DNA lesion bypass and the stochastic dynamics of transcription-coupled repair.

DNA base damage is a major source of oncogenic mutations and disruption to gene expression. The stalling of RNA polymerase II (RNAP) at sites of DNA damage and the subsequent triggering of repair processes have major roles in shaping the genome-wide distribution of mutations, clearing barriers to transcription, and minimizing the production of miscoded gene products. Despite its importance for genetic integrity, key mechanistic features of this transcription-coupled repair (TCR) process are controversial or unknown. Here, we exploited a well-powered in vivo mammalian model system to explore the mechanistic properties and parameters of TCR for alkylation damage at fine spatial resolution and with discrimination of the damaged DNA strand. For rigorous interpretation, a generalizable mathematical model of DNA damage and TCR was developed. Fitting experimental data to the model and simulation revealed that RNA polymerases frequently bypass lesions without triggering repair, indicating that small alkylation adducts are unlikely to be an efficient barrier to gene expression. Following a burst of damage, the efficiency of transcription-coupled repair gradually decays through gene bodies with implications for the occurrence and accurate inference of driver mutations in cancer. The reinitation of transcription from the repair site is not a general feature of transcription-coupled repair, and the observed data is consistent with reinitiation never taking place. Collectively, these results reveal how the directional but stochastic activity of TCR shapes the distribution of mutations following DNA damage.

Sequential mutations in exponentially growing populations.

Stochastic models of sequential mutation acquisition are widely used to quantify cancer and bacterial evolution. Across manifold scenarios, recurrent research questions are: how many cells are there with n alterations, and how long will it take for these cells to appear. For exponentially growing populations, these questions have been tackled only in special cases so far. Here, within a multitype branching process framework, we consider a general mutational path where mutations may be advantageous, neutral or deleterious. In the biologically relevant limiting regimes of large times and small mutation rates, we derive probability distributions for the number, and arrival time, of cells with n mutations. Surprisingly, the two quantities respectively follow Mittag-Leffler and logistic distributions regardless of n or the mutations' selective effects. Our results provide a rapid method to assess how altering the fundamental division, death, and mutation rates impacts the arrival time, and number, of mutant cells. We highlight consequences for mutation rate inference in fluctuation assays.

Signatures of TOP1 transcription-associated mutagenesis in cancer and germline.

The mutational landscape is shaped by many processes. Genic regions are vulnerable to mutation but are preferentially protected by transcription-coupled repair1. In microorganisms, transcription has been demonstrated to be mutagenic2,3; however, the impact of transcription-associated mutagenesis remains to be established in higher eukaryotes4. Here we show that ID4-a cancer insertion-deletion (indel) mutation signature of unknown aetiology5 characterized by short (2 to 5 base pair) deletions -is due to a transcription-associated mutagenesis process. We demonstrate that defective ribonucleotide excision repair in mammals is associated with the ID4 signature, with mutations occurring at a TNT sequence motif, implicating topoisomerase 1 (TOP1) activity at sites of genome-embedded ribonucleotides as a mechanistic basis. Such TOP1-mediated deletions occur somatically in cancer, and the ID-TOP1 signature is also found in physiological settings, contributing to genic de novo indel mutations in the germline. Thus, although topoisomerases protect against genome instability by relieving topological stress6, their activity may also be an important source of mutations in the human genome.

Response to comment on "Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2".

Further analysis of SARS-CoV-2 genome sequencing data identifies several highly recurrent genetic variants with low allele frequencies, which, if filtered out, provide estimates consistent with tighter transmission bottlenecks.

Quality and diagnostic performance of coronary computed tomography angiogram (CCTA): A comparison between pre-liver and pre-kidney transplant patients.

