1
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Ferrare JT, Good BH. Evolution of evolvability in rapidly adapting populations. Nat Ecol Evol 2024:10.1038/s41559-024-02527-0. [PMID: 39261599 DOI: 10.1038/s41559-024-02527-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 07/29/2024] [Indexed: 09/13/2024]
Abstract
Mutations can alter the short-term fitness of an organism, as well as the rates and benefits of future mutations. While numerous examples of these evolvability modifiers have been observed in rapidly adapting microbial populations, existing theory struggles to predict when they will be favoured by natural selection. Here we develop a mathematical framework for predicting the fates of genetic variants that modify the rates and benefits of future mutations in linked genomic regions. We derive analytical expressions showing how the fixation probabilities of these variants depend on the size of the population and the diversity of competing mutations. We find that competition between linked mutations can dramatically enhance selection for modifiers that increase the benefits of future mutations, even when they impose a strong direct cost on fitness. However, we also find that modest direct benefits can be sufficient to drive evolutionary dead ends to fixation. Our results suggest that subtle differences in evolvability could play an important role in shaping the long-term success of genetic variants in rapidly evolving microbial populations.
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Affiliation(s)
| | - Benjamin H Good
- Department of Applied Physics, Stanford University, Stanford, CA, USA.
- Department of Biology, Stanford University, Stanford, CA, USA.
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
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2
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Schmidlin K, Apodaca S, Newell D, Sastokas A, Kinsler G, Geiler-Samerotte K. Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs. eLife 2024; 13:RP94144. [PMID: 39255191 PMCID: PMC11386965 DOI: 10.7554/elife.94144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2024] Open
Abstract
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 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.
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Affiliation(s)
- Kara Schmidlin
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, United States
- School of Life Sciences, Arizona State University, Tempe, United States
| | - Sam Apodaca
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, United States
- School of Life Sciences, Arizona State University, Tempe, United States
| | - Daphne Newell
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, United States
- School of Life Sciences, Arizona State University, Tempe, United States
| | - Alexander Sastokas
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, United States
- School of Life Sciences, Arizona State University, Tempe, United States
| | - Grant Kinsler
- Department of Bioengineering, University of Pennsylvania, Philadelphia, United States
| | - Kerry Geiler-Samerotte
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, United States
- School of Life Sciences, Arizona State University, Tempe, United States
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3
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Zimmermann A, Prieto-Vivas JE, Voordeckers K, Bi C, Verstrepen KJ. Mutagenesis techniques for evolutionary engineering of microbes - exploiting CRISPR-Cas, oligonucleotides, recombinases, and polymerases. Trends Microbiol 2024; 32:884-901. [PMID: 38493013 DOI: 10.1016/j.tim.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 02/07/2024] [Accepted: 02/09/2024] [Indexed: 03/18/2024]
Abstract
The natural process of evolutionary adaptation is often exploited as a powerful tool to obtain microbes with desirable traits. For industrial microbes, evolutionary engineering is often used to generate variants that show increased yields or resistance to stressful industrial environments, thus obtaining superior microbial cell factories. However, even in large populations, the natural supply of beneficial mutations is typically low, which implies that obtaining improved microbes is often time-consuming and inefficient. To overcome this limitation, different techniques have been developed that boost mutation rates. While some of these methods simply increase the overall mutation rate across a genome, others use recent developments in DNA synthesis, synthetic biology, and CRISPR-Cas techniques to control the type and location of mutations. This review summarizes the most important recent developments and methods in the field of evolutionary engineering in model microorganisms. It discusses how both in vitro and in vivo approaches can increase the genetic diversity of the host, with a special emphasis on in vivo techniques for the optimization of metabolic pathways for precision fermentation.
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Affiliation(s)
- Anna Zimmermann
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium; CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, 3001, Belgium
| | - Julian E Prieto-Vivas
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium; CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, 3001, Belgium
| | - Karin Voordeckers
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium; CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, 3001, Belgium
| | - Changhao Bi
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China; College of Life Science, Tianjin Normal University, Tianjin, China
| | - Kevin J Verstrepen
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium; CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, 3001, Belgium; VIB-VIB Joint Center of Synthetic Biology, National Center of Technology Innovation for Synthetic Biology, Tianjin, China.
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4
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Abreu CI, Mathur S, Petrov DA. Environmental memory alters the fitness effects of adaptive mutations in fluctuating environments. Nat Ecol Evol 2024; 8:1760-1775. [PMID: 39020024 DOI: 10.1038/s41559-024-02475-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 06/11/2024] [Indexed: 07/19/2024]
Abstract
Evolution in a static laboratory environment often proceeds via large-effect beneficial mutations that may become maladaptive in other environments. Conversely, natural settings require populations to endure environmental fluctuations. A sensible assumption is that the fitness of a lineage in a fluctuating environment is the time average of its fitness over the sequence of static conditions it encounters. However, transitions between conditions may pose entirely new challenges, which could cause deviations from this time average. To test this, we tracked hundreds of thousands of barcoded yeast lineages evolving in static and fluctuating conditions and subsequently isolated 900 mutants for pooled fitness assays in 15 environments. Here we find that fitness in fluctuating environments indeed often deviates from the time average, leading to fitness non-additivity. Moreover, closer examination reveals that fitness in one component of a fluctuating environment is often strongly influenced by the previous component. We show that this environmental memory is especially common for mutants with high variance in fitness across tested environments. We use a simple mathematical model and whole-genome sequencing to propose mechanisms underlying this effect, including lag time evolution and sensing mutations. Our results show that environmental fluctuations impact fitness and suggest that variance in static environments can explain these impacts.
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Affiliation(s)
- Clare I Abreu
- Department of Biology, Stanford University, Stanford, CA, USA.
| | - Shaili Mathur
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Dmitri A Petrov
- Department of Biology, Stanford University, Stanford, CA, USA.
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5
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McGee RS, Kinsler G, Petrov D, Tikhonov M. Improving the Accuracy of Bulk Fitness Assays by Correcting Barcode Processing Biases. Mol Biol Evol 2024; 41:msae152. [PMID: 39041198 PMCID: PMC11316221 DOI: 10.1093/molbev/msae152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 06/28/2024] [Accepted: 07/11/2024] [Indexed: 07/24/2024] Open
Abstract
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 can induce nonuniform amplification or uneven sequencing coverage that causes 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, named REBAR (Removing the Effects of Bias through Analysis of Residuals), for inferring the effects of barcode processing bias by leveraging the structure of systematic deviations in the data. We illustrate this approach by applying it to two independent data sets, and demonstrate that this method estimates and corrects for bias more accurately than standard proxies, such as GC-based corrections. REBAR mitigates bias and improves fitness estimates in high-throughput assays without introducing additional complexity to the experimental protocols, with potential applications in a range of experimental evolution and mutation screening contexts.
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Affiliation(s)
| | - Grant Kinsler
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Dmitri Petrov
- Department of Biology, Stanford University, Palo Alto, CA, USA
| | - Mikhail Tikhonov
- Department of Physics, Washington University, St. Louis, MO, USA
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6
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Ardell S, Martsul A, Johnson MS, Kryazhimskiy S. Environment-independent distribution of mutational effects emerges from microscopic epistasis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.18.567655. [PMID: 38014325 PMCID: PMC10680819 DOI: 10.1101/2023.11.18.567655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Predicting how new mutations alter phenotypes is difficult because mutational effects vary across genotypes and environments. Recently discovered global epistasis, where the fitness effects of mutations scale with the fitness of the background genotype, can improve predictions, but how the environment modulates this scaling is unknown. We measured the fitness effects of ~100 insertion mutations in 42 strains of Saccharomyces cerevisiae in six laboratory environments and found that the global-epistasis scaling is nearly invariant across environments. Instead, the environment tunes one global parameter, the background fitness at which most mutations switch sign. As a consequence, the distribution of mutational effects is predictable across genotypes and environments. Our results suggest that the effective dimensionality of genotype-to-phenotype maps across environments is surprisingly low.
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Affiliation(s)
- Sarah Ardell
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA 92093
| | - Alena Martsul
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA 92093
| | - Milo S. Johnson
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA 94720
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Sergey Kryazhimskiy
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA 92093
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7
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Schmidlin K, Ogbunugafor CB, Geiler-Samerotte K. Environment by environment interactions (ExE) differ across genetic backgrounds (ExExG). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.08.593194. [PMID: 38766025 PMCID: PMC11100745 DOI: 10.1101/2024.05.08.593194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
While the terms "gene-by-gene interaction" (GxG) and "gene-by-environment interaction" (GxE) are commonplace within the fields of quantitative and evolutionary genetics, "environment-by-environment interaction" (ExE) is a term used less often. In this study, we find that environment-by-environment interactions are a meaningful driver of phenotypes, and that they differ across different genotypes (suggestive of ExExG). To reach this conclusion, we analyzed a large dataset of roughly 1,000 mutant yeast strains with varying degrees of resistance to different antifungal drugs. We show that the effectiveness of a drug combination, relative to single drugs, often varies across different drug resistant mutants. 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 better predicts the direction and magnitude of ExE interactions for some mutants. Studying how ExE interactions change across genotypes (ExExG) is not only important when modeling the evolution of pathogenic microbes, but also for broader efforts to understand the cell biology underlying these interactions and to resolve the source of phenotypic variance across populations. The relevance of ExExG interactions have been largely omitted from canon in evolutionary and population genetics, but these fields and others stand to benefit from perspectives that highlight how interactions between external forces craft the complex behavior of living systems.
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Affiliation(s)
- Kara Schmidlin
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ, 85287
- School of Life Sciences, Arizona State University, Tempe AZ, 85287
| | - C. Brandon Ogbunugafor
- Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT,06511
- Santa Fe Institute, Santa Fe, NM, 87501
| | - Kerry Geiler-Samerotte
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ, 85287
- School of Life Sciences, Arizona State University, Tempe AZ, 85287
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8
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Lobel JH, Ingolia NT. Precise measurement of molecular phenotypes with barcode-based CRISPRi systems. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.21.600132. [PMID: 38948701 PMCID: PMC11213135 DOI: 10.1101/2024.06.21.600132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Genome-wide CRISPR-Cas9 screens have untangled regulatory networks and revealed the genetic underpinnings of diverse biological processes. Their success relies on experimental designs that interrogate specific molecular phenotypes and distinguish key regulators from background effects. Here, we realize these goals with a generalizable platform for CRISPR interference with barcoded expression reporter sequencing (CiBER-seq) that dramatically improves the sensitivity and scope of genome-wide screens. We systematically address technical factors that distort phenotypic measurements by normalizing expression reporters against closely-matched control promoters, integrated together into the genome at single copy. To test our ability to capture post-transcriptional and post-translational regulation through sequencing, we screened for genes that affected nonsense-mediated mRNA decay and Doa10-mediated cytosolic protein decay. Our optimized CiBER-seq screens accurately capture the known components of well-studied RNA and protein quality control pathways with minimal background. These results demonstrate the precision and versatility of CiBER-seq for dissecting the genetic networks controlling cellular behaviors.
