrast to the other cell lines, the Molt-4 resistant subline continued to cycle, though at a rate slower relative to its control, rather than proceed to apoptosis. This led to a larger S-phase block in the Molt-4 resistant cell line, but not a higher rate of cell death. Gene expression of apoptosis, cell cycle, and repair genes were consistent with mechanistic dynamics described by the model. In summary, the mathematical model provides a quantitative assessment to compare the cell cycle effects of MP in cells with varying degrees of MP resistance

Thiopurine Methyltransferase Predicts the Extent of Cytotoxicty and DNA Damage in Astroglial Cells after Thioguanine Exposure

Thiopurine methyltransferase (Tpmt) is the primary enzyme responsible for deactivating thiopurine drugs. Thiopurine drugs (i.e., thioguanine [TG], mercaptopurine, azathioprine) are commonly used for the treatment of cancer, organ transplant, and autoimmune disorders. Chronic thiopurine therapy has been linked to the development of brain cancer (most commonly astrocytomas), and Tpmt status has been associated with this risk. Therefore, we investigated whether the level of Tpmt protein activity could predict TG-associated cytotoxicity and DNA damage in astrocytic cells. We found that TG induced cytotoxicity in a dose-dependent manner in Tpmt+/+, Tpmt+/− and Tpmt−/− primary mouse astrocytes and that a low Tpmt phenotype predicted significantly higher sensitivity to TG than did a high Tpmt phenotype. We also found that TG exposure induced significantly more DNA damage in the form of single strand breaks (SSBs) and double strand breaks (DSBs) in primary astrocytes with low Tpmt versus high Tpmt. More interestingly, we found that Tpmt+/− astrocytes had the highest degree of cytotoxicity and genotoxicity (i.e., IC50, SSBs and DSBs) after TG exposure. We then used human glioma cell lines as model astroglial cells to represent high (T98) and low (A172) Tpmt expressers and found that A172 had the highest degree of cytoxicity and SSBs after TG exposure. When we over-expressed Tpmt in the A172 cell line, we found that TG IC50 was significantly higher and SSB's were significantly lower as compared to mock transfected cells. This study shows that low Tpmt can lead to greater sensitivity to thiopurine therapy in astroglial cells. When Tpmt deactivation at the germ-line is considered, this study also suggests that heterozygosity may be subject to the greatest genotoxic effects of thiopurine therapy

Mirror, mirror on the wall: which microbiomes will help heal them all?

BACKGROUND: Clinicians have known for centuries that there is substantial variability between patients in their response to medications—some individuals exhibit a miraculous recovery while others fail to respond at all. Still others experience dangerous side effects. The hunt for the factors responsible for this variation has been aided by the ability to sequence the human genome, but this just provides part of the picture. Here, we discuss the emerging field of study focused on the human microbiome and how it may help to better predict drug response and improve the treatment of human disease. DISCUSSION: Various clinical disciplines characterize drug response using either continuous or categorical descriptors that are then correlated to environmental and genetic risk factors. However, these approaches typically ignore the microbiome, which can directly metabolize drugs into downstream metabolites with altered activity, clearance, and/or toxicity. Variations in the ability of each individual’s microbiome to metabolize drugs may be an underappreciated source of differences in clinical response. Complementary studies in humans and animal models are necessary to elucidate the mechanisms responsible and to test the feasibility of identifying microbiome-based biomarkers of treatment outcomes. SUMMARY: We propose that the predictive power of genetic testing could be improved by taking a more comprehensive view of human genetics that encompasses our human and microbial genomes. Furthermore, unlike the human genome, the microbiome is rapidly altered by diet, pharmaceuticals, and other interventions, providing the potential to improve patient care by re-shaping our associated microbial communities

A meta-analysis of genome-wide association studies of epigenetic age acceleration

Funding: Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Genotyping and DNA methylation profiling of the GS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” ((STRADL) Reference 104036/Z/14/Z)). Funding details for the cohorts included in the study by Lu et al. (2018) can be found in their publication. HCW is supported by a JMAS SIM fellowship from the Royal College of Physicians of Edinburgh and by an ESAT College Fellowship from the University of Edinburgh. AMM & HCW acknowledge the support of the Dr. Mortimer and Theresa Sackler Foundation. SH acknowledges support from grant 1U01AG060908-01. REM is supported by Alzheimer’s Research UK major project grant ARUK-PG2017B-10. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data Availability: Summary statistics from the research reported in the manuscript will be made available immediately following publication on the Edinburgh Data Share portal with a permanent digital object identifier (DOI). According to the terms of consent for Generation Scotland participants, requests for access to the individual-level data must be reviewed by the GS Access Committee ([email protected]). Individual-level data are not immediately available, due to confidentiality considerations and our legal obligation to protect personal information. These data will, however, be made available upon request and after review by the GS access committee, once ethical and data governance concerns regarding personal data have been addressed by the receiving institution through a Data Transfer Agreement.Peer reviewedPublisher PD