Fitness Effects Of Mutations Research Articles

Fitness effects of somatic mutations accumulating during vegetative growth

The unique life form of plants promotes the accumulation of somatic mutations that can be passed to offspring in the next generation, because the same meristem cells responsible for vegetative growth also generate gametes for sexual reproduction. However, little is known about the consequences of somatic mutation accumulation for offspring fitness. We evaluate the fitness effects of somatic mutations in Mimulus guttatus by comparing progeny from self-pollinations made within the same flower (autogamy) to progeny from self-pollinations made between stems on the same plant (geitonogamy). The effects of somatic mutations are evident from this comparison, as autogamy leads to homozygosity of a proportion of somatic mutations, but progeny from geitonogamy remain heterozygous for mutations unique to each stem. In two different experiments, we find consistent fitness effects of somatic mutations from individual stems. Surprisingly, several progeny groups from autogamous crosses displayed increases in fitness compared to progeny from geitonogamy crosses, likely indicating that beneficial somatic mutations occurred in some stems. These results support the hypothesis that somatic mutations accumulate during vegetative growth, but they are filtered by different forms of selection that occur throughout development, resulting in the culling of expressed deleterious mutations and the retention of beneficial mutations.

A Population-Genetic Lens into the Process of Gene Loss Following Whole-Genome Duplication.

Whole-genome duplications (WGDs) have occurred in many eukaryotic lineages. However, the underlying evolutionary forces and molecular mechanisms responsible for the long-term retention of gene duplicates created by WGDs are not well understood. We employ a population-genomic approach to understand the selective forces acting on paralogs and investigate ongoing duplicate-gene loss in multiple species of Paramecium that share an ancient WGD. We show that mutations that abolish protein function are more likely to be segregating in retained WGD paralogs than in single-copy genes, most likely because of ongoing nonfunctionalization post-WGD. This relaxation of purifying selection occurs in only one WGD paralog, accompanied by the gradual fixation of nonsynonymous mutations and reduction in levels of expression, and occurs over a long period of evolutionary time, “marking” one locus for future loss. Concordantly, the fitness effects of new nonsynonymous mutations and frameshift-causing indels are significantly more deleterious in the highly expressed copy compared with their paralogs with lower expression. Our results provide a novel mechanistic model of gene duplicate loss following WGDs, wherein selection acts on the sum of functional activity of both duplicate genes, allowing the two to wander in expression and functional space, until one duplicate locus eventually degenerates enough in functional efficiency or expression that its contribution to total activity is too insignificant to be retained by purifying selection. Retention of duplicates by such mechanisms predicts long times to duplicate-gene loss, which should not be falsely attributed to retention due to gain/change in function.

Population genetics of polymorphism and divergence in rapidly evolving populations.

In rapidly evolving populations, numerous beneficial and deleterious mutations can arise and segregate within a population at the same time. In this regime, evolutionary dynamics cannot be analyzed using traditional population genetic approaches that assume that sites evolve independently. Instead, the dynamics of many loci must be analyzed simultaneously. Recent work has made progress by first analyzing the fitness variation within a population, and then studying how individual lineages interact with this traveling fitness wave. However, these "traveling wave" models have previously been restricted to extreme cases where selection on individual mutations is either much faster or much slower than the typical coalescent timescale Tc. In this work, we show how the traveling wave framework can be extended to intermediate regimes in which the scaled fitness effects of mutations (Tcs) are neither large nor small compared to one. This enables us to describe the dynamics of populations subject to a wide range of fitness effects, and in particular, in cases where it is not immediately clear which mutations are most important in shaping the dynamics and statistics of genetic diversity. We use this approach to derive new expressions for the fixation probabilities and site frequency spectra of mutations as a function of their scaled fitness effects, along with related results for the coalescent timescale Tc and the rate of adaptation or Muller's ratchet. We find that competition between linked mutations can have a dramatic impact on the proportions of neutral and selected polymorphisms, which is not simply summarized by the scaled selection coefficient Tcs. We conclude by discussing the implications of these results for population genetic inferences.

The effect of weak clonal interference on average fitness trajectories in the presence of macroscopic epistasis.

