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Real-time monitoring of subcellular states with genetically encoded redox biosensor system (RBS) in yeast cell factories. Biosens Bioelectron 2023; 222:114988. [PMID: 36521204 DOI: 10.1016/j.bios.2022.114988] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/22/2022] [Accepted: 12/04/2022] [Indexed: 12/12/2022]
Abstract
During industrial fermentation, microbial cell factories are usually confronted with environmental or metabolic stresses, leading to the imbalance of intracellular redox and the reduction of cell metabolic capacity. Here, we constructed the genetically encoded redox biosensor system (RBS) based on redox-sensitive fluorescent proteins to detect redox metabolites, including reactive oxygen species (ROS), oxidized glutathione, NADH, and NADPH in Saccharomyces cerevisiae. The functional biosensors were quantitatively characterized and the orthogonal redox biosensor system (oRBS) was designed for detecting multiple redox metabolites. Furthermore, the compartment targeted redox biosensor system (ctRBS) was constructed to detect ROS and NADPH, revealing the distribution and spatiotemporal dynamics of ROS in yeast under various stress conditions. As a proof-of-concept, RBS was applied to evaluate the redox states of engineered yeast with stress resistance and heterogenous triterpene synthesis in vivo, elucidating the redox balance significantly affecting the growth and production phenotypes. The RBS in this study allowed the exploration of the diversity of compartmental redox state and real-time monitoring of the production process of yeast, providing a reliable and effective approach for accurate and in-depth profiling of bottlenecks of yeast cell factories.
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2
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García-Ríos E, Guillamón JM. Genomic Adaptations of Saccharomyces Genus to Wine Niche. Microorganisms 2022; 10:microorganisms10091811. [PMID: 36144411 PMCID: PMC9500811 DOI: 10.3390/microorganisms10091811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/05/2022] [Accepted: 09/08/2022] [Indexed: 11/16/2022] Open
Abstract
Wine yeast have been exposed to harsh conditions for millennia, which have led to adaptive evolutionary strategies. Thus, wine yeasts from Saccharomyces genus are considered an interesting and highly valuable model to study human-drive domestication processes. The rise of whole-genome sequencing technologies together with new long reads platforms has provided new understanding about the population structure and the evolution of wine yeasts. Population genomics studies have indicated domestication fingerprints in wine yeast, including nucleotide variations, chromosomal rearrangements, horizontal gene transfer or hybridization, among others. These genetic changes contribute to genetically and phenotypically distinct strains. This review will summarize and discuss recent research on evolutionary trajectories of wine yeasts, highlighting the domestication hallmarks identified in this group of yeast.
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Affiliation(s)
- Estéfani García-Ríos
- Department of Food Biotechnology, Instituto de Agroquímica y Tecnología de los Alimentos (CSIC), Avda. Agustín Escardino, 7, 46980 Paterna, Spain
- Department of Science, Universidad Internacional de Valencia-VIU, Pintor Sorolla 21, 46002 Valencia, Spain
- Correspondence:
| | - José Manuel Guillamón
- Department of Food Biotechnology, Instituto de Agroquímica y Tecnología de los Alimentos (CSIC), Avda. Agustín Escardino, 7, 46980 Paterna, Spain
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3
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Liu Y, Zhao H, Fu B, Jiang S, Wang J, Wan Y. Mapping Cell Phenomics with Multiparametric Flow Cytometry Assays. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:272-281. [PMID: 36939758 PMCID: PMC9590532 DOI: 10.1007/s43657-021-00031-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 09/28/2021] [Accepted: 10/11/2021] [Indexed: 11/26/2022]
Abstract
Phenomics explores the complex interactions among genes, epigenetics, symbiotic microorganisms, diet, and environmental exposure based on the physical, chemical, and biological characteristics of individuals and groups. Increasingly efficient and comprehensive phenotyping techniques have been integrated into modern phenomics-related research. Multicolor flow cytometry technology provides more measurement parameters than conventional flow cytometry. Based on detailed descriptions of cell phenotypes, rare cell populations and cell subsets can be distinguished, new cell phenotypes can be discovered, and cell apoptosis characteristics can be detected, which will expand the potential of cell phenomics research. Based on the enhancements in multicolor flow cytometry hardware, software, reagents, and method design, the present review summarizes the recent advances and applications of multicolor flow cytometry in cell phenomics, illuminating the potential of applying phenomics in future studies.
