1
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Mondal S, Becskei A. Gene choice in cancer cells is exclusive in ion transport but concurrent in DNA replication. Comput Struct Biotechnol J 2024; 23:2534-2547. [PMID: 38974885 PMCID: PMC11226983 DOI: 10.1016/j.csbj.2024.06.004] [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: 02/29/2024] [Revised: 06/04/2024] [Accepted: 06/04/2024] [Indexed: 07/09/2024] Open
Abstract
Cancers share common cellular and physiological features. Little is known about whether distinctive gene expression patterns can be displayed at the single-cell level by gene families in cancer cells. The expression of gene homologs within a family can exhibit concurrence and exclusivity. Concurrence can promote all-or-none expression patterns of related genes and underlie alternative physiological states. Conversely, exclusive gene families express the same or similar number of homologs in each cell, allowing a broad repertoire of cell identities to be generated. We show that gene families involved in the cell-cycle and antigen presentation are expressed concurrently. Concurrence in the DNA replication complex MCM reflects the replicative status of cells, including cell lines and cancer-derived organoids. Exclusive expression requires precise regulatory mechanism, but cancer cells retain this form of control for ion homeostasis and extend it to gene families involved in cell migration. Thus, the cell adhesion-based identity of healthy cells is transformed to an identity based on migration in the population of cancer cells, reminiscent of epithelial-mesenchymal transition.
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Affiliation(s)
- Samuel Mondal
- Biozentrum, University of Basel, Spitalstrasse 41, Basel 4056, Switzerland
| | - Attila Becskei
- Biozentrum, University of Basel, Spitalstrasse 41, Basel 4056, Switzerland
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2
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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; 31:1447-1459. [PMID: 38925113 PMCID: PMC11330362 DOI: 10.1016/j.chembiol.2024.05.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [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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3
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El Meouche I, Jain P, Jolly MK, Capp JP. Drug tolerance and persistence in bacteria, fungi and cancer cells: Role of non-genetic heterogeneity. Transl Oncol 2024; 49:102069. [PMID: 39121829 DOI: 10.1016/j.tranon.2024.102069] [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: 10/06/2023] [Revised: 07/17/2024] [Accepted: 08/01/2024] [Indexed: 08/12/2024] Open
Abstract
A common feature of bacterial, fungal and cancer cell populations upon treatment is the presence of tolerant and persistent cells able to survive, and sometimes grow, even in the presence of usually inhibitory or lethal drug concentrations, driven by non-genetic differences among individual cells in a population. Here we review and compare data obtained on drug survival in bacteria, fungi and cancer cells to unravel common characteristics and cellular pathways, and to point their singularities. This comparative work also allows to cross-fertilize ideas across fields. We particularly focus on the role of gene expression variability in the emergence of cell-cell non-genetic heterogeneity because it represents a possible common basic molecular process at the origin of most persistence phenomena and could be monitored and tuned to help improve therapeutic interventions.
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Affiliation(s)
- Imane El Meouche
- Université Paris Cité, Université Sorbonne Paris Nord, INSERM, IAME, F-75018 Paris, France.
| | - Paras Jain
- Department of Bioengineering, Indian Institute of Science, Bangalore, India
| | - Mohit Kumar Jolly
- Department of Bioengineering, Indian Institute of Science, Bangalore, India
| | - Jean-Pascal Capp
- Toulouse Biotechnology Institute, INSA/University of Toulouse, CNRS, INRAE, Toulouse, France.
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4
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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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5
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Pal S, Dhar R. Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making. FEBS Lett 2024; 598:1673-1691. [PMID: 38724715 DOI: 10.1002/1873-3468.14898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/23/2024] [Accepted: 03/27/2024] [Indexed: 07/23/2024]
Abstract
The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.
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Affiliation(s)
- Sampriti Pal
- Department of Bioscience and Biotechnology, IIT Kharagpur, India
| | - Riddhiman Dhar
- Department of Bioscience and Biotechnology, IIT Kharagpur, India
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6
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Zhang Z, Zabaikina I, Nieto C, Vahdat Z, Bokes P, Singh A. Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.28.601263. [PMID: 38979195 PMCID: PMC11230457 DOI: 10.1101/2024.06.28.601263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth, and the protein inhibits growth at high concentrations, establishing a positive feedback loop. For a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the commonly used single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.
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Affiliation(s)
- Zhanhao Zhang
- Department of Electrical and Computer Engineering, University of Delaware. Newark, DE 19716, USA
| | - Iryna Zabaikina
- Department of Applied Mathematics and Statistics, Comenius University, Bratislava 84248, Slovakia
| | - César Nieto
- Department of Electrical and Computer Engineering, University of Delaware. Newark, DE 19716, USA
| | - Zahra Vahdat
- Department of Electrical and Computer Engineering, University of Delaware. Newark, DE 19716, USA
| | - Pavol Bokes
- Department of Applied Mathematics and Statistics, Comenius University, Bratislava 84248, Slovakia
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware. Newark, DE 19716, USA
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7
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Whiting FJH, Househam J, Baker AM, Sottoriva A, Graham TA. Phenotypic noise and plasticity in cancer evolution. Trends Cell Biol 2024; 34:451-464. [PMID: 37968225 DOI: 10.1016/j.tcb.2023.10.002] [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: 07/13/2023] [Revised: 09/29/2023] [Accepted: 10/04/2023] [Indexed: 11/17/2023]
Abstract
Non-genetic alterations can produce changes in a cell's phenotype. In cancer, these phenomena can influence a cell's fitness by conferring access to heritable, beneficial phenotypes. Herein, we argue that current discussions of 'phenotypic plasticity' in cancer evolution ignore a salient feature of the original definition: namely, that it occurs in response to an environmental change. We suggest 'phenotypic noise' be used to distinguish non-genetic changes in phenotype that occur independently from the environment. We discuss the conceptual and methodological techniques used to identify these phenomena during cancer evolution. We propose that the distinction will guide efforts to define mechanisms of phenotype change, accelerate translational work to manipulate phenotypes through treatment, and, ultimately, improve patient outcomes.