Adaptation dynamics on fitness landscapes is often studied theoretically in the strong-selection, weak-mutation regime. However, in a large population, multiple beneficial mutants can emerge before any of them fixes in the population. Competition between mutants is known as clonal interference, and while it is known to slow down the rate of adaptation (when compared to the strong-selection, weak-mutation model with the same parameters), how it affects the shape of long-term fitness trajectories in the presence of epistasis is an open question. Here, by considering how changes in fixation probabilities arising from weak clonal interference affect the dynamics of adaptation on fitness-parameterized landscapes, we find that the change in the shape of fitness trajectory arises only through changes in the supply of beneficial mutations (or equivalently, the beneficial mutation rate). Furthermore, a depletion of beneficial mutations as a population climbs up the fitness landscape can speed up the rescaled fitness trajectory (where adaptation speed is measured relative to its value at the start of the experiment), while an enhancement of the beneficial mutation rate does the opposite of slowing it down. Our findings suggest that by carrying out evolution experiments in both regimes (with and without clonal interference), one could potentially distinguish the different sources of macroscopic epistasis (fitness effect of mutations vs change in fraction of beneficial mutations).

Inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data.

There still are no effective long-term protective vaccines against viruses that continuously evolve under immune pressure such as seasonal influenza, which has caused, and can cause, devastating epidemics in the human population. To find such a broadly protective immunization strategy, it is useful to know how easily the virus can escape via mutation from specific antibody responses. This information is encoded in the fitness landscape of the viral proteins (i.e., knowledge of the viral fitness as a function of sequence). Here we present a computational method to infer the intrinsic mutational fitness landscape of influenzalike evolving antigens from yearly sequence data. We test inference performance with computer-generated sequence data that are based on stochastic simulations mimicking basic features of immune-driven viral evolution. Although the numerically simulated model does create a phylogeny based on the allowed mutations, the inference scheme does not use this information. This provides a contrast to other methods that rely on reconstruction of phylogenetic trees. Our method just needs a sufficient number of samples over multiple years. With our method, we are able to infer single as well as pairwise mutational fitness effects from the simulated sequence time series for short antigenic proteins. Our fitness inference approach may have potential future use for the design of immunization protocols by identifying intrinsically vulnerable immune target combinations on antigens that evolve under immune-driven selection. In the future, this approach may be applied to influenza and other novel viruses such as SARS-CoV-2, which evolves and, like influenza, might continue to escape the natural and vaccine-mediated immune pressures.

Using deep mutational scanning to identify the determinants of antibiotic resistance

Current drug discovery platforms have proven incapable of supplying enough new antibiotic types to meet the challenge of rapidly developing clinical resistances. New techniques that specifically incorporate potential resistance adaptations during design are necessary. In cases of known specific resistance mechanisms, a biophysical approach may allow rational development of existing drugs that escape resistance. In this work, we use deep mutational scanning (DMS), a next-generation sequencing-based technique that reports on the site-specific fitness effects of mutations across an entire protein, to investigate the contribution of Virginiamycin acetyltransferase (VatD) to several streptogramin analogues.

The mutation effect reaction norm (mu-rn) highlights environmentally dependent mutation effects and epistatic interactions.

Since the modern synthesis, the fitness effects of mutations and epistasis have been central yet provocative concepts in evolutionary and population genetics. Studies of how the interactions between parcels of genetic information can change as a function of environmental context have added a layer of complexity to these discussions. Here, I introduce the "mutation effect reaction norm" (Mu-RN), a new instrument through which one can analyze the phenotypic consequences of mutations and interactions across environmental contexts. It embodies the fusion of measurements of genetic interactions with the reaction norm, a classic depiction of the performance of genotypes across environments. I demonstrate the utility of the Mu-RN through a case study: the signature of a "compensatory ratchet" mutation that undermines reverse evolution of antimicrobial resistance. In closing, I argue that the mutation effect reaction norm may help us resolve the dynamism and unpredictability of evolution, with implications for theoretical biology, biotechnology, and publichealth.

Genetic load and extinction in peripheral populations: the roles of migration, drift and demographic stochasticity

We analyse how migration from a large mainland influences genetic load and population numbers on an island, in a scenario where fitness-affecting variants are unconditionally deleterious, and where numbers decline with increasing load. Our analysis shows that migration can have qualitatively different effects, depending on the total mutation target and fitness effects of deleterious variants. In particular, we find that populations exhibit a genetic Allee effect across a wide range of parameter combinations, when variants are partially recessive, cycling between low-load (large-population) and high-load (sink) states. Increased migration reduces load in the sink state (by increasing heterozygosity) but further inflates load in the large-population state (by hindering purging). We identify various critical parameter thresholds at which one or other stable state collapses, and discuss how these thresholds are influenced by the genetic versus demographic effects of migration. Our analysis is based on a ‘semi-deterministic’ analysis, which accounts for genetic drift but neglects demographic stochasticity. We also compare against simulations which account for both demographic stochasticity and drift. Our results clarify the importance of gene flow as a key determinant of extinction risk in peripheral populations, even in the absence of ecological gradients.This article is part of the theme issue ‘Species’ ranges in the face of changing environments (part I)’.

