Biology subjects

Geiler-Samerotte, K.

Publications and source records attributed to Geiler-Samerotte, K..

13 recordsLinked to original sources

bioRxiv2025

Massively parallel evolution reveals a biophysical scaling law between cell size and internal cell density

Using a massively parallel evolution platform, we selected Saccharomyces cerevisiae for increased cell size to test how cellular architecture adapts to biophysical constraints. As cells evolved larger size, they became less spherical and showed reduced carrying capacity without changes in maximum growth rate. Optical diffraction tomography revealed that individual cells with greater volume consistently exhibited lower internal density, a relationship that persisted across replicate populations and evolved isolates. The largest cells often contained enlarged vacuoles, suggesting that vacuole expansion may sometimes contribute to reduced density. Extending this analysis beyond yeast, 68 species spanning major phylogenetic clades also demonstrate a negative scaling between cell density and cell size. Together, these results suggest that decreased internal density is a conserved consequence of increasing cell size, revealing a fundamental cellular trade-off between volume expansion and material concentration.

biophysics↗

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

Collateral fitness effects of mutation are not commonly caused by protein misfolding

Mutations in coding sequences are often assumed to harm cells by destabilizing proteins and creating toxic misfolded species. Here we directly test how fitness scales with predicted folding stability. Using deep mutational scanning of a gratuitously expressed protein in S. cerevisiae ({approx}2,000 YFP single-amino-acid variants) and meta-analyses of seven additional scans of gratuitous proteins in yeast and E. coli, we find that collateral fitness effects, costs that arise independently of protein function, do not correlate with predicted destabilization ({Delta}{Delta}G). Even variants predicted or biochemically shown to misfold frequently had no measurable collateral cost. In contrast, across matched datasets where the same proteins were required for growth, predicted destabilization strongly tracked primary fitness costs, and this association intensified as functional demand increased. These conclusions were robust to multiple stability predictors and to competitive fitness assays with high sensitivity. Together, our results indicate that misfolding is not a common driver of collateral fitness costs, whereas it often underlies primary costs when function matters. These findings overturn the long-standing assumption that misfolding universally drives the collateral costs of mutation, reframing misfolded proteins as only one piece of a broader puzzle and opening the way to identify alternative cellular vulnerabilities that shape evolution, disease, and aging.

molecular biology↗

bioRxiv2025

High-throughput single-cell transcriptomics informs mechanisms underlying cell-to-cell differences in stress responses

Cellular stress responses are central to survival, adaptation, and disease, yet individual cells experiencing the same environment can differ substantially in how they respond. Understanding which features of the stress response are shared among cells, and which vary across cells and stressors, requires resolving transcriptional responses at the level of individual cells. Here, we used ultra-high-throughput single-cell RNA sequencing to profile more than 16,000 Saccharomyces cerevisiae cells exposed to diverse stressors, including protein misfolding, nutrient limitation, and drug stress. We compared the canonical Environmental Stress Response (ESR), originally defined from population-level measurements, with stress-responsive programs identified by emphasizing expression changes that were consistent across individual cells. Although the canonical induced ESR (iESR) showed strong average activation in stressed populations, its activation varied substantially among their constituent cells. Stress-specific programs identified by our single-cell-aware analysis were more consistent predictors of individual cell stress, yet showed little overlap with the ESR or with one another across stressors. This stress-specificity was asymmetric: Induced programs varied considerably across stressors, whereas repressed programs were dominated by a conserved set of growth- and ribosome-related genes. Consequently, unstressed cells could be identified across environments much more reliably than stressed cells, revealing a transcriptional version of the Anna Karenina principle: Unstressed cells resemble one another in their shared growth programs, whereas stressed cells diverge in stress-specific ways. Single-cell resolution also revealed an alternative transcriptional state in which cells with weaker activation of canonical stress programs showed stronger transposable element (TE) expression. Although iESR and TE expression were positively associated across stressed populations, they were inversely related across individual cells, demonstrating that population averaging can obscure, and even reverse, relationships among transcriptional programs. Together, these results show that population-level and single-cell analyses capture complementary properties of stress responses. Both perspectives are important in understanding and ultimately predicting how cell populations survive and adapt under stressful conditions.

