1
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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 2024:10.1038/s41559-024-02475-9. [PMID: 39020024 DOI: 10.1038/s41559-024-02475-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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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2
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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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3
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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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4
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Chambers MJ, Scobell SB, Sadhu MJ. Systematic genetic characterization of the human PKR kinase domain highlights its functional malleability to escape a viral substrate mimic. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.29.596416. [PMID: 38903081 PMCID: PMC11188142 DOI: 10.1101/2024.05.29.596416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Evolutionary arms races can arise at the contact surfaces between host and viral proteins, producing dynamic spaces in which genetic variants are continually pursued. However, the sampling of genetic variation must be balanced with the need to maintain protein function. A striking case is given by protein kinase R (PKR), a member of the mammalian innate immune system. PKR detects viral replication within the host cell and halts protein synthesis to prevent viral replication by phosphorylating eIF2α, a component of the translation initiation machinery. PKR is targeted by many viral antagonists, including poxvirus pseudosubstrate antagonists that inhibit PKR by interacting with the same binding surface as eIF2α. Remarkably, PKR has several rapidly evolving residues at this interface, suggesting it is engaging in an evolutionary arms race, despite the surface's critical role in phosphorylating eIF2α. To systematically explore the evolutionary opportunities available at this dynamic interface, we generated and characterized a library of 426 SNP-accessible nonsynonymous variants of human PKR for their ability to escape inhibition by the model pseudosubstrate inhibitor K3 from vaccinia virus. We identified key sites in the PKR kinase domain that harbor K3-resistant variants, as well as critical sites where variation leads to loss of function. We find K3-resistant variants are readily available throughout the interface and are enriched at sites under positive selection. Moreover, variants beneficial against K3 were also beneficial against an enhanced variant of K3, indicating resilience to viral adaptation. Overall, we find that the eIF2α-binding surface of PKR is highly malleable, potentiating its evolutionary ability to combat viral inhibition.
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Affiliation(s)
- Michael J Chambers
- Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
- Department of Microbiology & Immunology, Georgetown University, Washington DC, USA
| | - Sophia B Scobell
- Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Meru J Sadhu
- Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
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5
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Nakayama Y, Fujiu K, Oshima T, Matsuda J, Sugita J, Matsubara TJ, Liu Y, Goto K, Kani K, Uchida R, Takeda N, Morita H, Xiao Y, Hayashi M, Maru Y, Hasumi E, Kojima T, Ishiguro S, Kijima Y, Yachie N, Yamazaki S, Yamamoto R, Kudo F, Nakanishi M, Iwama A, Fujiki R, Kaneda A, Ohara O, Nagai R, Manabe I, Komuro I. Heart failure promotes multimorbidity through innate immune memory. Sci Immunol 2024; 9:eade3814. [PMID: 38787963 DOI: 10.1126/sciimmunol.ade3814] [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: 08/12/2022] [Accepted: 05/02/2024] [Indexed: 05/26/2024]
Abstract
Patients with heart failure (HF) often experience repeated acute decompensation and develop comorbidities such as chronic kidney disease and frailty syndrome. Although this suggests pathological interaction among comorbidities, the mechanisms linking them are poorly understood. Here, we identified alterations in hematopoietic stem cells (HSCs) as a critical driver of recurrent HF and associated comorbidities. Bone marrow transplantation from HF-experienced mice resulted in spontaneous cardiac dysfunction and fibrosis in recipient mice, as well as increased vulnerability to kidney and skeletal muscle insults. HF enhanced the capacity of HSCs to generate proinflammatory macrophages. In HF mice, global chromatin accessibility analysis and single-cell RNA-seq showed that transforming growth factor-β (TGF-β) signaling was suppressed in HSCs, which corresponded with repressed sympathetic nervous activity in bone marrow. Transplantation of bone marrow from mice in which TGF-β signaling was inhibited similarly exacerbated cardiac dysfunction. Collectively, these results suggest that cardiac stress modulates the epigenome of HSCs, which in turn alters their capacity to generate cardiac macrophage subpopulations. This change in HSCs may be a common driver of repeated HF events and comorbidity by serving as a key carrier of "stress memory."
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Affiliation(s)
- Yukiteru Nakayama
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
| | - Katsuhito Fujiu
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
- Department of Advanced Cardiology, University of Tokyo, Tokyo, Japan
| | - Tsukasa Oshima
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
| | - Jun Matsuda
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
| | - Junichi Sugita
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
| | | | - Yuxiang Liu
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
| | - Kohsaku Goto
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
| | - Kunihiro Kani
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
| | - Ryoko Uchida
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
- Department of Advanced Cardiology, University of Tokyo, Tokyo, Japan
| | - Norifumi Takeda
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
| | - Hiroyuki Morita
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
| | - Yingda Xiao
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
| | - Michiko Hayashi
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
| | - Yujin Maru
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
| | - Eriko Hasumi
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
| | - Toshiya Kojima
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
| | - Soh Ishiguro
- School of Biomedical Engineering, Faculty of Applied Science and Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Yusuke Kijima
- School of Biomedical Engineering, Faculty of Applied Science and Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Aquatic Bioscience, Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan
| | - Nozomu Yachie
- School of Biomedical Engineering, Faculty of Applied Science and Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Synthetic Biology Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan
| | - Satoshi Yamazaki
- Division of Stem Cell Therapy, Center for Stem Cell Biology and Regenerative Medicine, Institute of Medical Science, University of Tokyo, Tokyo, Japan
- Laboratory of Stem Cell Therapy, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Ryo Yamamoto
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
| | - Fujimi Kudo
- Department of Systems Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Mio Nakanishi
- Department of Systems Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Atsushi Iwama
- Division of Stem Cell and Molecular Medicine, Center for Stem Cell Biology and Regenerative Medicine, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Ryoji Fujiki
- Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
- Department of Applied Genomics, Kazusa DNA Research Institute, Chiba, Japan
| | - Atsushi Kaneda
- Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Osamu Ohara
- Department of Applied Genomics, Kazusa DNA Research Institute, Chiba, Japan
| | - Ryozo Nagai
- Jichi Medical University, Shimotsuke, Tochigi, Japan
| | - Ichiro Manabe
- Department of Systems Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Issei Komuro
- Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan
- International University of Health and Welfare, Tokyo, Japan
- Department of Frontier Cardiovascular Science, Graduate School of Tokyo, University of Tokyo, Tokyo, Japan
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6
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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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7
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Dvoretskova E, Ho MC, Kittke V, Neuhaus F, Vitali I, Lam DD, Delgado I, Feng C, Torres M, Winkelmann J, Mayer C. Spatial enhancer activation influences inhibitory neuron identity during mouse embryonic development. Nat Neurosci 2024; 27:862-872. [PMID: 38528203 PMCID: PMC11088997 DOI: 10.1038/s41593-024-01611-9] [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: 02/02/2023] [Accepted: 02/23/2024] [Indexed: 03/27/2024]
Abstract
The mammalian telencephalon contains distinct GABAergic projection neuron and interneuron types, originating in the germinal zone of the embryonic basal ganglia. How genetic information in the germinal zone determines cell types is unclear. Here we use a combination of in vivo CRISPR perturbation, lineage tracing and ChIP-sequencing analyses and show that the transcription factor MEIS2 favors the development of projection neurons by binding enhancer regions in projection-neuron-specific genes during mouse embryonic development. MEIS2 requires the presence of the homeodomain transcription factor DLX5 to direct its functional activity toward the appropriate binding sites. In interneuron precursors, the transcription factor LHX6 represses the MEIS2-DLX5-dependent activation of projection-neuron-specific enhancers. Mutations of Meis2 result in decreased activation of regulatory enhancers, affecting GABAergic differentiation. We propose a differential binding model where the binding of transcription factors at cis-regulatory elements determines differential gene expression programs regulating cell fate specification in the mouse ganglionic eminence.
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Affiliation(s)
- Elena Dvoretskova
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Max Planck Institute of Neurobiology, Martinsried, Germany
| | - May C Ho
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Max Planck Institute of Neurobiology, Martinsried, Germany
| | - Volker Kittke
- Institute of Neurogenomics, Helmholtz Zentrum München GmbH, German Research Center for Environmental Health, Neuhererg, Germany
- TUM School of Medicine and Health, Institute of Human Genetics, Technical University of Munich, Munich, Germany
- DZPG (German Center for Mental Health), Munich, Germany
| | - Florian Neuhaus
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Max Planck Institute of Neurobiology, Martinsried, Germany
| | - Ilaria Vitali
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Max Planck Institute of Neurobiology, Martinsried, Germany
| | - Daniel D Lam
- Institute of Neurogenomics, Helmholtz Zentrum München GmbH, German Research Center for Environmental Health, Neuhererg, Germany
- TUM School of Medicine and Health, Institute of Human Genetics, Technical University of Munich, Munich, Germany
| | - Irene Delgado
- Cardiovascular Development Program, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
- Departamento de Genética, Fisiología y Microbiología, Facultad de Biología, Universidad Complutense de Madrid, Madrid, Spain
| | - Chao Feng
- Max Planck Institute for Biological Intelligence, Martinsried, Germany
- Max Planck Institute of Neurobiology, Martinsried, Germany
| | - Miguel Torres
- Cardiovascular Development Program, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
| | - Juliane Winkelmann
- Institute of Neurogenomics, Helmholtz Zentrum München GmbH, German Research Center for Environmental Health, Neuhererg, Germany
- TUM School of Medicine and Health, Institute of Human Genetics, Technical University of Munich, Munich, Germany
- DZPG (German Center for Mental Health), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Christian Mayer
- Max Planck Institute for Biological Intelligence, Martinsried, Germany.
- Max Planck Institute of Neurobiology, Martinsried, Germany.