Limits to detecting epistasis in the fitness landscape of HIV.

The rapid evolution of HIV is constrained by interactions between mutations which affect viral fitness. In this work, we explore the role of epistasis in determining the mutational fitness landscape of HIV for multiple drug target proteins, including Protease, Reverse Transcriptase, and Integrase. Epistatic interactions between residues modulate the mutation patterns involved in drug resistance, with unambiguous signatures of epistasis best seen in the comparison of the Potts model predicted and experimental HIV sequence "prevalences" expressed as higher-order marginals (beyond triplets) of the sequence probability distribution. In contrast, experimental measures of fitness such as viral replicative capacities generally probe fitness effects of point mutations in a single background, providing weak evidence for epistasis in viral systems. The detectable effects of epistasis are obscured by higher evolutionary conservation at sites. While double mutant cycles in principle, provide one of the best ways to probe epistatic interactions experimentally without reference to a particular background, we show that the analysis is complicated by the small dynamic range of measurements. Overall, we show that global pairwise interaction Potts models are necessary for predicting the mutational landscape of viral proteins.

Fitness Effects of Mutations: An Assessment of PROVEAN Predictions Using Mutation Accumulation Data.

Predicting fitness in natural populations is a major challenge in biology. It may be possible to leverage fast-accumulating genomic data sets to infer the fitness effects of mutant alleles, allowing evolutionary questions to be addressed in any organism. In this paper, we investigate the utility of one such tool, called PROVEAN. This program compares a query sequence with existing data to provide an alignment-based score for any protein variant, with scores categorized as neutral or deleterious based on a pre-set threshold. PROVEAN has been used widely in evolutionary studies, for example, to estimate mutation load in natural populations, but has not been formally tested as a predictor of aggregate mutational effects on fitness. Using three large published data sets on the genome sequences of laboratory mutation accumulation lines, we assessed how well PROVEAN predicted the actual fitness patterns observed, relative to other metrics. In most cases, we find that a simple count of the total number of mutant proteins is a better predictor of fitness than the number of proteins with variants scored as deleterious by PROVEAN. We also find that the sum of all mutant protein scores explains variation in fitness better than the number of mutant proteins in one of the data sets. We discuss the implications of these results for studies of populations in the wild.

Fine-scale quantification of GC-biased gene conversion intensity in mammals

GC-biased gene conversion (gBGC) is a molecular evolutionary force that favours GC over AT alleles irrespective of their fitness effect. Quantifying the variation in time and across genomes of its intensity is key to properly interpret patterns of molecular evolution. In particular, the existing literature is unclear regarding the relationship between gBGC strength and species effective population size, Ne. Here we analysed the nucleotide substitution pattern in coding sequences of closely related species of mammals, thus accessing a high resolution map of the intensity of gBGC. Our maximum likelihood approach shows that gBGC is pervasive, highly variable among species and genes, and of strength positively correlated with Ne in mammals. We estimate that gBGC explains up to 60% of the total amount of synonymous ATGC substitutions. We show that the fine-scale analysis of gBGC-induced nucleotide substitutions has the potential to inform on various aspects of molecular evolution, such as the distribution of fitness effects of mutations and the dynamics of recombination hotspots.

Mutation saturation for fitness effects at human CpG sites.

