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Mahilkar A, Nagendra P, Alugoju P, E R, Saini S. Public good-driven release of heterogeneous resources leads to genotypic diversification of an isogenic yeast population. Evolution 2022; 76:2811-2828. [PMID: 36181481 PMCID: PMC7614384 DOI: 10.1111/evo.14646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 09/22/2022] [Indexed: 01/22/2023]
Abstract
Understanding the basis of biological diversity remains a central problem in evolutionary biology. Using microbial systems, adaptive diversification has been studied in (a) spatially heterogeneous environments, (b) temporally segregated resources, and (c) resource specialization in a homogeneous environment. However, it is not well understood how adaptive diversification can take place in a homogeneous environment containing a single resource. Starting from an isogenic population of yeast Saccharomyces cerevisiae, we report rapid adaptive diversification, when propagated in an environment containing melibiose as the carbon source. The diversification is driven due to a public good enzyme α-galactosidase, which hydrolyzes melibiose into glucose and galactose. The diversification is driven by mutations at a single locus, in the GAL3 gene in the S. cerevisiae GAL/MEL regulon. We show that metabolic co-operation involving public resources could be an important mode of generating biological diversity. Our study demonstrates sympatric diversification of yeast starting from an isogenic population and provides detailed mechanistic insights into the factors and conditions responsible for generating and maintaining the population diversity.
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Affiliation(s)
- Anjali Mahilkar
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India
| | - Prachitha Nagendra
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India
| | - Phaniendra Alugoju
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India
| | - Rajeshkannan E
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India
| | - Supreet Saini
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India
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2
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The past determines the future: sugar source history and transcriptional memory. Curr Genet 2020; 66:1029-1035. [PMID: 32686056 PMCID: PMC7599190 DOI: 10.1007/s00294-020-01094-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 06/30/2020] [Accepted: 07/07/2020] [Indexed: 11/20/2022]
Abstract
Transcriptional reinduction memory is a phenomenon whereby cells “remember” their transcriptional response to a previous stimulus such that subsequent encounters with the same stimulus can result in altered gene expression kinetics. Chromatin structure is thought to play a role in certain transcriptional memory mechanisms, leading to questions as to whether and how memory can be actively maintained and inherited to progeny through cell division. Here we summarize efforts towards dissecting chromatin-based transcriptional memory inheritance of GAL genes in Saccharomyces cerevisiae. We focus on methods and analyses of GAL (as well as MAL and INO) memory in single cells and discuss the challenges in unraveling the underlying mechanisms in yeast and higher eukaryotes.
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3
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Kavatalkar V, Saini S, Bhat PJ. Role of Noise-Induced Cellular Variability in Saccharomyces cerevisiae During Metabolic Adaptation: Causes, Consequences and Ramifications. J Indian Inst Sci 2020. [DOI: 10.1007/s41745-020-00180-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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4
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Bheda P, Aguilar-Gómez D, Becker NB, Becker J, Stavrou E, Kukhtevich I, Höfer T, Maerkl S, Charvin G, Marr C, Kirmizis A, Schneider R. Single-Cell Tracing Dissects Regulation of Maintenance and Inheritance of Transcriptional Reinduction Memory. Mol Cell 2020; 78:915-925.e7. [DOI: 10.1016/j.molcel.2020.04.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 02/15/2020] [Accepted: 04/15/2020] [Indexed: 10/24/2022]
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5
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Ilan Y. Order Through Disorder: The Characteristic Variability of Systems. Front Cell Dev Biol 2020; 8:186. [PMID: 32266266 PMCID: PMC7098948 DOI: 10.3389/fcell.2020.00186] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 03/05/2020] [Indexed: 12/17/2022] Open
Abstract
Randomness characterizes many processes in nature, and therefore its importance cannot be overstated. In the present study, we investigate examples of randomness found in various fields, to underlie its fundamental processes. The fields we address include physics, chemistry, biology (biological systems from genes to whole organs), medicine, and environmental science. Through the chosen examples, we explore the seemingly paradoxical nature of life and demonstrate that randomness is preferred under specific conditions. Furthermore, under certain conditions, promoting or making use of variability-associated parameters may be necessary for improving the function of processes and systems.