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8
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Weile J, Ferra G, Boyle G, Pendyala S, Amorosi C, Yeh CL, Cote AG, Kishore N, Tabet D, van Loggerenberg W, Rayhan A, Fowler DM, Dunham MJ, Roth FP. Pacybara: accurate long-read sequencing for barcoded mutagenized allelic libraries. Bioinformatics 2024; 40:btae182. [PMID: 38569896 PMCID: PMC11021806 DOI: 10.1093/bioinformatics/btae182] [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: 12/19/2023] [Revised: 03/05/2024] [Accepted: 04/02/2024] [Indexed: 04/05/2024] Open
Abstract
MOTIVATION Long-read sequencing technologies, an attractive solution for many applications, often suffer from higher error rates. Alignment of multiple reads can improve base-calling accuracy, but some applications, e.g. sequencing mutagenized libraries where multiple distinct clones differ by one or few variants, require the use of barcodes or unique molecular identifiers. Unfortunately, sequencing errors can interfere with correct barcode identification, and a given barcode sequence may be linked to multiple independent clones within a given library. RESULTS Here we focus on the target application of sequencing mutagenized libraries in the context of multiplexed assays of variant effects (MAVEs). MAVEs are increasingly used to create comprehensive genotype-phenotype maps that can aid clinical variant interpretation. Many MAVE methods use long-read sequencing of barcoded mutant libraries for accurate association of barcode with genotype. Existing long-read sequencing pipelines do not account for inaccurate sequencing or nonunique barcodes. Here, we describe Pacybara, which handles these issues by clustering long reads based on the similarities of (error-prone) barcodes while also detecting barcodes that have been associated with multiple genotypes. Pacybara also detects recombinant (chimeric) clones and reduces false positive indel calls. In three example applications, we show that Pacybara identifies and correctly resolves these issues. AVAILABILITY AND IMPLEMENTATION Pacybara, freely available at https://github.com/rothlab/pacybara, is implemented using R, Python, and bash for Linux. It runs on GNU/Linux HPC clusters via Slurm, PBS, or GridEngine schedulers. A single-machine simplex version is also available.
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Affiliation(s)
- Jochen Weile
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Gabrielle Ferra
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States
| | - Gabriel Boyle
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States
| | - Sriram Pendyala
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States
| | - Clara Amorosi
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States
| | - Chiann-Ling Yeh
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States
| | - Atina G Cote
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Nishka Kishore
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Daniel Tabet
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Warren van Loggerenberg
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
- Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Ashyad Rayhan
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States
- Department of Bioengineering, University of Washington, Seattle, WA 98195, United States
- Brotman Baty Institute for Precision Medicine, Seattle, WA 98195, United States
| | - Maitreya J Dunham
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States
| | - Frederick P Roth
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
- Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, United States
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9
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Sintsova A, Ruscheweyh HJ, Field CM, Feer L, Nguyen BD, Daniel B, Hardt WD, Vorholt JA, Sunagawa S. mBARq: a versatile and user-friendly framework for the analysis of DNA barcodes from transposon insertion libraries, knockout mutants, and isogenic strain populations. Bioinformatics 2024; 40:btae078. [PMID: 38341646 PMCID: PMC10885212 DOI: 10.1093/bioinformatics/btae078] [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: 07/31/2023] [Revised: 12/18/2023] [Accepted: 02/08/2024] [Indexed: 02/12/2024] Open
Abstract
MOTIVATION DNA barcoding has become a powerful tool for assessing the fitness of strains in a variety of studies, including random transposon mutagenesis screens, attenuation of site-directed mutants, and population dynamics of isogenic strain pools. However, the statistical analysis, visualization, and contextualization of the data resulting from such experiments can be complex and require bioinformatic skills. RESULTS Here, we developed mBARq, a user-friendly tool designed to simplify these steps for diverse experimental setups. The tool is seamlessly integrated with an intuitive web app for interactive data exploration via the STRING and KEGG databases to accelerate scientific discovery. AVAILABILITY AND IMPLEMENTATION The tool is implemented in Python. The source code is freely available (https://github.com/MicrobiologyETHZ/mbarq) and the web app can be accessed at: https://microbiomics.io/tools/mbarq-app.
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Affiliation(s)
- Anna Sintsova
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
- Department of Biology, Institute of Microbiology, Swiss Institute of Bioinformatics, ETH Zurich, Zurich 8093, Switzerland
| | - Hans-Joachim Ruscheweyh
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
- Department of Biology, Institute of Microbiology, Swiss Institute of Bioinformatics, ETH Zurich, Zurich 8093, Switzerland
| | - Christopher M Field
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
- Department of Biology, Institute of Microbiology, Swiss Institute of Bioinformatics, ETH Zurich, Zurich 8093, Switzerland
| | - Lilith Feer
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
- Department of Biology, Institute of Microbiology, Swiss Institute of Bioinformatics, ETH Zurich, Zurich 8093, Switzerland
| | - Bidong D Nguyen
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
| | - Benjamin Daniel
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
| | - Wolf-Dietrich Hardt
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
| | - Julia A Vorholt
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
| | - Shinichi Sunagawa
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich 8093, Switzerland
- Department of Biology, Institute of Microbiology, Swiss Institute of Bioinformatics, ETH Zurich, Zurich 8093, Switzerland
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10
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Sun W, Perkins M, Huyghe M, Faraldo MM, Fre S, Perié L, Lyne AM. Extracting, filtering and simulating cellular barcodes using CellBarcode tools. NATURE COMPUTATIONAL SCIENCE 2024; 4:128-143. [PMID: 38374363 PMCID: PMC10899113 DOI: 10.1038/s43588-024-00595-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/16/2024] [Indexed: 02/21/2024]
Abstract
Identifying true DNA cellular barcodes among polymerase chain reaction and sequencing errors is challenging. Current tools are restricted in the diversity of barcode types supported or the analysis strategies implemented. As such, there is a need for more versatile and efficient tools for barcode extraction, as well as for tools to investigate which factors impact barcode detection and which filtering strategies to best apply. Here we introduce the package CellBarcode and its barcode simulation kit, CellBarcodeSim, that allows efficient and versatile barcode extraction and filtering for a range of barcode types from bulk or single-cell sequencing data using a variety of filtering strategies. Using the barcode simulation kit and biological data, we explore the technical and biological factors influencing barcode identification and provide a decision tree on how to optimize barcode identification for different barcode settings. We believe that CellBarcode and CellBarcodeSim have the capability to enhance the reproducibility and interpretation of barcode results across studies.
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Affiliation(s)
- Wenjie Sun
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France.
| | - Meghan Perkins
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Mathilde Huyghe
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Marisa M Faraldo
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Silvia Fre
- Institut Curie, Laboratory of Genetics and Developmental Biology, PSL Research University, INSERM U934, CNRS UMR3215, Paris, France
| | - Leïla Perié
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France.
| | - Anne-Marie Lyne
- Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France.
- INSERM U900, Paris, France.
- MINES ParisTech, CBIO-Centre for Computational Biology, PSL Research University, Paris, France.
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11
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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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12
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Weile J, Ferra G, Boyle G, Pendyala S, Amorosi C, Yeh CL, Cote AG, Kishore N, Tabet D, van Loggerenberg W, Rayhan A, Fowler DM, Dunham MJ, Roth FP. Pacybara: Accurate long-read sequencing for barcoded mutagenized allelic libraries. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.22.529427. [PMID: 36865234 PMCID: PMC9980134 DOI: 10.1101/2023.02.22.529427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Long read sequencing technologies, an attractive solution for many applications, often suffer from higher error rates. Alignment of multiple reads can improve base-calling accuracy, but some applications, e.g. sequencing mutagenized libraries where multiple distinct clones differ by one or few variants, require the use of barcodes or unique molecular identifiers. Unfortunately, sequencing errors can interfere with correct barcode identification, and a given barcode sequence may be linked to multiple independent clones within a given library. Here we focus on the target application of sequencing mutagenized libraries in the context of multiplexed assays of variant effects (MAVEs). MAVEs are increasingly used to create comprehensive genotype-phenotype maps that can aid clinical variant interpretation. Many MAVE methods use long-read sequencing of barcoded mutant libraries for accurate association of barcode with genotype. Existing long-read sequencing pipelines do not account for inaccurate sequencing or non-unique barcodes. Here, we describe Pacybara, which handles these issues by clustering long reads based on the similarities of (error-prone) barcodes while also detecting barcodes that have been associated with multiple genotypes. Pacybara also detects recombinant (chimeric) clones and reduces false positive indel calls. In three example applications, we show that Pacybara identifies and correctly resolves these issues.
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Affiliation(s)
- Jochen Weile
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 2E4
| | - Gabrielle Ferra
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Gabriel Boyle
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Sriram Pendyala
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Clara Amorosi
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Chiann-Ling Yeh
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Atina G Cote
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 2E4
| | - Nishka Kishore
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 2E4
| | - Daniel Tabet
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 2E4
| | - Warren van Loggerenberg
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 2E4
| | - Ashyad Rayhan
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 2E4
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA 98195, USA
| | - Maitreya J Dunham
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Frederick P Roth
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, M5G 1X5, Canada
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 2E4
- Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
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13
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Chambers MJ, Scobell S, Sadhu MJ. Systematic characterization of the local evolutionary space available to human PKR and vaccinia virus K3. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.21.568178. [PMID: 38076952 PMCID: PMC10705557 DOI: 10.1101/2023.11.21.568178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
The interfaces between host and viral proteins can be dynamic spaces in which genetic variants are continually pursued, giving rise to evolutionary arms races. One such scenario is found between the mammalian innate immunity protein PKR (protein kinase R) and the poxvirus antagonist K3. Once activated, PKR phosphorylates the natural substrate eIF2α, which halts protein synthesis within the cell and prevents viral replication. K3 acts as a pseudosubstrate antagonist against PKR by directly antagonizing this halt in protein synthesis, enabling poxviruses to replicate in the cell. Exploring the impact of genetic variants in both PKR and K3 is necessary not only to highlight residues of evolutionary constraint and opportunity but also to elucidate the mechanism by which human PKR is able to subvert a rapidly evolving viral antagonist. To systematically explore this dynamic interface, we have generated a combinatorial library of PKR and K3 missense variants to be co-expressed and characterized in a high-throughput yeast selection assay. This assay allows us to characterize hundreds of thousands of unique PKR-K3 genetic combinations in a single pooled experiment. Our results highlight specific missense variants available to PKR that subvert the K3 antagonist. We find that improved PKR variants are readily available at sites under positive selection, with limited opportunity at sites interfacing with K3 and eIF2α. Additionally, we find many variants that improve and disable K3 antagonism, suggesting a pliable interface. We reason that this approach can be leveraged to explore the evolutionary plasticity of many other host-virus interfaces.
