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Lin WH, Opoc FG, Liao CW, Roy K, Steinmetz L, Leu JY. Histone deacetylase Hos2 regulates protein expression noise by potentially modulating the protein translation machinery. Nucleic Acids Res 2024; 52:7556-7571. [PMID: 38783136 PMCID: PMC11260488 DOI: 10.1093/nar/gkae432] [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: 02/02/2024] [Revised: 05/05/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024] Open
Abstract
Non-genetic variations derived from expression noise at transcript or protein levels can result in cell-to-cell heterogeneity within an isogenic population. Although cells have developed strategies to reduce noise in some cellular functions, this heterogeneity can also facilitate varying levels of regulation and provide evolutionary benefits in specific environments. Despite several general characteristics of cellular noise having been revealed, the detailed molecular pathways underlying noise regulation remain elusive. Here, we established a dual-fluorescent reporter system in Saccharomyces cerevisiae and performed experimental evolution to search for mutations that increase expression noise. By analyzing evolved cells using bulk segregant analysis coupled with whole-genome sequencing, we identified the histone deacetylase Hos2 as a negative noise regulator. A hos2 mutant down-regulated multiple ribosomal protein genes and exhibited partially compromised protein translation, indicating that Hos2 may regulate protein expression noise by modulating the translation machinery. Treating cells with translation inhibitors or introducing mutations into several Hos2-regulated ribosomal protein genes-RPS9A, RPS28B and RPL42A-enhanced protein expression noise. Our study provides an effective strategy for identifying noise regulators and also sheds light on how cells regulate non-genetic variation through protein translation.
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Affiliation(s)
- Wei-Han Lin
- Doctoral Program in Microbial Genomics, National Chung Hsing University and Academia Sinica, Taiwan
- Institute of Molecular Biology, Academia Sinica, Taipei 115, Taiwan
| | - Florica J G Opoc
- Institute of Molecular Biology, Academia Sinica, Taipei 115, Taiwan
| | - Chia-Wei Liao
- Institute of Molecular Biology, Academia Sinica, Taipei 115, Taiwan
| | - Kevin R Roy
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA 94304, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Lars M Steinmetz
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA 94304, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg 69117, Germany
| | - Jun-Yi Leu
- Doctoral Program in Microbial Genomics, National Chung Hsing University and Academia Sinica, Taiwan
- Institute of Molecular Biology, Academia Sinica, Taipei 115, Taiwan
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2
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Church MC, Workman JL. The SWI/SNF chromatin remodeling complex: a critical regulator of metabolism. Biochem Soc Trans 2024; 52:1327-1337. [PMID: 38666605 DOI: 10.1042/bst20231141] [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: 02/06/2024] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 06/27/2024]
Abstract
The close relationship between chromatin and metabolism has been well-studied in recent years. Many metabolites have been found to be cofactors used to modify chromatin, and these modifications can in turn affect gene transcription. One chromatin-associated factor responsible for regulating transcription is the SWI/SNF complex, an ATP-dependent chromatin remodeler conserved throughout eukaryotes. SWI/SNF was originally described in yeast as regulating genes involved in carbon source metabolism and mating type switching, and its mammalian counterpart has been extensively studied for its role in diseases such as cancer. The yeast SWI/SNF complex is closely associated with activation of stress response genes, many of which have metabolic functions. It is now recognized that this is a conserved function of the complex, and recent work has shown that mammalian SWI/SNF is also a key regulator of metabolic transcription. Emerging evidence suggests that loss of SWI/SNF introduces vulnerabilities to cells due to this metabolic influence, and that this may present opportunities for treatment of SWI/SNF-deficient cancers.
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Affiliation(s)
- Michael C Church
- Stowers Institute of Medical Research, 1000 E 50th Street, Kansas City, MO 64118, U.S.A
| | - Jerry L Workman
- Stowers Institute of Medical Research, 1000 E 50th Street, Kansas City, MO 64118, U.S.A
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Helenek C, Krzysztoń R, Petreczky J, Wan Y, Cabral M, Coraci D, Balázsi G. Synthetic gene circuit evolution: Insights and opportunities at the mid-scale. Cell Chem Biol 2024:S2451-9456(24)00219-8. [PMID: 38925113 DOI: 10.1016/j.chembiol.2024.05.018] [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: 02/12/2024] [Revised: 05/07/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024]
Abstract
Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.
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Affiliation(s)
- Christopher Helenek
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA
| | - Rafał Krzysztoń
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Julia Petreczky
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
| | - Yiming Wan
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Mariana Cabral
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA
| | - Damiano Coraci
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Gábor Balázsi
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA; Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY 11794, USA.