PURPOSE: Assess and compare the quality and diagnostic performance of CCTA between pre-liver and pre-kidney transplant patients, and gauge impact of CCTA on ICA requirements. METHODS: Patients without known coronary artery disease (CAD) were selected for CCTA if considered high-risk or after abnormal stress testing. All pre-liver and pre-kidney CCTAs between March 2018 and August 2020 were retrospectively included. CCTA quality was qualitatively graded as excellent/good/fair/poor, and CAD graded as < or ≥50% stenosis. Heart rate, coronary artery calcium (CAC) scores, and fractional flow reserve CT (FFRCT) results were collected. CAD stenosis was graded on invasive coronary angiogram (ICA) images, with ≥50% stenosis defined as significant. RESULTS: 162 pre-transplant patients (91 pre-liver, 71 pre-kidney). Pre-kidney patients had poorer CCTA quality (p = 0.04) and higher heart rate (median: 65 bpm vs 60 bpm, p < 0.001). Out of 147 diagnostic CCTAs (pre-liver: 84, pre-kidney: 63), 73 (49.7%) had a ≥50% stenosis (pre-liver: 38 (45.2%), pre-kidney:35 (55.6%)). 12/38 (31.6%) had a significantly reduced FFRCT, and 19/53 (35.8%) had ≥50% stenosis on ICA. Among patients whose CCTA was diagnostic and had ICA, stenosis severity was concordant in 10/23 (43.5%) pre-liver and 10/25 (40%) pre-kidney patients. All discordant cases had stenosis 'over-called' on CCTA. CONCLUSION: Diagnostic-quality CCTAs in high-risk pre-transplant patients are achievable and can greatly reduce ICA requirements by excluding significant CAD. CCTA quality is poorer in pre-kidney transplant patients compared to pre-liver, possibly due to higher heart rate.

Breast tumours maintain a reservoir of subclonal diversity during expansion.

Our knowledge of copy number evolution during the expansion of primary breast tumours is limited1,2. Here, to investigate this process, we developed a single-cell, single-molecule DNA-sequencing method and performed copy number analysis of 16,178 single cells from 8 human triple-negative breast cancers and 4 cell lines. The results show that breast tumours and cell lines comprise a large milieu of subclones (7-22) that are organized into a few (3-5) major superclones. Evolutionary analysis suggests that after clonal TP53 mutations, multiple loss-of-heterozygosity events and genome doubling, there was a period of transient genomic instability followed by ongoing copy number evolution during the primary tumour expansion. By subcloning single daughter cells in culture, we show that tumour cells rediversify their genomes and do not retain isogenic properties. These data show that triple-negative breast cancers continue to evolve chromosome aberrations and maintain a reservoir of subclonal diversity during primary tumour growth.

Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2.

Superspreading events shaped the coronavirus disease 2019 (COVID-19) pandemic, and their rapid identification and containment are essential for disease control. Here, we provide a national-scale analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) superspreading during the first wave of infections in Austria, a country that played a major role in initial virus transmissions in Europe. Capitalizing on Austria's well-developed epidemiological surveillance system, we identified major SARS-CoV-2 clusters during the first wave of infections and performed deep whole-genome sequencing of more than 500 virus samples. Phylogenetic-epidemiological analysis enabled the reconstruction of superspreading events and charts a map of tourism-related viral spread originating from Austria in spring 2020. Moreover, we exploited epidemiologically well-defined clusters to quantify SARS-CoV-2 mutational dynamics, including the observation of low-frequency mutations that progressed to fixation within the infection chain. Time-resolved virus sequencing unveiled viral mutation dynamics within individuals with COVID-19, and epidemiologically validated infector-infectee pairs enabled us to determine an average transmission bottleneck size of 103 SARS-CoV-2 particles. In conclusion, this study illustrates the power of combining epidemiological analysis with deep viral genome sequencing to unravel the spread of SARS-CoV-2 and to gain fundamental insights into mutational dynamics and transmission properties.

Phenotypic delay in the evolution of bacterial antibiotic resistance: Mechanistic models and their implications.