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Affiliation(s)
- Michael J Chambers
- Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
- Department of Microbiology & Immunology, Georgetown University, Washington DC, USA
| | - Sophia Scobell
- Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Meru J Sadhu
- Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
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14
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Kosterlitz O, Grassi N, Werner B, McGee RS, Top EM, Kerr B. Evolutionary "Crowdsourcing": Alignment of Fitness Landscapes Allows for Cross-species Adaptation of a Horizontally Transferred Gene. Mol Biol Evol 2023; 40:msad237. [PMID: 37931146 PMCID: PMC10657783 DOI: 10.1093/molbev/msad237] [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] [Received: 04/06/2023] [Revised: 09/15/2023] [Accepted: 10/10/2023] [Indexed: 11/08/2023] Open
Abstract
Genes that undergo horizontal gene transfer (HGT) evolve in different genomic backgrounds. Despite the ubiquity of cross-species HGT, the effects of switching hosts on gene evolution remains understudied. Here, we present a framework to examine the evolutionary consequences of host-switching and apply this framework to an antibiotic resistance gene commonly found on conjugative plasmids. Specifically, we determined the adaptive landscape of this gene for a small set of mutationally connected genotypes in 3 enteric species. We uncovered that the landscape topographies were largely aligned with minimal host-dependent mutational effects. By simulating gene evolution over the experimentally gauged landscapes, we found that the adaptive evolution of the mobile gene in one species translated to adaptation in another. By simulating gene evolution over artificial landscapes, we found that sufficient alignment between landscapes ensures such "adaptive equivalency" across species. Thus, given adequate landscape alignment within a bacterial community, vehicles of HGT such as plasmids may enable a distributed form of genetic evolution across community members, where species can "crowdsource" adaptation.
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Affiliation(s)
- Olivia Kosterlitz
- Biology Department, University of Washington, Seattle, WA 98195, USA
- BEACON Center for the Study of Evolution in Action, East Lansing, MI 48824, USA
| | - Nathan Grassi
- Biology Department, University of Washington, Seattle, WA 98195, USA
| | - Bailey Werner
- Biology Department, University of Washington, Seattle, WA 98195, USA
| | - Ryan Seamus McGee
- BEACON Center for the Study of Evolution in Action, East Lansing, MI 48824, USA
- Department of Neuroscience, Washington University, St.Louis, MO 63110, USA
| | - Eva M Top
- BEACON Center for the Study of Evolution in Action, East Lansing, MI 48824, USA
- Department of Biological Sciences and Institute for Interdisciplinary Data Sciences, University of Idaho, Moscow, ID 83844, USA
| | - Benjamin Kerr
- Biology Department, University of Washington, Seattle, WA 98195, USA
- BEACON Center for the Study of Evolution in Action, East Lansing, MI 48824, USA
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15
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Han R, Qi J, Xue Y, Sun X, Zhang F, Gao X, Li G. HycDemux: a hybrid unsupervised approach for accurate barcoded sample demultiplexing in nanopore sequencing. Genome Biol 2023; 24:222. [PMID: 37798751 PMCID: PMC10552309 DOI: 10.1186/s13059-023-03053-1] [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: 01/09/2022] [Accepted: 09/08/2023] [Indexed: 10/07/2023] Open
Abstract
DNA barcodes enable Oxford Nanopore sequencing to sequence multiple barcoded DNA samples on a single flow cell. DNA sequences with the same barcode need to be grouped together through demultiplexing. As the number of samples increases, accurate demultiplexing becomes difficult. We introduce HycDemux, which incorporates a GPU-parallelized hybrid clustering algorithm that uses nanopore signals and DNA sequences for accurate data clustering, alongside a voting-based module to finalize the demultiplexing results. Comprehensive experiments demonstrate that our approach outperforms unsupervised tools in short sequence fragment clustering and performs more robustly than current state-of-the-art demultiplexing tools for complex multi-sample sequencing data.
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Affiliation(s)
- Renmin Han
- Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China
| | - Junhai Qi
- Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China
- BioMap Research, California, USA
| | - Yang Xue
- Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China
| | - Xiujuan Sun
- High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
| | - Fa Zhang
- School of Medical Technolgoy, Beijing Institute of Technology, Beijing, 100085, China.
| | - Xin Gao
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, 23955, Saudi Arabia.
| | - Guojun Li
- Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China.
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16
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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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17
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Negreira GH, de Groote R, Van Giel D, Monsieurs P, Maes I, de Muylder G, Van den Broeck F, Dujardin J, Domagalska MA. The adaptive roles of aneuploidy and polyclonality in Leishmania in response to environmental stress. EMBO Rep 2023; 24:e57413. [PMID: 37470283 PMCID: PMC10481652 DOI: 10.15252/embr.202357413] [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: 04/28/2023] [Revised: 06/15/2023] [Accepted: 06/30/2023] [Indexed: 07/21/2023] Open
Abstract
Aneuploidy is generally considered harmful, but in some microorganisms, it can act as an adaptive mechanism against environmental stress. Here, we use Leishmania-a protozoan parasite with remarkable genome plasticity-to study the early steps of aneuploidy evolution under high drug pressure (using antimony or miltefosine as stressors). By combining single-cell genomics, lineage tracing with cellular barcodes, and longitudinal genome characterization, we reveal that aneuploidy changes under antimony pressure result from polyclonal selection of pre-existing karyotypes, complemented by further and rapid de novo alterations in chromosome copy number along evolution. In the case of miltefosine, early parasite adaptation is associated with independent point mutations in a miltefosine transporter gene, while aneuploidy changes only emerge later, upon exposure to increased drug levels. Therefore, polyclonality and genome plasticity are hallmarks of parasite adaptation, but the scenario of aneuploidy dynamics depends on the nature and strength of the environmental stress as well as on the existence of other pre-adaptive mechanisms.
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Affiliation(s)
- Gabriel H Negreira
- Molecular Parasitology UnitInstitute of Tropical Medicine AntwerpAntwerpBelgium
| | - Robin de Groote
- Molecular Parasitology UnitInstitute of Tropical Medicine AntwerpAntwerpBelgium
| | - Dorien Van Giel
- Molecular Parasitology UnitInstitute of Tropical Medicine AntwerpAntwerpBelgium
| | - Pieter Monsieurs
- Molecular Parasitology UnitInstitute of Tropical Medicine AntwerpAntwerpBelgium
| | - Ilse Maes
- Molecular Parasitology UnitInstitute of Tropical Medicine AntwerpAntwerpBelgium
| | | | - Frederik Van den Broeck
- Molecular Parasitology UnitInstitute of Tropical Medicine AntwerpAntwerpBelgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical ResearchKatholieke Universiteit LeuvenLeuvenBelgium
| | - Jean‐Claude Dujardin
- Molecular Parasitology UnitInstitute of Tropical Medicine AntwerpAntwerpBelgium
- Department of Biomedical SciencesUniversity of AntwerpAntwerpBelgium
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18
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Baryshev A, La Fleur A, Groves B, Michel C, Baker D, Ljubetič A, Seelig G. Massively parallel protein-protein interaction measurement by sequencing (MP3-seq) enables rapid screening of protein heterodimers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.08.527770. [PMID: 36798377 PMCID: PMC9934699 DOI: 10.1101/2023.02.08.527770] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Protein-protein interactions (PPIs) regulate many cellular processes, and engineered PPIs have cell and gene therapy applications. Here we introduce massively parallel protein-protein interaction measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast-two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs, and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Finally, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics simulation energy terms to predict MP3-seq values. We find that AF-M and AF-M complex prediction-based models could be valuable for pre-screening interactions, but that measuring interactions experimentally remains necessary to rank their strengths quantitatively.
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Affiliation(s)
- Alexander Baryshev
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
| | - Alyssa La Fleur
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
| | - Benjamin Groves
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
| | - Cirstyn Michel
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| | - Ajasja Ljubetič
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Department for Synthetic Biology and Immunology, National Institute of Chemistry, Ljubljana SI-1000, Slovenia
| | - Georg Seelig
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
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19
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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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20
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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: 11] [Impact Index Per Article: 11.0] [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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21
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Xu H, Li C, Xu C, Zhang J. Chance promoter activities illuminate the origins of eukaryotic intergenic transcriptions. Nat Commun 2023; 14:1826. [PMID: 37005399 PMCID: PMC10067814 DOI: 10.1038/s41467-023-37610-w] [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: 08/04/2022] [Accepted: 03/23/2023] [Indexed: 04/04/2023] Open
Abstract
It is debated whether the pervasive intergenic transcription from eukaryotic genomes has functional significance or simply reflects the promiscuity of RNA polymerases. We approach this question by comparing chance promoter activities with the expression levels of intergenic regions in the model eukaryote Saccharomyces cerevisiae. We build a library of over 105 strains, each carrying a 120-nucleotide, chromosomally integrated, completely random sequence driving the potential transcription of a barcode. Quantifying the RNA concentration of each barcode in two environments reveals that 41-63% of random sequences have significant, albeit usually low, promoter activities. Therefore, even in eukaryotes, where the presence of chromatin is thought to repress transcription, chance transcription is prevalent. We find that only 1-5% of yeast intergenic transcriptions are unattributable to chance promoter activities or neighboring gene expressions, and these transcriptions exhibit higher-than-expected environment-specificity. These findings suggest that only a minute fraction of intergenic transcription is functional in yeast.
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Affiliation(s)
- Haiqing Xu
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Chuan Li
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
- Microsoft, Redmond, WA, USA
| | - Chuan Xu
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
- Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China
| | - Jianzhi Zhang
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.
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22
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English MA, Alcantar MA, Collins JJ. A self‐propagating, barcoded transposon system for the dynamic rewiring of genomic networks. Mol Syst Biol 2023:e11398. [DOI: 10.15252/msb.202211398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 03/08/2023] [Accepted: 03/10/2023] [Indexed: 03/29/2023] Open
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23
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Kijima Y, Evans-Yamamoto D, Toyoshima H, Yachie N. A universal sequencing read interpreter. SCIENCE ADVANCES 2023; 9:eadd2793. [PMID: 36598975 PMCID: PMC9812397 DOI: 10.1126/sciadv.add2793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
Massively parallel DNA sequencing has led to the rapid growth of highly multiplexed experiments in biology. These experiments produce unique sequencing results that require specific analysis pipelines to decode highly structured reads. However, no versatile framework that interprets sequencing reads to extract their encoded information for downstream biological analysis has been developed. Here, we report INTERSTELLAR (interpretation, scalable transformation, and emulation of large-scale sequencing reads) that decodes data values encoded in theoretically any type of sequencing read and translates them into sequencing reads of another structure of choice. We demonstrated that INTERSTELLAR successfully extracted information from a range of short- and long-read sequencing reads and translated those of single-cell (sc)RNA-seq, scATAC-seq, and spatial transcriptomics to be analyzed by different software tools that have been developed for conceptually the same types of experiments. INTERSTELLAR will greatly facilitate the development of sequencing-based experiments and sharing of data analysis pipelines.
