Biology subjects

Kinsler, G.

Publications and source records attributed to Kinsler, G..

9 recordsLinked to original sources

bioRxiv2025

Modularity, Limited Sparsity, and Extensive Pleiotropy in a Genotype-Phenotype-Fitness Map of Drug Resistance

Understanding how the myriad molecular impacts of mutation percolate to influence higher-order traits and ultimately fitness requires compressing a many-to-many mapping into something tractable. Decades of theoretical work suggest this may be possible because biological systems are modular: effects of perturbation are often funneled through particular pathways or subsystems rather than propagating freely through the organism. Yet few empirical systems have been able to demonstrate such modularity at scale. Here we show that, even across 774 diverse yeast lineages, fitness variation across 12 drug environments is organized by a strikingly low-dimensional structure defined by only a few inferred phenotypic axes that capture the main patterns of variation. Lineages drawn from multiple evolutionary histories reveal more of these phenotypic axes than those derived from a single selection pressure. Consistent with many of these lineages having evolved under strong selection pressure, their mutations often exhibit broad pleiotropy, affecting nearly all inferred phenotypic axes. However, fitness in any given drug depends on a much sparser subset of the phenotypic modules these axes reflect. By compressing many-to-many relationships, this low-dimensional framework exposes the modular phenotypic space, as well as the context-dependent contribution of each phenotypic module to fitness that can constrain the pleiotropic effects of adaptive mutations. It also highlights that the apparent complexity of genotype-phenotype-fitness maps depends not only on environmental context but also on the diversity of mutations through which they are observed, laying the groundwork for identifying the key phenotypic modules that matter for fitness.

evolutionary biology↗

bioRxiv2025

Lineage memory shapes viral resistance barriers in human skin

Individual cells within a given population exhibit striking variability in viral susceptibility, but it remains unknown whether this heterogeneity reflects memories encoded into the cellular lineage or true probabilistic variability. We used multi-color lineage tracing in a human primary organotypic skin model to reveal that viral resistance is encoded within specific cellular lineages. These lineages create distinct boundaries that block viral spread. Our lineage analyses in vitro confirmed that viral susceptibility exhibits strong heritability across cell generations, with siblings and cousins displaying remarkably similar infection outcomes. ATAC and proteomics profiling of resistant and susceptible clones revealed distinct epigenomic and proteomic states, with the transcription factor AP-1 emerging as a potential central regulator of lineage-encoded viral resistance. Inducing AP-1 activity with PMA rendered cells resistant to viral infection, suggesting a causative role in mediating resistance memory. Our findings demonstrate that antiviral resistance in human skin cells is encoded within cellular lineages and preserved through cell divisions, revealing how cell memory may shape infection dynamics and viral containment in tissues.

bioengineering↗

bioRxiv2025

Low-dimensional genotype-fitness mapping across divergent environments suggests a limiting functions model of fitness

A central goal in evolutionary biology is to be able to predict the effect of a genetic mutation on fitness. This is a major challenge because fitness depends both on phenotypic changes due to the mutation, and how these phenotypes map onto fitness in a particular environment. Genotype, phenotype, and environment spaces are extremely large and complex, rendering bottom-up prediction difficult. Here we show, using a large collection of adaptive yeast mutants, that fitness across a set of lab environments can be well-captured by low-dimensional linear models of abstract genotype-phenotype-fitness maps. We find that these maps are low-dimensional not only in the environment where the adaptive mutants evolved, but also in divergent environments. We further find that the genotype-phenotype-fitness spaces implied by these maps overlap only partially across environments. We argue that these patterns are consistent with a "limiting functions" model of fitness, whereby only a small number of limiting functions can be modified to affect fitness in any given environment. The pleiotropic side-effects on non-limiting functions are effectively hidden from natural selection locally, but can be revealed globally. These results combine to emphasize the importance of environmental context in genotype-phenotype-fitness mapping, and have implications for the predictability and trajectory of evolution in complex environments.

evolutionary biology↗

bioRxiv2025

SpaceBar enables clone tracing in spatial transcriptomic data

We report a cellular barcoding strategy, SpaceBar, that enables simultaneous clone tracing and spatial transcriptomics profiling. Our approach uses a library of 96 synthetic barcode sequences that can be robustly detected by imaging based spatial transcriptomics (seqFISH), delivered such that each cell is labeled with a combination of barcodes. We used these barcodes to label melanoma cells in a tumor xenograft model and profiled both clone identity and spatial gene expression in situ. We developed a gene scoring metric that quantifies how strongly gene expression is driven by intrinsic cellular cues or extrinsic environmental signals. Our framework distinguishes between clonal dynamics and environmentally-driven transcriptional regulation in complex tissue contexts.

genomics↗

bioRxiv2024

A shift from pleiotropic to modular adaptation revealed by a high-resolution two-step adaptive walk