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Affiliation(s)
- Joseph H. Lobel
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Nicholas T. Ingolia
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- Lead contact
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9
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Schmidlin, Apodaca, Newell, Sastokas, Kinsler, Geiler-Samerotte. Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.17.562616. [PMID: 37905147 PMCID: PMC10614906 DOI: 10.1101/2023.10.17.562616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
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.
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Affiliation(s)
- Schmidlin
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ
- School of Life Sciences, Arizona State University, Tempe AZ
| | - Apodaca
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ
- School of Life Sciences, Arizona State University, Tempe AZ
| | - Newell
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ
- School of Life Sciences, Arizona State University, Tempe AZ
| | - Sastokas
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ
- School of Life Sciences, Arizona State University, Tempe AZ
| | - Kinsler
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
| | - Geiler-Samerotte
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ
- School of Life Sciences, Arizona State University, Tempe AZ
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10
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Li W, Miller D, Liu X, Tosi L, Chkaiban L, Mei H, Hung PH, Parekkadan B, Sherlock G, Levy S. Arrayed in vivo barcoding for multiplexed sequence verification of plasmid DNA and demultiplexing of pooled libraries. Nucleic Acids Res 2024; 52:e47. [PMID: 38709890 PMCID: PMC11162764 DOI: 10.1093/nar/gkae332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/23/2024] [Accepted: 04/16/2024] [Indexed: 05/08/2024] Open
Abstract
Sequence verification of plasmid DNA is critical for many cloning and molecular biology workflows. To leverage high-throughput sequencing, several methods have been developed that add a unique DNA barcode to individual samples prior to pooling and sequencing. However, these methods require an individual plasmid extraction and/or in vitro barcoding reaction for each sample processed, limiting throughput and adding cost. Here, we develop an arrayed in vivo plasmid barcoding platform that enables pooled plasmid extraction and library preparation for Oxford Nanopore sequencing. This method has a high accuracy and recovery rate, and greatly increases throughput and reduces cost relative to other plasmid barcoding methods or Sanger sequencing. We use in vivo barcoding to sequence verify >45 000 plasmids and show that the method can be used to transform error-containing dispersed plasmid pools into sequence-perfect arrays or well-balanced pools. In vivo barcoding does not require any specialized equipment beyond a low-overhead Oxford Nanopore sequencer, enabling most labs to flexibly process hundreds to thousands of plasmids in parallel.
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Affiliation(s)
- Weiyi Li
- SLAC National Accelerator Laboratory, Stanford University, Stanford, CA, USA
| | - Darach Miller
- SLAC National Accelerator Laboratory, Stanford University, Stanford, CA, USA
| | - Xianan Liu
- SLAC National Accelerator Laboratory, Stanford University, Stanford, CA, USA
| | - Lorenzo Tosi
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA
| | - Lamia Chkaiban
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA
| | - Han Mei
- SLAC National Accelerator Laboratory, Stanford University, Stanford, CA, USA
| | - Po-Hsiang Hung
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Biju Parekkadan
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA
| | - Gavin Sherlock
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Sasha F Levy
- SLAC National Accelerator Laboratory, Stanford University, Stanford, CA, USA
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11
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Plavskin Y, de Biase MS, Ziv N, Janská L, Zhu YO, Hall DW, Schwarz RF, Tranchina D, Siegal ML. Spontaneous single-nucleotide substitutions and microsatellite mutations have distinct distributions of fitness effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.04.547687. [PMID: 37461506 PMCID: PMC10349969 DOI: 10.1101/2023.07.04.547687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
The fitness effects of new mutations determine key properties of evolutionary processes. Beneficial mutations drive evolution, yet selection is also shaped by the frequency of small-effect deleterious mutations, whose combined effect can burden otherwise adaptive lineages and alter evolutionary trajectories and outcomes in clonally evolving organisms such as viruses, microbes, and tumors. The small effect sizes of these important mutations have made accurate measurements of their rates difficult. In microbes, assessing the effect of mutations on growth can be especially instructive, as this complex phenotype is closely linked to fitness in clonally evolving organisms. Here, we perform high-throughput time-lapse microscopy on cells from mutation-accumulation strains to precisely infer the distribution of mutational effects on growth rate in the budding yeast, Saccharomyces cerevisiae. We show that mutational effects on growth rate are overwhelmingly negative, highly skewed towards very small effect sizes, and frequent enough to suggest that deleterious hitchhikers may impose a significant burden on evolving lineages. By using lines that accumulated mutations in either wild-type or slippage repair-defective backgrounds, we further disentangle the effects of two common types of mutations, single-nucleotide substitutions and simple sequence repeat indels, and show that they have distinct effects on yeast growth rate. Although the average effect of a simple sequence repeat mutation is very small (~0.3%), many do alter growth rate, implying that this class of frequent mutations has an important evolutionary impact.
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12
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Bao K, Strayer BR, Braker NP, Chan AA, Sharp NP. Mutations in yeast are deleterious on average regardless of the degree of adaptation to the testing environment. Proc Biol Sci 2024; 291:20240064. [PMID: 38889780 DOI: 10.1098/rspb.2024.0064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 04/29/2024] [Indexed: 06/20/2024] Open
Abstract
The role of spontaneous mutations in evolution depends on the distribution of their effects on fitness. Despite a general consensus that new mutations are deleterious on average, a handful of mutation accumulation experiments in diverse organisms instead suggest that beneficial and deleterious mutations can have comparable fitness impacts, i.e. the product of their respective rates and effects can be roughly equal. We currently lack a general framework for predicting when such a pattern will occur. One idea is that beneficial mutations will be more evident in genotypes that are not well adapted to the testing environment. We tested this prediction experimentally in the laboratory yeast Saccharomyces cerevisiae by allowing nine replicate populations to adapt to novel environments with complex sets of stressors. After >1000 asexual generations interspersed with 41 rounds of sexual reproduction, we assessed the mean effect of induced mutations on yeast growth in both the environment to which they had been adapting and the alternative novel environment. The mutations were deleterious on average, with the severity depending on the testing environment. However, we found no evidence that the adaptive match between genotype and environment is predictive of mutational fitness effects.
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Affiliation(s)
- Kevin Bao
- Department of Genetics, University of Wisconsin-Madison 425-G Henry Mall, Madison, Wisconsin 53706, USA
| | - Brant R Strayer
- Department of Genetics, University of Wisconsin-Madison 425-G Henry Mall, Madison, Wisconsin 53706, USA
| | - Neil P Braker
- Department of Genetics, University of Wisconsin-Madison 425-G Henry Mall, Madison, Wisconsin 53706, USA
| | - Alexandra A Chan
- Department of Genetics, University of Wisconsin-Madison 425-G Henry Mall, Madison, Wisconsin 53706, USA
| | - Nathaniel P Sharp
- Department of Genetics, University of Wisconsin-Madison 425-G Henry Mall, Madison, Wisconsin 53706, USA
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13
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Tawfeeq MT, Voordeckers K, van den Berg P, Govers SK, Michiels J, Verstrepen KJ. Mutational robustness and the role of buffer genes in evolvability. EMBO J 2024; 43:2294-2307. [PMID: 38719995 PMCID: PMC11183146 DOI: 10.1038/s44318-024-00109-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 03/19/2024] [Accepted: 04/17/2024] [Indexed: 06/19/2024] Open
Abstract
Organisms rely on mutations to fuel adaptive evolution. However, many mutations impose a negative effect on fitness. Cells may have therefore evolved mechanisms that affect the phenotypic effects of mutations, thus conferring mutational robustness. Specifically, so-called buffer genes are hypothesized to interact directly or indirectly with genetic variation and reduce its effect on fitness. Environmental or genetic perturbations can change the interaction between buffer genes and genetic variation, thereby unmasking the genetic variation's phenotypic effects and thus providing a source of variation for natural selection to act on. This review provides an overview of our understanding of mutational robustness and buffer genes, with the chaperone gene HSP90 as a key example. It discusses whether buffer genes merely affect standing variation or also interact with de novo mutations, how mutational robustness could influence evolution, and whether mutational robustness might be an evolved trait or rather a mere side-effect of complex genetic interactions.
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Affiliation(s)
- Mohammed T Tawfeeq
- VIB-KU Leuven Center for Microbiology, Leuven, Belgium
- Department of Microbial and Molecular Systems, KU Leuven, Leuven, Belgium
| | - Karin Voordeckers
- VIB-KU Leuven Center for Microbiology, Leuven, Belgium
- Department of Microbial and Molecular Systems, KU Leuven, Leuven, Belgium
| | - Pieter van den Berg
- Department of Microbial and Molecular Systems, KU Leuven, Leuven, Belgium
- Department of Biology, KU Leuven, Leuven, Belgium
| | | | - Jan Michiels
- VIB-KU Leuven Center for Microbiology, Leuven, Belgium
- Department of Microbial and Molecular Systems, KU Leuven, Leuven, Belgium
| | - Kevin J Verstrepen
- VIB-KU Leuven Center for Microbiology, Leuven, Belgium.
- Department of Microbial and Molecular Systems, KU Leuven, Leuven, Belgium.
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14
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Li XC, Gandara L, Ekelöf M, Richter K, Alexandrov T, Crocker J. Rapid response of fly populations to gene dosage across development and generations. Nat Commun 2024; 15:4551. [PMID: 38811562 PMCID: PMC11137061 DOI: 10.1038/s41467-024-48960-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 05/17/2024] [Indexed: 05/31/2024] Open
Abstract
Although the effects of genetic and environmental perturbations on multicellular organisms are rarely restricted to single phenotypic layers, our current understanding of how developmental programs react to these challenges remains limited. Here, we have examined the phenotypic consequences of disturbing the bicoid regulatory network in early Drosophila embryos. We generated flies with two extra copies of bicoid, which causes a posterior shift of the network's regulatory outputs and a decrease in fitness. We subjected these flies to EMS mutagenesis, followed by experimental evolution. After only 8-15 generations, experimental populations have normalized patterns of gene expression and increased survival. Using a phenomics approach, we find that populations were normalized through rapid increases in embryo size driven by maternal changes in metabolism and ovariole development. We extend our results to additional populations of flies, demonstrating predictability. Together, our results necessitate a broader view of regulatory network evolution at the systems level.