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Affiliation(s)
- Yusuke Kijima
- School of Biomedical Engineering, Faculty of Applied Science and Faculty of Medicine, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan
- Department of Aquatic Bioscience, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan
| | - Daniel Evans-Yamamoto
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0035, Japan
| | - Hiromi Toyoshima
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan
| | - Nozomu Yachie
- School of Biomedical Engineering, Faculty of Applied Science and Faculty of Medicine, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan
- Twitter: @yachielab
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24
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Wild SA, Cannell IG, Nicholls A, Kania K, Bressan D, Hannon GJ, Sawicka K. Clonal transcriptomics identifies mechanisms of chemoresistance and empowers rational design of combination therapies. eLife 2022; 11:e80981. [PMID: 36525288 PMCID: PMC9757829 DOI: 10.7554/elife.80981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 11/10/2022] [Indexed: 12/23/2022] Open
Abstract
Tumour heterogeneity is thought to be a major barrier to successful cancer treatment due to the presence of drug resistant clonal lineages. However, identifying the characteristics of such lineages that underpin resistance to therapy has remained challenging. Here, we utilise clonal transcriptomics with WILD-seq; Wholistic Interrogation of Lineage Dynamics by sequencing, in mouse models of triple-negative breast cancer (TNBC) to understand response and resistance to therapy, including BET bromodomain inhibition and taxane-based chemotherapy. These analyses revealed oxidative stress protection by NRF2 as a major mechanism of taxane resistance and led to the discovery that our tumour models are collaterally sensitive to asparagine deprivation therapy using the clinical stage drug L-asparaginase after frontline treatment with docetaxel. In summary, clonal transcriptomics with WILD-seq identifies mechanisms of resistance to chemotherapy that are also operative in patients and pin points asparagine bioavailability as a druggable vulnerability of taxane-resistant lineages.
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Affiliation(s)
- Sophia A Wild
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson WayCambridgeUnited Kingdom
| | - Ian G Cannell
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson WayCambridgeUnited Kingdom
| | - Ashley Nicholls
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson WayCambridgeUnited Kingdom
| | - Katarzyna Kania
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson WayCambridgeUnited Kingdom
| | - Dario Bressan
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson WayCambridgeUnited Kingdom
| | - Gregory J Hannon
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson WayCambridgeUnited Kingdom
| | - Kirsty Sawicka
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson WayCambridgeUnited Kingdom
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25
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Blois S, Goetz BM, Bull JJ, Sullivan CS. Interpreting and de-noising genetically engineered barcodes in a DNA virus. PLoS Comput Biol 2022; 18:e1010131. [PMID: 36413582 PMCID: PMC9725130 DOI: 10.1371/journal.pcbi.1010131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 12/06/2022] [Accepted: 11/08/2022] [Indexed: 11/23/2022] Open
Abstract
The concept of a nucleic acid barcode applied to pathogen genomes is easy to grasp and the many possible uses are straightforward. But implementation may not be easy, especially when growing through multiple generations or assaying the pathogen long-term. The potential problems include: the barcode might alter fitness, the barcode may accumulate mutations, and construction of the marked pathogens may result in unintended barcodes that are not as designed. Here, we generate approximately 5,000 randomized barcodes in the genome of the prototypic small DNA virus murine polyomavirus. We describe the challenges faced with interpreting the barcode sequences obtained from the library. Our Illumina NextSeq sequencing recalled much greater variation in barcode sequencing reads than the expected 5,000 barcodes-necessarily stemming from the Illumina library processing and sequencing error. Using data from defined control virus genomes cloned into plasmid backbones we develop a vetted post-sequencing method to cluster the erroneous reads around the true virus genome barcodes. These findings may foreshadow problems with randomized barcodes in other microbial systems and provide a useful approach for future work utilizing nucleic acid barcoded pathogens.
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Affiliation(s)
- Sylvain Blois
- Department of Molecular Biosciences, LaMontagne Center for Infectious Disease, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Biomedical Sciences, University of Cagliari, Monserrato, Cagliari, Italy
| | - Benjamin M. Goetz
- Center for Biomedical Research Support, The University of Texas at Austin, Austin, Texas, United States of America
| | - James J. Bull
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
| | - Christopher S. Sullivan
- Department of Molecular Biosciences, LaMontagne Center for Infectious Disease, The University of Texas at Austin, Austin, Texas, United States of America
- * E-mail:
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26
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Mullis MN, Ghione C, Lough-Stevens M, Goldstein I, Matsui T, Levy SF, Dean MD, Ehrenreich IM. Complex genetics cause and constrain fungal persistence in different parts of the mammalian body. Genetics 2022; 222:6698696. [PMID: 36103708 PMCID: PMC9630980 DOI: 10.1093/genetics/iyac138] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/26/2022] [Indexed: 12/05/2022] Open
Abstract
Determining how genetic polymorphisms enable certain fungi to persist in mammalian hosts can improve understanding of opportunistic fungal pathogenesis, a source of substantial human morbidity and mortality. We examined the genetic basis of fungal persistence in mice using a cross between a clinical isolate and the lab reference strain of the budding yeast Saccharomyces cerevisiae. Employing chromosomally encoded DNA barcodes, we tracked the relative abundances of 822 genotyped, haploid segregants in multiple organs over time and performed linkage mapping of their persistence in hosts. Detected loci showed a mix of general and antagonistically pleiotropic effects across organs. General loci showed similar effects across all organs, while antagonistically pleiotropic loci showed contrasting effects in the brain vs the kidneys, liver, and spleen. Persistence in an organ required both generally beneficial alleles and organ-appropriate pleiotropic alleles. This genetic architecture resulted in many segregants persisting in the brain or in nonbrain organs, but few segregants persisting in all organs. These results show complex combinations of genetic polymorphisms collectively cause and constrain fungal persistence in different parts of the mammalian body.
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Affiliation(s)
- Martin N Mullis
- University of Southern California Molecular and Computational Biology Section, Department of Biological Sciences, , Los Angeles, CA 90089, USA
| | - Caleb Ghione
- University of Southern California Molecular and Computational Biology Section, Department of Biological Sciences, , Los Angeles, CA 90089, USA
| | - Michael Lough-Stevens
- University of Southern California Molecular and Computational Biology Section, Department of Biological Sciences, , Los Angeles, CA 90089, USA
| | - Ilan Goldstein
- University of Southern California Molecular and Computational Biology Section, Department of Biological Sciences, , Los Angeles, CA 90089, USA
| | - Takeshi Matsui
- Stanford University Joint Initiative for Metrology in Biology, , CA 94305, USA
- SLAC National Accelerator Laboratory , Menlo Park, CA, 94025, USA
- Stanford University Department of Genetics, , Stanford, CA 94305, USA
| | - Sasha F Levy
- Stanford University Joint Initiative for Metrology in Biology, , CA 94305, USA
- SLAC National Accelerator Laboratory , Menlo Park, CA, 94025, USA
- Stanford University Department of Genetics, , Stanford, CA 94305, USA
| | - Matthew D Dean
- University of Southern California Molecular and Computational Biology Section, Department of Biological Sciences, , Los Angeles, CA 90089, USA
| | - Ian M Ehrenreich
- University of Southern California Molecular and Computational Biology Section, Department of Biological Sciences, , Los Angeles, CA 90089, USA
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27
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Tavakolian N, Frazão JG, Bendixsen D, Stelkens R, Li CB. Shepherd: Accurate Clustering for Correcting DNA Barcode Errors. Bioinformatics 2022; 38:3710-3716. [PMID: 35708611 PMCID: PMC9344852 DOI: 10.1093/bioinformatics/btac395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 03/26/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
Motivation DNA barcodes are short, random nucleotide sequences introduced into cell populations to track the relative counts of hundreds of thousands of individual lineages over time. Lineage tracking is widely applied, e.g. to understand evolutionary dynamics in microbial populations and the progression of breast cancer in humans. Barcode sequences are unknown upon insertion and must be identified using next-generation sequencing technology, which is error prone. In this study, we frame the barcode error correction task as a clustering problem with the aim to identify true barcode sequences from noisy sequencing data. We present Shepherd, a novel clustering method that is based on an indexing system of barcode sequences using k-mers, and a Bayesian statistical test incorporating a substitution error rate to distinguish true from error sequences. Results When benchmarking with synthetic data, Shepherd provides barcode count estimates that are significantly more accurate than state-of-the-art methods, producing 10–150 times fewer spurious lineages. For empirical data, Shepherd produces results that are consistent with the improvements seen on synthetic data. These improvements enable higher resolution lineage tracking and more accurate estimates of biologically relevant quantities, e.g. the detection of small effect mutations. Availability and implementation A Python implementation of Shepherd is freely available at: https://www.github.com/Nik-Tavakolian/Shepherd. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nik Tavakolian
- Department of Mathematics, Stockholm University, Stockholm, 10691, Sweden
| | | | - Devin Bendixsen
- Department of Zoology, Stockholm University, Stockholm, 10691, Sweden
| | - Rike Stelkens
- Department of Zoology, Stockholm University, Stockholm, 10691, Sweden
| | - Chun-Biu Li
- Department of Mathematics, Stockholm University, Stockholm, 10691, Sweden
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28
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Aggeli D, Marad DA, Liu X, Buskirk SW, Levy SF, Lang GI. Overdominant and partially dominant mutations drive clonal adaptation in diploid Saccharomyces cerevisiae. Genetics 2022; 221:6569837. [PMID: 35435209 PMCID: PMC9157133 DOI: 10.1093/genetics/iyac061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 04/06/2022] [Indexed: 11/14/2022] Open
Abstract
Identification of adaptive targets in experimental evolution typically relies on extensive replication and genetic reconstruction. An alternative approach is to directly assay all mutations in an evolved clone by generating pools of segregants that contain random combinations of evolved mutations. Here, we apply this method to six Saccharomyces cerevisiae clones isolated from four diploid populations that were clonally evolved for 2,000 generations in rich glucose medium. Each clone contains 17-26 mutations relative to the ancestor. We derived intermediate genotypes between the founder and the evolved clones by bulk mating sporulated cultures of the evolved clones to a barcoded haploid version of the ancestor. We competed the resulting barcoded diploids en masse and quantified fitness in the experimental and alternative environments by barcode sequencing. We estimated average fitness effects of evolved mutations using barcode-based fitness assays and whole genome sequencing for a subset of segregants. In contrast to our previous work with haploid evolved clones, we find that diploids carry fewer beneficial mutations, with modest fitness effects (up to 5.4%) in the environment in which they arose. In agreement with theoretical expectations, reconstruction experiments show that all mutations with a detectable fitness effect manifest some degree of dominance over the ancestral allele, and most are overdominant. Genotypes with lower fitness effects in alternative environments allowed us to identify conditions that drive adaptation in our system.