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Affiliation(s)
| | - Jacob Househam
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Ann-Marie Baker
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK; Computational Biology Research Centre, Human Technopole, Milan, Italy
| | - Trevor A Graham
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
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8
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Pina C. Contributions of transcriptional noise to leukaemia evolution: KAT2A as a case-study. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230052. [PMID: 38432321 PMCID: PMC10909511 DOI: 10.1098/rstb.2023.0052] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 12/04/2023] [Indexed: 03/05/2024] Open
Abstract
Transcriptional noise is proposed to participate in cell fate changes, but contributions to mammalian cell differentiation systems, including cancer, remain associative. Cancer evolution is driven by genetic variability, with modulatory or contributory participation of epigenetic variants. Accumulation of epigenetic variants enhances transcriptional noise, which can facilitate cancer cell fate transitions. Acute myeloid leukaemia (AML) is an aggressive cancer with strong epigenetic dependencies, characterized by blocked differentiation. It constitutes an attractive model to probe links between transcriptional noise and malignant cell fate regulation. Gcn5/KAT2A is a classical epigenetic transcriptional noise regulator. Its loss increases transcriptional noise and modifies cell fates in stem and AML cells. By reviewing the analysis of KAT2A-depleted pre-leukaemia and leukaemia models, I discuss that the net result of transcriptional noise is diversification of cell fates secondary to alternative transcriptional programmes. Cellular diversification can enable or hinder AML progression, respectively, by differentiation of cell types responsive to mutations, or by maladaptation of leukaemia stem cells. KAT2A-dependent noise-responsive genes participate in ribosome biogenesis and KAT2A loss destabilizes translational activity. I discuss putative contributions of perturbed translation to AML biology, and propose KAT2A loss as a model for mechanistic integration of transcriptional and translational control of noise and fate decisions. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.
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Affiliation(s)
- Cristina Pina
- College of Health, Medicine and Life Sciences, Brunel University London, Kingston Lane, Uxbridge, London, UB8 3PH, United Kingdom
- CenGEM – Centre for Genome Engineering and Maintenance, Brunel University London, Kingston Lane, Uxbridge, London, UB8 3PH, United Kingdom
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9
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Cai DL, Chan Y, Kong YM, Liu YZ, Guo Y, Cai AQ, Zhu BS. Ginsenoside Rg1 promotes fetal hemoglobin production in vitro: A potential therapeutic avenue for β-thalassemia. Eur J Pharmacol 2024; 968:176404. [PMID: 38382804 DOI: 10.1016/j.ejphar.2024.176404] [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: 11/10/2023] [Revised: 01/25/2024] [Accepted: 02/06/2024] [Indexed: 02/23/2024]
Abstract
β-thalassemia, a globally prevalent genetic disorder, urgently requires innovative treatment options. Fetal hemoglobin (HbF) induction stands as a key therapeutic approach. This investigation focused on Ginsenoside Rg1 from the Panax genus for HbF induction. Employing K562 cells and human erythroid precursor cells (ErPCs) derived from neonatal cord blood, the study tested Rg1 at different concentrations. We measured its effects on γ-globin mRNA levels and HbF expression, alongside assessments of cell proliferation and differentiation. In K562 cells, Rg1 at 400 μM significantly increased γ-globin mRNA expression by 4.24 ± 1.08-fold compared to the control. In ErPCs, the 800 μM concentration was most effective, leading to an over 80% increase in F-cells and a marked upregulation in HbF expression. Notably, Rg1 did not adversely affect cell proliferation or differentiation, with the 200 μM concentration showing an increase in γ-globin mRNA by 2.33 ± 0.58-fold, and the 800 μM concentration enhancing HbF expression by 2.59 ± 0.03-fold in K562 cells. Our results underscore Rg1's potential as an effective and safer alternative for β-thalassemia treatment. By significantly enhancing HbF levels without cytotoxicity, Rg1 offers a notable advantage over traditional treatments like Hydroxyurea. While promising, these in vitro findings warrant further in vivo exploration to confirm Rg1's therapeutic efficacy and to unravel its underlying mechanistic pathways.