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Affiliation(s)
- Yang Liu
- Biomedical Analysis Center, Army Medical University, Chongqing, 400038 China
- Chongqing Key Laboratory of Cytomics, Chongqing, 400038 China
| | - Haichu Zhao
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518055 China
| | - Boqiang Fu
- National Institute of Metrology, Beijing, 100029 China
| | - Shan Jiang
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518055 China
| | - Jing Wang
- National Institute of Metrology, Beijing, 100029 China
| | - Ying Wan
- Biomedical Analysis Center, Army Medical University, Chongqing, 400038 China
- Chongqing Key Laboratory of Cytomics, Chongqing, 400038 China
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4
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Phenomics approaches to understand genetic networks and gene function in yeast. Biochem Soc Trans 2022; 50:713-721. [PMID: 35285506 PMCID: PMC9162466 DOI: 10.1042/bst20210285] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/14/2022] [Accepted: 02/18/2022] [Indexed: 01/03/2023]
Abstract
Over the past decade, major efforts have been made to systematically survey the characteristics or phenotypes associated with genetic variation in a variety of model systems. These so-called phenomics projects involve the measurement of 'phenomes', or the set of phenotypic information that describes an organism or cell, in various genetic contexts or states, and in response to external factors, such as environmental signals. Our understanding of the phenome of an organism depends on the availability of reagents that enable systematic evaluation of the spectrum of possible phenotypic variation and the types of measurements that can be taken. Here, we highlight phenomics studies that use the budding yeast, a pioneer model organism for functional genomics research. We focus on genetic perturbation screens designed to explore genetic interactions, using a variety of phenotypic read-outs, from cell growth to subcellular morphology.
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5
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Ghanegolmohammadi F, Ohnuki S, Ohya Y. Assignment of unimodal probability distribution models for quantitative morphological phenotyping. BMC Biol 2022; 20:81. [PMID: 35361198 PMCID: PMC8969357 DOI: 10.1186/s12915-022-01283-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 03/17/2022] [Indexed: 01/02/2023] Open
Abstract
Background Cell morphology is a complex and integrative readout, and therefore, an attractive measurement for assessing the effects of genetic and chemical perturbations to cells. Microscopic images provide rich information on cell morphology; therefore, subjective morphological features are frequently extracted from digital images. However, measured datasets are fundamentally noisy; thus, estimation of the true values is an ultimate goal in quantitative morphological phenotyping. Ideal image analyses require precision, such as proper probability distribution analyses to detect subtle morphological changes, recall to minimize artifacts due to experimental error, and reproducibility to confirm the results. Results Here, we present UNIMO (UNImodal MOrphological data), a reliable pipeline for precise detection of subtle morphological changes by assigning unimodal probability distributions to morphological features of the budding yeast cells. By defining the data type, followed by validation using the model selection method, examination of 33 probability distributions revealed nine best-fitting probability distributions. The modality of the distribution was then clarified for each morphological feature using a probabilistic mixture model. Using a reliable and detailed set of experimental log data of wild-type morphological replicates, we considered the effects of confounding factors. As a result, most of the yeast morphological parameters exhibited unimodal distributions that can be used as basic tools for powerful downstream parametric analyses. The power of the proposed pipeline was confirmed by reanalyzing morphological changes in non-essential yeast mutants and detecting 1284 more mutants with morphological defects compared with a conventional approach (Box–Cox transformation). Furthermore, the combined use of canonical correlation analysis permitted global views on the cellular network as well as new insights into possible gene functions. Conclusions Based on statistical principles, we showed that UNIMO offers better predictions of the true values of morphological measurements. We also demonstrated how these concepts can provide biologically important information. This study draws attention to the necessity of employing a proper approach to do more with less. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01283-6.
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Affiliation(s)
- Farzan Ghanegolmohammadi
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Bldg. FSB-101, 5-1-5 Kashiwanoha, Kashiwa, Chiba Prefecture, 277-8562, Japan.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Shinsuke Ohnuki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Bldg. FSB-101, 5-1-5 Kashiwanoha, Kashiwa, Chiba Prefecture, 277-8562, Japan
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Bldg. FSB-101, 5-1-5 Kashiwanoha, Kashiwa, Chiba Prefecture, 277-8562, Japan. .,Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan.
