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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: 0] [Impact Index Per Article: 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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2
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Abstract
Gene-by-environment interactions play a crucial role in horizontal gene transfer by affecting how the transferred genes alter host fitness. However, how the environment modulates the fitness effect of transferred genes has not been tested systematically in an experimental study. We adapted a high-throughput technique for obtaining very precise estimates of bacterial fitness, in order to measure the fitness effects of 44 orthologs transferred from Salmonella Typhimurium to Escherichia coli in six physiologically relevant environments. We found that the fitness effects of individual genes were highly dependent on the environment, while the distributions of fitness effects across genes were not, with all tested environments resulting in distributions of same shape and spread. Furthermore, the extent to which the fitness effects of a gene varied between environments depended on the average fitness effect of that gene across all environments, with nearly neutral and nearly lethal genes having more consistent fitness effects across all environments compared to deleterious genes. Put together, our results reveal the unpredictable nature of how environmental conditions impact the fitness effects of each individual gene. At the same time, distributions of fitness effects across environments exhibit consistent features, pointing to the generalizability of factors that shape horizontal gene transfer of orthologous genes.
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Affiliation(s)
- Hande Acar Kirit
- Veterinary and Ecological Sciences, Institute of Infection, University of Liverpool, Liverpool, Merseyside, United Kingdom
- Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, Norman, OK
- Department of Anthropology, University of Oklahoma, Norman, OK
| | - Jonathan P Bollback
- Veterinary and Ecological Sciences, Institute of Infection, University of Liverpool, Liverpool, Merseyside, United Kingdom
| | - Mato Lagator
- School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
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3
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Miller JH, Fasanello VJ, Liu P, Longan ER, Botero CA, Fay JC. Using colony size to measure fitness in Saccharomyces cerevisiae. PLoS One 2022; 17:e0271709. [PMID: 36227888 PMCID: PMC9560512 DOI: 10.1371/journal.pone.0271709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 09/15/2022] [Indexed: 01/05/2023] Open
Abstract
Competitive fitness assays in liquid culture have been a mainstay for characterizing experimental evolution of microbial populations. Growth of microbial strains has also been extensively characterized by colony size and could serve as a useful alternative if translated to per generation measurements of relative fitness. To examine fitness based on colony size, we established a relationship between cell number and colony size for strains of Saccharomyces cerevisiae robotically pinned onto solid agar plates in a high-density format. This was used to measure growth rates and estimate relative fitness differences between evolved strains and their ancestors. After controlling for edge effects through both normalization and agar-trimming, we found that colony size is a sensitive measure of fitness, capable of detecting 1% differences. While fitnesses determined from liquid and solid mediums were not equivalent, our results demonstrate that colony size provides a sensitive means of measuring fitness that is particularly well suited to measurements across many environments.
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Affiliation(s)
- James H. Miller
- Department of Biology, University of Rochester, Rochester, New York, United States of America
| | - Vincent J. Fasanello
- Department of Biology, Washington University, St. Louis, Missouri, United States of America
| | - Ping Liu
- Department of Biology, Washington University, St. Louis, Missouri, United States of America
| | - Emery R. Longan
- Department of Biology, University of Rochester, Rochester, New York, United States of America
| | - Carlos A. Botero
- Department of Biology, Washington University, St. Louis, Missouri, United States of America
| | - Justin C. Fay
- Department of Biology, University of Rochester, Rochester, New York, United States of America
- * E-mail:
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4
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Parikh SB, Houghton C, Van Oss SB, Wacholder A, Carvunis A. Origins, evolution, and physiological implications of de novo genes in yeast. Yeast 2022; 39:471-481. [PMID: 35959631 PMCID: PMC9544372 DOI: 10.1002/yea.3810] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 08/08/2022] [Accepted: 08/09/2022] [Indexed: 12/03/2022] Open
Abstract
De novo gene birth is the process by which new genes emerge in sequences that were previously noncoding. Over the past decade, researchers have taken advantage of the power of yeast as a model and a tool to study the evolutionary mechanisms and physiological implications of de novo gene birth. We summarize the mechanisms that have been proposed to explicate how noncoding sequences can become protein-coding genes, highlighting the discovery of pervasive translation of the yeast transcriptome and its presumed impact on evolutionary innovation. We summarize current best practices for the identification and characterization of de novo genes. Crucially, we explain that the field is still in its nascency, with the physiological roles of most young yeast de novo genes identified thus far still utterly unknown. We hope this review inspires researchers to investigate the true contribution of de novo gene birth to cellular physiology and phenotypic diversity across yeast strains and species.