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Affiliation(s)
- Dong-Ling Cai
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, 650500, Yunnan Province, China; Medical School, Kunming University of Science and Technology, Kunming, 650500, Yunnan Province, China
| | - Ying Chan
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, 650500, Yunnan Province, China; Medical School, Kunming University of Science and Technology, Kunming, 650500, Yunnan Province, China; Department of Medical Genetics, NHC Key Laboratory of Preconception Health Birth in Western China, Yunnan Provincial Key Laboratory for Birth Defects and Genetic Diseases, First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, China
| | - Ya-Min Kong
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, 650500, Yunnan Province, China; Medical School, Kunming University of Science and Technology, Kunming, 650500, Yunnan Province, China; Department of Medical Genetics, NHC Key Laboratory of Preconception Health Birth in Western China, Yunnan Provincial Key Laboratory for Birth Defects and Genetic Diseases, First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, China
| | - Yi-Ze Liu
- Medical School, Kunming University of Science and Technology, Kunming, 650500, Yunnan Province, China
| | - Yan Guo
- Medical School, Kunming University of Science and Technology, Kunming, 650500, Yunnan Province, China
| | - Ai-Qi Cai
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, 650500, Yunnan Province, China; Medical School, Kunming University of Science and Technology, Kunming, 650500, Yunnan Province, China; Department of Medical Genetics, NHC Key Laboratory of Preconception Health Birth in Western China, Yunnan Provincial Key Laboratory for Birth Defects and Genetic Diseases, First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, China
| | - Bao-Sheng Zhu
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, 650500, Yunnan Province, China; Medical School, Kunming University of Science and Technology, Kunming, 650500, Yunnan Province, China; Department of Medical Genetics, NHC Key Laboratory of Preconception Health Birth in Western China, Yunnan Provincial Key Laboratory for Birth Defects and Genetic Diseases, First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, China.
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10
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Bose A, Datta S, Mandal R, Ray U, Dhar R. Increased heterogeneity in expression of genes associated with cancer progression and drug resistance. Transl Oncol 2024; 41:101879. [PMID: 38262110 PMCID: PMC10832509 DOI: 10.1016/j.tranon.2024.101879] [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: 10/27/2023] [Revised: 12/16/2023] [Accepted: 12/29/2023] [Indexed: 01/25/2024] Open
Abstract
Fluctuations in the number of regulatory molecules and differences in timings of molecular events can generate variation in gene expression among genetically identical cells in the same environmental condition. This variation, termed as expression noise, can create differences in metabolic state and cellular functions, leading to phenotypic heterogeneity. Expression noise and phenotypic heterogeneity have been recognized as important contributors to intra-tumor heterogeneity, and have been associated with cancer growth, progression, and therapy resistance. However, how expression noise changes with cancer progression in actual cancer patients has remained poorly explored. Such an analysis, through identification of genes with increasing expression noise, can provide valuable insights into generation of intra-tumor heterogeneity, and could have important implications for understanding immune-suppression, drug tolerance and therapy resistance. In this work, we performed a genome-wide identification of changes in gene expression noise with cancer progression using single-cell RNA-seq data of lung adenocarcinoma patients at different stages of cancer. We identified 37 genes in epithelial cells that showed an increasing noise trend with cancer progression, many of which were also associated with cancer growth, EMT and therapy resistance. We found that expression of several of these genes was positively associated with expression of mitochondrial genes, suggesting an important role of mitochondria in generation of heterogeneity. In addition, we uncovered substantial differences in sample-specific noise profiles which could have implications for personalized prognosis and treatment.
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Affiliation(s)
- Anwesha Bose
- Department of Bioscience and Biotechnology, Indian Institute of Technology (IIT) Kharagpur, India
| | - Subhasis Datta
- Department of Bioscience and Biotechnology, Indian Institute of Technology (IIT) Kharagpur, India
| | - Rakesh Mandal
- Department of Bioscience and Biotechnology, Indian Institute of Technology (IIT) Kharagpur, India
| | - Upasana Ray
- Department of Bioscience and Biotechnology, Indian Institute of Technology (IIT) Kharagpur, India
| | - Riddhiman Dhar
- Department of Bioscience and Biotechnology, Indian Institute of Technology (IIT) Kharagpur, India.
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11
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Martinez TC, McNerney ME. Haploinsufficient Transcription Factors in Myeloid Neoplasms. ANNUAL REVIEW OF PATHOLOGY 2024; 19:571-598. [PMID: 37906947 DOI: 10.1146/annurev-pathmechdis-051222-013421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Many transcription factors (TFs) function as tumor suppressor genes with heterozygous phenotypes, yet haploinsufficiency generally has an underappreciated role in neoplasia. This is no less true in myeloid cells, which are normally regulated by a delicately balanced and interconnected transcriptional network. Detailed understanding of TF dose in this circuitry sheds light on the leukemic transcriptome. In this review, we discuss the emerging features of haploinsufficient transcription factors (HITFs). We posit that: (a) monoallelic and biallelic losses can have distinct cellular outcomes; (b) the activity of a TF exists in a greater range than the traditional Mendelian genetic doses; and (c) how a TF is deleted or mutated impacts the cellular phenotype. The net effect of a HITF is a myeloid differentiation block and increased intercellular heterogeneity in the course of myeloid neoplasia.