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6
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Bunk D, Moriasy J, Thoma F, Jakubke C, Osman C, Hörl D. YeastMate: Neural network-assisted segmentation of mating and budding events in S. cerevisiae. Bioinformatics 2022; 38:2667-2669. [PMID: 35179572 PMCID: PMC9048668 DOI: 10.1093/bioinformatics/btac107] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/14/2022] [Accepted: 02/16/2022] [Indexed: 11/14/2022] Open
Abstract
SUMMARY Here, we introduce YeastMate, a user-friendly deep learning-based application for automated detection and segmentation of Saccharomyces cerevisiae cells and their mating and budding events in microscopy images. We build upon Mask R-CNN with a custom segmentation head for the subclassification of mother and daughter cells during lifecycle transitions. YeastMate can be used directly as a Python library or through a stand-alone GUI application and a Fiji plugin as easy to use frontends. AVAILABILITY AND IMPLEMENTATION The source code for YeastMate is freely available at https://github.com/hoerlteam/YeastMate under the MIT license. We offer installers for our software stack for Windows, macOS and Linux. A detailed user guide is available at https://yeastmate.readthedocs.io. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David Bunk
- Faculty of Biology, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Germany
| | - Julian Moriasy
- Faculty of Biology, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Germany
| | - Felix Thoma
- Faculty of Biology, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Germany
| | - Christopher Jakubke
- Faculty of Biology, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Germany
| | - Christof Osman
- Faculty of Biology, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Germany
| | - David Hörl
- Faculty of Biology, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Germany
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7
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Oliver SG. From Petri Plates to Petri Nets, a revolution in yeast biology. FEMS Yeast Res 2022; 22:6526310. [PMID: 35142857 PMCID: PMC8862034 DOI: 10.1093/femsyr/foac008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 01/26/2022] [Accepted: 02/07/2022] [Indexed: 11/22/2022] Open
Affiliation(s)
- Stephen G Oliver
- Department of Biochemistry, University of Cambridge, Sanger Building, 80 Tennis Court Road, Cambridge CB2 1GA, UK
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8
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Itto-Nakama K, Watanabe S, Kondo N, Ohnuki S, Kikuchi R, Nakamura T, Ogasawara W, Kasahara K, Ohya Y. AI-based forecasting of ethanol fermentation using yeast morphological data. Biosci Biotechnol Biochem 2021; 86:125-134. [PMID: 34751736 DOI: 10.1093/bbb/zbab188] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 10/25/2021] [Indexed: 11/12/2022]
Abstract
Several industries require getting information of products as soon as possible during fermentation. However, the trade-off between sensing speed and data quantity presents challenges for forecasting fermentation product yields. In this study, we tried to develop AI models to forecast ethanol yields in yeast fermentation cultures, using cell morphological data. Our platform involves the quick acquisition of yeast morphological images using a non-staining protocol, extraction of high-dimensional morphological data using image processing software, and forecasting of ethanol yields via supervised machine learning. We found that the neural network algorithm produced the best performance, which had a coefficient of determination of > 0.9 even at 30 and 60 min in the future. The model was validated using test data collected using the CalMorph-PC(10) system, which enables rapid image acquisition within 10 min. AI-based forecasting of product yields based on cell morphology will facilitate the management and stable production of desired biocommodities.
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Affiliation(s)
- Kaori Itto-Nakama
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwano-ha, Kashiwa, Chiba, Japan
| | - Shun Watanabe
- Chitose Laboratory Corp., Biotechnology Research Center, 907 Nogawa, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Naoko Kondo
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwano-ha, Kashiwa, Chiba, Japan
| | - Shinsuke Ohnuki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwano-ha, Kashiwa, Chiba, Japan
| | - Ryota Kikuchi
- Chitose Laboratory Corp., Biotechnology Research Center, 907 Nogawa, Miyamae-ku, Kawasaki, Kanagawa, Japan.,Circular Bioeconomy Development, Office of Society Academia Collaboration for Innovation, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto, Kyoto, Japan
| | - Toru Nakamura
- NRI System Techno Ltd., 134 Godo-cho, Hodogaya-ku, Yokohama, Kanagawa, Japan
| | - Wataru Ogasawara
- Department of Bioengineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata, Japan
| | - Ken Kasahara
- Chitose Laboratory Corp., Biotechnology Research Center, 907 Nogawa, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwano-ha, Kashiwa, Chiba, Japan.,Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, Japan
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9
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Mattiazzi Usaj M, Yeung CHL, Friesen H, Boone C, Andrews BJ. Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations. Cell Syst 2021; 12:608-621. [PMID: 34139168 DOI: 10.1016/j.cels.2021.05.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 04/26/2021] [Accepted: 05/12/2021] [Indexed: 12/26/2022]
Abstract
Single-cell image analysis provides a powerful approach for studying cell-to-cell heterogeneity, which is an important attribute of isogenic cell populations, from microbial cultures to individual cells in multicellular organisms. This phenotypic variability must be explained at a mechanistic level if biologists are to fully understand cellular function and address the genotype-to-phenotype relationship. Variability in single-cell phenotypes is obscured by bulk readouts or averaging of phenotypes from individual cells in a sample; thus, single-cell image analysis enables a higher resolution view of cellular function. Here, we consider examples of both small- and large-scale studies carried out with isogenic cell populations assessed by fluorescence microscopy, and we illustrate the advantages, challenges, and the promise of quantitative single-cell image analysis.
