The Epistatic Relationship between BRCA2 and the Other RAD51 Mediators in Homologous Recombination

RAD51 recombinase polymerizes at the site of double-strand breaks (DSBs) where it performs DSB repair. The loss of RAD51 causes extensive chromosomal breaks, leading to apoptosis. The polymerization of RAD51 is regulated by a number of RAD51 mediators, such as BRCA1, BRCA2, RAD52, SFR1, SWS1, and the five RAD51 paralogs, including XRCC3. We here show that brca2-null mutant cells were able to proliferate, indicating that RAD51 can perform DSB repair in the absence of BRCA2. We disrupted the BRCA1, RAD52, SFR1, SWS1, and XRCC3 genes in the brca2-null cells. All the resulting double-mutant cells displayed a phenotype that was very similar to that of the brca2-null cells. We suggest that BRCA2 might thus serve as a platform to recruit various RAD51 mediators at the appropriate position at the DNA–damage site.</p

The Epistatic Relationship between BRCA2 and the Other RAD51 Mediators in Homologous Recombination

RAD51 recombinase polymerizes at the site of double-strand breaks (DSBs) where it performs DSB repair. The loss of RAD51 causes extensive chromosomal breaks, leading to apoptosis. The polymerization of RAD51 is regulated by a number of RAD51 mediators, such as BRCA1, BRCA2, RAD52, SFR1, SWS1, and the five RAD51 paralogs, including XRCC3. We here show that brca2-null mutant cells were able to proliferate, indicating that RAD51 can perform DSB repair in the absence of BRCA2. We disrupted the BRCA1, RAD52, SFR1, SWS1, and XRCC3 genes in the brca2-null cells. All the resulting double-mutant cells displayed a phenotype that was very similar to that of the brca2-null cells. We suggest that BRCA2 might thus serve as a platform to recruit various RAD51 mediators at the appropriate position at the DNA–damage site

Mutant cells defective in DNA repair pathways provide a sensitive high-throughput assay for genotoxicity.

Chemicals used industrially and commercially are required by law to be assessed for their genotoxic potential. However, all currently used assays have major limitations and despite intense effort, there is no universal agreement on which tests should be employed, or how to interpret results. We have developed a new assay system using the chicken DT40 B cell line that offers a number of significant advantages over current methodologies. Our assay could provide enhanced sensitivity using genetically defined and phenotypically characterized mutants defective in DNA repair pathways. Furthermore, analysis of the mutants, using DNA repair proficient wild-type cells as a negative control, minimizes false negative outcomes. Assessing the different responses of a panel of mutants representative of all repair pathways, mechanistic detail of genotoxicity can be determined. This unique feature, as well as reducing the false positive rate, strengthens positive identifications and is useful when extrapolating results to the human context. Our panel of mutants is likely to be useful in screening large compound libraries for an emerging class of chemotherapeutic drugs, which includes inhibitors of DNA repair enzymes such as PARP and DNA polymerases

Evolution of Pre-Existing versus Acquired Resistance to Platinum Drugs and PARP Inhibitors in BRCA-Associated Cancers

<div><p>Platinum drugs and PARP inhibitors (“PARPis”) are considered to be effective in BRCA-associated cancers with impaired DNA repair. These agents cause stalled and collapsed replication forks and create double-strand breaks effectively in the absence of repair mechanisms, resulting in arrest of the cell cycle and induction of cell death. However, recent studies have shown failure of these chemotherapeutic agents due to emerging drug resistance. In this study, we developed a stochastic model of BRCA-associated cancer progression in which there are four cancer populations: those with (i) functional BRCA, (ii) dysfunctional BRCA, (iii) functional BRCA and a growth advantage, and (iv) dysfunctional BRCA and a growth advantage. These four cancer populations expand from one cancer cell with normal repair function until the total cell number reaches a detectable amount. We derived formulas for the probability and expected numbers of each population at the time of detection. Furthermore, we extended the model to consider the tumor dynamics during treatment. Results from the model were validated and showed good agreement with clinical and experimental evidence in BRCA-associated cancers. Based on the model, we investigated conditions in which drug resistance during the treatment course originated from either a pre-existing drug-resistant population or a <i>de novo</i> population, due to secondary mutations. Finally, we found that platinum drugs and PARPis were effective if (i) BRCA inactivation is present, (ii) the cancer was diagnosed early, and (iii) tumor growth is rapid. Our results indicate that different types of cancers have a preferential way of acquiring resistance to platinum drugs and PARPis according to their growth and mutational characteristics.</p></div

Interference in DNA Replication Can Cause Mitotic Chromosomal Breakage Unassociated with Double-Strand Breaks

