The Immortal DNA strand hypothesis.

<p><b>a)</b> During replication of the ancestral DNA strand, errors (dashed line) might occur. If these errors are not corrected by intrinsic DNA repair mechanisms, they become permanently fixed in daughter cells after the next cell division. However, the original ancestral strand is still present and can provide the blue print for additional non-mutated copies of DNA. <b>b)</b> In principle, a stem cell driven tissue allows for non-random DNA strand segregation. Preferentially segregating ancestral DNA strands into stem cells and duplicated strands into differentiated cells with limited life span can drastically reduce the accumulation of somatic mutations in tissues.</p

Mutational burden and variance in healthy human tissues.

<p>Mutational burden and variance of the mutational burden in colon, small intestine liver and skin tissue in healthy adult humans of different ages, data taken from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006233#pcbi.1006233.ref013" target="_blank">13</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006233#pcbi.1006233.ref014" target="_blank">14</a>]. Open circle represent mutational burden of single cells, whereas dark grey dots represent the mean mutational burden or variance respectively. In all cases, the data well supports our expectation of a linearly increasing mean and variance with age. Linear regressions (dashed lines) give estimates for the change of the mutational burden and the variance with age, see main text (uncertainties represent standard errors). Eqs (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006233#pcbi.1006233.e004" target="_blank">3</a>) and (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006233#pcbi.1006233.e005" target="_blank">4</a>) then allow to estimate the non-random strand segregation probability as well as the per-cell mutation rate per cell division. In all cases, the probability of non-random strand segregation is high (median: <i>p</i> = 0.979 (0.97,0.99)), whereas the mutation rate per cell division varies between tissues and is highest in skin, see insets and main text for tissue specific estimates.</p

Variation of mutational burden in healthy human tissues suggests non-random strand segregation and allows measuring somatic mutation rates

<div><p>The immortal strand hypothesis poses that stem cells could produce differentiated progeny while conserving the original template strand, thus avoiding accumulating somatic mutations. However, quantitating the extent of non-random DNA strand segregation in human stem cells remains difficult <i>in vivo</i>. Here we show that the change of the mean and variance of the mutational burden with age in healthy human tissues allows estimating strand segregation probabilities and somatic mutation rates. We analysed deep sequencing data from healthy human colon, small intestine, liver, skin and brain. We found highly effective non-random DNA strand segregation in all adult tissues (mean strand segregation probability: 0.98, standard error bounds (0.97,0.99)). In contrast, non-random strand segregation efficiency is reduced to 0.87 (0.78,0.88) in neural tissue during early development, suggesting stem cell pool expansions due to symmetric self-renewal. Healthy somatic mutation rates differed across tissue types, ranging from 3.5 × 10⁻⁹/bp/division in small intestine to 1.6 × 10⁻⁷/bp/division in skin.</p></div

Dependence of parameter inferences on stem cell proliferation rate.

<p>Inferences of <b>a)</b> the DNA strand segregation probability and <b>b)</b> mutation rate per cell division are robust against wide ranges of the stem cell proliferation rate <i>λ</i>. If stem cells divide once per week this implies (Eq (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006233#pcbi.1006233.e004" target="_blank">3</a>)) for the probability of DNA strand segregation in colon: <i>p</i> = 0.973 (0.892; 0.996), small intestine: <i>p</i> = 0.969 (0.877; 0.995), liver: <i>p</i> = 0.988 (0.952; 0.998), prefrontal cortex: <i>p</i> = 0.999 (0.997; 0.9999), hippocampal dentale gyrus: <i>p</i> = 0.999 (0.997; 0.9999), skin: <i>p</i> = 0.985 (0.94; 0.998). Numbers in brackets correspond to the range of the DNA strand segregation probabilities for stem cell replication rates between once per month and every day respectively. In contrast for neurons during early development we find: <i>p</i> = 0.876 (0.67; 0.96) if cells divide every 48h (number in brackets correspond to cell divisions once per week and twice a day respectively). Based on Eq (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006233#pcbi.1006233.e005" target="_blank">4</a>) we find for the <i>in vivo</i> mutation rate per base pair per cell division in colon: <i>μ</i> = 4.37 (4.27; 4.77) × 10⁻⁹, small intestine: <i>μ</i> = 3.54 (3.45; 3.91) × 10⁻⁹, liver: <i>μ</i> = 8.48 (8.39; 8.8) × 10⁻⁹, prefrontal cortex: <i>μ</i> = 7.68 (7.67; 7.7) × 10⁻⁸, hippocampal dentale gyrus: <i>μ</i> = 1.14 (1.14; 1.15) × 10⁻⁷, neurons during early development: <i>μ</i> = 1.23 (1.02; 1.47) × 10⁻⁸ and skin: <i>μ</i> = 1.57 (1.56; 1.65) × 10⁻⁷.</p

Predicted mutational burden in individual stem cells with age.