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Affiliation(s)
- Tanner C Martinez
- Department of Pathology, Department of Pediatrics, Section of Hematology/Oncology, The University of Chicago Medicine Comprehensive Cancer Center, The University of Chicago, Chicago, Illinois, USA;
- Medical Scientist Training Program, The University of Chicago, Chicago, Illinois, USA
| | - Megan E McNerney
- Department of Pathology, Department of Pediatrics, Section of Hematology/Oncology, The University of Chicago Medicine Comprehensive Cancer Center, The University of Chicago, Chicago, Illinois, USA;
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12
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Wan Y, Mu Q, Krzysztoń R, Cohen J, Coraci D, Helenek C, Tompkins C, Lin A, Farquhar K, Cross E, Wang J, Balázsi G. Adaptive DNA amplification of synthetic gene circuit opens a way to overcome cancer chemoresistance. Proc Natl Acad Sci U S A 2023; 120:e2303114120. [PMID: 38019857 DOI: 10.1073/pnas.2303114120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 09/27/2023] [Indexed: 12/01/2023] Open
Abstract
Drug resistance continues to impede the success of cancer treatments, creating a need for experimental model systems that are broad, yet simple, to allow the identification of mechanisms and novel countermeasures applicable to many cancer types. To address these needs, we investigated a set of engineered mammalian cell lines with synthetic gene circuits integrated into their genome that evolved resistance to Puromycin. We identified DNA amplification as the mechanism underlying drug resistance in 4 out of 6 replicate populations. Triplex-forming oligonucleotide (TFO) treatment combined with Puromycin could efficiently suppress the growth of cell populations with DNA amplification. Similar observations in human cancer cell lines suggest that TFOs could be broadly applicable to mitigate drug resistance, one of the major difficulties in treating cancer.
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Affiliation(s)
- Yiming Wan
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
| | - Quanhua Mu
- Department of Chemical and Biological Engineering, Division of Life Science, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region 999077, China
| | - Rafał Krzysztoń
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
| | - Joseph Cohen
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
| | - Damiano Coraci
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
| | - Christopher Helenek
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
| | | | - Annie Lin
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
| | - Kevin Farquhar
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
| | | | - Jiguang Wang
- Department of Chemical and Biological Engineering, Division of Life Science, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region 999077, China
| | - Gábor Balázsi
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
- Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY 11794
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13
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George JT. Optimal phenotypic adaptation in fluctuating environments. Biophys J 2023; 122:4414-4424. [PMID: 37876159 PMCID: PMC10698328 DOI: 10.1016/j.bpj.2023.10.019] [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: 04/05/2023] [Revised: 08/28/2023] [Accepted: 10/18/2023] [Indexed: 10/26/2023] Open
Abstract
Phenotypic adaptation is a universal feature of biological systems navigating highly variable environments. Recent empirical data support the role of memory-driven decision making in cellular systems navigating uncertain future nutrient landscapes, wherein a distinct growth phenotype emerges in fluctuating conditions. We develop a simple stochastic mathematical model to describe memory-driven cellular adaptation required for systems to optimally navigate such uncertainty. In this framework, adaptive populations traverse dynamic environments by inferring future variation from a memory of prior states, and memory capacity imposes a fundamental trade-off between the speed and accuracy of adaptation to new fluctuating environments. Our results suggest that the observed growth reductions that occur in fluctuating environments are a direct consequence of optimal decision making and result from bet hedging and occasional phenotypic-environmental mismatch. We anticipate that this modeling framework will be useful for studying the role of memory in phenotypic adaptation, including in the design of temporally varying therapies against adaptive systems.
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Affiliation(s)
- Jason T George
- Department of Biomedical Engineering, Texas A&M University, College Station, Texas; Engineering Medicine Program, Texas A&M University, Houston, Texas; Center for Theoretical Biological Physics, Rice University, Houston, Texas.
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14
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Feng H, Li F, Wang T, Xing XH, Zeng AP, Zhang C. Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision. SCIENCE ADVANCES 2023; 9:eadg5296. [PMID: 37939173 PMCID: PMC10631719 DOI: 10.1126/sciadv.adg5296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 10/06/2023] [Indexed: 11/10/2023]
Abstract
Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli, from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli.
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Affiliation(s)
- Huibao Feng
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Fan Li
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Tianmin Wang
- Tsinghua-Peking Center for Life Sciences, School of Medicine, Tsinghua University, Beijing 100084, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Xin-hui Xing
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - An-ping Zeng
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg 21073, Germany
- Center of Synthetic Biology and Integrated Bioengineering, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Chong Zhang
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
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15
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Shepherd MJ, Pierce AP, Taylor TB. Evolutionary innovation through transcription factor rewiring in microbes is shaped by levels of transcription factor activity, expression, and existing connectivity. PLoS Biol 2023; 21:e3002348. [PMID: 37871011 PMCID: PMC10621929 DOI: 10.1371/journal.pbio.3002348] [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: 05/22/2023] [Revised: 11/02/2023] [Accepted: 09/25/2023] [Indexed: 10/25/2023] Open
Abstract
The survival of a population during environmental shifts depends on whether the rate of phenotypic adaptation keeps up with the rate of changing conditions. A common way to achieve this is via change to gene regulatory network (GRN) connections-known as rewiring-that facilitate novel interactions and innovation of transcription factors. To understand the success of rapidly adapting organisms, we therefore need to determine the rules that create and constrain opportunities for GRN rewiring. Here, using an experimental microbial model system with the soil bacterium Pseudomonas fluorescens, we reveal a hierarchy among transcription factors that are rewired to rescue lost function, with alternative rewiring pathways only unmasked after the preferred pathway is eliminated. We identify 3 key properties-high activation, high expression, and preexisting low-level affinity for novel target genes-that facilitate transcription factor innovation. Ease of acquiring these properties is constrained by preexisting GRN architecture, which was overcome in our experimental system by both targeted and global network alterations. This work reveals the key properties that determine transcription factor evolvability, and as such, the evolution of GRNs.