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Affiliation(s)
- Xueying C Li
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
- College of Life Sciences, Beijing Normal University, Beijing, China.
| | - Lautaro Gandara
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Måns Ekelöf
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Kerstin Richter
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Theodore Alexandrov
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- Molecular Medicine Partnership Unit between EMBL and Heidelberg University, Heidelberg, Germany
- BioInnovation Institute, Copenhagen, Denmark
| | - Justin Crocker
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
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15
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Hale JJ, Matsui T, Goldstein I, Mullis MN, Roy KR, Ville CN, Miller D, Wang C, Reynolds T, Steinmetz LM, Levy SF, Ehrenreich IM. Genome-scale analysis of interactions between genetic perturbations and natural variation. Nat Commun 2024; 15:4234. [PMID: 38762544 PMCID: PMC11102447 DOI: 10.1038/s41467-024-48626-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 04/30/2024] [Indexed: 05/20/2024] Open
Abstract
Interactions between genetic perturbations and segregating loci can cause perturbations to show different phenotypic effects across genetically distinct individuals. To study these interactions on a genome scale in many individuals, we used combinatorial DNA barcode sequencing to measure the fitness effects of 8046 CRISPRi perturbations targeting 1721 distinct genes in 169 yeast cross progeny (or segregants). We identified 460 genes whose perturbation has different effects across segregants. Several factors caused perturbations to show variable effects, including baseline segregant fitness, the mean effect of a perturbation across segregants, and interacting loci. We mapped 234 interacting loci and found four hub loci that interact with many different perturbations. Perturbations that interact with a given hub exhibit similar epistatic relationships with the hub and show enrichment for cellular processes that may mediate these interactions. These results suggest that an individual's response to perturbations is shaped by a network of perturbation-locus interactions that cannot be measured by approaches that examine perturbations or natural variation alone.
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Affiliation(s)
- Joseph J Hale
- Department of Biological Sciences, Molecular and Computational Biology Section, University of Southern California, Los Angeles, CA, 90089, USA
| | - Takeshi Matsui
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
| | - Ilan Goldstein
- Department of Biological Sciences, Molecular and Computational Biology Section, University of Southern California, Los Angeles, CA, 90089, USA
| | - Martin N Mullis
- Department of Biological Sciences, Molecular and Computational Biology Section, University of Southern California, Los Angeles, CA, 90089, USA
| | - Kevin R Roy
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Christopher Ne Ville
- Department of Biological Sciences, Molecular and Computational Biology Section, University of Southern California, Los Angeles, CA, 90089, USA
| | - Darach Miller
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
| | - Charley Wang
- Department of Biological Sciences, Molecular and Computational Biology Section, University of Southern California, Los Angeles, CA, 90089, USA
| | - Trevor Reynolds
- Department of Biological Sciences, Molecular and Computational Biology Section, University of Southern California, Los Angeles, CA, 90089, USA
| | - Lars M Steinmetz
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Sasha F Levy
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.
- BacStitch DNA, Los Altos, CA, USA.
| | - Ian M Ehrenreich
- Department of Biological Sciences, Molecular and Computational Biology Section, University of Southern California, Los Angeles, CA, 90089, USA.
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16
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García-Pintos LP. Limits on the evolutionary rates of biological traits. Sci Rep 2024; 14:11314. [PMID: 38760507 PMCID: PMC11101453 DOI: 10.1038/s41598-024-61872-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 05/10/2024] [Indexed: 05/19/2024] Open
Abstract
This paper focuses on the maximum speed at which biological evolution can occur. I derive inequalities that limit the rate of evolutionary processes driven by natural selection, mutations, or genetic drift. These rate limits link the variability in a population to evolutionary rates. In particular, high variances in the fitness of a population and of a quantitative trait allow for fast changes in the trait's average. In contrast, low variability makes a trait less susceptible to random changes due to genetic drift. The results in this article generalize Fisher's fundamental theorem of natural selection to dynamics that allow for mutations and genetic drift, via trade-off relations that constrain the evolutionary rates of arbitrary traits. The rate limits can be used to probe questions in various evolutionary biology and ecology settings. They apply, for instance, to trait dynamics within or across species or to the evolution of bacteria strains. They apply to any quantitative trait, e.g., from species' weights to the lengths of DNA strands.
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Affiliation(s)
- Luis Pedro García-Pintos
- Theoretical Division (T4), Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
- Joint Center for Quantum Information and Computer Science and Joint Quantum Institute, NIST/University of Maryland, College Park, MD, 20742, USA.
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17
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Ordon J, Thouin J, Nakano RT, Ma KW, Zhang P, Huettel B, Garrido-Oter R, Schulze-Lefert P. Chromosomal barcodes for simultaneous tracking of near-isogenic bacterial strains in plant microbiota. Nat Microbiol 2024; 9:1117-1129. [PMID: 38503974 PMCID: PMC10994850 DOI: 10.1038/s41564-024-01619-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 01/22/2024] [Indexed: 03/21/2024]
Abstract
DNA-amplicon-based microbiota profiling can estimate species diversity and abundance but cannot resolve genetic differences within individuals of the same species. Here we report the development of modular bacterial tags (MoBacTags) encoding DNA barcodes that enable tracking of near-isogenic bacterial commensals in an array of complex microbiome communities. Chromosomally integrated DNA barcodes are then co-amplified with endogenous marker genes of the community by integrating corresponding primer binding sites into the barcode. We use this approach to assess the contributions of individual bacterial genes to Arabidopsis thaliana root microbiota establishment with synthetic communities that include MoBacTag-labelled strains of Pseudomonas capeferrum. Results show reduced root colonization for certain mutant strains with defects in gluconic-acid-mediated host immunosuppression, which would not be detected with traditional amplicon sequencing. Our work illustrates how MoBacTags can be applied to assess scaling of individual bacterial genetic determinants in the plant microbiota.
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Affiliation(s)
- Jana Ordon
- Department of Plant-Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany
- Institute of Plant Molecular Biology, University of Zurich, Zurich, Switzerland
| | - Julien Thouin
- Department of Plant-Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Ryohei Thomas Nakano
- Department of Plant-Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany
- Department of Biological Sciences, Faculty of Science, Hokkaido University, Sapporo, Japan
| | - Ka-Wai Ma
- Department of Plant-Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany
- Institute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan
| | - Pengfan Zhang
- Department of Plant-Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany
- Innovative Genomics Institute (IGI), University of California, Berkeley, CA, USA
| | - Bruno Huettel
- Department of Plant-Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Ruben Garrido-Oter
- Department of Plant-Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany
- Cluster of Excellence on Plant Sciences (CEPLAS), Max Planck Institute for Plant Breeding Research, Cologne, Germany
- Earlham Institute, Norwich, UK
| | - Paul Schulze-Lefert
- Department of Plant-Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany.
- Cluster of Excellence on Plant Sciences (CEPLAS), Max Planck Institute for Plant Breeding Research, Cologne, Germany.
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18
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Brettner L, Eder R, Schmidlin K, Geiler-Samerotte K. An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts. Yeast 2024; 41:242-255. [PMID: 38282330 PMCID: PMC11146634 DOI: 10.1002/yea.3927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 01/04/2024] [Accepted: 01/16/2024] [Indexed: 01/30/2024] Open
Abstract
Yeasts are naturally diverse, genetically tractable, and easy to grow such that researchers can investigate any number of genotypes, environments, or interactions thereof. However, studies of yeast transcriptomes have been limited by the processing capabilities of traditional RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform, SPLiT-seq (Split Pool Ligation-based Transcriptome sequencing), for yeasts and apply it to 43,388 cells of multiple species and ploidies. 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 SPLiT-seq uses 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 perturbations 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 detect transcriptionally distinct cell states related to cell cycle, ploidy, metabolic strategies, and so forth, 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.
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Affiliation(s)
- Leandra Brettner
- Biodesign Institute Center for Mechanisms of Evolution, Arizona State University, Tempe, Arizona, USA
| | - Rachel Eder
- Biodesign Institute Center for Mechanisms of Evolution, Arizona State University, Tempe, Arizona, USA
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Kara Schmidlin
- Biodesign Institute Center for Mechanisms of Evolution, Arizona State University, Tempe, Arizona, USA
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
| | - Kerry Geiler-Samerotte
- Biodesign Institute Center for Mechanisms of Evolution, Arizona State University, Tempe, Arizona, USA
- School of Life Sciences, Arizona State University, Tempe, Arizona, USA
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19
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Yu Q, Ascensao JA, Okada T, Boyd O, Volz E, Hallatschek O. Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England. PLoS Pathog 2024; 20:e1012090. [PMID: 38620033 PMCID: PMC11045146 DOI: 10.1371/journal.ppat.1012090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 04/25/2024] [Accepted: 03/03/2024] [Indexed: 04/17/2024] Open
Abstract
Genetic drift in infectious disease transmission results from randomness of transmission and host recovery or death. The strength of genetic drift for SARS-CoV-2 transmission is expected to be high due to high levels of superspreading, and this is expected to substantially impact disease epidemiology and evolution. However, we don't yet have an understanding of how genetic drift changes over time or across locations. Furthermore, noise that results from data collection can potentially confound estimates of genetic drift. To address this challenge, we develop and validate a method to jointly infer genetic drift and measurement noise from time-series lineage frequency data. Our method is highly scalable to increasingly large genomic datasets, which overcomes a limitation in commonly used phylogenetic methods. We apply this method to over 490,000 SARS-CoV-2 genomic sequences from England collected between March 2020 and December 2021 by the COVID-19 Genomics UK (COG-UK) consortium and separately infer the strength of genetic drift for pre-B.1.177, B.1.177, Alpha, and Delta. We find that even after correcting for measurement noise, the strength of genetic drift is consistently, throughout time, higher than that expected from the observed number of COVID-19 positive individuals in England by 1 to 3 orders of magnitude, which cannot be explained by literature values of superspreading. Our estimates of genetic drift suggest low and time-varying establishment probabilities for new mutations, inform the parametrization of SARS-CoV-2 evolutionary models, and motivate future studies of the potential mechanisms for increased stochasticity in this system.
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Affiliation(s)
- QinQin Yu
- Department of Physics, University of California, Berkeley, California, United States of America
| | - Joao A. Ascensao
- Department of Bioengineering, University of California, Berkeley, California, United States of America
| | - Takashi Okada
- Department of Physics, University of California, Berkeley, California, United States of America
- Department of Integrative Biology, University of California, Berkeley, California, United States of America
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan
- RIKEN iTHEMS, Wako, Saitama, Japan
| | | | - Olivia Boyd
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Oskar Hallatschek
- Department of Physics, University of California, Berkeley, California, United States of America
- Department of Integrative Biology, University of California, Berkeley, California, United States of America
- Peter Debye Institute for Soft Matter Physics, Leipzig University, Leipzig, Germany
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20
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Mulla Y, Bollenbach T. Invade to evade: E. coli's gutsy survival strategies. Cell Host Microbe 2024; 32:300-301. [PMID: 38484709 DOI: 10.1016/j.chom.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 02/09/2024] [Accepted: 02/09/2024] [Indexed: 03/19/2024]
Abstract
Antibiotic resistance is often studied in vitro, limiting the understanding of in vivo mechanisms that affect antibiotic treatment. In this issue of Cell Host & Microbe, Rodrigues et al. show that specific mutations allow bacteria to invade intestinal cells in a mouse model, thereby evading antibiotic treatment.