Whole exome sequences have now been collected for millions of humans, with the related goals of identifying pathogenic mutations in patients and establishing reference repositories of data from unaffected individuals. As a result, we are approaching an important limit, in which datasets are large enough that, in the absence of natural selection, every highly mutable site will have experienced at least one mutation in the genealogical history of the sample. Here, we focus on CpG sites that are methylated in the germline and experience mutations to T at an elevated rate of ~10-7 per site per generation; considering synonymous mutations in a sample of 390,000 individuals, ~ 99 % of such CpG sites harbor a C/T polymorphism. Methylated CpG sites provide a natural mutation saturation experiment for fitness effects: as we show, at nt sample sizes, not seeing a non-synonymous polymorphism is indicative of strong selection against that mutation. We rely on this idea in order to directly identify a subset of CpG transitions that are likely to be highly deleterious, including ~27 % of possible loss-of-function mutations, and up to 20 % of possible missense mutations, depending on the type of functional site in which they occur. Unlike methylated CpGs, most mutation types, with rates on the order of 10-8 or 10-9, remain very far from saturation. We discuss what these findings imply for interpreting the potential clinical relevance of mutations from their presence or absence in reference databases and for inferences about the fitness effects of new mutations.

What does the distribution of fitness effects of new mutations reflect? Insights from plants.

The distribution of fitness effects (DFE) of new mutations plays a central role in molecular evolution. It is therefore crucial to be able to estimate it accurately from genomic data and to understand the factors that shape it. After a rapid overview of available methods to characterize the fitness effects of mutations, we review what is known on the factors affecting them in plants. Available data indicate that life history traits (e.g. mating system and longevity) have a major effect on the DFE. By contrast, the impact of demography within species appears to be more limited. These results remain to be confirmed, and methods to estimate the joint evolution of demography, life history traits, and the DFE need to be developed.

Tracing the Evolution of Clonal Hematopoiesis to AML Using Longitudinal Pre-Diagnosis Blood Samples

▪The acquisition of somatic mutations in hematopoietic stem and progenitor cells (HSPCs) is increasingly common with age ('clonal hematopoiesis'). If sequential acquisition and clonal expansion of mutations occurs, progression to Acute Myeloid Leukemia (AML) can occur. While the mutational landscape of clonal hematopoiesis antecedent to AML development has been well-defined (Abelson et al. 2018, Desai et al. 2018), the timing of acquisition and growth dynamics of these high-risk mutations remain largely unknown. At what age are these mutations acquired? Are the fitness effects (growth rates) conferred by specific mutations predictable from person-to-person and how do fitness effects change with additive mutations? Are the clonal dynamics that precede AML development characterised by strong competition between clones (clonal interference)?To answer these questions, we identified 220 women from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) who were cancer-free at enrolment but subsequently developed AML during the >12 years follow-up. 50 of these women had annual blood samples collected at multiple time-points pre-AML diagnosis (mean: 5 time-points, range: 2-11). Deep error-corrected duplex sequencing, with a variant allele frequency (VAF) detection limit of 0.1%, was performed on peripheral blood DNA from these women, as well as from age- and timepoint-matched controls who remained blood cancer free. A custom designed next-generation sequencing (NGS) panel was used to enable detection of mutations in 34 clonal hematopoiesis/AML-associated genes, genome-wide mosaic chromosomal alterations (mCAs) and AML-associated translocations.Having samples from multiple timepoints enabled the fitness effects (growth rates) of mutations to be calculated, as well as the additive effect of further mutations. These growth rates, in combination with insights from evolutionary theory, allowed the acquisition time of many mutations to be estimated, with initiating driver mutations often arising in the first 2 decades of life in the pre-AML cases. Growth trajectory dynamics of co-occurring mutations enabled the clonal composition to be inferred in many instances and revealed linear evolution of successive mutations in some pre-AML cases, but a branching pattern with clear evidence of clonal interference in others. Specific variants, which we have previously identified as 'highly fit' in clonal hematopoiesis (Watson et al. 2020), were significantly enriched in pre-AML cases compared to controls and were often detectable at VAFs >10% more than 5 years pre-diagnosis. NPM1 mutations, which characteristically occur ‘late’ in AML development, could be detected as early as 2 years pre-diagnosis, highlighting the benefit afforded by error-corrected low VAF variant calling, particularly in high-risk individuals.Our findings, exploiting longitudinal blood samples collected pre-AML combined with an integrated assessment of multiple types of genetic changes, reveal key insights into the evolutionary dynamics of mutations in the years preceding AML development. Understanding which features distinguish pre-malignant from benign clonal evolution is key for risk stratification of individuals with clonal hematopoiesis to allow rational monitoring and identification of individuals that may benefit from early intervention studies. [Display omitted] DisclosuresWatson: Johnson & Johnson: Consultancy; Inivata: Consultancy. Menon: Abcodia Ltd: Current holder of individual stocks in a privately-held company. Blundell: Johnson & Johnson: Consultancy; Inivata: Consultancy.