Phenotypic delay-the time delay between genetic mutation and expression of the corresponding phenotype-is generally neglected in evolutionary models, yet recent work suggests that it may be more common than previously assumed. Here, we use computer simulations and theory to investigate the significance of phenotypic delay for the evolution of bacterial resistance to antibiotics. We consider three mechanisms which could potentially cause phenotypic delay: effective polyploidy, dilution of antibiotic-sensitive molecules and accumulation of resistance-enhancing molecules. We find that the accumulation of resistant molecules is relevant only within a narrow parameter range, but both the dilution of sensitive molecules and effective polyploidy can cause phenotypic delay over a wide range of parameters. We further investigate whether these mechanisms could affect population survival under drug treatment and thereby explain observed discrepancies in mutation rates estimated by Luria-Delbrück fluctuation tests. While the effective polyploidy mechanism does not affect population survival, the dilution of sensitive molecules leads both to decreased probability of survival under drug treatment and underestimation of mutation rates in fluctuation tests. The dilution mechanism also changes the shape of the Luria-Delbrück distribution of mutant numbers, and we show that this modified distribution provides an improved fit to previously published experimental data.

Competing evolutionary paths in growing populations with applications to multidrug resistance.

Investigating the emergence of a particular cell type is a recurring theme in models of growing cellular populations. The evolution of resistance to therapy is a classic example. Common questions are: when does the cell type first occur, and via which sequence of steps is it most likely to emerge? For growing populations, these questions can be formulated in a general framework of branching processes spreading through a graph from a root to a target vertex. Cells have a particular fitness value on each vertex and can transition along edges at specific rates. Vertices represent cell states, say genotypes or physical locations, while possible transitions are acquiring a mutation or cell migration. We focus on the setting where cells at the root vertex have the highest fitness and transition rates are small. Simple formulas are derived for the time to reach the target vertex and for the probability that it is reached along a given path in the graph. We demonstrate our results on several scenarios relevant to the emergence of drug resistance, including: the orderings of resistance-conferring mutations in bacteria and the impact of imperfect drug penetration in cancer.

Universal Asymptotic Clone Size Distribution for General Population Growth.

Deterministically growing (wild-type) populations which seed stochastically developing mutant clones have found an expanding number of applications from microbial populations to cancer. The special case of exponential wild-type population growth, usually termed the Luria-Delbrück or Lea-Coulson model, is often assumed but seldom realistic. In this article, we generalise this model to different types of wild-type population growth, with mutants evolving as a birth-death branching process. Our focus is on the size distribution of clones-that is the number of progeny of a founder mutant-which can be mapped to the total number of mutants. Exact expressions are derived for exponential, power-law and logistic population growth. Additionally, for a large class of population growth, we prove that the long-time limit of the clone size distribution has a general two-parameter form, whose tail decays as a power-law. Considering metastases in cancer as the mutant clones, upon analysing a data-set of their size distribution, we indeed find that a power-law tail is more likely than an exponential one.

Modelling 99Tc concentrations in Fucus vesiculosus from the north-east Irish Sea.

In 1994 there were substantial increases in the quantity of 99Tc discharged into the north-east Irish Sea from BNFL Sellafield (UK), concomitant with improvements in waste treatment procedures. As a consequence, the concentration of 99Tc observed in seawater and biota samples, taken from the Irish Sea coastline, increased significantly. Elevated concentrations were also reported in Dutch, Danish, Norwegian, Swedish and Arctic waters in subsequent years. In the present study a simple numerical model was developed and applied to time-series data of 99Tc concentrations in the brown seaweed Fucus vesiculosus, collected from three UK sites in the vicinity of Sellafield (St. Bees, Heysham, Port William). The model considered site-specific scaling effects, lag times, previous discharge history and potential seasonal variation in uptake. In general, there was a good fit between predicted and observed concentrations, but the degree of uncertainty varied inversely with the frequency of sampling. We did not observe a significant seasonal variation. The modelled lag times to the three sites were consistent with transport times based on observations of the water column distribution of 99Tc. The model was applied to a variety of discharge scenarios, reflecting current discussion on the future management of 99Tc releases. Concentrations in Fucus reached asymptotic values in 3-10 years, depending on the scenario and sampling site under consideration.