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Affiliation(s)
- Yuval Mulla
- Institute for Biological Physics, University of Cologne, 50937 Cologne, Germany; Molecular Microbiology Section, Amsterdam Institute for Life and Environment (A-Life), Vrije Universiteit, 1081BT Amsterdam, The Netherlands
| | - Tobias Bollenbach
- Institute for Biological Physics, University of Cologne, 50937 Cologne, Germany; Center for Data and Simulation Science, University of Cologne, 50931 Cologne, Germany.
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21
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Razo-Mejia M, Mani M, Petrov D. Bayesian inference of relative fitness on high-throughput pooled competition assays. PLoS Comput Biol 2024; 20:e1011937. [PMID: 38489348 PMCID: PMC10971673 DOI: 10.1371/journal.pcbi.1011937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 03/27/2024] [Accepted: 02/21/2024] [Indexed: 03/17/2024] Open
Abstract
The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.
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Affiliation(s)
- Manuel Razo-Mejia
- Department of Biology, Stanford University, Stanford, California, United States of America
| | - Madhav Mani
- NSF-Simons Center for Quantitative Biology, Northwestern University, Chicago, Illinois, United States of America
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Chicago, Illinois, United States of America
| | - Dmitri Petrov
- Department of Biology, Stanford University, Stanford, California, United States of America
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, United States of America
- Chan Zuckerberg Biohub, San Francisco, California, United States of America
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22
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Park K, Bae Y. Operator model for evolutionary dynamics. Biosystems 2024; 237:105130. [PMID: 38309419 DOI: 10.1016/j.biosystems.2024.105130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/24/2024] [Accepted: 01/25/2024] [Indexed: 02/05/2024]
Abstract
Drift, selection, and mutation are integral evolutionary factors. In this article, operator model is newly suggested to intuitively represent those evolutionary factors into mathematical operators, and to ultimately offer unconventional methodology for understanding evolutionary dynamics. To be specific, each of the drift, selection, and mutation was respectively interpreted as operator which in essence is a random matrix that acts upon the vector which contains population distribution information. The simulation results from the operator model coincided with the previous theoretical results for beneficial mutation accumulation rate in concurrent and successional regimes for asexually reproducing case. Furthermore, beneficial mutation accumulation in strong drift regime for asexually reproducing case was observed from the simulation while allowing the interactions of mutations with diverse selection coefficients. Lastly, methods to justify, reinforce, apply, and expand the operator model were discussed to scrutinize the implications of the model. With the operator model's unique characteristics, the model is expected to broaden perspective and to offer effective methodology for understanding the evolutionary process.
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Affiliation(s)
- Kangbien Park
- Department of Physics, College of Natural Science, Yonsei University, Seoul, 03722, Republic of Korea.
| | - Yonghee Bae
- Department of Physics, College of Natural Science, Yonsei University, Seoul, 03722, Republic of Korea
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23
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Ascensao JA, Denk J, Lok K, Yu Q, Wetmore KM, Hallatschek O. Rediversification following ecotype isolation reveals hidden adaptive potential. Curr Biol 2024; 34:855-867.e6. [PMID: 38325377 PMCID: PMC10911448 DOI: 10.1016/j.cub.2024.01.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 11/09/2023] [Accepted: 01/10/2024] [Indexed: 02/09/2024]
Abstract
Microbial communities play a critical role in ecological processes, and their diversity is key to their functioning. However, little is known about whether communities can regenerate ecological diversity following ecotype removal or extinction and how the rediversified communities would compare to the original ones. Here, we show that simple two-ecotype communities from the E. coli long-term evolution experiment (LTEE) consistently rediversified into two ecotypes following the isolation of one of the ecotypes, coexisting via negative frequency-dependent selection. Communities separated by more than 30,000 generations of evolutionary time rediversify in similar ways. The rediversified ecotype appears to share a number of growth traits with the ecotype it replaces. However, the rediversified community is also different from the original community in ways relevant to the mechanism of ecotype coexistence-for example, in stationary phase response and survival. We found substantial variation in the transcriptional states between the two original ecotypes, whereas the differences within the rediversified community were comparatively smaller, although the rediversified community showed unique patterns of differential expression. Our results suggest that evolution may leave room for alternative diversification processes even in a maximally reduced community of only two strains. We hypothesize that the presence of alternative evolutionary pathways may be even more pronounced in communities of many species where there are even more potential niches, highlighting an important role for perturbations, such as species removal, in evolving ecological communities.
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Affiliation(s)
- Joao A Ascensao
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA
| | - Jonas Denk
- Department of Physics, University of California Berkeley Berkeley, CA, USA
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
| | - Kristen Lok
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA
- Present affiliation: Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - QinQin Yu
- Department of Physics, University of California Berkeley Berkeley, CA, USA
- Present affiliation: Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
| | - Kelly M Wetmore
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA
| | - Oskar Hallatschek
- Department of Physics, University of California Berkeley Berkeley, CA, USA
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103 Leipzig, Germany
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24
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Wong DPGH, Good BH. Quantifying the adaptive landscape of commensal gut bacteria using high-resolution lineage tracking. Nat Commun 2024; 15:1605. [PMID: 38383538 PMCID: PMC10881964 DOI: 10.1038/s41467-024-45792-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 02/05/2024] [Indexed: 02/23/2024] Open
Abstract
Gut microbiota can adapt to their host environment by rapidly acquiring new mutations. However, the dynamics of this process are difficult to characterize in dominant gut species in their complex in vivo environment. Here we show that the fine-scale dynamics of genome-wide transposon libraries can enable quantitative inferences of these in vivo evolutionary forces. By analyzing >400,000 lineages across four human Bacteroides strains in gnotobiotic mice, we observed positive selection on thousands of cryptic variants - most of which were unrelated to their original gene knockouts. The spectrum of fitness benefits varied between species, and displayed diverse tradeoffs over time and in different dietary conditions, enabling inferences of their underlying function. These results suggest that within-host adaptations arise from an intense competition between numerous contending variants, which can strongly influence their emergent evolutionary tradeoffs.
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Affiliation(s)
- Daniel P G H Wong
- Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA
| | - Benjamin H Good
- Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA.
- Department of Biology, Stanford University, Stanford, CA, 94305, USA.
- Chan Zuckerberg Biohub-San Francisco, San Francisco, CA, 94158, USA.
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25
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Melissa MJ, Desai MM. A dynamical limit to evolutionary adaptation. Proc Natl Acad Sci U S A 2024; 121:e2312845121. [PMID: 38241432 PMCID: PMC10823227 DOI: 10.1073/pnas.2312845121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 12/06/2023] [Indexed: 01/21/2024] Open
Abstract
Natural selection makes evolutionary adaptation possible even if the overwhelming majority of new mutations are deleterious. However, in rapidly evolving populations where numerous linked mutations occur and segregate simultaneously, clonal interference and genetic hitchhiking can limit the efficiency of selection, allowing deleterious mutations to accumulate over time. This can in principle overwhelm the fitness increases provided by beneficial mutations, leading to an overall fitness decline. Here, we analyze the conditions under which evolution will tend to drive populations to higher versus lower fitness. Our analysis focuses on quantifying the boundary between these two regimes, as a function of parameters such as population size, mutation rates, and selection pressures. This boundary represents a state in which adaptation is precisely balanced by Muller's ratchet, and we show that it can be characterized by rapid molecular evolution without any net fitness change. Finally, we consider the implications of global fitness-mediated epistasis and find that under some circumstances, this can drive populations toward the boundary state, which can thus represent a long-term evolutionary attractor.
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Affiliation(s)
- Matthew J. Melissa
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA02138
- Department of Physics, Harvard University, Cambridge, MA02138
- Quantitative Biology Initiative, Harvard University, Cambridge, MA02138
- National Science Foundation (NSF)-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, MA02138
| | - Michael M. Desai
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA02138
- Department of Physics, Harvard University, Cambridge, MA02138
- Quantitative Biology Initiative, Harvard University, Cambridge, MA02138
- National Science Foundation (NSF)-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, MA02138
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26
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Hale JJ, Matsui T, Goldstein I, Mullis MN, Roy KR, Ville CN, Miller D, Wang C, Reynolds T, Steinmetz LM, Levy SF, Ehrenreich IM. Genome-scale analysis of interactions between genetic perturbations and natural variation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.06.539663. [PMID: 38293072 PMCID: PMC10827069 DOI: 10.1101/2023.05.06.539663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Interactions between genetic perturbations and segregating loci can cause perturbations to show different phenotypic effects across genetically distinct individuals. To study these interactions on a genome scale in many individuals, we used combinatorial DNA barcode sequencing to measure the fitness effects of 7,700 CRISPRi perturbations targeting 1,712 distinct genes in 169 yeast cross progeny (or segregants). We identified 460 genes whose perturbation has different effects across segregants. Several factors caused perturbations to show variable effects, including baseline segregant fitness, the mean effect of a perturbation across segregants, and interacting loci. We mapped 234 interacting loci and found four hub loci that interact with many different perturbations. Perturbations that interact with a given hub exhibit similar epistatic relationships with the hub and show enrichment for cellular processes that may mediate these interactions. These results suggest that an individual's response to perturbations is shaped by a network of perturbation-locus interactions that cannot be measured by approaches that examine perturbations or natural variation alone.
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Affiliation(s)
- Joseph J. Hale
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Takeshi Matsui
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
| | - Ilan Goldstein
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Martin N. Mullis
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Kevin R. Roy
- Stanford Genome Technology Center, Stanford University, Palo Alto, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Chris Ne Ville
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Darach Miller
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
| | - Charley Wang
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Trevor Reynolds
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Lars M. Steinmetz
- Stanford Genome Technology Center, Stanford University, Palo Alto, California, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Sasha F. Levy
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
- Present address: BacStitch DNA, Los Altos, California, USA
| | - Ian M. Ehrenreich
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
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27
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Jiang G, Zhang Y, Chen M, Ramoneda J, Han L, Shi Y, Peyraud R, Wang Y, Shi X, Chen X, Ding W, Jousset A, Hikichi Y, Ohnishi K, Zhao FJ, Xu Y, Shen Q, Dini-Andreote F, Zhang Y, Wei Z. Effects of plant tissue permeability on invasion and population bottlenecks of a phytopathogen. Nat Commun 2024; 15:62. [PMID: 38167266 PMCID: PMC10762237 DOI: 10.1038/s41467-023-44234-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
Pathogen genetic diversity varies in response to environmental changes. However, it remains unclear whether plant barriers to invasion could be considered a genetic bottleneck for phytopathogen populations. Here, we implement a barcoding approach to generate a pool of 90 isogenic and individually barcoded Ralstonia solanacearum strains. We used 90 of these strains to inoculate tomato plants with different degrees of physical permeability to invasion (intact roots, wounded roots and xylem inoculation) and quantify the phytopathogen population dynamics during invasion. Our results reveal that the permeability of plant roots impacts the degree of population bottleneck, genetic diversity, and composition of Ralstonia populations. We also find that selection is the main driver structuring pathogen populations when barriers to infection are less permeable, i.e., intact roots, the removal of root physical and immune barriers results in the predominance of stochasticity in population assembly. Taken together, our study suggests that plant root permeability constitutes a bottleneck for phytopathogen invasion and genetic diversity.