DNAが切れていないのに発生する染色体断裂の発見 -ヒトの被爆線量を測定する手法に異議あり-. 京都大学プレスリリース. 2013-04-04.Morphological analysis of mitotic chromosomes is used to detect mutagenic chemical compounds and to estimate the dose of ionizing radiation to be administered. It has long been believed that chromosomal breaks are always associated with double-strand breaks (DSBs). We here provide compelling evidence against this canonical theory. We employed a genetic approach using two cell lines, chicken DT40 and human Nalm-6. We measured the number of chromosomal breaks induced by three replication-blocking agents (aphidicolin, 5-fluorouracil, and hydroxyurea) in DSB-repair-proficient wild-type cells and cells deficient in both homologous recombination and nonhomologous end-joining (the two major DSB-repair pathways). Exposure of cells to the three replication-blocking agents for at least two cell cycles resulted in comparable numbers of chromosomal breaks for RAD54−/−/KU70−/− DT40 clones and wild-type cells. Likewise, the numbers of chromosomal breaks induced in RAD54−/−/LIG4−/− Nalm-6 clones and wild-type cells were also comparable. These data indicate that the replication-blocking agents can cause chromosomal breaks unassociated with DSBs. In contrast with DSB-repair-deficient cells, chicken DT40 cells deficient in PIF1 or ATRIP, which molecules contribute to the completion of DNA replication, displayed higher numbers of mitotic chromosomal breaks induced by aphidicolin than did wild-type cells, suggesting that single-strand gaps left unreplicated may result in mitotic chromosomal breaks

Proportion of clinically significant populations at diagnosis.

<p>(A–C) The proportion of type-2 and -3 cells with a growth advantage among the total population at diagnosis is shown over a wide range of <i>u</i>₁, <i>u</i>₂, and the relative growth rate of type-2 and -3 cells to that of type-0 and -1 cells is (<i>a</i>–<i>b</i>)/(<i>r</i>–<i>d</i>). (D–F) The proportion of type-1 and -3 cells (drug-sensitive cells) among the total population is shown. (G–I) The proportion of type-3 cells arising from type-1 cells among the total type-3 population is shown. Each population at diagnosis was calculated by the formulas, Eq. (S12), Eq. (S13), and Eq. (S22). Parameter values used in the figure are <i>u</i>₂ = 10⁻⁷, <i>u</i>₃ = 0.01, <i>M</i> = 10⁶, <i>r</i> = 0.2, <i>a</i> = 0.3, <i>d</i> = <i>b</i> = 0.1 (panel A, D, and G), <i>u</i>₁ = 10⁻² (panel B, E, and H), and <i>u</i>₁ = 10⁻⁷ (panel C, F, and I).</p

Expected numbers of type-3 cells at diagnosis.

<p>The dependence of the expected number of type-3 cells at diagnosis on various parameters is shown. The curves indicate the predictions of the analytical approximation, Eq. (S22), while the circles indicate the results of the direct computer simulations (system S1). Standard parameter values used in the figure are <i>u</i>₁ = <i>u</i>₂ = 5.0⋅10⁻⁷, <i>u</i>₃ = 0.01, <i>M</i> = 10⁶, <i>r</i> = 0.2, <i>a</i> = 0.3, and <i>d</i> = <i>b</i> = 0.1.</p

Population composition at relapse and recurrence time intervals.

<p>The population compositions at diagnosis (the time of initial treatment) and at the time of recurrence after treatment with 60 parameter sets are shown in the pie charts. The time periods until recurrence after treatment are shown as numbers under the pie charts. The time of recurrence is defined as the time point when the total number has exceeded 10% of the number at diagnosis. Each result is obtained by averaging many trials by stochastic simulations of the model under treatment (system S23). Parameter values used in the simulations, except the treatment effects, <i>γ</i> and <i>η</i>, are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105724#pone-0105724-t001" target="_blank">Table 1</a>. The letters in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105724#pone-0105724-t001" target="_blank">Table 1</a> correspond to those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105724#pone-0105724-g005" target="_blank">Figure 5</a>. Treatment effects are shown at the top of the pie charts as the reduction effects on growth rates of sensitive populations (<i>γ</i>) and those on resistant populations (<i>η</i>). We show the results separately by different values of <i>u</i>₁; <i>u</i>₁ is 0.01 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105724#pone-0105724-g005" target="_blank">Figure 5(1)</a>, and 10⁻⁷ in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105724#pone-0105724-g005" target="_blank">Figure 5(2)</a>. The parameter values used in the figure, but not shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105724#pone-0105724-t001" target="_blank">Table 1</a> are <i>u</i>₄ = 0.01, and <i>d</i> = <i>b</i> = 0.1.</p

Probability of type-3 cells at diagnosis.

<p>The dependence of the probability of type-3 cell existence at diagnosis on various parameters is shown. The curves indicate the predictions of the analytical approximation, Eq. (S11), while the circles indicate the results of the direct computer simulations (system S1). Standard parameter values used in the figure are <i>u</i>₁ = <i>u</i>₂ = 5.0⋅10⁻⁷, <i>u</i>₃ = 0.01, <i>M</i> = 10⁶, <i>r</i> = 0.2, <i>a</i> = 0.3, and <i>d</i> = <i>b</i> = 0.1.</p

Parameter sets used for the analysis in Figure 5 and the expected numbers of cells at diagnosis.

<p>The expected numbers of type-1, -2, and -3 cells at diagnosis were calculated using Eq. (S12), Eq. (S13), and Eq. (S22). The remainder of the total number is considered to comprise the number of type-0 cells. The proportion of each type is shown in parentheses. Parameter values used in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105724#pone-0105724-g005" target="_blank">Figure 5</a> are <i>r</i> = 0.2 and <i>d</i> = <i>b</i> = 0.1.</p><p>Parameter sets used for the analysis in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105724#pone-0105724-g005" target="_blank">Figure 5</a> and the expected numbers of cells at diagnosis.</p