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Affiliation(s)
- Yaron Ilan
- Department of Medicine, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
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6
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Abstract
Background Organisms can be primed by metabolic exposures to continue expressing response genes even once the metabolite is no longer available, and can affect the speed and magnitude of responsive gene expression during subsequent exposures. This “metabolic transcriptional memory” can have a profound impact on the survivability of organisms in fluctuating environments. Scope of review Here I present several examples of metabolic transcriptional memory in the microbial world and discuss what is known so far regarding the underlying mechanisms, which mainly focus on chromatin modifications, protein inheritance, and broad changes in metabolic network. From these lessons learned in microbes, some insights into the yet understudied human metabolic memory can be gained. I thus discuss the implications of metabolic memory in disease progression in humans – i.e., the memory of high blood sugar exposure and the resulting effects on diabetic complications. Major conclusions Carbon source shifts from glucose to other less preferred sugars such as lactose, galactose, and maltose for energy metabolism as well as starvation of a signal transduction precursor sugar inositol are well-studied examples of metabolic transcriptional memory in Escherichia coli and Saccharomyces cerevisiae. Although the specific factors guiding metabolic transcriptional memory are not necessarily conserved from microbes to humans, the same basic mechanisms are in play, as is observed in hyperglycemic memory. Exploration of new metabolic transcriptional memory systems as well as further detailed mechanistic analyses of known memory contexts in microbes is therefore central to understanding metabolic memory in humans, and may be of relevance for the successful treatment of the ever-growing epidemic of diabetes. Metabolic exposures can prime genes to have memory. Memory of carbon source shifts occurs in all kingdoms of life. Memory is maintained through multiple mechanisms including chromatin modifications, proteins, and metabolic network. Metabolic transcriptional memory in unicellular organisms is a part of “bet-hedging” strategies to ensure survival. Hyperglycemic memory in humans contributes to diabetes and aging.
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Affiliation(s)
- Poonam Bheda
- Institute of Functional Epigenetics, Helmholtz Zentrum München, Neuherberg, Germany.
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7
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Li G, Neuert G. Multiplex RNA single molecule FISH of inducible mRNAs in single yeast cells. Sci Data 2019; 6:94. [PMID: 31209217 PMCID: PMC6572782 DOI: 10.1038/s41597-019-0106-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 05/17/2019] [Indexed: 12/31/2022] Open
Abstract
Transcript levels powerfully influence cell behavior and phenotype and are carefully regulated at several steps. Recently developed single cell approaches such as RNA single molecule fluorescence in-situ hybridization (smFISH) have produced advances in our understanding of how these steps work within the cell. In comparison to single-cell sequencing, smFISH provides more accurate quantification of RNA levels. Additionally, transcript subcellular localization is directly visualized, enabling the analysis of transcription (initiation and elongation), RNA export and degradation. As part of our efforts to investigate how this type of analysis can generate improved models of gene expression, we used smFISH to quantify the kinetic expression of STL1 and CTT1 mRNAs in single Saccharomyces cerevisiae cells upon 0.2 and 0.4 M NaCl osmotic stress. In this Data Descriptor, we outline our procedure along with our data in the form of raw images and processed mRNA counts. We discuss how these data can be used to develop single cell modelling approaches, to study fundamental processes in transcription regulation and develop single cell image processing approaches.
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Affiliation(s)
- Guoliang Li
- Department of Molecular Physiology and Biophysics, School of Medicine, Vanderbilt University, Nashville, TN, 37232, USA
| | - Gregor Neuert
- Department of Molecular Physiology and Biophysics, School of Medicine, Vanderbilt University, Nashville, TN, 37232, USA.
- Department of Biomedical Engineering, School of Engineering, Vanderbilt University, Nashville, TN, 37232, USA.
- Department of Pharmacology, School of Medicine, Vanderbilt University, Nashville, TN, 37232, USA.