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Affiliation(s)
- Gaofei Jiang
- College of Resources and Environment, College of Plant Protection, Interdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin, Southwest University, Chongqing, China
- Key Laboratory of Plant Immunity, Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China
| | - Yuling Zhang
- Key Laboratory of Plant Immunity, Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China
| | - Min Chen
- College of Environmental Science and Engineering, Shaanxi University of Science & Technology, Xi'an, China
| | - Josep Ramoneda
- Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
| | - Liangliang Han
- Department of Biomedical Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Yu Shi
- State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng, Henan, China
| | - Rémi Peyraud
- iMEAN, Ramonville Saint Agne, Occitanie, FR, France
| | - Yikui Wang
- Vegetable Research Institute, Guangxi Academy of Agricultural Science, Nanning, China
| | - Xiaojun Shi
- College of Resources and Environment, College of Plant Protection, Interdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin, Southwest University, Chongqing, China
| | - Xinping Chen
- College of Resources and Environment, College of Plant Protection, Interdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin, Southwest University, Chongqing, China
| | - Wei Ding
- College of Resources and Environment, College of Plant Protection, Interdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin, Southwest University, Chongqing, China
| | - Alexandre Jousset
- Key Laboratory of Plant Immunity, Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China
| | - Yasufumi Hikichi
- Faculty of Agriculture and Marine Science, Kochi University, Nankoku, Japan
| | - Kouhei Ohnishi
- Faculty of Agriculture and Marine Science, Kochi University, Nankoku, Japan
| | - Fang-Jie Zhao
- Key Laboratory of Plant Immunity, Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China
| | - Yangchun Xu
- Key Laboratory of Plant Immunity, Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China
| | - Qirong Shen
- Key Laboratory of Plant Immunity, Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China
| | - Francisco Dini-Andreote
- Department of Plant Science & Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
- The One Health Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Yong Zhang
- College of Resources and Environment, College of Plant Protection, Interdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin, Southwest University, Chongqing, China.
- College of Environmental Science and Engineering, Shaanxi University of Science & Technology, Xi'an, China.
| | - Zhong Wei
- Key Laboratory of Plant Immunity, Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing, China.
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28
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MacGillivray KA, Ng SL, Wiesenfeld S, Guest RL, Jubery T, Silhavy TJ, Ratcliff WC, Hammer BK. Trade-offs constrain adaptive pathways to the type VI secretion system survival. iScience 2023; 26:108332. [PMID: 38025790 PMCID: PMC10679819 DOI: 10.1016/j.isci.2023.108332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 08/25/2023] [Accepted: 10/22/2023] [Indexed: 12/01/2023] Open
Abstract
The Type VI Secretion System (T6SS) is a nano-harpoon used by many bacteria to inject toxins into neighboring cells. While much is understood about mechanisms of T6SS-mediated toxicity, less is known about the ways that competitors can defend themselves against this attack, especially in the absence of their own T6SS. Here we subjected eight replicate populations of Escherichia coli to T6SS attack by Vibrio cholerae. Over ∼500 generations of competition, isolates of the E. coli populations evolved to survive T6SS attack an average of 27-fold better, through two convergently evolved pathways: apaH was mutated in six of the eight replicate populations, while the other two populations each had mutations in both yejM and yjeP. However, the mutations we identified are pleiotropic, reducing cellular growth rates, and increasing susceptibility to antibiotics and elevated pH. These trade-offs help us understand how the T6SS shapes the evolution of bacterial interactions.
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Affiliation(s)
- Kathryn A. MacGillivray
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering & Bioscience, Georgia Institute of Technology, Atlanta, GA, USA
- Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA, USA
| | - Siu Lung Ng
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering & Bioscience, Georgia Institute of Technology, Atlanta, GA, USA
- Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA, USA
| | - Sophia Wiesenfeld
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering & Bioscience, Georgia Institute of Technology, Atlanta, GA, USA
- Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA, USA
| | - Randi L. Guest
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Tahrima Jubery
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering & Bioscience, Georgia Institute of Technology, Atlanta, GA, USA
- Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA, USA
| | - Thomas J. Silhavy
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - William C. Ratcliff
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering & Bioscience, Georgia Institute of Technology, Atlanta, GA, USA
- Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA, USA
| | - Brian K. Hammer
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering & Bioscience, Georgia Institute of Technology, Atlanta, GA, USA
- Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA, USA
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29
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Li J, Stenberg S, Yue JX, Mikhalev E, Thompson D, Warringer J, Liti G. Genome instability footprint under rapamycin and hydroxyurea treatments. PLoS Genet 2023; 19:e1011012. [PMID: 37931001 PMCID: PMC10653606 DOI: 10.1371/journal.pgen.1011012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 11/16/2023] [Accepted: 10/10/2023] [Indexed: 11/08/2023] Open
Abstract
The mutational processes dictating the accumulation of mutations in genomes are shaped by genetic background, environment and their interactions. Accurate quantification of mutation rates and spectra under drugs has important implications in disease treatment. Here, we used whole-genome sequencing and time-resolved growth phenotyping of yeast mutation accumulation lines to give a detailed view of the mutagenic effects of rapamycin and hydroxyurea on the genome and cell growth. Mutation rates depended on the genetic backgrounds but were only marginally affected by rapamycin. As a remarkable exception, rapamycin treatment was associated with frequent chromosome XII amplifications, which compensated for rapamycin induced rDNA repeat contraction on this chromosome and served to maintain rDNA content homeostasis and fitness. In hydroxyurea, a wide range of mutation rates were elevated regardless of the genetic backgrounds, with a particularly high occurrence of aneuploidy that associated with dramatic fitness loss. Hydroxyurea also induced a high T-to-G and low C-to-A transversion rate that reversed the common G/C-to-A/T bias in yeast and gave rise to a broad range of structural variants, including mtDNA deletions. The hydroxyurea mutation footprint was consistent with the activation of error-prone DNA polymerase activities and non-homologues end joining repair pathways. Taken together, our study provides an in-depth view of mutation rates and signatures in rapamycin and hydroxyurea and their impact on cell fitness, which brings insights for assessing their chronic effects on genome integrity.
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Affiliation(s)
- Jing Li
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
- Université Côte d’Azur, CNRS, INSERM, IRCAN, Nice, France
| | - Simon Stenberg
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Jia-Xing Yue
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
- Université Côte d’Azur, CNRS, INSERM, IRCAN, Nice, France
| | | | - Dawn Thompson
- Ginkgo Bioworks, Boston, Massachusetts, United States of America
| | - Jonas Warringer
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Gianni Liti
- Université Côte d’Azur, CNRS, INSERM, IRCAN, Nice, France
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30
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Helsen J, Sherlock G, Dey G. Experimental evolution for cell biology. Trends Cell Biol 2023; 33:903-912. [PMID: 37188561 PMCID: PMC10592577 DOI: 10.1016/j.tcb.2023.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 04/18/2023] [Accepted: 04/19/2023] [Indexed: 05/17/2023]
Abstract
Evolutionary cell biology explores the origins, principles, and core functions of cellular features and regulatory networks through the lens of evolution. This emerging field relies heavily on comparative experiments and genomic analyses that focus exclusively on extant diversity and historical events, providing limited opportunities for experimental validation. In this opinion article, we explore the potential for experimental laboratory evolution to augment the evolutionary cell biology toolbox, drawing inspiration from recent studies that combine laboratory evolution with cell biological assays. Primarily focusing on approaches for single cells, we provide a generalizable template for adapting experimental evolution protocols to provide fresh insight into long-standing questions in cell biology.
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Affiliation(s)
- Jana Helsen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Cell Biology and Biophysics, European Molecular Biology Laboratory, Heidelberg, Germany.
| | - Gavin Sherlock
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
| | - Gautam Dey
- Cell Biology and Biophysics, European Molecular Biology Laboratory, Heidelberg, Germany.
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31
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Chen V, Johnson MS, Hérissant L, Humphrey PT, Yuan DC, Li Y, Agarwala A, Hoelscher SB, Petrov DA, Desai MM, Sherlock G. Evolution of haploid and diploid populations reveals common, strong, and variable pleiotropic effects in non-home environments. eLife 2023; 12:e92899. [PMID: 37861305 PMCID: PMC10629826 DOI: 10.7554/elife.92899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 09/27/2023] [Indexed: 10/21/2023] Open
Abstract
Adaptation is driven by the selection for beneficial mutations that provide a fitness advantage in the specific environment in which a population is evolving. However, environments are rarely constant or predictable. When an organism well adapted to one environment finds itself in another, pleiotropic effects of mutations that made it well adapted to its former environment will affect its success. To better understand such pleiotropic effects, we evolved both haploid and diploid barcoded budding yeast populations in multiple environments, isolated adaptive clones, and then determined the fitness effects of adaptive mutations in 'non-home' environments in which they were not selected. We find that pleiotropy is common, with most adaptive evolved lineages showing fitness effects in non-home environments. Consistent with other studies, we find that these pleiotropic effects are unpredictable: they are beneficial in some environments and deleterious in others. However, we do find that lineages with adaptive mutations in the same genes tend to show similar pleiotropic effects. We also find that ploidy influences the observed adaptive mutational spectra in a condition-specific fashion. In some conditions, haploids and diploids are selected with adaptive mutations in identical genes, while in others they accumulate mutations in almost completely disjoint sets of genes.