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Affiliation(s)
- Matthew J. Shepherd
- Milner Centre for Evolution, Department of Life Sciences, University of Bath, Bath, United Kingdom
- Division of Evolution and Genomic Sciences, School of Biological Sciences, University of Manchester, Manchester, United Kingdom
| | - Aidan P. Pierce
- Milner Centre for Evolution, Department of Life Sciences, University of Bath, Bath, United Kingdom
| | - Tiffany B. Taylor
- Milner Centre for Evolution, Department of Life Sciences, University of Bath, Bath, United Kingdom
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16
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Charlebois DA. Quantitative systems-based prediction of antimicrobial resistance evolution. NPJ Syst Biol Appl 2023; 9:40. [PMID: 37679446 PMCID: PMC10485028 DOI: 10.1038/s41540-023-00304-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/24/2023] [Indexed: 09/09/2023] Open
Abstract
Predicting evolution is a fundamental problem in biology with practical implications for treating antimicrobial resistance, which is a complex system-level phenomenon. In this perspective article, we explore the limits of predicting antimicrobial resistance evolution, quantitatively define the predictability and repeatability of microevolutionary processes, and speculate on how these quantities vary across temporal, biological, and complexity scales. The opportunities and challenges for predicting antimicrobial resistance in the context of systems biology are also discussed. Based on recent research, we conclude that the evolution of antimicrobial resistance can be predicted using a systems biology approach integrating quantitative models with multiscale data from microbial evolution experiments.
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Affiliation(s)
- Daniel A Charlebois
- Department of Physics, University of Alberta, Edmonton, AB, T6G-2E1, Canada.
- Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G-2E9, Canada.
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17
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Torres A, Cockerell S, Phillips M, Balázsi G, Ghosh K. MaxCal can infer models from coupled stochastic trajectories of gene expression and cell division. Biophys J 2023; 122:2623-2635. [PMID: 37218129 PMCID: PMC10397576 DOI: 10.1016/j.bpj.2023.05.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/03/2023] [Accepted: 05/18/2023] [Indexed: 05/24/2023] Open
Abstract
Gene expression is inherently noisy due to small numbers of proteins and nucleic acids inside a cell. Likewise, cell division is stochastic, particularly when tracking at the level of a single cell. The two can be coupled when gene expression affects the rate of cell division. Single-cell time-lapse experiments can measure both fluctuations by simultaneously recording protein levels inside a cell and its stochastic division. These information-rich noisy trajectory data sets can be harnessed to learn about the underlying molecular and cellular details that are often not known a priori. A critical question is: How can we infer a model given data where fluctuations at two levels-gene expression and cell division-are intricately convoluted? We show the principle of maximum caliber (MaxCal)-integrated within a Bayesian framework-can be used to infer several cellular and molecular details (division rates, protein production, and degradation rates) from these coupled stochastic trajectories (CSTs). We demonstrate this proof of concept using synthetic data generated from a known model. An additional challenge in data analysis is that trajectories are often not in protein numbers, but in noisy fluorescence that depends on protein number in a probabilistic manner. We again show that MaxCal can infer important molecular and cellular rates even when data are in fluorescence, another example of CST with three confounding factors-gene expression noise, cell division noise, and fluorescence distortion-all coupled. Our approach will provide guidance to build models in synthetic biology experiments as well as general biological systems where examples of CSTs are abundant.
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Affiliation(s)
- Andrew Torres
- Department of Physics and Astronomy, University of Denver, Denver, Colorado
| | - Spencer Cockerell
- Department of Physics and Astronomy, University of Denver, Denver, Colorado
| | - Michael Phillips
- Department of Physics and Astronomy, University of Denver, Denver, Colorado
| | - Gábor Balázsi
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Kingshuk Ghosh
- Molecular and Cellular Biophysics, University of Denver, Denver, Colorado; Department of Physics and Astronomy, University of Denver, Denver, Colorado.
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18
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Wan Y, Cohen J, Szenk M, Farquhar KS, Coraci D, Krzysztoń R, Azukas J, Van Nest N, Smashnov A, Chern YJ, De Martino D, Nguyen LC, Bien H, Bravo-Cordero JJ, Chan CH, Rosner MR, Balázsi G. Nonmonotone invasion landscape by noise-aware control of metastasis activator levels. Nat Chem Biol 2023; 19:887-899. [PMID: 37231268 PMCID: PMC10299915 DOI: 10.1038/s41589-023-01344-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 04/18/2023] [Indexed: 05/27/2023]
Abstract
A major pharmacological assumption is that lowering disease-promoting protein levels is generally beneficial. For example, inhibiting metastasis activator BACH1 is proposed to decrease cancer metastases. Testing such assumptions requires approaches to measure disease phenotypes while precisely adjusting disease-promoting protein levels. Here we developed a two-step strategy to integrate protein-level tuning, noise-aware synthetic gene circuits into a well-defined human genomic safe harbor locus. Unexpectedly, engineered MDA-MB-231 metastatic human breast cancer cells become more, then less and then more invasive as we tune BACH1 levels up, irrespective of the native BACH1. BACH1 expression shifts in invading cells, and expression of BACH1's transcriptional targets confirm BACH1's nonmonotone phenotypic and regulatory effects. Thus, chemical inhibition of BACH1 could have unwanted effects on invasion. Additionally, BACH1's expression variability aids invasion at high BACH1 expression. Overall, precisely engineered, noise-aware protein-level control is necessary and important to unravel disease effects of genes to improve clinical drug efficacy.