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8
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Perez-Samper G, Cerulus B, Jariani A, Vermeersch L, Barrajón Simancas N, Bisschops MMM, van den Brink J, Solis-Escalante D, Gallone B, De Maeyer D, van Bael E, Wenseleers T, Michiels J, Marchal K, Daran-Lapujade P, Verstrepen KJ. The Crabtree Effect Shapes the Saccharomyces cerevisiae Lag Phase during the Switch between Different Carbon Sources. mBio 2018; 9:e01331-18. [PMID: 30377274 PMCID: PMC6212832 DOI: 10.1128/mbio.01331-18] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 09/20/2018] [Indexed: 12/16/2022] Open
Abstract
When faced with environmental changes, microbes often enter a temporary growth arrest during which they reprogram the expression of specific genes to adapt to the new conditions. A prime example of such a lag phase occurs when microbes need to switch from glucose to other, less-preferred carbon sources. Despite its industrial relevance, the genetic network that determines the duration of the lag phase has not been studied in much detail. Here, we performed a genome-wide Bar-Seq screen to identify genetic determinants of the Saccharomyces cerevisiae glucose-to-galactose lag phase. The results show that genes involved in respiration, and specifically those encoding complexes III and IV of the electron transport chain, are needed for efficient growth resumption after the lag phase. Anaerobic growth experiments confirmed the importance of respiratory energy conversion in determining the lag phase duration. Moreover, overexpression of the central regulator of respiration, HAP4, leads to significantly shorter lag phases. Together, these results suggest that the glucose-induced repression of respiration, known as the Crabtree effect, is a major determinant of microbial fitness in fluctuating carbon environments.IMPORTANCE The lag phase is arguably one of the prime characteristics of microbial growth. Longer lag phases result in lower competitive fitness in variable environments, and the duration of the lag phase is also important in many industrial processes where long lag phases lead to sluggish, less efficient fermentations. Despite the immense importance of the lag phase, surprisingly little is known about the exact molecular processes that determine its duration. Our study uses the molecular toolbox of S. cerevisiae combined with detailed growth experiments to reveal how the transition from fermentative to respirative metabolism is a key bottleneck for cells to overcome the lag phase. Together, our findings not only yield insight into the key molecular processes and genes that influence lag duration but also open routes to increase the efficiency of industrial fermentations and offer an experimental framework to study other types of lag behavior.
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Affiliation(s)
- Gemma Perez-Samper
- VIB - KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
| | - Bram Cerulus
- VIB - KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
| | - Abbas Jariani
- VIB - KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
| | - Lieselotte Vermeersch
- VIB - KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
| | | | - Markus M M Bisschops
- Department of Biotechnology, Delft University of Technology, Delft, The Netherlands
| | - Joost van den Brink
- Department of Biotechnology, Delft University of Technology, Delft, The Netherlands
| | | | - Brigida Gallone
- VIB - KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
| | - Dries De Maeyer
- Centre of Microbial and Plant Genetics, KU Leuven, Leuven, Belgium
| | - Elise van Bael
- VIB - KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
| | - Tom Wenseleers
- Laboratory of Socioecology and Social Evolution, Department of Biology, KU Leuven, Leuven, Belgium
| | - Jan Michiels
- VIB - KU Leuven Center for Microbiology, Leuven, Belgium
- Centre of Microbial and Plant Genetics, KU Leuven, Leuven, Belgium
| | - Kathleen Marchal
- Centre of Microbial and Plant Genetics, KU Leuven, Leuven, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
| | | | - Kevin J Verstrepen
- VIB - KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
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9
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Cerulus B, Jariani A, Perez-Samper G, Vermeersch L, Pietsch JMJ, Crane MM, New AM, Gallone B, Roncoroni M, Dzialo MC, Govers SK, Hendrickx JO, Galle E, Coomans M, Berden P, Verbandt S, Swain PS, Verstrepen KJ. Transition between fermentation and respiration determines history-dependent behavior in fluctuating carbon sources. eLife 2018; 7:e39234. [PMID: 30299256 PMCID: PMC6211830 DOI: 10.7554/elife.39234] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 10/05/2018] [Indexed: 01/24/2023] Open
Abstract
Cells constantly adapt to environmental fluctuations. These physiological changes require time and therefore cause a lag phase during which the cells do not function optimally. Interestingly, past exposure to an environmental condition can shorten the time needed to adapt when the condition re-occurs, even in daughter cells that never directly encountered the initial condition. Here, we use the molecular toolbox of Saccharomyces cerevisiae to systematically unravel the molecular mechanism underlying such history-dependent behavior in transitions between glucose and maltose. In contrast to previous hypotheses, the behavior does not depend on persistence of proteins involved in metabolism of a specific sugar. Instead, presence of glucose induces a gradual decline in the cells' ability to activate respiration, which is needed to metabolize alternative carbon sources. These results reveal how trans-generational transitions in central carbon metabolism generate history-dependent behavior in yeast, and provide a mechanistic framework for similar phenomena in other cell types.
