Mutation rates and fitness consequences of mosaic chromosomal alterations in blood.

Mosaic chromosomal alterations (mCAs) are common in cancers and can arise decades before diagnosis. A quantitative understanding of the rate at which these events occur, and their functional consequences, could improve cancer risk prediction and our understanding of somatic evolution. Using mCA clone size estimates from the blood of approximately 500,000 UK Biobank participants, we estimate mutation rates and fitness consequences of acquired gain, loss and copy-neutral loss of heterozygosity events. Most mCAs have moderate to high fitness effects but occur at a low rate, being more than tenfold less common than equivalently fit single-nucleotide variants. Notable exceptions are mosaic loss of X and Y, which we estimate have roughly 1,000-fold higher mutation rates than autosomal mCAs. Although the way in which most mCAs increase in prevalence with age is consistent with constant growth rates, some mCAs exhibit different behavior, suggesting that their fitness may depend on inherited variants, extrinsic factors or distributions of fitness effects.

Systematic Profiling of DNMT3A Variants Reveals Protein Instability Mediated by the DCAF8 E3 Ubiquitin Ligase Adaptor.

Clonal hematopoiesis is a prevalent age-related condition associated with a greatly increased risk of hematologic disease; mutations in DNA methyltransferase 3A (DNMT3A) are the most common driver of this state. DNMT3A variants occur across the gene with some particularly associated with malignancy, but the functional relevance and mechanisms of pathogenesis of the majority of mutations are unknown. Here, we systematically investigated the methyltransferase activity and protein stability of 253 disease-associated DNMT3A mutations, and found that 74% were loss-of-function mutations. Half of these variants exhibited reduced protein stability and, as a class, correlated with greater clonal expansion and acute myeloid leukemia development. We investigated the mechanisms underlying the instability using a CRISPR screen and uncovered regulated destruction of DNMT3A mediated by the DCAF8 E3 ubiquitin ligase adaptor. We establish a new paradigm to classify novel variants that has prognostic and potential therapeutic significance for patients with hematologic disease. SIGNIFICANCE: DNMT3A has emerged as the most important epigenetic regulator and tumor suppressor in the hematopoietic system. Our study represents a systematic and high-throughput method to characterize the molecular impact of DNMT3A missense mutations and the discovery of a regulated destruction mechanism of DNMT3A offering new prognostic and future therapeutic avenues.See related commentary by Ma and Will, p. 23.This article is highlighted in the In This Issue feature, p. 1.

Synonymous mutations reveal genome-wide levels of positive selection in healthy tissues.

Genetic alterations under positive selection in healthy tissues have implications for cancer risk. However, total levels of positive selection across the genome remain unknown. Passenger mutations are influenced by all driver mutations, regardless of type or location in the genome. Therefore, the total number of passengers can be used to estimate the total number of drivers-including unidentified drivers outside of cancer genes that are traditionally missed. Here we analyze the variant allele frequency spectrum of synonymous mutations from healthy blood and esophagus to quantify levels of missing positive selection. In blood, we find that only 30% of passengers can be explained by single-nucleotide variants in driver genes, suggesting high levels of positive selection for mutations elsewhere in the genome. In contrast, more than half of all passengers in the esophagus can be explained by just the two driver genes NOTCH1 and TP53, suggesting little positive selection elsewhere.

The evolutionary dynamics and fitness landscape of clonal hematopoiesis.

Somatic mutations acquired in healthy tissues as we age are major determinants of cancer risk. Whether variants confer a fitness advantage or rise to detectable frequencies by chance remains largely unknown. Blood sequencing data from ~50,000 individuals reveal how mutation, genetic drift, and fitness shape the genetic diversity of healthy blood (clonal hematopoiesis). We show that positive selection, not drift, is the major force shaping clonal hematopoiesis, provide bounds on the number of hematopoietic stem cells, and quantify the fitness advantages of key pathogenic variants, at single-nucleotide resolution, as well as the distribution of fitness effects (fitness landscape) within commonly mutated driver genes. These data are consistent with clonal hematopoiesis being driven by a continuing risk of mutations and clonal expansions that become increasingly detectable with age.

