The contribution of inherited genotype to breast cancer

The etiology of breast cancer is complex, and is likely to involve the actions of genes at multiple levels along the multistage carcinogenesis process. These inherited genotypes include those that affect the propensity to be exposed to breast carcinogens, and those associated with breast tumorigenesis directly. In addition, inherited genotypes may influence response to breast cancer chemoprevention and treatment. Studies relating inherited genotypes with breast cancer incidence and mortality should consider a broader spectrum of genes and their potential roles in multistage carcinogenesis than have been typically evaluated to date. Understanding the role of inherited genotype at different stages of carcinogenesis could improve our understanding of cancer biology, may identify specific exposures or events that correlate with carcinogenesis, or target relevant biochemical pathways for the development of preventive or therapeutic interventions

Modulation of the DNA-binding activity of Saccharomyces cerevisiae MSH2–MSH6 complex by the high-mobility group protein NHP6A, in vitro

DNA mismatch repair corrects mispaired bases and small insertions/deletions in DNA. In eukaryotes, the mismatch repair complex MSH2–MSH6 binds to mispairs with only slightly higher affinity than to fully paired DNA in vitro. Recently, the high-mobility group box1 protein, (HMGB1), has been shown to stimulate the mismatch repair reaction in vitro. In yeast, the closest homologs of HMGB1 are NHP6A and NHP6B. These proteins have been shown to be required for genome stability maintenance and mutagenesis control. In this work, we show that MSH2–MSH6 and NHP6A modulate their binding to DNA in vitro. Binding of the yeast MSH2–MSH6 to homoduplex regions of DNA significantly stimulates the loading of NHP6A. Upon binding of NHP6A to DNA, MSH2–MSH6 is excluded from binding unless a mismatch is present. A DNA binding-impaired MSH2–MSH6F337A significantly reduced the loading of NHP6A to DNA, suggesting that MSH2–MSH6 binding is a requisite for NHP6A loading. MSH2–MSH6 and NHP6A form a stable complex, which is responsive to ATP on mismatched substrates. These results suggest that MSH2–MSH6 binding to homoduplex regions of DNA recruits NHP6A, which then prevents further binding of MSH2–MSH6 to these sites unless a mismatch is present

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

Mapping the yeast host cell response to recombinant membrane protein production:relieving the biological bottlenecks

Understanding the structures and functions of membrane proteins is an active area of research within bioscience. Membrane proteins are key players in essential cellular processes such as the uptake of nutrients, the export of waste products, and the way in which cells communicate with their environment. It is therefore not surprising that membrane proteins are targeted by over half of all prescription drugs. Since most membrane proteins are not abundant in their native membranes, it is necessary to produce them in recombinant host cells to enable further structural and functional studies. Unfortunately, achieving the required yields of functional recombinant membrane proteins is still a bottleneck in contemporary bioscience. This has highlighted the need for defined and rational optimization strategies based upon experimental observation rather than relying on trial and error. We have published a transcriptome and subsequent genetic analysis that has identified genes implicated in high-yielding yeast cells. These results have highlighted a role for alterations to a cell's protein synthetic capacity in the production of high yields of recombinant membrane protein: paradoxically, reduced protein synthesis favors higher yields. These results highlight a potential bottleneck at the protein folding or translocation stage of protein production

Nomenclature for alleles of the thiopurine methyltransferase gene

The drug-metabolizing enzyme thiopurine methyltransferase (TPMT) has become one of the best examples of pharmacogenomics to be translated into routine clinical practice. TPMT metabolizes the thiopurines 6-mercaptopurine, 6-thioguanine, and azathioprine, drugs that are widely used for treatment of acute leukemias, inflammatory bowel diseases, and other disorders of immune regulation. Since the discovery of genetic polymorphisms in the TPMT gene, many sequence variants that cause a decreased enzyme activity have been identified and characterized. Increasingly, to optimize dose, pretreatment determination of TPMT status before commencing thiopurine therapy is now routine in many countries. Novel TPMT sequence variants are currently numbered sequentially using PubMed as a source of information; however, this has caused some problems as exemplified by two instances in which authors' articles appeared on PubMed at the same time, resulting in the same allele numbers given to different polymorphisms. Hence, there is an urgent need to establish an order and consensus to the numbering of known and novel TPMT sequence variants. To address this problem, a TPMT nomenclature committee was formed in 2010, to define the nomenclature and numbering of novel variants for the TPMT gene. A website (http://www.imh.liu.se/tpmtalleles) serves as a platform for this work. Researchers are encouraged to submit novel TPMT alleles to the committee for designation and reservation of unique allele numbers. The committee has decided to renumber two alleles: nucleotide position 106 (G>A) from TPMT*24 to TPMT*30 and position 611 (T>C, rs79901429) from TPMT*28 to TPMT*31. Nomenclature for all other known alleles remains unchanged

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