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Affiliation(s)
- Vivian Chen
- Department of Biology, Stanford UniversityStanfordUnited States
| | - Milo S Johnson
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Quantitative Biology Initiative, Harvard UniversityCambridgeUnited States
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard UniversityBostonUnited States
| | - Lucas Hérissant
- Department of Genetics, Stanford UniversityStanfordUnited States
| | - Parris T Humphrey
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
| | - David C Yuan
- Department of Biology, Stanford UniversityStanfordUnited States
| | - Yuping Li
- Department of Biology, Stanford UniversityStanfordUnited States
| | - Atish Agarwala
- Department of Physics, Stanford UniversityStanfordUnited States
| | | | - Dmitri A Petrov
- Department of Biology, Stanford UniversityStanfordUnited States
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard UniversityCambridgeUnited States
- Quantitative Biology Initiative, Harvard UniversityCambridgeUnited States
- NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard UniversityBostonUnited States
- Department of Physics, Harvard UniversityCambridgeUnited States
| | - Gavin Sherlock
- Department of Genetics, Stanford UniversityStanfordUnited States
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32
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Li W, Miller D, Liu X, Tosi L, Chkaiban L, Mei H, Hung PH, Parekkadan B, Sherlock G, Levy SF. Arrayed in vivo barcoding for multiplexed sequence verification of plasmid DNA and demultiplexing of pooled libraries. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.13.562064. [PMID: 37873145 PMCID: PMC10592806 DOI: 10.1101/2023.10.13.562064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Sequence verification of plasmid DNA is critical for many cloning and molecular biology workflows. To leverage high-throughput sequencing, several methods have been developed that add a unique DNA barcode to individual samples prior to pooling and sequencing. However, these methods require an individual plasmid extraction and/or in vitro barcoding reaction for each sample processed, limiting throughput and adding cost. Here, we develop an arrayed in vivo plasmid barcoding platform that enables pooled plasmid extraction and library preparation for Oxford Nanopore sequencing. This method has a high accuracy and recovery rate, and greatly increases throughput and reduces cost relative to other plasmid barcoding methods or Sanger sequencing. We use in vivo barcoding to sequence verify >45,000 plasmids and show that the method can be used to transform error-containing dispersed plasmid pools into sequence-perfect arrays or well-balanced pools. In vivo barcoding does not require any specialized equipment beyond a low-overhead Oxford Nanopore sequencer, enabling most labs to flexibly process hundreds to thousands of plasmids in parallel.
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Affiliation(s)
- Weiyi Li
- SLAC National Accelerator Laboratory, Stanford University, Stanford, CA, USA
| | - Darach Miller
- SLAC National Accelerator Laboratory, Stanford University, Stanford, CA, USA
| | - Xianan Liu
- SLAC National Accelerator Laboratory, Stanford University, Stanford, CA, USA
| | - Lorenzo Tosi
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA
| | - Lamia Chkaiban
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA
| | - Han Mei
- SLAC National Accelerator Laboratory, Stanford University, Stanford, CA, USA
| | - Po-Hsiang Hung
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Biju Parekkadan
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA
| | - Gavin Sherlock
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Sasha F Levy
- SLAC National Accelerator Laboratory, Stanford University, Stanford, CA, USA
- Present Address: BacStitch DNA, Los Altos, CA, USA
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33
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Razo-Mejia M, Mani M, Petrov D. Bayesian inference of relative fitness on high-throughput pooled competition assays. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.14.562365. [PMID: 37904971 PMCID: PMC10614806 DOI: 10.1101/2023.10.14.562365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.
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Affiliation(s)
| | - Madhav Mani
- NSF-Simons Center for Quantitative Biology, Northwestern University
- Department of Molecular Biosciences, Northwestern University
| | - Dmitri Petrov
- Department of Biology, Stanford University
- Stanford Cancer Institute, Stanford University School of Medicine
- Chan Zuckerberg Biohub
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34
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Alperovich N, Scott BM, Ross D. Automation protocol for high-efficiency and high-quality genomic DNA extraction from Saccharomyces cerevisiae. PLoS One 2023; 18:e0292401. [PMID: 37847718 PMCID: PMC10581484 DOI: 10.1371/journal.pone.0292401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/19/2023] [Indexed: 10/19/2023] Open
Abstract
Although many protocols have been previously developed for genomic DNA (gDNA) extraction from S. cerevisiae, to take advantage of recent advances in laboratory automation and DNA-barcode sequencing, there is a need for automated methods that can provide high-quality gDNA at high efficiency. Here, we describe and demonstrate a fully automated protocol that includes five basic steps: cell wall and RNA digestion, cell lysis, DNA binding to magnetic beads, washing with ethanol, and elution. Our protocol avoids the use of hazardous reagents (e.g., phenol, chloroform), glass beads for mechanical cell disruption, or incubation of samples at 100°C (i.e., boiling). We show that our protocol can extract gDNA with high efficiency both from cells grown in liquid culture and from colonies grown on agar plates. We also show results from gel electrophoresis that demonstrate that the resulting gDNA is of high quality.
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Affiliation(s)
- Nina Alperovich
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Benjamin M. Scott
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, United States of America
| | - David Ross
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
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35
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Abreu CI, Mathur S, Petrov DA. Strong environmental memory revealed by experimental evolution in static and fluctuating environments. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.14.557739. [PMID: 37745585 PMCID: PMC10515930 DOI: 10.1101/2023.09.14.557739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Evolution in a static environment, such as a laboratory setting with constant and uniform conditions, often proceeds via large-effect beneficial mutations that may become maladaptive in other environments. Conversely, natural settings require populations to endure environmental fluctuations. A sensible assumption is that the fitness of a lineage in a fluctuating environment is the time-average of its fitness over the sequence of static conditions it encounters. However, transitions between conditions may pose entirely new challenges, which could cause deviations from this time-average. To test this, we tracked hundreds of thousands of barcoded yeast lineages evolving in static and fluctuating conditions and subsequently isolated 900 mutants for pooled fitness assays in 15 environments. We find that fitness in fluctuating environments indeed often deviates from the expectation based on static components, leading to fitness non-additivity. Moreover, closer examination reveals that fitness in one component of a fluctuating environment is often strongly influenced by the previous component. We show that this environmental memory is especially common for mutants with high variance in fitness across tested environments, even if the components of the focal fluctuating environment are excluded from this variance. We employ a simple mathematical model and whole-genome sequencing to propose mechanisms underlying this effect, including lag time evolution and sensing mutations. Our results demonstrate that environmental fluctuations have large impacts on fitness and suggest that variance in static environments can explain these impacts.
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Affiliation(s)
- Clare I. Abreu
- Department of Biology, Stanford University; Stanford CA, USA
| | - Shaili Mathur
- Department of Biology, Stanford University; Stanford CA, USA
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36
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Quan N, Eguchi Y, Geiler-Samerotte K. Intra- FCY1: a novel system to identify mutations that cause protein misfolding. Front Genet 2023; 14:1198203. [PMID: 37745845 PMCID: PMC10512024 DOI: 10.3389/fgene.2023.1198203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 08/22/2023] [Indexed: 09/26/2023] Open
Abstract
Protein misfolding is a common intracellular occurrence. Most mutations to coding sequences increase the propensity of the encoded protein to misfold. These misfolded molecules can have devastating effects on cells. Despite the importance of protein misfolding in human disease and protein evolution, there are fundamental questions that remain unanswered, such as, which mutations cause the most misfolding? These questions are difficult to answer partially because we lack high-throughput methods to compare the destabilizing effects of different mutations. Commonly used systems to assess the stability of mutant proteins in vivo often rely upon essential proteins as sensors, but misfolded proteins can disrupt the function of the essential protein enough to kill the cell. This makes it difficult to identify and compare mutations that cause protein misfolding using these systems. Here, we present a novel in vivo system named Intra-FCY1 that we use to identify mutations that cause misfolding of a model protein [yellow fluorescent protein (YFP)] in Saccharomyces cerevisiae. The Intra-FCY1 system utilizes two complementary fragments of the yeast cytosine deaminase Fcy1, a toxic protein, into which YFP is inserted. When YFP folds, the Fcy1 fragments associate together to reconstitute their function, conferring toxicity in media containing 5-fluorocytosine and hindering growth. But mutations that make YFP misfold abrogate Fcy1 toxicity, thus strains possessing misfolded YFP variants rise to high frequency in growth competition experiments. This makes such strains easier to study. The Intra-FCY1 system cancels localization of the protein of interest, thus can be applied to study the relative stability of mutant versions of diverse cellular proteins. Here, we confirm this method can identify novel mutations that cause misfolding, highlighting the potential for Intra-FCY1 to illuminate the relationship between protein sequence and stability.
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Affiliation(s)
- N. Quan
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ, United States
- School of Life Sciences, Arizona State University, Tempe, AZ, United States
| | - Y. Eguchi
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ, United States
| | - K. Geiler-Samerotte
- Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ, United States
- School of Life Sciences, Arizona State University, Tempe, AZ, United States
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37
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Melissa MJ, Desai MM. A dynamical limit to evolutionary adaptation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.31.551320. [PMID: 37577473 PMCID: PMC10418092 DOI: 10.1101/2023.07.31.551320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Natural selection makes evolutionary adaptation possible even if the overwhelming majority of new mutations are deleterious. However, in rapidly evolving populations where numerous linked mutations occur and segregate simultaneously, clonal interference and genetic hitchhiking can limit the efficiency of selection, allowing deleterious mutations to accumulate over time. This can in principle overwhelm the fitness increases provided by beneficial mutations, leading to an overall fitness decline. Here, we analyze the conditions under which evolution will tend to drive populations to higher versus lower fitness. Our analysis focuses on quantifying the boundary between these two regimes, as a function of parameters such as population size, mutation rates, and selection pressures. This boundary represents a state in which adaptation is precisely balanced by Muller's ratchet, and we show that it can be characterized by rapid molecular evolution without any net fitness change. Finally, we consider the implications of global fitness-mediated epistasis, and find that under some circumstances this can drive populations towards the boundary state, which can thus represent a long-term evolutionary attractor.
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Affiliation(s)
- Matthew J. Melissa
- Department of Organismic and Evolutionary Biology, Department of Physics, Quantitative Biology Initiative, and NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University
| | - Michael M. Desai
- Department of Organismic and Evolutionary Biology, Department of Physics, Quantitative Biology Initiative, and NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University
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38
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Alexander HK. Quantifying stochastic establishment of mutants in microbial adaptation. MICROBIOLOGY (READING, ENGLAND) 2023; 169:001365. [PMID: 37561015 PMCID: PMC10482372 DOI: 10.1099/mic.0.001365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/10/2023] [Indexed: 08/11/2023]
Abstract
Studies of microbial evolution, especially in applied contexts, have focused on the role of selection in shaping predictable, adaptive responses to the environment. However, chance events - the appearance of novel genetic variants and their establishment, i.e. outgrowth from a single cell to a sizeable population - also play critical initiating roles in adaptation. Stochasticity in establishment has received little attention in microbiology, potentially due to lack of awareness as well as practical challenges in quantification. However, methods for high-replicate culturing, mutant labelling and detection, and statistical inference now make it feasible to experimentally quantify the establishment probability of specific adaptive genotypes. I review methods that have emerged over the past decade, including experimental design and mathematical formulas to estimate establishment probability from data. Quantifying establishment in further biological settings and comparing empirical estimates to theoretical predictions represent exciting future directions. More broadly, recognition that adaptive genotypes may be stochastically lost while rare is significant both for interpreting common lab assays and for designing interventions to promote or inhibit microbial evolution.