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Affiliation(s)
- Bram Cerulus
- VIB Laboratory for Systems BiologyVIB-KU Leuven Center for MicrobiologyLeuvenBelgium
- Departement Microbiële en Moleculaire Systemen (M2S)CMPG Laboratory of Genetics and GenomicsLeuvenBelgium
| | - Abbas Jariani
- VIB Laboratory for Systems BiologyVIB-KU Leuven Center for MicrobiologyLeuvenBelgium
- Departement Microbiële en Moleculaire Systemen (M2S)CMPG Laboratory of Genetics and GenomicsLeuvenBelgium
| | - Gemma Perez-Samper
- VIB Laboratory for Systems BiologyVIB-KU Leuven Center for MicrobiologyLeuvenBelgium
- Departement Microbiële en Moleculaire Systemen (M2S)CMPG Laboratory of Genetics and GenomicsLeuvenBelgium
| | - Lieselotte Vermeersch
- VIB Laboratory for Systems BiologyVIB-KU Leuven Center for MicrobiologyLeuvenBelgium
- Departement Microbiële en Moleculaire Systemen (M2S)CMPG Laboratory of Genetics and GenomicsLeuvenBelgium
| | - Julian MJ Pietsch
- Centre for Synthetic and Systems Biology, School of Biological SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Matthew M Crane
- Centre for Synthetic and Systems Biology, School of Biological SciencesUniversity of EdinburghEdinburghUnited Kingdom
- Department of PathologyUniversity of WashingtonWashingtonUnited States
| | - Aaron M New
- VIB Laboratory for Systems BiologyVIB-KU Leuven Center for MicrobiologyLeuvenBelgium
- Departement Microbiële en Moleculaire Systemen (M2S)CMPG Laboratory of Genetics and GenomicsLeuvenBelgium
| | - Brigida Gallone
- VIB Laboratory for Systems BiologyVIB-KU Leuven Center for MicrobiologyLeuvenBelgium
- Departement Microbiële en Moleculaire Systemen (M2S)CMPG Laboratory of Genetics and GenomicsLeuvenBelgium
- Department of Plant Biotechnology and BioinformaticsGhent UniversityGhentBelgium
- VIB Center for Plant Systems BiologyGhentBelgium
| | - Miguel Roncoroni
- VIB Laboratory for Systems BiologyVIB-KU Leuven Center for MicrobiologyLeuvenBelgium
- Departement Microbiële en Moleculaire Systemen (M2S)CMPG Laboratory of Genetics and GenomicsLeuvenBelgium
| | - Maria C Dzialo
- VIB Laboratory for Systems BiologyVIB-KU Leuven Center for MicrobiologyLeuvenBelgium
- Departement Microbiële en Moleculaire Systemen (M2S)CMPG Laboratory of Genetics and GenomicsLeuvenBelgium
| | - Sander K Govers
- VIB Laboratory for Systems BiologyVIB-KU Leuven Center for MicrobiologyLeuvenBelgium
- Departement Microbiële en Moleculaire Systemen (M2S)CMPG Laboratory of Genetics and GenomicsLeuvenBelgium
| | - Jhana O Hendrickx
- VIB Laboratory for Systems BiologyVIB-KU Leuven Center for MicrobiologyLeuvenBelgium
- Departement Microbiële en Moleculaire Systemen (M2S)CMPG Laboratory of Genetics and GenomicsLeuvenBelgium
| | - Eva Galle
- VIB Laboratory for Systems BiologyVIB-KU Leuven Center for MicrobiologyLeuvenBelgium
- Departement Microbiële en Moleculaire Systemen (M2S)CMPG Laboratory of Genetics and GenomicsLeuvenBelgium
| | - Maarten Coomans
- VIB Laboratory for Systems BiologyVIB-KU Leuven Center for MicrobiologyLeuvenBelgium
- Departement Microbiële en Moleculaire Systemen (M2S)CMPG Laboratory of Genetics and GenomicsLeuvenBelgium
| | - Pieter Berden
- VIB Laboratory for Systems BiologyVIB-KU Leuven Center for MicrobiologyLeuvenBelgium
- Departement Microbiële en Moleculaire Systemen (M2S)CMPG Laboratory of Genetics and GenomicsLeuvenBelgium
| | - Sara Verbandt
- VIB Laboratory for Systems BiologyVIB-KU Leuven Center for MicrobiologyLeuvenBelgium
- Departement Microbiële en Moleculaire Systemen (M2S)CMPG Laboratory of Genetics and GenomicsLeuvenBelgium
| | - Peter S Swain
- Centre for Synthetic and Systems Biology, School of Biological SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Kevin J Verstrepen
- VIB Laboratory for Systems BiologyVIB-KU Leuven Center for MicrobiologyLeuvenBelgium
- Departement Microbiële en Moleculaire Systemen (M2S)CMPG Laboratory of Genetics and GenomicsLeuvenBelgium
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10
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Richard M, Chuffart F, Duplus-Bottin H, Pouyet F, Spichty M, Fulcrand E, Entrevan M, Barthelaix A, Springer M, Jost D, Yvert G. Assigning function to natural allelic variation via dynamic modeling of gene network induction. Mol Syst Biol 2018; 14:e7803. [PMID: 29335276 PMCID: PMC5787706 DOI: 10.15252/msb.20177803] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