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Affiliation(s)
- Yiming Wan
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Joseph Cohen
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Mariola Szenk
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Kevin S Farquhar
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
- Genetics and Epigenetics Graduate Program, The University of Texas MD Anderson Cancer Center, UT Health Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Damiano Coraci
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Rafał Krzysztoń
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Joshua Azukas
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Nicholas Van Nest
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Alex Smashnov
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Yi-Jye Chern
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY, USA
- Department of Molecular and Cellular Biology, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Daniela De Martino
- Department of Medicine, Division of Hematology and Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Long Chi Nguyen
- Ben May Department for Cancer Research, University of Chicago, Chicago, IL, USA
| | - Harold Bien
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Jose Javier Bravo-Cordero
- Department of Medicine, Division of Hematology and Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chia-Hsin Chan
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY, USA
- Department of Molecular and Cellular Biology, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Marsha Rich Rosner
- Ben May Department for Cancer Research, University of Chicago, Chicago, IL, USA
| | - Gábor Balázsi
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.
- Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA.
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19
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Howland KK, Brock A. Cellular barcoding tracks heterogeneous clones through selective pressures and phenotypic transitions. Trends Cancer 2023; 9:591-601. [PMID: 37105856 PMCID: PMC10339273 DOI: 10.1016/j.trecan.2023.03.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/27/2023] [Accepted: 03/28/2023] [Indexed: 04/29/2023]
Abstract
Genomic DNA barcoding has emerged as a sensitive and flexible tool to measure the fates of clonal subpopulations within a heterogeneous cancer cell population. Coupling cellular barcoding with single-cell transcriptomics permits the longitudinal analysis of molecular mechanisms with detailed clone-level resolution. Numerous recent studies have employed these tools to track clonal cell states in cancer progression and treatment response. With these new technologies comes the opportunity to examine longstanding questions about the origins and contributions of tumor cell heterogeneity and the roles of selection and phenotypic plasticity in disease progression and treatment.
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Affiliation(s)
- Kennedy K Howland
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78734, USA
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78734, USA.
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20
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Bi S, Kargeti M, Colin R, Farke N, Link H, Sourjik V. Dynamic fluctuations in a bacterial metabolic network. Nat Commun 2023; 14:2173. [PMID: 37061520 PMCID: PMC10105761 DOI: 10.1038/s41467-023-37957-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 04/06/2023] [Indexed: 04/17/2023] Open
Abstract
The operation of the central metabolism is typically assumed to be deterministic, but dynamics and high connectivity of the metabolic network make it potentially prone to generating fluctuations. However, time-resolved measurements of metabolite levels in individual cells that are required to characterize such fluctuations remained a challenge, particularly in small bacterial cells. Here we use single-cell metabolite measurements based on Förster resonance energy transfer, combined with computer simulations, to explore the real-time dynamics of the metabolic network of Escherichia coli. We observe that steplike exposure of starved E. coli to glycolytic carbon sources elicits large periodic fluctuations in the intracellular concentration of pyruvate in individual cells. These fluctuations are consistent with predicted oscillatory dynamics of E. coli metabolic network, and they are primarily controlled by biochemical reactions around the pyruvate node. Our results further indicate that fluctuations in glycolysis propagate to other cellular processes, possibly leading to temporal heterogeneity of cellular states within a population.
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Affiliation(s)
- Shuangyu Bi
- Max Planck Institute for Terrestrial Microbiology and Center for Synthetic Microbiology (SYNMIKRO), D-35043, Marburg, Germany
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, 266237, China
| | - Manika Kargeti
- Max Planck Institute for Terrestrial Microbiology and Center for Synthetic Microbiology (SYNMIKRO), D-35043, Marburg, Germany
| | - Remy Colin
- Max Planck Institute for Terrestrial Microbiology and Center for Synthetic Microbiology (SYNMIKRO), D-35043, Marburg, Germany
| | - Niklas Farke
- University of Tübingen, D-72076, Tübingen, Germany
| | - Hannes Link
- University of Tübingen, D-72076, Tübingen, Germany
| | - Victor Sourjik
- Max Planck Institute for Terrestrial Microbiology and Center for Synthetic Microbiology (SYNMIKRO), D-35043, Marburg, Germany.
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21
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Pillai M, Hojel E, Jolly MK, Goyal Y. Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools. NATURE COMPUTATIONAL SCIENCE 2023; 3:301-313. [PMID: 38177938 DOI: 10.1038/s43588-023-00427-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 03/03/2023] [Indexed: 01/06/2024]
Abstract
Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.
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Affiliation(s)
- Maalavika Pillai
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Emilia Hojel
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India.
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA.
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA.
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22
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Parab L, Pal S, Dhar R. Transcription factor binding process is the primary driver of noise in gene expression. PLoS Genet 2022; 18:e1010535. [PMID: 36508455 PMCID: PMC9779669 DOI: 10.1371/journal.pgen.1010535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 12/22/2022] [Accepted: 11/16/2022] [Indexed: 12/14/2022] Open
Abstract
Noise in expression of individual genes gives rise to variations in activity of cellular pathways and generates heterogeneity in cellular phenotypes. Phenotypic heterogeneity has important implications for antibiotic persistence, mutation penetrance, cancer growth and therapy resistance. Specific molecular features such as the presence of the TATA box sequence and the promoter nucleosome occupancy have been associated with noise. However, the relative importance of these features in noise regulation is unclear and how well these features can predict noise has not yet been assessed. Here through an integrated statistical model of gene expression noise in yeast we found that the number of regulating transcription factors (TFs) of a gene was a key predictor of noise, whereas presence of the TATA box and the promoter nucleosome occupancy had poor predictive power. With an increase in the number of regulatory TFs, there was a rise in the number of cooperatively binding TFs. In addition, an increased number of regulatory TFs meant more overlaps in TF binding sites, resulting in competition between TFs for binding to the same region of the promoter. Through modeling of TF binding to promoter and application of stochastic simulations, we demonstrated that competition and cooperation among TFs could increase noise. Thus, our work uncovers a process of noise regulation that arises out of the dynamics of gene regulation and is not dependent on any specific transcription factor or specific promoter sequence.