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Affiliation(s)
- Helen K. Alexander
- Institute of Ecology & Evolution, University of Edinburgh, Edinburgh, UK
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39
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Stevenson ZC, Moerdyk-Schauwecker MJ, Banse SA, Patel DS, Lu H, Phillips PC. High-throughput library transgenesis in Caenorhabditis elegans via Transgenic Arrays Resulting in Diversity of Integrated Sequences (TARDIS). eLife 2023; 12:RP84831. [PMID: 37401921 PMCID: PMC10328503 DOI: 10.7554/elife.84831] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023] Open
Abstract
High-throughput transgenesis using synthetic DNA libraries is a powerful method for systematically exploring genetic function. Diverse synthesized libraries have been used for protein engineering, identification of protein-protein interactions, characterization of promoter libraries, developmental and evolutionary lineage tracking, and various other exploratory assays. However, the need for library transgenesis has effectively restricted these approaches to single-cell models. Here, we present Transgenic Arrays Resulting in Diversity of Integrated Sequences (TARDIS), a simple yet powerful approach to large-scale transgenesis that overcomes typical limitations encountered in multicellular systems. TARDIS splits the transgenesis process into a two-step process: creation of individuals carrying experimentally introduced sequence libraries, followed by inducible extraction and integration of individual sequences/library components from the larger library cassette into engineered genomic sites. Thus, transformation of a single individual, followed by lineage expansion and functional transgenesis, gives rise to thousands of genetically unique transgenic individuals. We demonstrate the power of this system using engineered, split selectable TARDIS sites in Caenorhabditis elegans to generate (1) a large set of individually barcoded lineages and (2) transcriptional reporter lines from predefined promoter libraries. We find that this approach increases transformation yields up to approximately 1000-fold over current single-step methods. While we demonstrate the utility of TARDIS using C. elegans, in principle the process is adaptable to any system where experimentally generated genomic loci landing pads and diverse, heritable DNA elements can be generated.
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Affiliation(s)
| | | | - Stephen A Banse
- Institute of Ecology and Evolution, University of OregonEugeneUnited States
| | - Dhaval S Patel
- School of Chemical & Biomolecular Engineering, Georgia Institute of TechnologyAtlantaUnited States
- Petit Institute for Bioengineering and Bioscience, Georgia Institute of TechnologyAtlantaUnited States
| | - Hang Lu
- School of Chemical & Biomolecular Engineering, Georgia Institute of TechnologyAtlantaUnited States
- Petit Institute for Bioengineering and Bioscience, Georgia Institute of TechnologyAtlantaUnited States
| | - Patrick C Phillips
- Institute of Ecology and Evolution, University of OregonEugeneUnited States
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40
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Rezenman S, Knafo M, Tsigalnitski I, Barad S, Jona G, Levi D, Dym O, Reich Z, Kapon R. gUMI-BEAR, a modular, unsupervised population barcoding method to track variants and evolution at high resolution. PLoS One 2023; 18:e0286696. [PMID: 37285353 DOI: 10.1371/journal.pone.0286696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 05/19/2023] [Indexed: 06/09/2023] Open
Abstract
Cellular lineage tracking provides a means to observe population makeup at the clonal level, allowing exploration of heterogeneity, evolutionary and developmental processes and individual clones' relative fitness. It has thus contributed significantly to understanding microbial evolution, organ differentiation and cancer heterogeneity, among others. Its use, however, is limited because existing methods are highly specific, expensive, labour-intensive, and, critically, do not allow the repetition of experiments. To address these issues, we developed gUMI-BEAR (genomic Unique Molecular Identifier Barcoded Enriched Associated Regions), a modular, cost-effective method for tracking populations at high resolution. We first demonstrate the system's application and resolution by applying it to track tens of thousands of Saccharomyces cerevisiae lineages growing together under varying environmental conditions applied across multiple generations, revealing fitness differences and lineage-specific adaptations. Then, we demonstrate how gUMI-BEAR can be used to perform parallel screening of a huge number of randomly generated variants of the Hsp82 gene. We further show how our method allows isolation of variants, even if their frequency in the population is low, thus enabling unsupervised identification of modifications that lead to a behaviour of interest.
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Affiliation(s)
- Shahar Rezenman
- Department of Biomolecular Sciences, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Maor Knafo
- Department of Biomolecular Sciences, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Ivgeni Tsigalnitski
- Department of Biomolecular Sciences, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Shiri Barad
- Department of Biomolecular Sciences, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Ghil Jona
- Life Sciences Core Facilities, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Dikla Levi
- Life Sciences Core Facilities, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Orly Dym
- The Dana and Yossie Hollander Center for Structural Proteomics, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Ziv Reich
- Department of Biomolecular Sciences, Weizmann Institute of Science Rehovot, Rehovot, Israel
| | - Ruti Kapon
- Department of Biomolecular Sciences, Weizmann Institute of Science Rehovot, Rehovot, Israel
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41
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Hsu P, Cheng Y, Liao C, Litan RRR, Jhou Y, Opoc FJG, Amine AAA, Leu J. Rapid evolutionary repair by secondary perturbation of a primary disrupted transcriptional network. EMBO Rep 2023; 24:e56019. [PMID: 37009824 PMCID: PMC10240213 DOI: 10.15252/embr.202256019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 03/16/2023] [Accepted: 03/17/2023] [Indexed: 04/04/2023] Open
Abstract
The discrete steps of transcriptional rewiring have been proposed to occur neutrally to ensure steady gene expression under stabilizing selection. A conflict-free switch of a regulon between regulators may require an immediate compensatory evolution to minimize deleterious effects. Here, we perform an evolutionary repair experiment on the Lachancea kluyveri yeast sef1Δ mutant using a suppressor development strategy. Complete loss of SEF1 forces cells to initiate a compensatory process for the pleiotropic defects arising from misexpression of TCA cycle genes. Using different selective conditions, we identify two adaptive loss-of-function mutations of IRA1 and AZF1. Subsequent analyses show that Azf1 is a weak transcriptional activator regulated by the Ras1-PKA pathway. Azf1 loss-of-function triggers extensive gene expression changes responsible for compensatory, beneficial, and trade-off phenotypes. The trade-offs can be alleviated by higher cell density. Our results not only indicate that secondary transcriptional perturbation provides rapid and adaptive mechanisms potentially stabilizing the initial stage of transcriptional rewiring but also suggest how genetic polymorphisms of pleiotropic mutations could be maintained in the population.
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Affiliation(s)
- Po‐Chen Hsu
- Institute of Molecular BiologyAcademia SinicaTaipeiTaiwan
| | - Yu‐Hsuan Cheng
- Institute of Molecular BiologyAcademia SinicaTaipeiTaiwan
- Present address:
Morgridge Institute for ResearchMadisonWIUSA
- Present address:
Howard Hughes Medical InstituteUniversity of Wisconsin‐MadisonMadisonWIUSA
| | - Chia‐Wei Liao
- Institute of Molecular BiologyAcademia SinicaTaipeiTaiwan
| | | | - Yu‐Ting Jhou
- Institute of Molecular BiologyAcademia SinicaTaipeiTaiwan
| | | | | | - Jun‐Yi Leu
- Institute of Molecular BiologyAcademia SinicaTaipeiTaiwan
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42
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Theodosiou L, Farr AD, Rainey PB. Barcoding Populations of Pseudomonas fluorescens SBW25. J Mol Evol 2023; 91:254-262. [PMID: 37186220 PMCID: PMC10275814 DOI: 10.1007/s00239-023-10103-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 03/13/2023] [Indexed: 05/17/2023]
Abstract
In recent years, evolutionary biologists have developed an increasing interest in the use of barcoding strategies to study eco-evolutionary dynamics of lineages within evolving populations and communities. Although barcoded populations can deliver unprecedented insight into evolutionary change, barcoding microbes presents specific technical challenges. Here, strategies are described for barcoding populations of the model bacterium Pseudomonas fluorescens SBW25, including the design and cloning of barcoded regions, preparation of libraries for amplicon sequencing, and quantification of resulting barcoded lineages. In so doing, we hope to aid the design and implementation of barcoding methodologies in a broad range of model and non-model organisms.
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Affiliation(s)
- Loukas Theodosiou
- Department of Microbial Population Biology, Max Planck Institute for Evolutionary Biology, Plön, Germany.
- Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding, Cologne, Germany.
| | - Andrew D Farr
- Department of Microbial Population Biology, Max Planck Institute for Evolutionary Biology, Plön, Germany
| | - Paul B Rainey
- Department of Microbial Population Biology, Max Planck Institute for Evolutionary Biology, Plön, Germany
- Laboratory of Biophysics and Evolution, CBI, ESPCI Paris, Université PSL, CNRS, Paris, France
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43
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Geiler-Samerotte K, Lang GI. Best Practices in Microbial Experimental Evolution. J Mol Evol 2023; 91:237-240. [PMID: 37209159 PMCID: PMC10885815 DOI: 10.1007/s00239-023-10119-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 05/11/2023] [Indexed: 05/22/2023]
Affiliation(s)
- Kerry Geiler-Samerotte
- School of Life Sciences and Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ, 85287, USA.
| | - Gregory I Lang
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, 18015, USA
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44
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Kinsler G, Schmidlin K, Newell D, Eder R, Apodaca S, Lam G, Petrov D, Geiler-Samerotte K. Extreme Sensitivity of Fitness to Environmental Conditions: Lessons from #1BigBatch. J Mol Evol 2023; 91:293-310. [PMID: 37237236 PMCID: PMC10276131 DOI: 10.1007/s00239-023-10114-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 04/30/2023] [Indexed: 05/28/2023]
Abstract
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 barcodes, all methods are limited in their precision to differentiate strains with small fitness differences. In this study, we rule out some major sources of imprecision, but still find that fitness measurements vary substantially from replicate to replicate. Our data suggest that very subtle and difficult to avoid environmental differences between replicates create systematic variation across fitness measurements. We conclude by discussing how fitness measurements should be interpreted given their extreme environment 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.
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Affiliation(s)
| | - Kara Schmidlin
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
| | - Daphne Newell
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
- School of Life Sciences, Arizona State University, Tempe, USA
| | - Rachel Eder
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
- School of Life Sciences, Arizona State University, Tempe, USA
| | - Sam Apodaca
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
- School of Life Sciences, Arizona State University, Tempe, USA
| | | | | | - Kerry Geiler-Samerotte
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA.
- School of Life Sciences, Arizona State University, Tempe, USA.