Engraftment of rare, pathogenic donor hematopoietic mutations in unrelated hematopoietic stem cell transplantation.

Clonal hematopoiesis is associated with various age-related morbidities. Error-corrected sequencing (ECS) of human blood samples, with a limit of detection of ≥0.0001, has demonstrated that nearly every healthy individual >50 years old harbors rare hematopoietic clones below the detection limit of standard high-throughput sequencing. If these rare mutations confer survival or proliferation advantages, then the clone(s) could expand after a selective pressure such as chemotherapy, radiotherapy, or chronic immunosuppression. Given these observations and the lack of quantitative data regarding clonal hematopoiesis in adolescents and young adults, who are more likely to serve as unrelated hematopoietic stem cell donors, we completed this pilot study to determine whether younger adults harbored hematopoietic clones with pathogenic mutations, how often those clones were transferred to recipients, and what happened to these clones over time after transplantation. We performed ECS on 125 blood and marrow samples from 25 matched unrelated donors and recipients. Clonal mutations, with a median variant allele frequency of 0.00247, were found in 11 donors (44%; median, 36 years old). Of the mutated clones, 84.2% of mutations were predicted to be molecularly pathogenic and 100% engrafted in recipients. Recipients also demonstrated de novo clonal expansion within the first 100 days after hematopoietic stem cell transplant (HSCT). Given this pilot demonstration that rare, pathogenic clonal mutations are far more prevalent in younger adults than previously appreciated, and they engraft in recipients and persist over time, larger studies with longer follow-up are necessary to correlate clonal engraftment with post-HSCT morbidity.

The dynamics of adaptive genetic diversity during the early stages of clonal evolution.

The dynamics of genetic diversity in large clonally evolving cell populations are poorly understood, despite having implications for the treatment of cancer and microbial infections. Here, we combine barcode lineage tracking, sequencing of adaptive clones and mathematical modelling of mutational dynamics to understand adaptive diversity changes during experimental evolution of Saccharomyces cerevisiae under nitrogen and carbon limitation. We find that, despite differences in beneficial mutational mechanisms and fitness effects, early adaptive genetic diversity increases predictably, driven by the expansion of many single-mutant lineages. However, a crash in adaptive diversity follows, caused by highly fit double-mutant 'jackpot' clones that are fed from exponentially growing single mutants, a process closely related to the classic Luria-Delbrück experiment. The diversity crash is likely to be a general feature of asexual evolution with clonal interference; however, both its timing and magnitude are stochastic and depend on the population size, the distribution of beneficial fitness effects and patterns of epistasis.

A scalable double-barcode sequencing platform for characterization of dynamic protein-protein interactions.

Several large-scale efforts have systematically catalogued protein-protein interactions (PPIs) of a cell in a single environment. However, little is known about how the protein interactome changes across environmental perturbations. Current technologies, which assay one PPI at a time, are too low throughput to make it practical to study protein interactome dynamics. Here, we develop a highly parallel protein-protein interaction sequencing (PPiSeq) platform that uses a novel double barcoding system in conjunction with the dihydrofolate reductase protein-fragment complementation assay in Saccharomyces cerevisiae. PPiSeq detects PPIs at a rate that is on par with current assays and, in contrast with current methods, quantitatively scores PPIs with enough accuracy and sensitivity to detect changes across environments. Both PPI scoring and the bulk of strain construction can be performed with cell pools, making the assay scalable and easily reproduced across environments. PPiSeq is therefore a powerful new tool for large-scale investigations of dynamic PPIs.

Development of a Comprehensive Genotype-to-Fitness Map of Adaptation-Driving Mutations in Yeast.

Adaptive evolution plays a large role in generating the phenotypic diversity observed in nature, yet current methods are impractical for characterizing the molecular basis and fitness effects of large numbers of individual adaptive mutations. Here, we used a DNA barcoding approach to generate the genotype-to-fitness map for adaptation-driving mutations from a Saccharomyces cerevisiae population experimentally evolved by serial transfer under limiting glucose. We isolated and measured the fitness of thousands of independent adaptive clones and sequenced the genomes of hundreds of clones. We found only two major classes of adaptive mutations: self-diploidization and mutations in the nutrient-responsive Ras/PKA and TOR/Sch9 pathways. Our large sample size and precision of measurement allowed us to determine that there are significant differences in fitness between mutations in different genes, between different paralogs, and even between different classes of mutations within the same gene.