molecular biology↗

bioRxiv2025

INgen: Intracellular Genomic DNA Amplification for Downstream Applications in Sequencing and Sorting

Here, we introduce intracellular genomic amplification (INgen), a method that harnesses the cell membrane as a natural reaction chamber to amplify DNA within fixed, permeabilized cells. INgen employs a strand-displacing, isothermal polymerase together with biotinylated primers to achieve robust intracellular DNA amplification and efficient recovery of the amplified material for sequencing. This approach overcomes a long-standing barrier that has prevented important advances in single-cell and rare-cell genomics: the inability to amplify and recover sequenceable DNA from fixed cells. By pushing past this barrier, INgen provides a critical and previously inaccessible step toward scalable single-cell DNA sequencing without the need to isolate each cell into a separate reaction vessel. Using INgen, we demonstrate targeted and whole-genome amplification across diverse organisms, including Saccharomyces cerevisiae, Bacillus subtilis, and Escherichia coli. Together, these capabilities position INgen as a foundational advance that paves the way for the next generation of single cell sequencing methods including high-throughput single-cell sequencing without physical isolation, contamination-resistant amplification within intact cells, and rare-cell enrichment prior to genomic analysis.

molecular biology↗

bioRxiv2024

Environment by environment interactions (ExE) differ across genetic backgrounds (ExExG)

While the terms "gene-by-gene interaction" (GxG) and "gene-by-environment interaction" (GxE) are widely recognized in the fields of quantitative and evolutionary genetics, "environment-byenvironment interaction" (ExE) is a term used less often. In this study, we find that environmentby-environment interactions are a meaningful driver of phenotypes, and moreover, that they differ across different genotypes (suggestive of ExExG). To support this conclusion, we analyzed a large dataset of roughly 1,000 mutant yeast strains with varying degrees of resistance to different antifungal drugs. Our findings reveal that the effectiveness of a drug combination, relative to single drugs, often differs across drug resistant mutants. Remarkably, even mutants that differ by only a single nucleotide change can have dramatically different drug x drug (ExE) interactions. We also introduce a new framework that more accurately predicts the direction and magnitude of ExE interactions for some mutants. Understanding how ExE interactions change across genotypes (ExExG) is crucial not only for modeling the evolution of pathogenic microbes, but also for enhancing our knowledge of the underlying cell biology and the sources of phenotypic variance within populations. While the significance of ExExG interactions has been overlooked in evolutionary and population genetics, these fields and others stand to benefit from understanding how these interactions shape the complex behavior of living systems.

evolutionary biology↗

bioRxiv2024

Single-cell heterogeneity and the growth laws

Across species and environments, the ribosome content of cell populations correlates with population growth rate. The robustness and universality of this correlation have led to its classification as a "growth law." This law has fueled theories about how evolution selects for microbial organisms that maximize their growth rate based on nutrient availability, and it has informed models about how individual cells regulate their growth rates and ribosomal content. However, due to methodological limitations, this growth law has rarely been studied at the level of individual cells. While populations of fast-growing cells tend to have more ribosomes than populations of slow-growing cells, it is unclear whether individual cells tightly regulate their ribosome content to match their environment. Here, we employ recent groundbreaking single-cell RNA sequencing techniques to study this growth law at the single-cell level in two different microbes, S. cerevisiae (a single-celled yeast and eukaryote) and B. subtilis (a bacterium and prokaryote). In both species, we observe significant variation in the ribosomal content of single cells that is not predictive of growth rate. Fast-growing populations include cells exhibiting transcriptional signatures of slow growth and stress, as do cells with the highest ribosome content we survey. Broadening our focus to non-ribosomal transcripts reveals subpopulations of cells in unique transcriptional states suggestive that they have evolved to do things other than maximize their rate of growth. Overall, these results indicate that single-cell ribosome levels are not finely tuned to match population growth rates or nutrient availability and cannot be predicted by a Gaussian process model that assumes measurements are sampled from a normal distribution centered on the population average. This work encourages the expansion of growth law and other models that predict how growth rates are regulated or how they evolve to consider single-cell heterogeneity. To this end, we provide extensive data and analysis of ribosomal and transcriptomic variation across thousands of single cells from multiple conditions, replicates, and species.