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Affiliation(s)
- Dimitra Aggeli
- Department of Biological Sciences, Lehigh University, Bethlehem, PA18015, USA
| | - Daniel A Marad
- Department of Biological Sciences, Lehigh University, Bethlehem, PA18015, USA
| | - Xianan Liu
- Joint Initiative for Metrology in Biology, SLAC National Accelerator Laboratory, Stanford University, Stanford, CA94025, USA
| | - Sean W Buskirk
- Department of Biological Sciences, Lehigh University, Bethlehem, PA18015, USA.,Department of Biology, West Chester University, West Chester, PA19383, USA
| | - Sasha F Levy
- Joint Initiative for Metrology in Biology, SLAC National Accelerator Laboratory, Stanford University, Stanford, CA94025, USA
| | - Gregory I Lang
- Department of Biological Sciences, Lehigh University, Bethlehem, PA18015, USA
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29
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Konno N, Kijima Y, Watano K, Ishiguro S, Ono K, Tanaka M, Mori H, Masuyama N, Pratt D, Ideker T, Iwasaki W, Yachie N. Deep distributed computing to reconstruct extremely large lineage trees. Nat Biotechnol 2022; 40:566-575. [PMID: 34992246 PMCID: PMC9934975 DOI: 10.1038/s41587-021-01111-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 10/01/2021] [Indexed: 02/07/2023]
Abstract
Phylogeny estimation (the reconstruction of evolutionary trees) has recently been applied to CRISPR-based cell lineage tracing, allowing the developmental history of an individual tissue or organism to be inferred from a large number of mutated sequences in somatic cells. However, current computational methods are not able to construct phylogenetic trees from extremely large numbers of input sequences. Here, we present a deep distributed computing framework to comprehensively trace accurate large lineages (FRACTAL) that substantially enhances the scalability of current lineage estimation software tools. FRACTAL first reconstructs only an upstream lineage of the input sequences and recursively iterates the same produce for its downstream lineages using independent computing nodes. We demonstrate the utility of FRACTAL by reconstructing lineages from >235 million simulated sequences and from >16 million cells from a simulated experiment with a CRISPR system that accumulates mutations during cell proliferation. We also successfully applied FRACTAL to evolutionary tree reconstructions and to an experiment using error-prone PCR (EP-PCR) for large-scale sequence diversification.
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Affiliation(s)
- Naoki Konno
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.,Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan.,Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Yusuke Kijima
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.,Department of Aquatic Bioscience, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.,School of Biomedical Engineering, Faculty of Applied Science and Faculty of Medicine, The University of British Columbia, Vancouver, British Columbia, Canada.,These authors contributed equally: Yusuke Kijima, Keito Watano, Soh Ishiguro
| | - Keito Watano
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.,Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan.,Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.,These authors contributed equally: Yusuke Kijima, Keito Watano, Soh Ishiguro
| | - Soh Ishiguro
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.,School of Biomedical Engineering, Faculty of Applied Science and Faculty of Medicine, The University of British Columbia, Vancouver, British Columbia, Canada.,These authors contributed equally: Yusuke Kijima, Keito Watano, Soh Ishiguro
| | - Keiichiro Ono
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Mamoru Tanaka
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Hideto Mori
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.,Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Graduate School of Media and Governance, Keio University, Fujisawa, Japan
| | - Nanami Masuyama
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.,School of Biomedical Engineering, Faculty of Applied Science and Faculty of Medicine, The University of British Columbia, Vancouver, British Columbia, Canada.,Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan.,Graduate School of Media and Governance, Keio University, Fujisawa, Japan
| | - Dexter Pratt
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Trey Ideker
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA.,Departments of Bioengineering and Computer Science, University of California San Diego, La Jolla, CA, USA
| | - Wataru Iwasaki
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan.,Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Nozomu Yachie
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan. .,School of Biomedical Engineering, Faculty of Applied Science and Faculty of Medicine, The University of British Columbia, Vancouver, British Columbia, Canada. .,Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan. .,Graduate School of Media and Governance, Keio University, Fujisawa, Japan.
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30
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Matsui T, Mullis MN, Roy KR, Hale JJ, Schell R, Levy SF, Ehrenreich IM. The interplay of additivity, dominance, and epistasis on fitness in a diploid yeast cross. Nat Commun 2022; 13:1463. [PMID: 35304450 PMCID: PMC8933436 DOI: 10.1038/s41467-022-29111-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 02/22/2022] [Indexed: 12/27/2022] Open
Abstract
In diploid species, genetic loci can show additive, dominance, and epistatic effects. To characterize the contributions of these different types of genetic effects to heritable traits, we use a double barcoding system to generate and phenotype a panel of ~200,000 diploid yeast strains that can be partitioned into hundreds of interrelated families. This experiment enables the detection of thousands of epistatic loci, many whose effects vary across families. Here, we show traits are largely specified by a small number of hub loci with major additive and dominance effects, and pervasive epistasis. Genetic background commonly influences both the additive and dominance effects of loci, with multiple modifiers typically involved. The most prominent dominance modifier in our data is the mating locus, which has no effect on its own. Our findings show that the interplay between additivity, dominance, and epistasis underlies a complex genotype-to-phenotype map in diploids.
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Affiliation(s)
- Takeshi Matsui
- Joint Initiative for Metrology in Biology, Stanford, CA, 94305, USA
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Martin N Mullis
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA, 90089, USA
- Twist Bioscience, 681 Gateway Blvd, South San Francisco, CA, 94080, USA
| | - Kevin R Roy
- Joint Initiative for Metrology in Biology, Stanford, CA, 94305, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA
| | - Joseph J Hale
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA, 90089, USA
| | - Rachel Schell
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA, 90089, USA
| | - Sasha F Levy
- Joint Initiative for Metrology in Biology, Stanford, CA, 94305, USA.
- SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, 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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31
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Abell NS, DeGorter MK, Gloudemans MJ, Greenwald E, Smith KS, He Z, Montgomery SB. Multiple causal variants underlie genetic associations in humans. Science 2022; 375:1247-1254. [PMID: 35298243 DOI: 10.1126/science.abj5117] [Citation(s) in RCA: 65] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Associations between genetic variation and traits are often in noncoding regions with strong linkage disequilibrium (LD), where a single causal variant is assumed to underlie the association. We applied a massively parallel reporter assay (MPRA) to functionally evaluate genetic variants in high, local LD for independent cis-expression quantitative trait loci (eQTL). We found that 17.7% of eQTLs exhibit more than one major allelic effect in tight LD. The detected regulatory variants were highly and specifically enriched for activating chromatin structures and allelic transcription factor binding. Integration of MPRA profiles with eQTL/complex trait colocalizations across 114 human traits and diseases identified causal variant sets demonstrating how genetic association signals can manifest through multiple, tightly linked causal variants.
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Affiliation(s)
- Nathan S Abell
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Marianne K DeGorter
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA 94305, USA
| | | | - Emily Greenwald
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Kevin S Smith
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA.,Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Stephen B Montgomery
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA 94305, USA.,Department of Pathology, School of Medicine, Stanford University, Stanford, CA 94305, USA
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32
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Single-cell delineation of lineage and genetic identity in the mouse brain. Nature 2022; 601:404-409. [PMID: 34912118 PMCID: PMC8770128 DOI: 10.1038/s41586-021-04237-0] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 11/12/2021] [Indexed: 01/25/2023]
Abstract
During neurogenesis, mitotic progenitor cells lining the ventricles of the embryonic mouse brain undergo their final rounds of cell division, giving rise to a wide spectrum of postmitotic neurons and glia1,2. The link between developmental lineage and cell-type diversity remains an open question. Here we used massively parallel tagging of progenitors to track clonal relationships and transcriptomic signatures during mouse forebrain development. We quantified clonal divergence and convergence across all major cell classes postnatally, and found diverse types of GABAergic neuron that share a common lineage. Divergence of GABAergic clones occurred during embryogenesis upon cell-cycle exit, suggesting that differentiation into subtypes is initiated as a lineage-dependent process at the progenitor cell level.
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Abstract
The use of DNA barcodes for determining changes in genotype frequencies has been instrumental to increase the scale at which we can phenotype strain libraries by using next-generation sequencing technologies. Here, we describe the determination of strain fitness for thousands of yeast strains simultaneously in a single assay using recent innovations that increase the precision of these measurements, such as the inclusion of unique-molecular identifiers (UMIs) and purification by solid-phase reverse immobilization (SPRI) beads.
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Affiliation(s)
- Claire A Chochinov
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
- Department of Biology, University of Toronto at Mississauga, Mississauga, ON, Canada
| | - Alex N Nguyen Ba
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada.
- Department of Biology, University of Toronto at Mississauga, Mississauga, ON, Canada.
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34
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Rubin BE, Diamond S, Cress BF, Crits-Christoph A, Lou YC, Borges AL, Shivram H, He C, Xu M, Zhou Z, Smith SJ, Rovinsky R, Smock DCJ, Tang K, Owens TK, Krishnappa N, Sachdeva R, Barrangou R, Deutschbauer AM, Banfield JF, Doudna JA. Species- and site-specific genome editing in complex bacterial communities. Nat Microbiol 2022; 7:34-47. [PMID: 34873292 PMCID: PMC9261505 DOI: 10.1038/s41564-021-01014-7] [Citation(s) in RCA: 108] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 10/29/2021] [Indexed: 12/13/2022]
Abstract
Understanding microbial gene functions relies on the application of experimental genetics in cultured microorganisms. However, the vast majority of bacteria and archaea remain uncultured, precluding the application of traditional genetic methods to these organisms and their interactions. Here, we characterize and validate a generalizable strategy for editing the genomes of specific organisms in microbial communities. We apply environmental transformation sequencing (ET-seq), in which nontargeted transposon insertions are mapped and quantified following delivery to a microbial community, to identify genetically tractable constituents. Next, DNA-editing all-in-one RNA-guided CRISPR-Cas transposase (DART) systems for targeted DNA insertion into organisms identified as tractable by ET-seq are used to enable organism- and locus-specific genetic manipulation in a community context. Using a combination of ET-seq and DART in soil and infant gut microbiota, we conduct species- and site-specific edits in several bacteria, measure gene fitness in a nonmodel bacterium and enrich targeted species. These tools enable editing of microbial communities for understanding and control.
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Affiliation(s)
- Benjamin E Rubin
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Spencer Diamond
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
| | - Brady F Cress
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | | | - Yue Clare Lou
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
| | - Adair L Borges
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Environmental Science, Policy and Management, University of California, Berkeley, CA, USA
| | - Haridha Shivram
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Christine He
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
- Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
| | - Michael Xu
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Zeyi Zhou
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Sara J Smith
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Rachel Rovinsky
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Dylan C J Smock
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Kimberly Tang
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Trenton K Owens
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | | | - Rohan Sachdeva
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
| | - Rodolphe Barrangou
- Department of Food, Bioprocessing and Nutrition Sciences, North Carolina State University, Raleigh, NC, USA
| | - Adam M Deutschbauer
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jillian F Banfield
- Innovative Genomics Institute, University of California, Berkeley, CA, USA.