Evolution by natural selection is expected to be a slow and gradual process. In particular, the mutations that drive evolution are predicted to be small and modular, incrementally improving a small number of traits. However, adaptive mutations identified early in microbial evolution experiments, cancer, and other systems often provide substantial fitness gains and pleiotropically improve multiple traits at once. We asked whether such pleiotropically adaptive mutations are common throughout adaptation or are instead a rare feature of early steps in evolution that tend to target key signaling pathways. To do so, we conducted barcoded second-step evolution experiments initiated from five first-step mutations identified from a prior yeast evolution experiment. We then isolated hundreds of second-step mutations from these evolution experiments, measured their fitness and performance in several growth phases, and conducted whole-genome sequencing of the second-step clones. Here, we found that while the vast majority of mutants isolated from the first-step of evolution in this condition show patterns of pleiotropic adaptation - improving both performance in fermentation and respiration growth phases - second-step mutations show a shift towards modular adaptation, mostly improving respiration performance and only rarely improving fermentation performance. We also identified a shift in the molecular basis of adaptation from genes in cellular signaling pathways towards genes involved in respiration and mitochondrial function. Our results suggest that the genes in cellular signaling pathways are particularly capable of providing large, adaptively pleiotropic benefits to the organism due to their ability to coherently affect many phenotypes at once. As such, these genes may serve as the source of pleiotropic adaptation in the early stages of evolution, and once these become exhausted, organisms then adapt more gradually, acquiring smaller, more modular mutations.

evolutionary biology↗

bioRxiv2023

Improving the accuracy of bulk fitness assays by correcting barcode processing biases

Measuring the fitnesses of genetic variants is a fundamental objective in evolutionary biology. A standard approach for measuring microbial fitnesses in bulk involves labeling a library of genetic variants with unique sequence barcodes, competing the labeled strains in batch culture, and using deep sequencing to track changes in the barcode abundances over time. However, idiosyncratic properties of barcodes (e.g., GC content) can induce non-uniform amplification or uneven sequencing coverage that cause some barcodes to be over-or under-represented in samples. This systematic bias can result in erroneous read count trajectories and misestimates of fitness. Here we develop a computational method for inferring the effects of processing bias by leveraging the structure of systematic deviations in the data. We illustrate this approach by applying it to fitness assay data collected for a large library of yeast variants, and show that this method estimates and corrects for bias more accurately than standard proxies, such as GC-based corrections. Our method mitigates bias and improves fitness estimates in high-throughput assays with-out introducing additional complexity to the experimental protocols, with potential value in a range of experimental evolution and mutation screening contexts.

evolutionary biology↗

bioRxiv2023

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs across hundreds of fluconazole-resistant yeast strains

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into 6 classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

evolutionary biology↗

bioRxiv2022

Extreme sensitivity of fitness to environmental conditions; lessons from #1BigBatch

The phrase "survival of the fittest" has become an iconic descriptor of how natural selection works. And yet, precisely measuring fitness, even for single-celled microbial populations growing in controlled laboratory conditions, remains a challenge. While numerous methods exist to perform these measurements, including recently developed methods utilizing DNA barcoding, all methods seem limited in their precision to differentiate strains with small fitness differences. This limit on precision is relevant in many fields, including the field of experimental evolution. In this study, we hone in on the factors that contribute to noisy fitness measurements and suggest solutions to avoid certain sources of noise. Surprisingly, even when common sources of technical noise are controlled for, we find that fitness measurements are still very noisy. Our data suggest that subtle environmental differences among replicates create substantial variation across fitness measurements. We conclude by providing best practices for obtaining precise fitness measurements and by discussing how these measurements should be interpreted given their extreme context dependence. This work was inspired by the scientific community who followed us and gave us tips as we live-tweeted a high-replicate fitness measurement experiment at #1BigBatch.

evolutionary biology↗

bioRxiv2020

A genotype-phenotype-fitness map reveals local modularity and global pleiotropy of adaptation

Building a genotype-phenotype-fitness map of adaptation is a central goal in evolutionary biology. It is notoriously difficult even when the adaptive mutations are known because it is hard to enumerate which phenotypes make these mutations adaptive. We address this problem by first quantifying how the fitness of hundreds of adaptive yeast mutants responds to subtle environmental shifts and then modeling the number of phenotypes they must collectively influence by decomposing these patterns of fitness variation. We find that a small number of phenotypes predicts fitness of the adaptive mutations near their original glucose-limited evolution condition. Importantly, phenotypes that matter little to fitness at or near the evolution condition can matter strongly in distant environments. This suggests that adaptive mutations are locally modular--affecting a small number of phenotypes that matter to fitness in the environment where they evolved--yet globally pleiotropic--affecting additional phenotypes that may reduce or improve fitness in new environments.

evolutionary biology↗