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Hematopoietic differentiation is characterized by a transient peak of entropy at a single-cell level. BMC Biol 2022; 20:60. [PMID: 35260165 PMCID: PMC8905725 DOI: 10.1186/s12915-022-01264-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 02/22/2022] [Indexed: 12/11/2022] Open
Abstract
Background Mature blood cells arise from hematopoietic stem cells in the bone marrow by a process of differentiation along one of several different lineage trajectories. This is often represented as a series of discrete steps of increasing progenitor cell commitment to a given lineage, but as for differentiation in general, whether the process is instructive or stochastic remains controversial. Here, we examine this question by analyzing single-cell transcriptomic data from human bone marrow cells, assessing cell-to-cell variability along the trajectories of hematopoietic differentiation into four different types of mature blood cells. The instructive model predicts that cells will be following the same sequence of instructions and that there will be minimal variability of gene expression between them throughout the process, while the stochastic model predicts a role for cell-to-cell variability when lineage commitments are being made. Results Applying Shannon entropy to measure cell-to-cell variability among human hematopoietic bone marrow cells at the same stage of differentiation, we observed a transient peak of gene expression variability occurring at characteristic points in all hematopoietic differentiation pathways. Strikingly, the genes whose cell-to-cell variation of expression fluctuated the most over the course of a given differentiation trajectory are pathway-specific genes, whereas genes which showed the greatest variation of mean expression are common to all pathways. Finally, we showed that the level of cell-to-cell variation is increased in the most immature compartment of hematopoiesis in myelodysplastic syndromes. Conclusions These data suggest that human hematopoietic differentiation could be better conceptualized as a dynamical stochastic process with a transient stage of cellular indetermination, and strongly support the stochastic view of differentiation. They also highlight the need to consider the role of stochastic gene expression in complex physiological processes and pathologies such as cancers, paving the way for possible noise-based therapies through epigenetic regulation. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01264-9.
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McGuigan K, Hoffmann AA, Sgrò CM. How is epigenetics predicted to contribute to climate change adaptation? What evidence do we need? Philos Trans R Soc Lond B Biol Sci 2021; 376:20200119. [PMID: 33866811 PMCID: PMC8059617 DOI: 10.1098/rstb.2020.0119] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/14/2020] [Indexed: 12/13/2022] Open
Abstract
Transgenerational effects that are interpreted in terms of epigenetics have become an important research focus at a time when rapid environmental changes are occurring. These effects are usually interpreted as enhancing fitness extremely rapidly, without depending on the slower process of natural selection changing DNA-encoded (fixed) genetic variants in populations. Supporting evidence comes from a variety of sources, including environmental associations with epialleles, cross-generation responses of clonal material exposed to different environmental conditions, and altered patterns of methylation or frequency changes in epialleles across time. Transgenerational environmental effects have been postulated to be larger than those associated with DNA-encoded genetic changes, based on (for instance) stronger associations between epialleles and environmental conditions. Yet environmental associations for fixed genetic differences may always be weak under polygenic models where multiple combinations of alleles can lead to the same evolutionary outcome. The ultimate currency of adaptation is fitness, and few transgenerational studies have robustly determined fitness effects, particularly when compared to fixed genetic variants. Not all transgenerational modifications triggered by climate change will increase fitness: stressful conditions often trigger negative fitness effects across generations that can eliminate benefits. Epigenetic responses and other transgenerational effects will undoubtedly play a role in climate change adaptation, but further, well-designed, studies are required to test their importance relative to DNA-encoded changes. This article is part of the theme issue 'How does epigenetics influence the course of evolution?'
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Affiliation(s)
- Katrina McGuigan
- School of Biological Science, The University of Queensland, Brisbane 4072, Queensland, Australia
| | - Ary A. Hoffmann
- School of Biosciences and Bio21 Institute, The University of Melbourne, Melbourne 3010, Australia
| | - Carla M. Sgrò
- School of Biological Sciences, Monash University, Melbourne 3800, Australia
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Capturing and Understanding the Dynamics and Heterogeneity of Gene Expression in the Living Cell. Int J Mol Sci 2020; 21:ijms21218278. [PMID: 33167354 PMCID: PMC7663833 DOI: 10.3390/ijms21218278] [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: 09/23/2020] [Revised: 10/29/2020] [Accepted: 11/03/2020] [Indexed: 11/21/2022] Open
Abstract
The regulation of gene expression is a fundamental process enabling cells to respond to internal and external stimuli or to execute developmental programs. Changes in gene expression are highly dynamic and depend on many intrinsic and extrinsic factors. In this review, we highlight the dynamic nature of transient gene expression changes to better understand cell physiology and development in general. We will start by comparing recent in vivo procedures to capture gene expression in real time. Intrinsic factors modulating gene expression dynamics will then be discussed, focusing on chromatin modifications. Furthermore, we will dissect how cell physiology or age impacts on dynamic gene regulation and especially discuss molecular insights into acquired transcriptional memory. Finally, this review will give an update on the mechanisms of heterogeneous gene expression among genetically identical individual cells. We will mainly focus on state-of-the-art developments in the yeast model but also cover higher eukaryotic systems.
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Guinn MT, Wan Y, Levovitz S, Yang D, Rosner MR, Balázsi G. Observation and Control of Gene Expression Noise: Barrier Crossing Analogies Between Drug Resistance and Metastasis. Front Genet 2020; 11:586726. [PMID: 33193723 PMCID: PMC7662081 DOI: 10.3389/fgene.2020.586726] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 09/29/2020] [Indexed: 12/12/2022] Open
Affiliation(s)
- Michael Tyler Guinn
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States.,Stony Brook Medical Scientist Training Program, Stony Brook, NY, United States
| | - Yiming Wan
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
| | - Sarah Levovitz
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
| | - Dongbo Yang
- Ben May Department for Cancer Research, The University of Chicago, Chicago, IL, United States
| | - Marsha R Rosner
- Ben May Department for Cancer Research, The University of Chicago, Chicago, IL, United States
| | - Gábor Balázsi
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
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