The distribution of mutational effects on fitness in Caenorhabditis elegans inferred from standing genetic variation.

The distribution of fitness effects (DFE) for new mutations is one of the most theoretically important but difficult to estimate properties in population genetics. A crucial challenge to inferring the DFE from natural genetic variation is the sensitivity of the site frequency spectrum to factors like population size change, population substructure, genome structure, and nonrandom mating. Although inference methods aim to control for population size changes, the influence of nonrandom mating remains incompletely understood, despite being a common feature of many species. We report the DFE estimated from 326 genomes of Caenorhabditis elegans, a nematode roundworm with a high rate of self-fertilization. We evaluate the robustness of DFE inferences using simulated data that mimics the genomic structure and reproductive life history of C. elegans. Our observations demonstrate how the combined influence of self-fertilization, genome structure, and natural selection on linked sites can conspire to compromise estimates of the DFE from extant polymorphisms with existing methods. These factors together tend to bias inferences toward weakly deleterious mutations, making it challenging to have full confidence in the inferred DFE of new mutations as deduced from standing genetic variation in species like C. elegans. Improved methods for inferring the DFE are needed to appropriately handle strong linked selection and selfing. These results highlight the importance of understanding the combined effects of processes that can bias our interpretations of evolution in natural populations.

Selection on Accessible Chromatin Regions in Capsella grandiflora.

Accurate estimates of genome-wide rates and fitness effects of new mutations are essential for an improved understanding of molecular evolutionary processes. Although eukaryotic genomes generally contain a large noncoding fraction, functional noncoding regions and fitness effects of mutations in such regions are still incompletely characterized. A promising approach to characterize functional noncoding regions relies on identifying accessible chromatin regions (ACRs) tightly associated with regulatory DNA. Here, we applied this approach to identify and estimate selection on ACRs in Capsella grandiflora, a crucifer species ideal for population genomic quantification of selection due to its favorable population demography. We describe a population-wide ACR distribution based on ATAC-seq data for leaf samples of 16 individuals from a natural population. We use population genomic methods to estimate fitness effects and proportions of positively selected fixations (α) in ACRs and find that intergenic ACRs harbor a considerable fraction of weakly deleterious new mutations, as well as a significantly higher proportion of strongly deleterious mutations than comparable inaccessible intergenic regions. ACRs are enriched for expression quantitative trait loci (eQTL) and depleted of transposable element insertions, as expected if intergenic ACRs are under selection because they harbor regulatory regions. By integrating empirical identification of intergenic ACRs with analyses of eQTL and population genomic analyses of selection, we demonstrate that intergenic regulatory regions are an important source of nearly neutral mutations. These results improve our understanding of selection on noncoding regions and the role of nearly neutral mutations for evolutionary processes in outcrossing Brassicaceae species.

Sex differences in deleterious mutational effects in Drosophila melanogaster: combining quantitative and population genetic insights.

Fitness effects of deleterious mutations can differ between females and males due to: (i) sex differences in the strength of purifying selection; and (ii) sex differences in ploidy. Although sex differences in fitness effects have important broader implications (e.g., for the evolution of sex and lifespan), few studies have quantified their scope. Those that have belong to one of two distinct empirical traditions: (i) quantitative genetics, which focusses on multi-locus genetic variances in each sex, but is largely agnostic about their genetic basis; and (ii) molecular population genetics, which focusses on comparing autosomal and X-linked polymorphism, but is poorly suited for inferring contemporary sex differences. Here, we combine both traditions to present a comprehensive analysis of female and male adult reproductive fitness among 202 outbred, laboratory-adapted, hemiclonal genomes of Drosophila melanogaster. While we find no clear evidence for sex differences in the strength of purifying selection, sex differences in ploidy generate multiple signals of enhanced purifying selection for X-linked loci. These signals are present in quantitative genetic metrics-i.e., a disproportionate contribution of the X to male (but not female) fitness variation-and population genetic metrics-i.e., steeper regressions of an allele's average fitness effect on its frequency, and proportionally less nonsynonymous polymorphism on the X than autosomes. Fitting our data to models for both sets of metrics, we infer that deleterious alleles are partially recessive. Given the often-large gap between quantitative and population genetic estimates of evolutionary parameters, our study showcases the benefits of combining genomic and fitness data when estimating such parameters.