- Department of Earth and Planetary Science, University of California, Berkeley, CA, USA.
- Environmental Science, Policy and Management, University of California, Berkeley, CA, USA.
- School of Earth Sciences, University of Melbourne, Melbourne, Victoria, Australia.
| | - Jennifer A Doudna
- Innovative Genomics Institute, University of California, Berkeley, CA, USA.
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA.
- Department of Chemistry, University of California, Berkeley, CA, USA.
- Howard Hughes Medical Institute, University of California, Berkeley, CA, USA.
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA.
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35
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Savinov A, Brandsen BM, Angell BE, Cuperus JT, Fields S. Effects of sequence motifs in the yeast 3' untranslated region determined from massively parallel assays of random sequences. Genome Biol 2021; 22:293. [PMID: 34663436 PMCID: PMC8522215 DOI: 10.1186/s13059-021-02509-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 09/30/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND The 3' untranslated region (UTR) plays critical roles in determining the level of gene expression through effects on activities such as mRNA stability and translation. Functional elements within this region have largely been identified through analyses of native genes, which contain multiple co-evolved sequence features. RESULTS To explore the effects of 3' UTR sequence elements outside of native sequence contexts, we analyze hundreds of thousands of random 50-mers inserted into the 3' UTR of a reporter gene in the yeast Saccharomyces cerevisiae. We determine relative protein expression levels from the fitness of transformants in a growth selection. We find that the consensus 3' UTR efficiency element significantly boosts expression, independent of sequence context; on the other hand, the consensus positioning element has only a small effect on expression. Some sequence motifs that are binding sites for Puf proteins substantially increase expression in the library, despite these proteins generally being associated with post-transcriptional downregulation of native mRNAs. Our measurements also allow a systematic examination of the effects of point mutations within efficiency element motifs across diverse sequence backgrounds. These mutational scans reveal the relative in vivo importance of individual bases in the efficiency element, which likely reflects their roles in binding the Hrp1 protein involved in cleavage and polyadenylation. CONCLUSIONS The regulatory effects of some 3' UTR sequence features, like the efficiency element, are consistent regardless of sequence context. In contrast, the consequences of other 3' UTR features appear to be strongly dependent on their evolved context within native genes.
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Affiliation(s)
- Andrew Savinov
- Department of Genome Sciences, University of Washington, Box 355065, Seattle, WA, 98195, USA
- Present address: Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA
| | - Benjamin M Brandsen
- Department of Genome Sciences, University of Washington, Box 355065, Seattle, WA, 98195, USA
- Department of Chemistry and Biochemistry, Creighton University, Omaha, NE, 68178, USA
| | - Brooke E Angell
- Department of Genome Sciences, University of Washington, Box 355065, Seattle, WA, 98195, USA
- Present address: Interdisciplinary Biological Sciences Graduate Program, Northwestern University, Evanston, IL, 60208, USA
| | - Josh T Cuperus
- Department of Genome Sciences, University of Washington, Box 355065, Seattle, WA, 98195, USA.
| | - Stanley Fields
- Department of Genome Sciences, University of Washington, Box 355065, Seattle, WA, 98195, USA.
- Department of Medicine, University of Washington, Box 357720, Seattle, WA, 98195, USA.
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36
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Changes in the distribution of fitness effects and adaptive mutational spectra following a single first step towards adaptation. Nat Commun 2021; 12:5193. [PMID: 34465770 PMCID: PMC8408183 DOI: 10.1038/s41467-021-25440-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 08/11/2021] [Indexed: 01/17/2023] Open
Abstract
Historical contingency and diminishing returns epistasis have been typically studied for relatively divergent genotypes and/or over long evolutionary timescales. Here, we use Saccharomyces cerevisiae to study the extent of diminishing returns and the changes in the adaptive mutational spectra following a single first adaptive mutational step. We further evolve three clones that arose under identical conditions from a common ancestor. We follow their evolutionary dynamics by lineage tracking and determine adaptive outcomes using fitness assays and whole genome sequencing. We find that diminishing returns manifests as smaller fitness gains during the 2nd step of adaptation compared to the 1st step, mainly due to a compressed distribution of fitness effects. We also find that the beneficial mutational spectra for the 2nd adaptive step are contingent on the 1st step, as we see both shared and diverging adaptive strategies. Finally, we find that adaptive loss-of-function mutations, such as nonsense and frameshift mutations, are less common in the second step of adaptation than in the first step. Analyses of both natural and experimental evolution suggest that adaptation depends on the evolutionary past and adaptive potential decreases over time. Here, by tracking yeast adaptation with DNA barcoding, the authors show that such evolutionary phenomena can be observed even after a single adaptive step.
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37
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Espinoza DA, Mortlock RD, Koelle SJ, Wu C, Dunbar CE. Interrogation of clonal tracking data using barcodetrackR. NATURE COMPUTATIONAL SCIENCE 2021; 1:280-289. [PMID: 37621673 PMCID: PMC10449013 DOI: 10.1038/s43588-021-00057-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/17/2021] [Indexed: 08/26/2023]
Abstract
Clonal tracking methods provide quantitative insights into the cellular output of genetically labelled progenitor cells across time and cellular compartments. In the context of gene and cell therapies, clonal tracking methods have enabled the tracking of progenitor cell output both in humans receiving therapies and in corresponding animal models, providing valuable insight into lineage reconstitution, clonal dynamics, and vector genotoxicity. However, the absence of a toolbox for analysis of clonal tracking data has precluded the development of standardized analytical frameworks within the field. Thus, we developed barcodetrackR, an R package and accompanying Shiny app containing diverse tools for the analysis and visualization of clonal tracking data. We demonstrate the utility of barcodetrackR in exploring longitudinal clonal patterns and lineage relationships in a number of clonal tracking studies of hematopoietic stem and progenitor cells (HSPCs) in humans receiving HSPC gene therapy and in animals receiving lentivirally transduced HSPC transplants or tumor cells.
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Affiliation(s)
- Diego A. Espinoza
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Translational Stem Cell Biology Branch, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ryland D. Mortlock
- Translational Stem Cell Biology Branch, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Samson J. Koelle
- Translational Stem Cell Biology Branch, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Statistics, University of Washington, Seattle, WA, USA
| | - Chuanfeng Wu
- Translational Stem Cell Biology Branch, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Cynthia E. Dunbar
- Translational Stem Cell Biology Branch, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
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38
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Tack DS, Tonner PD, Pressman A, Olson ND, Levy SF, Romantseva EF, Alperovich N, Vasilyeva O, Ross D. The genotype-phenotype landscape of an allosteric protein. Mol Syst Biol 2021; 17:e10179. [PMID: 33784029 PMCID: PMC8009258 DOI: 10.15252/msb.202010179] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 02/15/2021] [Accepted: 02/18/2021] [Indexed: 12/18/2022] Open
Abstract
Allostery is a fundamental biophysical mechanism that underlies cellular sensing, signaling, and metabolism. Yet a quantitative understanding of allosteric genotype-phenotype relationships remains elusive. Here, we report the large-scale measurement of the genotype-phenotype landscape for an allosteric protein: the lac repressor from Escherichia coli, LacI. Using a method that combines long-read and short-read DNA sequencing, we quantitatively measure the dose-response curves for nearly 105 variants of the LacI genetic sensor. The resulting data provide a quantitative map of the effect of amino acid substitutions on LacI allostery and reveal systematic sequence-structure-function relationships. We find that in many cases, allosteric phenotypes can be quantitatively predicted with additive or neural-network models, but unpredictable changes also occur. For example, we were surprised to discover a new band-stop phenotype that challenges conventional models of allostery and that emerges from combinations of nearly silent amino acid substitutions.
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Affiliation(s)
- Drew S Tack
- National Institute of Standards and TechnologyGaithersburgMDUSA
| | - Peter D Tonner
- National Institute of Standards and TechnologyGaithersburgMDUSA
| | - Abe Pressman
- National Institute of Standards and TechnologyGaithersburgMDUSA
| | - Nathan D Olson
- National Institute of Standards and TechnologyGaithersburgMDUSA
| | - Sasha F Levy
- SLAC National Accelerator LaboratoryMenlo ParkCAUSA
- Joint Initiative for Metrology in BiologyStanfordCAUSA
| | | | - Nina Alperovich
- National Institute of Standards and TechnologyGaithersburgMDUSA
| | - Olga Vasilyeva
- National Institute of Standards and TechnologyGaithersburgMDUSA
| | - David Ross
- National Institute of Standards and TechnologyGaithersburgMDUSA
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39
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van Tatenhove-Pel RJ, Zwering E, Boreel DF, Falk M, van Heerden JH, Kes MBMJ, Kranenburg CI, Botman D, Teusink B, Bachmann H. Serial propagation in water-in-oil emulsions selects for Saccharomyces cerevisiae strains with a reduced cell size or an increased biomass yield on glucose. Metab Eng 2021; 64:1-14. [PMID: 33418011 DOI: 10.1016/j.ymben.2020.12.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/26/2020] [Accepted: 12/15/2020] [Indexed: 11/19/2022]
Abstract
In S. cerevisiae and many other micro-organisms an increase in metabolic efficiency (i.e. ATP yield on carbon) is accompanied by a decrease in growth rate. From a fundamental point of view, studying these yield-rate trade-offs provides insight in for example microbial evolution and cellular regulation. From a biotechnological point of view, increasing the ATP yield on carbon might increase the yield of anabolic products. We here aimed to select S. cerevisiae mutants with an increased biomass yield. Serial propagation of individual cells in water-in-oil emulsions previously enabled the selection of lactococci with increased biomass yields, and adapting this protocol for yeast allowed us to enrich an engineered Crabtree-negative S. cerevisiae strain with a high biomass yield on glucose. When we started the selection with an S. cerevisiae deletion collection, serial propagation in emulsion enriched hxk2Δ and reg1Δ strains with an increased biomass yield on glucose. Surprisingly, a tps1Δ strain was highly abundant in both emulsion- and suspension-propagated populations. In a separate experiment we propagated a chemically mutagenized S. cerevisiae population in emulsion, which resulted in mutants with a higher cell number yield on glucose, but no significantly changed biomass yield. Genome analyses indicate that genes involved in glucose repression and cell cycle processes play a role in the selected phenotypes. The repeated identification of mutations in genes involved in glucose-repression indicates that serial propagation in emulsion is a valuable tool to study metabolic efficiency in S. cerevisiae.