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Affiliation(s)
- Mojca Mattiazzi Usaj
- Department of Chemistry and Biology, Ryerson University, Toronto, ON M5B 2K3, Canada
| | - Clarence Hue Lok Yeung
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Helena Friesen
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Charles Boone
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada; RIKEN Centre for Sustainable Resource Science, Wako, Saitama 351-0198, Japan
| | - Brenda J Andrews
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada.
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10
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Rocabert C, Beslon G, Knibbe C, Bernard S. Phenotypic noise and the cost of complexity. Evolution 2020; 74:2221-2237. [PMID: 32820537 DOI: 10.1111/evo.14083] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Accepted: 08/13/2020] [Indexed: 11/28/2022]
Abstract
Experimental studies demonstrate the existence of phenotypic diversity despite constant genotype and environment. Theoretical models based on a single phenotypic character predict that during an adaptation event, phenotypic noise should be positively selected far from the fitness optimum because it increases the fitness of the genotype, and then be selected against when the population reaches the optimum. It is suggested that because of this fitness gain, phenotypic noise should promote adaptive evolution. However, it is unclear how the selective advantage of phenotypic noise is linked to the rate of evolution, and whether any advantage would hold for more realistic, multidimensional phenotypes. Indeed, complex organisms suffer a cost of complexity, where beneficial mutations become rarer as the number of phenotypic characters increases. Using a quantitative genetics approach, we first show that for a one-dimensional phenotype, phenotypic noise promotes adaptive evolution on plateaus of positive fitness, independently from the direct selective advantage on fitness. Second, we show that for multidimensional phenotypes, phenotypic noise evolves to a low-dimensional configuration, with elevated noise in the direction of the fitness optimum. Such a dimensionality reduction of the phenotypic noise promotes adaptive evolution and numerical simulations show that it reduces the cost of complexity.
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Affiliation(s)
- Charles Rocabert
- Inria, 78150 Rocquencourt, France.,Synthetic and Systems Biology Unit, Biological Research Centre, Szeged, 6726, Hungary
| | - Guillaume Beslon
- Inria, 78150 Rocquencourt, France.,LIRIS, University of Lyon, INSA-Lyon, UMR5205, Lyon, F-69621, France
| | - Carole Knibbe
- Inria, 78150 Rocquencourt, France.,CarMeN Laboratory, University of Lyon, INSA-Lyon, INSERM U1060, Lyon, F-69621, France
| | - Samuel Bernard
- Inria, 78150 Rocquencourt, France.,Institut Camille Jordan, CNRS, University of Lyon, UMR5208, Lyon, F-69622, France
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11
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Nishimura S, Matsumori N. Chemical diversity and mode of action of natural products targeting lipids in the eukaryotic cell membrane. Nat Prod Rep 2020; 37:677-702. [PMID: 32022056 DOI: 10.1039/c9np00059c] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Covering: up to 2019Nature furnishes bioactive compounds (natural products) with complex chemical structures, yet with simple, sophisticated molecular mechanisms. When natural products exhibit their activities in cells or bodies, they first have to bind or react with a target molecule in/on the cell. The cell membrane is a major target for bioactive compounds. Recently, our understanding of the molecular mechanism of interactions between natural products and membrane lipids progressed with the aid of newly-developed analytical methods. New technology reconnects old compounds with membrane lipids, while new membrane-targeting molecules are being discovered through the screening for antimicrobial potential of natural products. This review article focuses on natural products that bind to eukaryotic membrane lipids, and includes clinically important molecules and key research tools. The chemical diversity of membrane-targeting natural products and the molecular basis of lipid recognition are described. The history of how their mechanism was unveiled, and how these natural products are used in research are also mentioned.