Obstruction of adaptation in diploids by recessive, strongly deleterious alleles.

Recessive deleterious mutations are common, causing many genetic disorders in humans and producing inbreeding depression in the majority of sexually reproducing diploids. The abundance of recessive deleterious mutations in natural populations suggests they are likely to be present on a chromosome when a new adaptive mutation occurs, yet the dynamics of recessive deleterious hitchhikers and their impact on adaptation remains poorly understood. Here we model how a recessive deleterious mutation impacts the fate of a genetically linked dominant beneficial mutation. The frequency trajectory of the adaptive mutation in this case is dramatically altered and results in what we have termed a "staggered sweep." It is named for its three-phased trajectory: (i) Initially, the two linked mutations have a selective advantage while rare and will increase in frequency together, then (ii), at higher frequencies, the recessive hitchhiker is exposed to selection and can cause a balanced state via heterozygote advantage (the staggered phase), and (iii) finally, if recombination unlinks the two mutations, then the beneficial mutation can complete the sweep to fixation. Using both analytics and simulations, we show that strongly deleterious recessive mutations can substantially decrease the probability of fixation for nearby beneficial mutations, thus creating zones in the genome where adaptation is suppressed. These mutations can also significantly prolong the number of generations a beneficial mutation takes to sweep to fixation, and cause the genomic signature of selection to resemble that of soft or partial sweeps. We show that recessive deleterious variation could impact adaptation in humans and Drosophila.

Quantitative evolutionary dynamics using high-resolution lineage tracking.

Evolution of large asexual cell populations underlies ∼30% of deaths worldwide, including those caused by bacteria, fungi, parasites, and cancer. However, the dynamics underlying these evolutionary processes remain poorly understood because they involve many competing beneficial lineages, most of which never rise above extremely low frequencies in the population. To observe these normally hidden evolutionary dynamics, we constructed a sequencing-based ultra high-resolution lineage tracking system in Saccharomyces cerevisiae that allowed us to monitor the relative frequencies of ∼500,000 lineages simultaneously. In contrast to some expectations, we found that the spectrum of fitness effects of beneficial mutations is neither exponential nor monotonic. Early adaptation is a predictable consequence of this spectrum and is strikingly reproducible, but the initial small-effect mutations are soon outcompeted by rarer large-effect mutations that result in variability between replicates. These results suggest that early evolutionary dynamics may be deterministic for a period of time before stochastic effects become important.

Beyond genome sequencing: lineage tracking with barcodes to study the dynamics of evolution, infection, and cancer.

Evolving cellular communities, such as the gut microbiome, pathogenic infections, and cancer, consist of large populations of ~10(7)-10(14) cells. Because of their large population sizes, adaptation within these populations can be driven by many beneficial mutations that never rise above extremely low frequencies. Genome sequencing methods such as clonal, single cell, or whole population sequencing are poorly suited to detect these rare beneficial lineages, and, more generally, to characterize which mutations are most important to the population dynamics. Here, we introduce an alternative approach: high-resolution lineage tracking with DNA barcodes. In contrast to whole genome sequencing, lineage tracking can detect a beneficial mutation at an extremely low frequency within the population, and estimate its time of occurrence and fitness effect. Many lineage trajectories can be observed in parallel, allowing one to observe the population dynamics in exquisite detail. We describe some of the technical and analytical challenges to lineage tracking with DNA barcodes and discuss its applications to studies of evolution, infectious disease and cancer.

Frequency factors in a landscape model of filamentous protein aggregation.

Using quantitative measurements of protein aggregation rates, we develop a kinetic picture of protein conversion from a soluble to a fibrillar state which shows that a single free energy barrier to aggregation controls the addition of protein molecules into amyloid fibrils, while the characteristic sublinear concentration dependence emerges as a natural consequence of finite diffusion times. These findings suggest that this reaction does not follow a simple chemical mechanism, but rather operates in a way analogous to the landscape models of protein folding defined by stochastic dynamics on a characteristic energy surface.