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Affiliation(s)
- Rinke Johanna van Tatenhove-Pel
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, VU University Amsterdam, de Boelelaan 1108, 1081HV Amsterdam, the Netherlands
| | - Emile Zwering
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, VU University Amsterdam, de Boelelaan 1108, 1081HV Amsterdam, the Netherlands
| | - Daan Floris Boreel
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, VU University Amsterdam, de Boelelaan 1108, 1081HV Amsterdam, the Netherlands
| | - Martijn Falk
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, VU University Amsterdam, de Boelelaan 1108, 1081HV Amsterdam, the Netherlands
| | - Johan Hendrik van Heerden
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, VU University Amsterdam, de Boelelaan 1108, 1081HV Amsterdam, the Netherlands
| | - Mariah B M J Kes
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, VU University Amsterdam, de Boelelaan 1108, 1081HV Amsterdam, the Netherlands
| | - Cindy Iris Kranenburg
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, VU University Amsterdam, de Boelelaan 1108, 1081HV Amsterdam, the Netherlands
| | - Dennis Botman
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, VU University Amsterdam, de Boelelaan 1108, 1081HV Amsterdam, the Netherlands
| | - Bas Teusink
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, VU University Amsterdam, de Boelelaan 1108, 1081HV Amsterdam, the Netherlands
| | - Herwig Bachmann
- Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, VU University Amsterdam, de Boelelaan 1108, 1081HV Amsterdam, the Netherlands; NIZO Food Research, Kernhemseweg 2, 6718ZB, Ede, the Netherlands.
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40
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Liu Z, Miller D, Li F, Liu X, Levy SF. A large accessory protein interactome is rewired across environments. eLife 2020; 9:e62365. [PMID: 32924934 PMCID: PMC7577743 DOI: 10.7554/elife.62365] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 09/04/2020] [Indexed: 12/30/2022] Open
Abstract
To characterize how protein-protein interaction (PPI) networks change, we quantified the relative PPI abundance of 1.6 million protein pairs in the yeast Saccharomyces cerevisiae across nine growth conditions, with replication, for a total of 44 million measurements. Our multi-condition screen identified 13,764 pairwise PPIs, a threefold increase over PPIs identified in one condition. A few 'immutable' PPIs are present across all conditions, while most 'mutable' PPIs are rarely observed. Immutable PPIs aggregate into highly connected 'core' network modules, with most network remodeling occurring within a loosely connected 'accessory' module. Mutable PPIs are less likely to co-express, co-localize, and be explained by simple mass action kinetics, and more likely to contain proteins with intrinsically disordered regions, implying that environment-dependent association and binding is critical to cellular adaptation. Our results show that protein interactomes are larger than previously thought and contain highly dynamic regions that reorganize to drive or respond to cellular changes.
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Affiliation(s)
- Zhimin Liu
- Department of Biochemistry, Stony Brook UniversityStony BrookUnited States
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
| | - Darach Miller
- Joint Initiative for Metrology in BiologyStanfordUnited States
- Department of Genetics, Stanford UniversityStanfordUnited States
| | - Fangfei Li
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
- Department of Applied Mathematics and Statistics, Stony Brook UniversityStony BrookUnited States
| | - Xianan Liu
- Department of Biochemistry, Stony Brook UniversityStony BrookUnited States
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
| | - Sasha F Levy
- Department of Biochemistry, Stony Brook UniversityStony BrookUnited States
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
- Joint Initiative for Metrology in BiologyStanfordUnited States
- Department of Genetics, Stanford UniversityStanfordUnited States
- Department of Applied Mathematics and Statistics, Stony Brook UniversityStony BrookUnited States
- SLAC National Accelerator LaboratoryMenlo ParkUnited States
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41
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Beneke T, Gluenz E. Bar-seq strategies for the LeishGEdit toolbox. Mol Biochem Parasitol 2020; 239:111295. [PMID: 32659298 DOI: 10.1016/j.molbiopara.2020.111295] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 06/16/2020] [Accepted: 06/22/2020] [Indexed: 11/24/2022]
Abstract
The number of fully sequenced genomes increases steadily but the function of many genes remains unstudied. To accelerate dissection of gene function in Leishmania spp. and other kinetoplastids we previously developed a streamlined pipeline for CRISPR-Cas9 gene editing, which we termed LeishGEdit. To facilitate high-throughput mutant screens we have adapted this pipeline by barcoding mutants with unique 17-nucleotide barcodes, allowing loss-of-function screens in mixed populations. Here we present primer design and analysis tools that facilitate these bar-seq strategies. We have developed a standalone easy-to-use pipeline to design CRISPR primers suitable for the LeishGEdit toolbox for any given genome and have generated a list of 14,995 barcodes. Barcodes and oligo sequences are now accessible through our website www.leishgedit.net allowing researchers to pursue bar-seq experiments in all currently available TriTrypDB genomes (release 41). This will streamline CRISPR bar-seq assays in kinetoplastids, enabling pooled mutant screens across the community.
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Affiliation(s)
- Tom Beneke
- Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford, OX1 3RE, UK.
| | - Eva Gluenz
- Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford, OX1 3RE, UK; The Wellcome Centre for Integrative Parasitology, Institute of Infection, Immunity and Inflammation, University of Glasgow, 120 University Place, Glasgow G12 8TA, UK
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42
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Chromosomal barcoding of E. coli populations reveals lineage diversity dynamics at high resolution. Nat Ecol Evol 2020; 4:437-452. [PMID: 32094541 DOI: 10.1038/s41559-020-1103-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 01/08/2020] [Indexed: 01/28/2023]
Abstract
Evolutionary dynamics in large asexual populations is strongly influenced by multiple competing beneficial lineages, most of which segregate at very low frequencies. However, technical barriers to tracking a large number of these rare lineages in bacterial populations have so far prevented a detailed elucidation of evolutionary dynamics. Here, we overcome this hurdle by developing a chromosomal-barcoding technique that allows simultaneous tracking of approximately 450,000 distinct lineages in Escherichia coli, which we use to test the effect of sub-inhibitory concentrations of common antibiotics on the evolutionary dynamics of low-frequency lineages. We find that populations lose lineage diversity at distinct rates that correspond to their antibiotic regimen. We also determine that some lineages have similar fates across independent experiments. By analysing the trajectory dynamics, we attribute the reproducible fates of these lineages to the presence of pre-existing beneficial mutations, and we demonstrate how the relative contribution of pre-existing and de novo mutations varies across drug regimens. Finally, we reproduce the observed lineage dynamics by simulations. Altogether, our results provide a valuable methodology for studying bacterial evolution as well as insights into evolution under sub-inhibitory antibiotic levels.
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43
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Li Y, Petrov DA, Sherlock G. Single nucleotide mapping of trait space reveals Pareto fronts that constrain adaptation. Nat Ecol Evol 2019; 3:1539-1551. [PMID: 31611676 PMCID: PMC7011918 DOI: 10.1038/s41559-019-0993-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 08/21/2019] [Indexed: 01/18/2023]
Abstract
Trade-offs constrain the improvement of performance of multiple traits simultaneously. Such trade-offs define Pareto fronts, which represent a set of optimal individuals that cannot be improved in any one trait without reducing performance in another. Surprisingly, experimental evolution often yields genotypes with improved performance in all measured traits, perhaps indicating an absence of trade-offs at least in the short term. Here we densely sample adaptive mutations in Saccharomyces cerevisiae to ask whether first-step adaptive mutations result in trade-offs during the growth cycle. We isolated thousands of adaptive clones evolved under carefully chosen conditions and quantified their performances in each part of the growth cycle. We too find that some first-step adaptive mutations can improve all traits to a modest extent. However, our dense sampling allowed us to identify trade-offs and establish the existence of Pareto fronts between fermentation and respiration, and between respiration and stationary phases. Moreover, we establish that no single mutation in the ancestral genome can circumvent the detected trade-offs. Finally, we sequenced hundreds of these adaptive clones, revealing new targets of adaptation and defining the genetic basis of the identified trade-offs.
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Affiliation(s)
- Yuping Li
- Departments of Biology, Stanford University, Stanford, CA, USA
| | - Dmitri A Petrov
- Departments of Biology, Stanford University, Stanford, CA, USA.
| | - Gavin Sherlock
- Departments of Genetics, Stanford University, Stanford, CA, USA.
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44
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Sample PJ, Wang B, Reid DW, Presnyak V, McFadyen IJ, Morris DR, Seelig G. Human 5' UTR design and variant effect prediction from a massively parallel translation assay. Nat Biotechnol 2019; 37:803-809. [PMID: 31267113 PMCID: PMC7100133 DOI: 10.1038/s41587-019-0164-5] [Citation(s) in RCA: 169] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2018] [Accepted: 05/21/2019] [Indexed: 12/20/2022]
Abstract
The ability to predict the impact of cis-regulatory sequences on gene expression would facilitate discovery in fundamental and applied biology. Here we combine polysome profiling of a library of 280,000 randomized 5' untranslated regions (UTRs) with deep learning to build a predictive model that relates human 5' UTR sequence to translation. Together with a genetic algorithm, we use the model to engineer new 5' UTRs that accurately direct specified levels of ribosome loading, providing the ability to tune sequences for optimal protein expression. We show that the same approach can be extended to chemically modified RNA, an important feature for applications in mRNA therapeutics and synthetic biology. We test 35,212 truncated human 5' UTRs and 3,577 naturally occurring variants and show that the model predicts ribosome loading of these sequences. Finally, we provide evidence of 45 single-nucleotide variants (SNVs) associated with human diseases that substantially change ribosome loading and thus may represent a molecular basis for disease.
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Affiliation(s)
- Paul J Sample
- Department of Electrical Engineering, University of Washington, Seattle, WA, USA
| | - Ban Wang
- Department of Electrical Engineering, University of Washington, Seattle, WA, USA
| | | | | | | | - David R Morris
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Georg Seelig
- Department of Electrical Engineering, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
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45
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Liu X, Liu Z, Dziulko AK, Li F, Miller D, Morabito RD, Francois D, Levy SF. iSeq 2.0: A Modular and Interchangeable Toolkit for Interaction Screening in Yeast. Cell Syst 2019; 8:338-344.e8. [PMID: 30954477 PMCID: PMC6483859 DOI: 10.1016/j.cels.2019.03.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 01/10/2019] [Accepted: 03/06/2019] [Indexed: 11/24/2022]
Abstract
We developed a flexible toolkit for combinatorial screening in Saccharomyces cerevisiae, which generates large libraries of cells, each uniquely barcoded to mark a combination of DNA elements. This interaction sequencing platform (iSeq 2.0) includes genomic landing pads that assemble combinations through sequential integration of plasmids or yeast mating, 15 barcoded plasmid libraries containing split selectable markers (URA3AI, KanMXAI, HphMXAI, and NatMXAI), and an array of ∼24,000 "double-barcoder" strains that can make existing yeast libraries iSeq compatible. Various DNA elements are compatible with iSeq: DNA introduced on integrating plasmids, engineered genomic modifications, or entire genetic backgrounds. DNA element libraries are modular and interchangeable, and any two libraries can be combined, making iSeq capable of performing many new combinatorial screens by short-read sequencing.