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Affiliation(s)
- Shinichi Nishimura
- Department of Biotechnology, Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Tokyo 113-8657, Japan.
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12
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Mattiazzi Usaj M, Sahin N, Friesen H, Pons C, Usaj M, Masinas MPD, Shuteriqi E, Shkurin A, Aloy P, Morris Q, Boone C, Andrews BJ. Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability. Mol Syst Biol 2020; 16:e9243. [PMID: 32064787 PMCID: PMC7025093 DOI: 10.15252/msb.20199243] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 12/16/2019] [Accepted: 01/15/2020] [Indexed: 12/13/2022] Open
Abstract
Our ability to understand the genotype-to-phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single-cell level. To systematically assess cell-to-cell phenotypic variability, we combined automated yeast genetics, high-content screening and neural network-based image analysis of single cells, focussing on genes that influence the architecture of four subcellular compartments of the endocytic pathway as a model system. Our unbiased assessment of the morphology of these compartments-endocytic patch, actin patch, late endosome and vacuole-identified 17 distinct mutant phenotypes associated with ~1,600 genes (~30% of all yeast genes). Approximately half of these mutants exhibited multiple phenotypes, highlighting the extent of morphological pleiotropy. Quantitative analysis also revealed that incomplete penetrance was prevalent, with the majority of mutants exhibiting substantial variability in phenotype at the single-cell level. Our single-cell analysis enabled exploration of factors that contribute to incomplete penetrance and cellular heterogeneity, including replicative age, organelle inheritance and response to stress.
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Affiliation(s)
| | - Nil Sahin
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | | | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute for Science and TechnologyBarcelona, CataloniaSpain
| | - Matej Usaj
- The Donnelly CentreUniversity of TorontoTorontoONCanada
| | | | | | - Aleksei Shkurin
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute for Science and TechnologyBarcelona, CataloniaSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)Barcelona, CataloniaSpain
| | - Quaid Morris
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
- Computational and Systems Biology ProgramMemorial Sloan Kettering Cancer CenterNew YorkNYUSA
| | - Charles Boone
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
- RIKEN Centre for Sustainable Resource ScienceWakoSaitamaJapan
| | - Brenda J Andrews
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
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13
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Chessel A, Carazo Salas RE. From observing to predicting single-cell structure and function with high-throughput/high-content microscopy. Essays Biochem 2019; 63:197-208. [PMID: 31243141 PMCID: PMC6610450 DOI: 10.1042/ebc20180044] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 05/24/2019] [Accepted: 05/24/2019] [Indexed: 02/08/2023]
Abstract
In the past 15 years, cell-based microscopy has evolved its focus from observing cell function to aiming to predict it. In particular-powered by breakthroughs in computer vision, large-scale image analysis and machine learning-high-throughput and high-content microscopy imaging have enabled to uniquely harness single-cell information to systematically discover and annotate genes and regulatory pathways, uncover systems-level interactions and causal links between cellular processes, and begin to clarify and predict causal cellular behaviour and decision making. Here we review these developments, discuss emerging trends in the field, and describe how single-cell 'omics and single-cell microscopy are imminently in an intersecting trajectory. The marriage of these two fields will make possible an unprecedented understanding of cell and tissue behaviour and function.
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Affiliation(s)
- Anatole Chessel
- École polytechnique, Université Paris-Saclay, 91128 Palaiseau Cedex, France
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14
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Abstract
Sake yeast was first isolated as a single yeast strain, Saccharomyces sake, during the Meiji era. Yeast strains suitable for sake fermentation were subsequently isolated from sake brewers and distributed as Kyokai yeast strains. Sake yeast strains that produce characteristic flavors have been bred in response to various market demands and individual preferences. Interestingly, both genetic and morphological studies have indicated that sake yeast used during the Meiji era differs from new sake yeasts derived from Kyokai Strain No. 7 lineage. Here, we discuss the history of sake yeast breeding, from the discovery of sake yeast to the present day, to highlight the achievements of great Japanese scientists and engineers.