Decomposing the sources of SARS-CoV-2 fitness variation in the United States.

The fitness of a pathogen is a composite phenotype determined by many different factors influencing growth rates both within and between hosts. Determining what factors shape fitness at the host population-level is especially challenging because both intrinsic factors like pathogen genetics and extrinsic factors such as host behavior influence between-host transmission potential. This challenge has been highlighted by controversy surrounding the population-level fitness effects of mutations in the SARS-CoV-2 genome and their relative importance when compared against non-genetic factors shaping transmission dynamics. Building upon phylodynamic birth–death models, we develop a new framework to learn how hundreds of genetic and non-genetic factors have shaped the fitness of SARS-CoV-2. We estimate the fitness effects of all amino acid variants and several structural variants that have circulated in the United States between February 2020 and March 2021 from viral phylogenies. We also estimate how much fitness variation among pathogen lineages is attributable to genetic versus non-genetic factors such as spatial heterogeneity in transmission rates. Before September 2020, most fitness variation between lineages can be explained by background spatial heterogeneity in transmission rates across geographic regions. Starting in late 2020, genetic variation in fitness increased dramatically with the emergence of several new lineages including B.1.1.7, B.1.427, B.1.429 and B.1.526. Our analysis also indicates that genetic variants in less well-explored genomic regions outside of Spike may be contributing significantly to overall fitness variation in the viral population.

Mutation Edgotype Drives Fitness Effect in Human.

Missense mutations are known to perturb protein-protein interaction networks (known as interactome networks) in different ways. However, it remains unknown how different interactome perturbation patterns (“edgotypes”) impact organismal fitness. Here, we estimate the fitness effect of missense mutations with different interactome perturbation patterns in human, by calculating the fractions of neutral and deleterious mutations that do not disrupt PPIs (“quasi-wild-type”), or disrupt PPIs either by disrupting the binding interface (“edgetic”) or by disrupting overall protein stability (“quasi-null”). We first map pathogenic mutations and common non-pathogenic mutations onto homology-based three-dimensional structural models of proteins and protein-protein interactions in human. Next, we perform structure-based calculations to classify each mutation as either quasi-wild-type, edgetic, or quasi-null. Using our predicted as well as experimentally determined interactome perturbation patterns, we estimate that >∼40% of quasi-wild-type mutations are effectively neutral and the remaining are mostly mildly deleterious, that >∼75% of edgetic mutations are only mildly deleterious, and that up to ∼75% of quasi-null mutations may be strongly detrimental. These estimates are the first such estimates of fitness effect for different network perturbation patterns in any interactome. Our results suggest that while mutations that do not disrupt the interactome tend to be effectively neutral, the majority of human PPIs are under strong purifying selection and the stability of most human proteins is essential to human life.

Precise measurement of the fitness effects of spontaneous mutations by droplet digital PCR in Burkholderia cenocepacia.

Understanding how mutations affect survivability is a key component to knowing how organisms and complex traits evolve. However, most mutations have a minor effect on fitness and these effects are difficult to resolve using traditional molecular techniques. Therefore, there is a dire need for more accurate and precise fitness measurements methods. Here, we measured the fitness effects in Burkholderia cenocepacia HI2424 mutation accumulation (MA) lines using droplet-digital polymerase chain reaction (ddPCR). Overall, the fitness measurements from ddPCR-MA are correlated positively with fitness measurements derived from traditional phenotypic marker assays (r = 0.297, P = 0.05), but showed some differences. First, ddPCR had significantly lower measurement variance in fitness (F = 3.78, P < 2.6 × 10-13) in control experiments. Second, the mean fitness from ddPCR-MA measurements were significantly lower than phenotypic marker assays (-0.0041 vs -0.0071, P = 0.006). Consistent with phenotypic marker assays, ddPCR-MA measurements observed multiple (27/43) lineages that significantly deviated from mean fitness, suggesting that a majority of the mutations are neutral or slightly deleterious and intermixed with a few mutations that have extremely large effects. Of these mutations, we found a significant excess of mutations within DNA excinuclease and Lys R transcriptional regulators that have extreme deleterious and beneficial effects, indicating that modifications to transcription and replication may have a strong effect on organismal fitness. This study demonstrates the power of ddPCR as a ubiquitous method for high-throughput fitness measurements in both DNA- and RNA-based organisms regardless of cell type or physiology.