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Affiliation(s)
- Xianan Liu
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Zhimin Liu
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Adam K Dziulko
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Fangfei Li
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA; Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-5215, USA
| | - Darach Miller
- SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA; Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA
| | - Robert D Morabito
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Danielle Francois
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Sasha F Levy
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA; Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-5215, USA; Joint Initiative for Metrology in Biology, Stanford, CA 94305-4245, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA; Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA.
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46
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Simpkins SW, Deshpande R, Nelson J, Li SC, Piotrowski JS, Ward HN, Yashiroda Y, Osada H, Yoshida M, Boone C, Myers CL. Using BEAN-counter to quantify genetic interactions from multiplexed barcode sequencing experiments. Nat Protoc 2019; 14:415-440. [PMID: 30635653 DOI: 10.1038/s41596-018-0099-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The construction of genome-wide mutant collections has enabled high-throughput, high-dimensional quantitative characterization of gene and chemical function, particularly via genetic and chemical-genetic interaction experiments. As the throughput of such experiments increases with improvements in sequencing technology and sample multiplexing, appropriate tools must be developed to handle the large volume of data produced. Here, we describe how to apply our approach to high-throughput, fitness-based profiling of pooled mutant yeast collections using the BEAN-counter software pipeline (Barcoded Experiment Analysis for Next-generation sequencing) for analysis. The software has also successfully processed data from Schizosaccharomyces pombe, Escherichia coli, and Zymomonas mobilis mutant collections. We provide general recommendations for the design of large-scale, multiplexed barcode sequencing experiments. The procedure outlined here was used to score interactions for ~4 million chemical-by-mutant combinations in our recently published chemical-genetic interaction screen of nearly 14,000 chemical compounds across seven diverse compound collections. Here we selected a representative subset of these data on which to demonstrate our analysis pipeline. BEAN-counter is open source, written in Python, and freely available for academic use. Users should be proficient at the command line; advanced users who wish to analyze larger datasets with hundreds or more conditions should also be familiar with concepts in analysis of high-throughput biological data. BEAN-counter encapsulates the knowledge we have accumulated from, and successfully applied to, our multiplexed, pooled barcode sequencing experiments. This protocol will be useful to those interested in generating their own high-dimensional, quantitative characterizations of gene or chemical function in a high-throughput manner.
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Affiliation(s)
- Scott W Simpkins
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Raamesh Deshpande
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Justin Nelson
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Sheena C Li
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Jeff S Piotrowski
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan.,Yumanity Therapeutics, Cambridge, MA, USA
| | - Henry Neil Ward
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Yoko Yashiroda
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Hiroyuki Osada
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Minoru Yoshida
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Charles Boone
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan.,Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Chad L Myers
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota Twin Cities, Minneapolis, MN, USA. .,Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA.
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47
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Qiu C, Kaplan CD. Functional assays for transcription mechanisms in high-throughput. Methods 2019; 159-160:115-123. [PMID: 30797033 PMCID: PMC6589137 DOI: 10.1016/j.ymeth.2019.02.017] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 02/18/2019] [Indexed: 01/12/2023] Open
Abstract
Dramatic increases in the scale of programmed synthesis of nucleic acid libraries coupled with deep sequencing have powered advances in understanding nucleic acid and protein biology. Biological systems centering on nucleic acids or encoded proteins greatly benefit from such high-throughput studies, given that large DNA variant pools can be synthesized and DNA, or RNA products of transcription, can be easily analyzed by deep sequencing. Here we review the scope of various high-throughput functional assays for studies of nucleic acids and proteins in general, followed by discussion of how these types of study have yielded insights into the RNA Polymerase II (Pol II) active site as an example. We discuss methodological considerations in the design and execution of these experiments that should be valuable to studies in any system.
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Affiliation(s)
- Chenxi Qiu
- Department of Medicine, Division of Translational Therapeutics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA; Cancer Research Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Craig D Kaplan
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA.
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48
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Srivastava A, Malik L, Smith T, Sudbery I, Patro R. Alevin efficiently estimates accurate gene abundances from dscRNA-seq data. Genome Biol 2019; 20:65. [PMID: 30917859 PMCID: PMC6437997 DOI: 10.1186/s13059-019-1670-y] [Citation(s) in RCA: 122] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 03/05/2019] [Indexed: 12/15/2022] Open
Abstract
We introduce alevin, a fast end-to-end pipeline to process droplet-based single-cell RNA sequencing data, performing cell barcode detection, read mapping, unique molecular identifier (UMI) deduplication, gene count estimation, and cell barcode whitelisting. Alevin's approach to UMI deduplication considers transcript-level constraints on the molecules from which UMIs may have arisen and accounts for both gene-unique reads and reads that multimap between genes. This addresses the inherent bias in existing tools which discard gene-ambiguous reads and improves the accuracy of gene abundance estimates. Alevin is considerably faster, typically eight times, than existing gene quantification approaches, while also using less memory.
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Affiliation(s)
- Avi Srivastava
- Department of Computer Science, Stony Brook University, Stony Brook, USA
| | - Laraib Malik
- Department of Computer Science, Stony Brook University, Stony Brook, USA
| | - Tom Smith
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA UK
| | - Ian Sudbery
- Sheffield Institute for Nucleic Acids, Department of Molecular Biology and Biotechnology, The University of Sheffield, Sheffield, S10 2TN UK
| | - Rob Patro
- Department of Computer Science, Stony Brook University, Stony Brook, USA
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49
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Swamy KBS, Zhou N. Experimental evolution: its principles and applications in developing stress-tolerant yeasts. Appl Microbiol Biotechnol 2019; 103:2067-2077. [PMID: 30659332 DOI: 10.1007/s00253-019-09616-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 01/03/2019] [Accepted: 01/03/2019] [Indexed: 10/27/2022]
Abstract
Stress tolerance and resistance in industrial yeast strains are important attributes for cost-effective bioprocessing. The source of stress-tolerant yeasts ranges from extremophilic environments to laboratory engineered strains. However, industrial stress-tolerant yeasts are very rare in nature as the natural environment forces them to evolve traits that optimize survival and reproduction and not the ability to withstand harsh habitat-irrelevant industrial conditions. Experimental evolution is a frequent method used to uncover the mechanisms of evolution and microbial adaption towards environmental stresses. It optimizes biological systems by means of adaptation to environmental stresses and thus has immense power of development of robust stress-tolerant yeasts. This mini-review briefly outlines the basics and implications of evolution experiments and their applications to industrial biotechnology. This work is meant to serve as an introduction to those new to the field of experimental evolution, and as a guide to biologists working in the field of yeast stress response. Future perspectives of experimental evolution for potential biotechnological applications have also been elucidated.
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Affiliation(s)
| | - Nerve Zhou
- Department of Biological Sciences and Biotechnology, Botswana International University of Science and Technology, P Bag 16, Palapye, Botswana
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50
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Lauer S, Avecilla G, Spealman P, Sethia G, Brandt N, Levy SF, Gresham D. Single-cell copy number variant detection reveals the dynamics and diversity of adaptation. PLoS Biol 2018; 16:e3000069. [PMID: 30562346 PMCID: PMC6298651 DOI: 10.1371/journal.pbio.3000069] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 11/14/2018] [Indexed: 12/13/2022] Open
Abstract
Copy number variants (CNVs) are a pervasive source of genetic variation and evolutionary potential, but the dynamics and diversity of CNVs within evolving populations remain unclear. Long-term evolution experiments in chemostats provide an ideal system for studying the molecular processes underlying CNV formation and the temporal dynamics with which they are generated, selected, and maintained. Here, we developed a fluorescent CNV reporter to detect de novo gene amplifications and deletions in individual cells. We used the CNV reporter in Saccharomyces cerevisiae to study CNV formation at the GAP1 locus, which encodes the general amino acid permease, in different nutrient-limited chemostat conditions. We find that under strong selection, GAP1 CNVs are repeatedly generated and selected during the early stages of adaptive evolution, resulting in predictable dynamics. Molecular characterization of CNV-containing lineages shows that the CNV reporter detects different classes of CNVs, including aneuploidies, nonreciprocal translocations, tandem duplications, and complex CNVs. Despite GAP1's proximity to repeat sequences that facilitate intrachromosomal recombination, breakpoint analysis revealed that short inverted repeat sequences mediate formation of at least 50% of GAP1 CNVs. Inverted repeat sequences are also found at breakpoints at the DUR3 locus, where CNVs are selected in urea-limited chemostats. Analysis of 28 CNV breakpoints indicates that inverted repeats are typically 8 nucleotides in length and separated by 40 bases. The features of these CNVs are consistent with origin-dependent inverted-repeat amplification (ODIRA), suggesting that replication-based mechanisms of CNV formation may be a common source of gene amplification. We combined the CNV reporter with barcode lineage tracking and found that 102-104 independent CNV-containing lineages initially compete within populations, resulting in extreme clonal interference. However, only a small number (18-21) of CNV lineages ever constitute more than 1% of the CNV subpopulation, and as selection progresses, the diversity of CNV lineages declines. Our study introduces a novel means of studying CNVs in heterogeneous cell populations and provides insight into their dynamics, diversity, and formation mechanisms in the context of adaptive evolution.
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Affiliation(s)
- Stephanie Lauer
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, United States of America
- Department of Biology, New York University, New York, New York, United States of America
| | - Grace Avecilla
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, United States of America
- Department of Biology, New York University, New York, New York, United States of America
| | - Pieter Spealman
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, United States of America
- Department of Biology, New York University, New York, New York, United States of America
| | - Gunjan Sethia
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, United States of America
- Department of Biology, New York University, New York, New York, United States of America
| | - Nathan Brandt
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, United States of America
- Department of Biology, New York University, New York, New York, United States of America
| | - Sasha F. Levy
- Joint Initiative for Metrology in Biology, National Institute of Standards and Technology, Stanford University, Stanford, California, United States of America
| | - David Gresham
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, United States of America
- Department of Biology, New York University, New York, New York, United States of America
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