More and more natural DNA variants are being linked to physiological traits. Yet, understanding what differences they make on molecular regulations remains challenging. Important properties of gene regulatory networks can be captured by computational models. If model parameters can be “personalized” according to the genotype, their variation may then reveal how DNA variants operate in the network. Here, we combined experiments and computations to visualize natural alleles of the yeast GAL3 gene in a space of model parameters describing the galactose response network. Alleles altering the activation of Gal3p by galactose were discriminated from those affecting its activity (production/degradation or efficiency of the activated protein). The approach allowed us to correctly predict that a non‐synonymous SNP would change the binding affinity of Gal3p with the Gal80p transcriptional repressor. Our results illustrate how personalizing gene regulatory models can be used for the mechanistic interpretation of genetic variants.
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Affiliation(s)
- Magali Richard
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France .,Univ. Grenoble Alpes, CNRS CHU Grenoble Alpes Grenoble INP TIMC-IMAG, Grenoble, France
| | - Florent Chuffart
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
| | - Hélène Duplus-Bottin
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
| | - Fanny Pouyet
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
| | - Martin Spichty
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
| | - Etienne Fulcrand
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
| | - Marianne Entrevan
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
| | - Audrey Barthelaix
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
| | - Michael Springer
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Daniel Jost
- Univ. Grenoble Alpes, CNRS CHU Grenoble Alpes Grenoble INP TIMC-IMAG, Grenoble, France
| | - Gaël Yvert
- Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
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11
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Stockwell SR, Rifkin SA. A living vector field reveals constraints on galactose network induction in yeast. Mol Syst Biol 2017; 13:908. [PMID: 28137775 PMCID: PMC5293160 DOI: 10.15252/msb.20167323] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
When a cell encounters a new environment, its transcriptional response can be constrained by its history. For example, yeast cells in galactose induce GAL genes with a speed and unanimity that depends on previous nutrient conditions. Cellular memory of long-term glucose exposure delays GAL induction and makes it highly variable with in a cell population, while other nutrient histories lead to rapid, uniform responses. To investigate how cell-level gene expression dynamics produce population-level phenotypes, we built living vector fields from thousands of single-cell time courses of the proteins Gal3p and Gal1p as cells switched to galactose from various nutrient histories. We show that, after sustained glucose exposure, the lack of these GAL transducers leads to induction delays that are long but also variable; that cellular resources constrain induction; and that bimodally distributed expression levels arise from lineage selection-a subpopulation of cells induces more quickly and outcompetes the rest. Our results illuminate cellular memory in this important model system and illustrate how resources and randomness interact to shape the response of a population to a new environment.
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Affiliation(s)
- Sarah R Stockwell
- Section of Ecology, Behavior, and Evolution, Division of Biological Sciences, University of California, San Diego La Jolla, CA, USA
| | - Scott A Rifkin
- Section of Ecology, Behavior, and Evolution, Division of Biological Sciences, University of California, San Diego La Jolla, CA, USA
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