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Affiliation(s)
- Yoshikazu Ohya
- a Department of Integrated Biosciences, Graduate School of Frontier Sciences , University of Tokyo , Kashiwa , Japan
| | - Mao Kashima
- a Department of Integrated Biosciences, Graduate School of Frontier Sciences , University of Tokyo , Kashiwa , Japan
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15
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Sing TL, Hung MP, Ohnuki S, Suzuki G, San Luis BJ, McClain M, Unruh JR, Yu Z, Ou J, Marshall-Sheppard J, Huh WK, Costanzo M, Boone C, Ohya Y, Jaspersen SL, Brown GW. The budding yeast RSC complex maintains ploidy by promoting spindle pole body insertion. J Cell Biol 2018; 217:2445-2462. [PMID: 29875260 PMCID: PMC6028538 DOI: 10.1083/jcb.201709009] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 02/13/2018] [Accepted: 05/09/2018] [Indexed: 01/31/2023] Open
Abstract
Sing et al. characterize an unanticipated role for the Saccharomyces cerevisiae RSC complex in ploidy maintenance. They show that RSC promotes the distribution of Nbp1 and Ndc1 to the spindle pole body (SPB) to facilitate SPB maturation and accurate chromosome segregation. Ploidy is tightly regulated in eukaryotic cells and is critical for cell function and survival. Cells coordinate multiple pathways to ensure replicated DNA is segregated accurately to prevent abnormal changes in chromosome number. In this study, we characterize an unanticipated role for the Saccharomyces cerevisiae “remodels the structure of chromatin” (RSC) complex in ploidy maintenance. We show that deletion of any of six nonessential RSC genes causes a rapid transition from haploid to diploid DNA content because of nondisjunction events. Diploidization is accompanied by diagnostic changes in cell morphology and is stably maintained without further ploidy increases. We find that RSC promotes chromosome segregation by facilitating spindle pole body (SPB) duplication. More specifically, RSC plays a role in distributing two SPB insertion factors, Nbp1 and Ndc1, to the new SPB. Thus, we provide insight into a role for a SWI/SNF family complex in SPB duplication and ploidy maintenance.
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Affiliation(s)
- Tina L Sing
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Minnie P Hung
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Shinsuke Ohnuki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Chiba, Japan
| | - Godai Suzuki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Chiba, Japan
| | - Bryan-Joseph San Luis
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | | | - Jay R Unruh
- Stowers Institute for Medical Research, Kansas City, MO
| | - Zulin Yu
- Stowers Institute for Medical Research, Kansas City, MO
| | - Jiongwen Ou
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Jesse Marshall-Sheppard
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Won-Ki Huh
- Department of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Michael Costanzo
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Charles Boone
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Chiba, Japan
| | - Sue L Jaspersen
- Stowers Institute for Medical Research, Kansas City, MO.,Department of Molecular and Integrative Physiology, University of Kansas Medical Centre, Kansas City, KS
| | - Grant W Brown
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada .,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
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16
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Ohnuki S, Ohya Y. High-dimensional single-cell phenotyping reveals extensive haploinsufficiency. PLoS Biol 2018; 16:e2005130. [PMID: 29768403 PMCID: PMC5955526 DOI: 10.1371/journal.pbio.2005130] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 04/06/2018] [Indexed: 12/17/2022] Open
Abstract
Haploinsufficiency, a dominant phenotype caused by a heterozygous loss-of-function mutation, has been rarely observed. However, high-dimensional single-cell phenotyping of yeast morphological characteristics revealed haploinsufficiency phenotypes for more than half of 1,112 essential genes under optimal growth conditions. Additionally, 40% of the essential genes with no obvious phenotype under optimal growth conditions displayed haploinsufficiency under severe growth conditions. Haploinsufficiency was detected more frequently in essential genes than in nonessential genes. Similar haploinsufficiency phenotypes were observed mostly in mutants with heterozygous deletion of functionally related genes, suggesting that haploinsufficiency phenotypes were caused by functional defects of the genes. A global view of the gene network was presented based on the similarities of the haploinsufficiency phenotypes. Our dataset contains rich information regarding essential gene functions, providing evidence that single-cell phenotyping is a powerful approach, even in the heterozygous condition, for analyzing complex biological systems. Diploid organisms harboring a wild-type gene and a loss-of-function mutation are called heterozygotes. They are expected to have weak or no individual phenotypes because the mutation is compensated for by the intact allele. The dominant inheritance of phenotypes in heterozygotes is an exceptional phenomenon called haploinsufficiency. Haploinsufficiency was thought to be a rare occurrence; however, a sensitive technique called high-dimensional single-cell phenotyping challenges this perspective. Investigations of single-cell phenotypes revealed that a large extent of the essential genes in yeast exhibit haploinsufficiency. Our analyses also provided crucial information on gene functional networks based on haploinsufficiency phenotypes. This work shows that high-dimensional single-cell phenotyping is a useful tool that can be used to better understand complex biological systems.