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45
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Spealman P, De T, Chuong JN, Gresham D. Best Practices in Microbial Experimental Evolution: Using Reporters and Long-Read Sequencing to Identify Copy Number Variation in Experimental Evolution. J Mol Evol 2023; 91:356-368. [PMID: 37012421 PMCID: PMC10275804 DOI: 10.1007/s00239-023-10102-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 02/21/2023] [Indexed: 04/05/2023]
Abstract
Copy number variants (CNVs), comprising gene amplifications and deletions, are a pervasive class of heritable variation. CNVs play a key role in rapid adaptation in both natural, and experimental, evolution. However, despite the advent of new DNA sequencing technologies, detection and quantification of CNVs in heterogeneous populations has remained challenging. Here, we summarize recent advances in the use of CNV reporters that provide a facile means of quantifying de novo CNVs at a specific locus in the genome, and nanopore sequencing, for resolving the often complex structures of CNVs. We provide guidance for the engineering and analysis of CNV reporters and practical guidelines for single-cell analysis of CNVs using flow cytometry. We summarize recent advances in nanopore sequencing, discuss the utility of this technology, and provide guidance for the bioinformatic analysis of these data to define the molecular structure of CNVs. The combination of reporter systems for tracking and isolating CNV lineages and long-read DNA sequencing for characterizing CNV structures enables unprecedented resolution of the mechanisms by which CNVs are generated and their evolutionary dynamics.
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Affiliation(s)
- Pieter Spealman
- Department of Biology, New York University, New York, NY, 10003, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, 10003, USA
| | - Titir De
- Department of Biology, New York University, New York, NY, 10003, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, 10003, USA
| | - Julie N Chuong
- Department of Biology, New York University, New York, NY, 10003, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, 10003, USA
| | - David Gresham
- Department of Biology, New York University, New York, NY, 10003, USA.
- Center for Genomics and Systems Biology, New York University, New York, NY, 10003, USA.
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46
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Karlsson K, Przybilla MJ, Kotler E, Khan A, Xu H, Karagyozova K, Sockell A, Wong WH, Liu K, Mah A, Lo YH, Lu B, Houlahan KE, Ma Z, Suarez CJ, Barnes CP, Kuo CJ, Curtis C. Deterministic evolution and stringent selection during preneoplasia. Nature 2023; 618:383-393. [PMID: 37258665 PMCID: PMC10247377 DOI: 10.1038/s41586-023-06102-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 04/19/2023] [Indexed: 06/02/2023]
Abstract
The earliest events during human tumour initiation, although poorly characterized, may hold clues to malignancy detection and prevention1. Here we model occult preneoplasia by biallelic inactivation of TP53, a common early event in gastric cancer, in human gastric organoids. Causal relationships between this initiating genetic lesion and resulting phenotypes were established using experimental evolution in multiple clonally derived cultures over 2 years. TP53 loss elicited progressive aneuploidy, including copy number alterations and structural variants prevalent in gastric cancers, with evident preferred orders. Longitudinal single-cell sequencing of TP53-deficient gastric organoids similarly indicates progression towards malignant transcriptional programmes. Moreover, high-throughput lineage tracing with expressed cellular barcodes demonstrates reproducible dynamics whereby initially rare subclones with shared transcriptional programmes repeatedly attain clonal dominance. This powerful platform for experimental evolution exposes stringent selection, clonal interference and a marked degree of phenotypic convergence in premalignant epithelial organoids. These data imply predictability in the earliest stages of tumorigenesis and show evolutionary constraints and barriers to malignant transformation, with implications for earlier detection and interception of aggressive, genome-instable tumours.
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Affiliation(s)
- Kasper Karlsson
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
- Science for Life Laboratory and Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Moritz J Przybilla
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
- Wellcome Sanger Institute & University of Cambridge, Hinxton, UK
| | - Eran Kotler
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Aziz Khan
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Hang Xu
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Kremena Karagyozova
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Alexandra Sockell
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Wing H Wong
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Katherine Liu
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Amanda Mah
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Yuan-Hung Lo
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Bingxin Lu
- Department of Cell and Developmental Biology, University College London, London, UK
| | - Kathleen E Houlahan
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Zhicheng Ma
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Carlos J Suarez
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London, UK
| | - Calvin J Kuo
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christina Curtis
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
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47
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Johnson MS, Venkataram S, Kryazhimskiy S. Best Practices in Designing, Sequencing, and Identifying Random DNA Barcodes. J Mol Evol 2023; 91:263-280. [PMID: 36651964 PMCID: PMC10276077 DOI: 10.1007/s00239-022-10083-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 12/15/2022] [Indexed: 01/19/2023]
Abstract
Random DNA barcodes are a versatile tool for tracking cell lineages, with applications ranging from development to cancer to evolution. Here, we review and critically evaluate barcode designs as well as methods of barcode sequencing and initial processing of barcode data. We first demonstrate how various barcode design decisions affect data quality and propose a new design that balances all considerations that we are currently aware of. We then discuss various options for the preparation of barcode sequencing libraries, including inline indices and Unique Molecular Identifiers (UMIs). Finally, we test the performance of several established and new bioinformatic pipelines for the extraction of barcodes from raw sequencing reads and for error correction. We find that both alignment and regular expression-based approaches work well for barcode extraction, and that error-correction pipelines designed specifically for barcode data are superior to generic ones. Overall, this review will help researchers to approach their barcoding experiments in a deliberate and systematic way.
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Affiliation(s)
- Milo S Johnson
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, 94720, USA
| | - Sandeep Venkataram
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA, 92093, USA
| | - Sergey Kryazhimskiy
- Department of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, CA, 92093, USA.
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48
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Hung PH, Liao CW, Ko FH, Tsai HK, Leu JY. Differential Hsp90-dependent gene expression is strain-specific and common among yeast strains. iScience 2023; 26:106635. [PMID: 37138775 PMCID: PMC10149407 DOI: 10.1016/j.isci.2023.106635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 02/21/2023] [Accepted: 04/05/2023] [Indexed: 05/05/2023] Open
Abstract
Enhanced phenotypic diversity increases a population's likelihood of surviving catastrophic conditions. Hsp90, an essential molecular chaperone and a central network hub in eukaryotes, has been observed to suppress or enhance the effects of genetic variation on phenotypic diversity in response to environmental cues. Because many Hsp90-interacting genes are involved in signaling transduction pathways and transcriptional regulation, we tested how common Hsp90-dependent differential gene expression is in natural populations. Many genes exhibited Hsp90-dependent strain-specific differential expression in five diverse yeast strains. We further identified transcription factors (TFs) potentially contributing to variable expression. We found that on Hsp90 inhibition or environmental stress, activities or abundances of Hsp90-dependent TFs varied among strains, resulting in differential strain-specific expression of their target genes, which consequently led to phenotypic diversity. We provide evidence that individual strains can readily display specific Hsp90-dependent gene expression, suggesting that the evolutionary impacts of Hsp90 are widespread in nature.
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Affiliation(s)
- Po-Hsiang Hung
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei 115, Taiwan
- Institute of Molecular Biology, Academia Sinica, Taipei 115, Taiwan
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
| | - Chia-Wei Liao
- Institute of Molecular Biology, Academia Sinica, Taipei 115, Taiwan
| | - Fu-Hsuan Ko
- Institute of Molecular Biology, Academia Sinica, Taipei 115, Taiwan
| | - Huai-Kuang Tsai
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei 115, Taiwan
- Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
- Corresponding author
| | - Jun-Yi Leu
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei 115, Taiwan
- Institute of Molecular Biology, Academia Sinica, Taipei 115, Taiwan
- Corresponding author
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49
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Li F, Mahadevan A, Sherlock G. An improved algorithm for inferring mutational parameters from bar-seq evolution experiments. BMC Genomics 2023; 24:246. [PMID: 37149606 PMCID: PMC10164349 DOI: 10.1186/s12864-023-09345-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 04/27/2023] [Indexed: 05/08/2023] Open
Abstract
BACKGROUND Genetic barcoding provides a high-throughput way to simultaneously track the frequencies of large numbers of competing and evolving microbial lineages. However making inferences about the nature of the evolution that is taking place remains a difficult task. RESULTS Here we describe an algorithm for the inference of fitness effects and establishment times of beneficial mutations from barcode sequencing data, which builds upon a Bayesian inference method by enforcing self-consistency between the population mean fitness and the individual effects of mutations within lineages. By testing our inference method on a simulation of 40,000 barcoded lineages evolving in serial batch culture, we find that this new method outperforms its predecessor, identifying more adaptive mutations and more accurately inferring their mutational parameters. CONCLUSION Our new algorithm is particularly suited to inference of mutational parameters when read depth is low. We have made Python code for our serial dilution evolution simulations, as well as both the old and new inference methods, available on GitHub ( https://github.com/FangfeiLi05/FitMut2 ), in the hope that it can find broader use by the microbial evolution community.
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Affiliation(s)
- Fangfei Li
- Department of Genetics, Stanford University, Stanford, California, US
| | - Aditya Mahadevan
- Department of Physics, Stanford University, Stanford, California, US
| | - Gavin Sherlock
- Department of Genetics, Stanford University, Stanford, California, US.
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50
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Ascensao JA, Denk J, Lok K, Yu Q, Wetmore KM, Hallatschek O. Rediversification Following Ecotype Isolation Reveals Hidden Adaptive Potential. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.03.539206. [PMID: 37205326 PMCID: PMC10187175 DOI: 10.1101/2023.05.03.539206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Microbial communities play a critical role in ecological processes, and their diversity is key to their functioning. However, little is known about if communities can regenerate ecological diversity following species removal or extinction, and how the rediversified communities would compare to the original ones. Here we show that simple two-ecotype communities from the E. coli Long Term Evolution Experiment (LTEE) consistently rediversified into two ecotypes following the isolation of one of the ecotypes, coexisting via negative frequency-dependent selection. Communities separated by more than 30,000 generations of evolutionary time rediversify in similar ways. The rediversified ecotype appears to share a number of growth traits with the ecotype it replaces. However, the rediversified community is also different compared to the original community in ways relevant to the mechanism of ecotype coexistence, for example in stationary phase response and survival. We found substantial variation in the transcriptional states between the two original ecotypes, whereas the differences within the rediversified community were comparatively smaller, but with unique patterns of differential expression. Our results suggest that evolution may leave room for alternative diversification processes even in a maximally reduced community of only two strains. We hypothesize that the presence of alternative evolutionary pathways may be even more pronounced in communities of many species, highlighting an important role for perturbations, such as species removal, in evolving ecological communities.
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Affiliation(s)
- Joao A Ascensao
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA
| | - Jonas Denk
- Department of Physics, University of California Berkeley Berkeley, CA, USA
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
| | - Kristen Lok
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA
| | - QinQin Yu
- Department of Physics, University of California Berkeley Berkeley, CA, USA
- Present affiliation: Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
| | - Kelly M Wetmore
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA
| | - Oskar Hallatschek
- Department of Physics, University of California Berkeley Berkeley, CA, USA
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
- Peter Debye Institute for Soft Matter Physics, Leipzig University, 04103 Leipzig, Germany
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