Biology subjects

Schmidlin, K.

Publications and source records attributed to Schmidlin, K..

8 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

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↗

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

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↗

bioRxiv2021

A novel lineage of polyomaviruses identified in bark scorpions

Polyomaviruses are non-enveloped viruses with circular double-stranded DNA genomes that range in size from [~]4-7 kilobasepairs. Initially identified in mammals, polyomaviruses have now been identified in birds and a few fish species. Although fragmentary polyomavirus-like sequences have been detected as apparent hitchhikers in shotgun genomics datasets for various arthropods, the possible diversity of these viruses in invertebrates remains unclear. In general, polyomaviruses are host-specific, showing strong evidence of host-virus co-evolution. Identification of polyomaviruses in a broader range of animals could shed useful light on the evolutionary history of this medically important group of viruses. Scorpions are predatory arachnids that are among the oldest terrestrial animals. Thus far, viromes of arachnids have been under-sampled and understudied. Here, high-throughput sequencing and traditional molecular techniques were used to explore the diversity of circular DNA viruses associated with bark scorpions (Centruroides sculpturatus) from the greater Phoenix area, Arizona, USA. The complete genomes of eight novel polyomaviruses were identified. Analysis of Centruroides transcriptomic datasets elucidated the splicing of the viral late gene array, which is more complex than that of vertebrate polyomaviruses. Phylogenetic analysis provides further evidence of co-divergence of polyomaviruses with their hosts, suggesting that at least one ancestral species of polyomaviruses was circulating amongst the primitive common ancestors of arthropods and chordates.

microbiology↗