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Affiliation(s)
- Lavisha Parab
- Department of Biotechnology, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, West Bengal, India
- Max-Planck-Institute for Evolutionary Biology, Plön, Germany
| | - Sampriti Pal
- Department of Biotechnology, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, West Bengal, India
| | - Riddhiman Dhar
- Department of Biotechnology, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, West Bengal, India
- * E-mail:
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23
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Guthrie J, Charlebois D. Non-genetic resistance facilitates survival while hindering the evolution of drug resistance due to intraspecific competition. Phys Biol 2022; 19. [PMID: 35998624 DOI: 10.1088/1478-3975/ac8c17] [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: 07/29/2022] [Accepted: 08/23/2022] [Indexed: 11/11/2022]
Abstract
Rising rates of resistance to antimicrobial drugs threaten the effective treatment of infections across the globe. Drug resistance has been established to emerge from non-genetic mechanisms as well as from genetic mechanisms. However, it is still unclear how non-genetic resistance affects the evolution of genetic drug resistance. We develop deterministic and stochastic population models that incorporate resource competition to quantitatively investigate the transition from non-genetic to genetic resistance during the exposure to static and cidal drugs. We find that non-genetic resistance facilitates the survival of cell populations during drug treatment while hindering the development of genetic resistance due to competition between the non-genetically and genetically resistant subpopulations. Non-genetic resistance in the presence of subpopulation competition increases the fixation times of drug resistance mutations, while increasing the probability of mutation before population extinction during cidal drug treatment. Intense intraspecific competition during drug treatment leads to extinction of susceptible and non-genetically resistant subpopulations. Alternating between drug and no drug conditions results in oscillatory population dynamics, increased resistance mutation fixation timescales, and reduced population survival. These findings advance our fundamental understanding of the evolution of resistance and may guide novel treatment strategies for patients with drug-resistant infections.
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Affiliation(s)
- Joshua Guthrie
- Department of Physics, University of Alberta, 11455 Saskatchewan Drive NW, Edmonton, Alberta, T6G 2E1, CANADA
| | - Daniel Charlebois
- Departments of Physics and Biological Sciences, University of Alberta, 11455 Saskatchewan Drive NW, Edmonton, Alberta, T6G 2E1, CANADA
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24
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Guilbert M, Courtade E, Thommen Q. Cellular Environment and Phenotypic Heterogeneity: How Data-Driven Modeling Finds the Smoking Gun. Int J Mol Sci 2022; 23:ijms23126536. [PMID: 35742979 PMCID: PMC9223694 DOI: 10.3390/ijms23126536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/06/2022] [Accepted: 06/08/2022] [Indexed: 02/01/2023] Open
Abstract
The cellular environment modifies cellular phenotypes, in particular, the stress response phenotype, which easily exhibits high phenotypic heterogeneity due to the common characteristics of its regulatory networks. The aim of this work is to quantify and interpret the impact of collagen type I, a major component of the cellular environment, on the phenotypic heterogeneity of the cellular response. Our approach combines in an original way the monitoring of the response of a single cell and the mathematical modeling of the network. After a detailed statistical description of the phenotypic heterogeneity of the cellular response, the mathematical modeling explains how the observed changes can be explained by an induced increase in the average expression of a central protein of the regulatory network. The predictions of the data-driven model are fully consistent with the biochemical measurements performed. The framework presented here is also a new general methodology to study phenotypic heterogeneity, although we focus here on the response to proteotoxic stress in HeLa cells.
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Affiliation(s)
- Marie Guilbert
- CNRS, UMR 8523-PhLAM-Physique des Lasers Atomes et Molécules, University of Lille, F-59000 Lille, France; (M.G.); (E.C.)
| | - Emmanuel Courtade
- CNRS, UMR 8523-PhLAM-Physique des Lasers Atomes et Molécules, University of Lille, F-59000 Lille, France; (M.G.); (E.C.)