cell 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↗

bioRxiv2023

Dissecting genetic correlation through recombinantperturbations: the role of developmental bias

Genetic correlation represents an important class of evolutionary constraint, which is itself evolvable. Empirical studies have found mixed results on whether genetic correlations change rapidly or slowly. This uncertainty challenges our ability to predict the outcome of selection. Despite the tremendous diversity and complexity of life forms, there are certain forms of life that are never observed. This might be because of developmental biases that restrict how organisms can evolve, or because they have low fitness in any environment yet available on Earth. Given that both developmental bias and selection can generate similar phenotypes, it is difficult to distinguish between the two causes of evolutionary stasis among related taxa. For example, remarkably invariant traits are observed spanning million years, such as wing shape in Drosophila wherein qualitative differences are rare within genera. Here, we ask whether the absence of certain combinations of traits, as indicated by genetic correlation, reflects developmental bias. However, much confusion and controversy remain over definitions of developmental bias, and probing it is challenging. We thus present a novel approach aiming to dissect genetic correlations and estimate the relative contribution of developmental bias in maintaining genetic correlations. We do so by leveraging a common but under-utilized type of data: genetic crosses. Through empirical analyses, we find that our approach can distinguish whether genetically correlated traits are developmentally constrained to covary. We also find that our developmental bias metric is an indicator of genetic correlation stability across conditions. Our framework presents a feasible way to dissect the mechanisms underlying genetic correlation and pleiotropy.

evolutionary biology↗

bioRxiv2022

An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts

Yeasts are naturally diverse, genetically tractable, and easy to grow in a myriad of experimental conditions such that researchers have the ability to investigate any number of genotypes, strains, environments, or the interaction thereof. However, studies of variation in the yeast transcriptome have been limited by the processing capabilities of available RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform for yeasts. This platform utilizes a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at once. This method can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches. Thus, our technology permits researchers to leverage "the awesome power of yeast" by allowing us to survey the transcriptome of hundreds of strains and environments in a short period of time, and with no specialized equipment. The key to this method is that sequential barcodes are probabilistically appended to cDNA copies of RNA while the molecules remain trapped inside of each cell. Thus, the transcriptome of each cell is labeled with a unique combination of barcodes. Since we use the cell membrane as a container for this reaction, many cells can be processed together without the need to physically isolate them from one another in separate wells or droplets. Further, the first barcode in the sequence can be chosen intentionally to identify samples from different environments or genetic backgrounds, enabling multiplexing of hundreds of unique samples in a single experiment. In addition to greater multiplexing capabilities, our method also facilitates a deeper investigation of biological heterogeneity given its single-cell nature. For example, in the data presented here we report transcriptionally distinct cell states related to cell cycle, growth rate, metabolic strategies, stress responses, etc. all within clonal yeast populations grown in the same environment. Hence, our technology has two obvious and impactful applications for yeast research: the first is the general study of transcriptional phenotypes across many strains and environments, and the second is investigating cell-to-cell heterogeneity across the entire transcriptome.

molecular 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↗

bioRxiv2020

The complete genome sequence of the Staphylococcus bacteriophage Metroid

Phages infecting bacteria of the genus Staphylococcus play an important role in their hosts ecology and evolution. On one hand, horizontal gene transfer from phage can encourage the rapid adaptation of pathogenic Staphylococcus enabling them to escape host immunity or access novel environments. On the other hand, lytic phages are promising agents for the treatment of bacterial infections, especially those resistant to antibiotics. As part of an ongoing effort to gain novel insights into bacteriophage diversity, we characterized the complete genome of the Staphylococcus bacteriophage Metroid, a cluster C phage with a genome size of 151kb, encompassing 254 predicted protein-coding genes as well as 4 tRNAs. A comparative genomic analysis highlights strong similarities - including a conservation of the lysis cassette - with other Staphylococcus cluster C1 bacteriophages, several of which were previously characterized for therapeutic applications.

genomics↗