Publications by authors named "Jamie R Blundell"

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.

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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.

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Clonal hematopoiesis is a prevalent age-related condition associated with a greatly increased risk of hematologic disease; mutations in DNA methyltransferase 3A () are the most common driver of this state. 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 mutations, and found that 74% were loss-of-function mutations.

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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).

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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