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Affiliation(s)
- Saurin B. Parikh
- Department of Computational and Systems Biology, School of Medicine, Pittsburgh Center for Evolutionary Biology and EvolutionUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Carly Houghton
- Department of Computational and Systems Biology, School of Medicine, Pittsburgh Center for Evolutionary Biology and EvolutionUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - S. Branden Van Oss
- Department of Computational and Systems Biology, School of Medicine, Pittsburgh Center for Evolutionary Biology and EvolutionUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Aaron Wacholder
- Department of Computational and Systems Biology, School of Medicine, Pittsburgh Center for Evolutionary Biology and EvolutionUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Anne‐Ruxandra Carvunis
- Department of Computational and Systems Biology, School of Medicine, Pittsburgh Center for Evolutionary Biology and EvolutionUniversity of PittsburghPittsburghPennsylvaniaUSA
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5
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Rana A, Patton D, Turner NT, Dillon MM, Cooper VS, Sung W. Precise measurement of the fitness effects of spontaneous mutations by droplet digital PCR in Burkholderia cenocepacia. Genetics 2021; 219:6325026. [PMID: 34849876 DOI: 10.1093/genetics/iyab117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 07/07/2021] [Accepted: 07/09/2021] [Indexed: 11/12/2022] Open
Abstract
Understanding how mutations affect survivability is a key component to knowing how organisms and complex traits evolve. However, most mutations have a minor effect on fitness and these effects are difficult to resolve using traditional molecular techniques. Therefore, there is a dire need for more accurate and precise fitness measurements methods. Here, we measured the fitness effects in Burkholderia cenocepacia HI2424 mutation accumulation (MA) lines using droplet-digital polymerase chain reaction (ddPCR). Overall, the fitness measurements from ddPCR-MA are correlated positively with fitness measurements derived from traditional phenotypic marker assays (r = 0.297, P = 0.05), but showed some differences. First, ddPCR had significantly lower measurement variance in fitness (F = 3.78, P < 2.6 × 10-13) in control experiments. Second, the mean fitness from ddPCR-MA measurements were significantly lower than phenotypic marker assays (-0.0041 vs -0.0071, P = 0.006). Consistent with phenotypic marker assays, ddPCR-MA measurements observed multiple (27/43) lineages that significantly deviated from mean fitness, suggesting that a majority of the mutations are neutral or slightly deleterious and intermixed with a few mutations that have extremely large effects. Of these mutations, we found a significant excess of mutations within DNA excinuclease and Lys R transcriptional regulators that have extreme deleterious and beneficial effects, indicating that modifications to transcription and replication may have a strong effect on organismal fitness. This study demonstrates the power of ddPCR as a ubiquitous method for high-throughput fitness measurements in both DNA- and RNA-based organisms regardless of cell type or physiology.
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Affiliation(s)
- Anita Rana
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - David Patton
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Nathan T Turner
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Marcus M Dillon
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario M5S3B2, Canada
| | - Vaughn S Cooper
- Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, PA 15219, USA
| | - Way Sung
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
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Nuismer SL, C. Layman N, Redwood AJ, Chan B, Bull JJ. Methods for measuring the evolutionary stability of engineered genomes to improve their longevity. Synth Biol (Oxf) 2021; 6:ysab018. [PMID: 34712842 PMCID: PMC8546616 DOI: 10.1093/synbio/ysab018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 07/05/2021] [Accepted: 07/26/2021] [Indexed: 11/14/2022] Open
Abstract
Diverse applications rely on engineering microbes to carry and express foreign transgenes. This engineered baggage rarely benefits the microbe and is thus prone to rapid evolutionary loss when the microbe is propagated. For applications where a transgene must be maintained for extended periods of growth, slowing the rate of transgene evolution is critical and can be achieved by reducing either the rate of mutation or the strength of selection. Because the benefits realized by changing these quantities will not usually be equal, it is important to know which will yield the greatest improvement to the evolutionary half-life of the engineering. Here, we provide a method for jointly estimating the mutation rate of transgene loss and the strength of selection favoring these transgene-free, revertant individuals. The method requires data from serial transfer experiments in which the frequency of engineered genomes is monitored periodically. Simple mathematical models are developed that use these estimates to predict the half-life of the engineered transgene and provide quantitative predictions for how alterations to mutation and selection will influence longevity. The estimation method and predictive tools have been implemented as an interactive web application, MuSe.
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Affiliation(s)
- Scott L Nuismer
- Department of Biological Sciences, University of Idaho, 875 Perimeter Dr, Moscow, Idaho 83844, USA
- Department of Mathematics, University of Idaho, 875 Perimeter Dr, Moscow, Idaho 83844, USA
| | - Nathan C. Layman
- Department of Biological Sciences, University of Idaho, 875 Perimeter Dr, Moscow, Idaho 83844, USA
| | - Alec J Redwood
- School of Biomedical Sciences, University of Western Australia, Perth, Western Australia, Australia
- The Institute for Respiratory Health, Nedlands, Western Australia, Australia
| | - Baca Chan
- School of Biomedical Sciences, University of Western Australia, Perth, Western Australia, Australia
- The Institute for Respiratory Health, Nedlands, Western Australia, Australia
| | - James J Bull
- Department of Biological Sciences, University of Idaho, 875 Perimeter Dr, Moscow, Idaho 83844, USA
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