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Affiliation(s)
- Shinsuke Ohnuki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba, Japan
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba, Japan
- AIST-UTokyo Advanced Operando-Measurement Technology Open Innovation Laboratory (OPERANDO-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Kashiwa, Chiba, Japan
- * E-mail:
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17
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Suzuki G, Wang Y, Kubo K, Hirata E, Ohnuki S, Ohya Y. Global study of holistic morphological effectors in the budding yeast Saccharomyces cerevisiae. BMC Genomics 2018; 19:149. [PMID: 29458326 PMCID: PMC5819264 DOI: 10.1186/s12864-018-4526-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Accepted: 02/05/2018] [Indexed: 11/16/2022] Open
Abstract
Background The size of the phenotypic effect of a gene has been thoroughly investigated in terms of fitness and specific morphological traits in the budding yeast Saccharomyces cerevisiae, but little is known about gross morphological abnormalities. Results We identified 1126 holistic morphological effectors that cause severe gross morphological abnormality when deleted, and 2241 specific morphological effectors with weak holistic effects but distinctive effects on yeast morphology. Holistic effectors fell into many gene function categories and acted as network hubs, affecting a large number of morphological traits, interacting with a large number of genes, and facilitating high protein expression. Holistic morphological abnormality was useful for estimating the importance of a gene to morphology. The contribution of gene importance to fitness and morphology could be used to efficiently classify genes into functional groups. Conclusion Holistic morphological abnormality can be used as a reproducible and reliable gene feature for high-dimensional morphological phenotyping. It can be used in many functional genomic applications. Electronic supplementary material The online version of this article (10.1186/s12864-018-4526-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Godai Suzuki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Bldg. FSB-101, 5-1-5 Kashiwanoha, Kashiwa, Chiba Prefecture, 277-8562, Japan
| | - Yang Wang
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Bldg. FSB-101, 5-1-5 Kashiwanoha, Kashiwa, Chiba Prefecture, 277-8562, Japan
| | - Karen Kubo
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Bldg. FSB-101, 5-1-5 Kashiwanoha, Kashiwa, Chiba Prefecture, 277-8562, Japan
| | - Eri Hirata
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Bldg. FSB-101, 5-1-5 Kashiwanoha, Kashiwa, Chiba Prefecture, 277-8562, Japan
| | - Shinsuke Ohnuki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Bldg. FSB-101, 5-1-5 Kashiwanoha, Kashiwa, Chiba Prefecture, 277-8562, Japan
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Bldg. FSB-101, 5-1-5 Kashiwanoha, Kashiwa, Chiba Prefecture, 277-8562, Japan. .,AIST-UTokyo Advanced Operando-Measurement Technology Open Innovation Laboratory (OPERANDO-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Bldg. Kashiwa Research Complex 2, 5-1-5 Kahiwanoha, Kashiwa, Chiba Prefecture, 277-8565, Japan.
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18
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Abstract
Although evolution by natural selection is widely regarded as the most important principle of biology, it is unknown whether phenotypic variations within and between species are mostly adaptive or neutral due to the lack of relevant studies of large, unbiased samples of phenotypic traits. Here, we examine 210 yeast morphological traits chosen because of experimental feasibility irrespective of their potential adaptive values. Our analysis is based on the premise that, under neutrality, the rate of phenotypic evolution measured in the unit of mutational size declines as the trait becomes more important to fitness, analogous to the neutral paradigm that functional genes evolve more slowly than functionless pseudogenes. However, we find faster evolution of more important morphological traits within and between species, rejecting the neutral hypothesis. By contrast, an analysis of 3,466 gene expression traits fails to refute neutrality. Thus, at least in yeast, morphological evolution appears largely adaptive, but the same may not apply to other classes of phenotypes. Our neutrality test is applicable to other species, especially genetic model organisms, for which estimations of mutational size and trait importance are relatively straightforward.