<p><b>a)</b> We show simulated stochastic mutation accumulation in a stem cell population of constant size. Here <i>N</i> = 20,000 stem cells segregating DNA strands with probability <i>p</i> = 0.7 and a mutation rate of <i>μ</i> = 6 per cell division (corresponding to a mutation rate of <i>μ</i> = 10⁻⁹ per bp per cell division). <b>b)</b> Mutational burden and <b>c)</b> variance of the mutational burden increase linear. Linear regression (dashed lines) gives and . The expected exact values based on above parameters and Eqs (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006233#pcbi.1006233.e002" target="_blank">1</a>) and (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006233#pcbi.1006233.e003" target="_blank">2</a>) are and . Eqs (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006233#pcbi.1006233.e004" target="_blank">3</a>) and (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006233#pcbi.1006233.e005" target="_blank">4</a>) yield for the strand segregation probability <i>p</i> = 0.702 and for the mutation rate <i>μ</i> = 6.03, (exact values imposed on the simulation were <i>p</i> = 0.7 and <i>μ</i> = 6).</p

Individual based stochastic simulation as two type Moran process.

<p>We assume two possible cell types, wild type leukemic cancer cells sensitive to Imatinib (yellow) and resistant cancer cells (blue). In total, cells were included in the experiment and simulations. At time there are resistant cells and wild type cells. During the next time step three cases are possible: (i) the number of resistant cells increases by one with probability , (ii) stays the same with probability (iii) or decreases by one with probability , (see equation (2a) and (2b)).</p

Average fitness observed for samples A) with and B) without detected mutations.

<p>The black dots are due to data and the green dots due to simulations. The evaluated parameters differ (see the inset of the plots), the fitness of the mutated cells is on average higher but their transformation rate is much smaller.</p

Dynamics of resistance development in the experiment and the mathematical model.

<p><b>A</b>) Average fitness of the total population due to experiments (black dots), stochastic simulations (green dots) and analytical results (black line, due to equation (7)). The parameters of simulation and calculation were chosen to , , days and . The dashed red line shows the linear decrease of the wild type fitness, the slope is determined by the critical time . <b>B</b>) The average frequency of wild type cells due to simulation (yellow dots) and calculation (black line), as well as the frequency of resistant cancer cells (blue dots) over time. We always start with wild types only, but since the system selects for resistant cells, they fixate in the long run. This is why the average fitness of the total population exhibits a minimum. At the start of the experiments, the fitness of wild types decreases, but after some time resistant cells take over and the fitness increases until it saturates.</p

Stochastic simulations with three subpopulations.

<p><b>A</b>) Transitions between the three types considered, wild types (W), resistant types without observed mutations (T) and resistant types with observed mutations (M). The switching probabilities are according to the arrows. <b>B</b>) Average fitness of the system described in a) due to simulations (green dots) and experimental data (black dots, as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0028955#pone-0028955-g003" target="_blank">figure 3</a>). The parameters are those observed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0028955#pone-0028955-g004" target="_blank">figure 4</a>. <b>C</b>) Average frequency of the three types. Initially, only wild types (yellow line) are present, at day 37 of the experiment resistant types without observed mutations (T, dashed blue line) reach almost fixation, but in the long run mutated types (M, blue dots) take over due to their fitness advantage. At day 120 of the experiment we have 0.57 T types and 0.43 M types (6 T and 4 M types were observed in the experiment at day 120).</p

ECJ Judges read the morning papers. Explaining the turnaround of European citizenship jurisprudence

<p>Recent jurisprudence of the European Court of Justice (ECJ) marks a striking shift towards a more restrictive interpretation of EU citizens’ rights. The Court's turnaround is not only highly relevant for practical debates about ‘Social Europe’ or ‘welfare migration’, but also enlightening from a more general, theoretical viewpoint. Several recent studies on the ECJ have argued that the Court is largely constrained by member state governments’ threats of legislative override and non-compliance. We show that an additional mechanism is necessary to explain the Court's turnaround on citizenship. While the ECJ extended EU citizens’ rights even against strong opposition by member state governments, its recent shift reflects changes in the broader political context, i.e., the politicization of free movement in the European Union (EU). The article theorises Court responsiveness to politicization and demonstrates empirically, how the Court's jurisprudence corresponds with changing public debates about EU citizenship.</p