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19
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Abstract
Sake yeast was developed exclusively in Japan. Its diversification during breeding remains largely uncharacterized. To evaluate the breeding processes of the sake lineage, we thoroughly investigated the phenotypes and differentiation of 27 sake yeast strains using high-dimensional, single-cell, morphological phenotyping. Although the genetic diversity of the sake yeast lineage is relatively low, its morphological diversity has expanded substantially compared to that of the Saccharomycescerevisiae species as a whole. Evaluation of the different types of breeding processes showed that the generation of hybrids (crossbreeding) has more profound effects on cell morphology than the isolation of mutants (mutation breeding). Analysis of phenotypic robustness revealed that some sake yeast strains are more morphologically heterogeneous, possibly due to impairment of cellular network hubs. This study provides a new perspective for studying yeast breeding genetics and micro-organism breeding strategies.
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20
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Exploiting Single-Cell Quantitative Data to Map Genetic Variants Having Probabilistic Effects. PLoS Genet 2016; 12:e1006213. [PMID: 27479122 PMCID: PMC4968810 DOI: 10.1371/journal.pgen.1006213] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Accepted: 07/02/2016] [Indexed: 01/11/2023] Open
Abstract
Despite the recent progress in sequencing technologies, genome-wide association studies (GWAS) remain limited by a statistical-power issue: many polymorphisms contribute little to common trait variation and therefore escape detection. The small contribution sometimes corresponds to incomplete penetrance, which may result from probabilistic effects on molecular regulations. In such cases, genetic mapping may benefit from the wealth of data produced by single-cell technologies. We present here the development of a novel genetic mapping method that allows to scan genomes for single-cell Probabilistic Trait Loci that modify the statistical properties of cellular-level quantitative traits. Phenotypic values are acquired on thousands of individual cells, and genetic association is obtained from a multivariate analysis of a matrix of Kantorovich distances. No prior assumption is required on the mode of action of the genetic loci involved and, by exploiting all single-cell values, the method can reveal non-deterministic effects. Using both simulations and yeast experimental datasets, we show that it can detect linkages that are missed by classical genetic mapping. A probabilistic effect of a single SNP on cell shape was detected and validated. The method also detected a novel locus associated with elevated gene expression noise of the yeast galactose regulon. Our results illustrate how single-cell technologies can be exploited to improve the genetic dissection of certain common traits. The method is available as an open source R package called ptlmapper.
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21
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Large-Scale Survey of Intraspecific Fitness and Cell Morphology Variation in a Protoploid Yeast Species. G3-GENES GENOMES GENETICS 2016; 6:1063-71. [PMID: 26888866 PMCID: PMC4825641 DOI: 10.1534/g3.115.026682] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
It is now clear that the exploration of the genetic and phenotypic diversity of nonmodel species greatly improves our knowledge in biology. In this context, we recently launched a population genomic analysis of the protoploid yeast Lachancea kluyveri (formerly Saccharomyces kluyveri), highlighting a broad genetic diversity (π = 17 × 10−3) compared to the yeast model organism, S. cerevisiae (π = 4 × 10−3). Here, we sought to generate a comprehensive view of the phenotypic diversity in this species. In total, 27 natural L. kluyveri isolates were subjected to trait profiling using the following independent approaches: (i) analyzing growth in 55 growth conditions and (ii) investigating 501 morphological changes at the cellular level. Despite higher genetic diversity, the fitness variance observed in L. kluyveri is lower than that in S. cerevisiae. However, morphological features show an opposite trend. In addition, there is no correlation between the origins (ecological or geographical) of the isolate and the phenotypic patterns, demonstrating that trait variation follows neither population history nor source environment in L. kluyveri. Finally, pairwise comparisons between growth rate correlation and genetic diversity show a clear decrease in phenotypic variability linked to genome variation increase, whereas no such a trend was identified for morphological changes. Overall, this study reveals for the first time the phenotypic diversity of a distantly related species to S. cerevisiae. Given its genetic properties, L. kluyveri might be useful in further linkage mapping analyses of complex traits, and could ultimately provide a better insight into the evolution of the genotype–phenotype relationship across yeast species.
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