| | - Quentin Thommen
- CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277-CANTHER-Cancer Heterogeneity Plasticity and Resistance to Therapies, University of Lille, F-59000 Lille, France
- Correspondence:
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25
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Kovalev RA, Fedorova ND, Pantina RA, Semenova EV, Filatov MV, Varfolomeeva EY. Stochasticity of p53 Protein Expression in Cells of Primary and Transferable Human Lines. Biophysics (Nagoya-shi) 2022. [DOI: 10.1134/s0006350922030101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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26
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Cancer: More than a geneticist’s Pandora’s box. J Biosci 2022. [DOI: 10.1007/s12038-022-00254-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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27
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Drug-dependent growth curve reshaping reveals mechanisms of antifungal resistance in Saccharomyces cerevisiae. Commun Biol 2022; 5:292. [PMID: 35361876 PMCID: PMC8971432 DOI: 10.1038/s42003-022-03228-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 03/07/2022] [Indexed: 11/15/2022] Open
Abstract
Microbial drug resistance is an emerging global challenge. Current drug resistance assays tend to be simplistic, ignoring complexities of resistance manifestations and mechanisms, such as multicellularity. Here, we characterize multicellular and molecular sources of drug resistance upon deleting the AMN1 gene responsible for clumping multicellularity in a budding yeast strain, causing it to become unicellular. Computational analysis of growth curve changes upon drug treatment indicates that the unicellular strain is more sensitive to four common antifungals. Quantitative models uncover entwined multicellular and molecular processes underlying these differences in sensitivity and suggest AMN1 as an antifungal target in clumping pathogenic yeasts. Similar experimental and mathematical modeling pipelines could reveal multicellular and molecular drug resistance mechanisms, leading to more effective treatments against various microbial infections and possibly even cancers. Combined growth curve experiments and quantitative modeling reveal antifungal responses of planktonic yeast, providing a future framework to examine antimicrobial drug resistance.
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Liu Y, Huang Y, Lu R, Xin F, Liu G. Synthetic biology applications of the yeast mating signal pathway. Trends Biotechnol 2021; 40:620-631. [PMID: 34666896 DOI: 10.1016/j.tibtech.2021.09.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 12/18/2022]
Abstract
Cell fusion is a fundamental biological process that is involved in the development of most eukaryotic organisms. During the fusion process in Saccharomyces cerevisiae, cells respond to pheromones to trigger the MAPK (mitogen-activated protein kinase) cascade to initiate mating, followed by polarization, cell-wall remodeling, membrane fusion, and karyogamy. We highlight the applications of the yeast mating signal pathway in promoter engineering for tuning the expression of output genes, as well as in metabolic engineering for decoupling growth and metabolism, biosensors for sensitive detection and signal amplification, genetic circuits for programmable biological functionalities, and artificial consortia for cell-cell communication. Strategies such as exploiting rational engineering of modular circuits and optimizing the reproductive pathway to precisely maneuver physiological events have implications for scientific research and industrial development.
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Affiliation(s)
- Ying Liu
- College of Biological and Pharmaceutical Engineering, Nanjing Tech University, Jiangsu Province, China
| | - Yuxin Huang
- College of Biological and Pharmaceutical Engineering, Nanjing Tech University, Jiangsu Province, China
| | - Ran Lu
- College of Biological and Pharmaceutical Engineering, Nanjing Tech University, Jiangsu Province, China
| | - Fengxue Xin
- College of Biological and Pharmaceutical Engineering, Nanjing Tech University, Jiangsu Province, China
| | - Guannan Liu
- College of Biological and Pharmaceutical Engineering, Nanjing Tech University, Jiangsu Province, China; Jiangsu Synergetic Innovation Center for Advanced Bio-Manufacture, Nanjing Tech University, Jiangsu Province, China.
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29
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Catania F, Ujvari B, Roche B, Capp JP, Thomas F. Bridging Tumorigenesis and Therapy Resistance With a Non-Darwinian and Non-Lamarckian Mechanism of Adaptive Evolution. Front Oncol 2021; 11:732081. [PMID: 34568068 PMCID: PMC8462274 DOI: 10.3389/fonc.2021.732081] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 08/25/2021] [Indexed: 12/13/2022] Open
Abstract
Although neo-Darwinian (and less often Lamarckian) dynamics are regularly invoked to interpret cancer's multifarious molecular profiles, they shine little light on how tumorigenesis unfolds and often fail to fully capture the frequency and breadth of resistance mechanisms. This uncertainty frames one of the most problematic gaps between science and practice in modern times. Here, we offer a theory of adaptive cancer evolution, which builds on a molecular mechanism that lies outside neo-Darwinian and Lamarckian schemes. This mechanism coherently integrates non-genetic and genetic changes, ecological and evolutionary time scales, and shifts the spotlight away from positive selection towards purifying selection, genetic drift, and the creative-disruptive power of environmental change. The surprisingly simple use-it or lose-it rationale of the proposed theory can help predict molecular dynamics during tumorigenesis. It also provides simple rules of thumb that should help improve therapeutic approaches in cancer.
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Affiliation(s)
- Francesco Catania
- Institute for Evolution and Biodiversity, University of Münster, Münster, Germany
| | - Beata Ujvari
- Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Deakin, VIC, Australia
| | - Benjamin Roche
- CREEC/CANECEV, MIVEGEC (CREES), Centre de Recherches Ecologiques et Evolutives sur le Cancer, University of Montpellier, CNRS, IRD, Montpellier, France
| | - Jean-Pascal Capp
- Toulouse Biotechnology Institute, University of Toulouse, INSA, CNRS, INRAE, Toulouse, France
| | - Frédéric Thomas
- CREEC/CANECEV, MIVEGEC (CREES), Centre de Recherches Ecologiques et Evolutives sur le Cancer, University of Montpellier, CNRS, IRD, Montpellier, France
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30
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Derrien J, Guérin-Charbonnel C, Gaborit V, Campion L, Devic M, Douillard E, Roi N, Avet-Loiseau H, Decaux O, Facon T, Mallm JP, Eils R, Munshi NC, Moreau P, Herrmann C, Magrangeas F, Minvielle S. The DNA methylation landscape of multiple myeloma shows extensive inter- and intrapatient heterogeneity that fuels transcriptomic variability. Genome Med 2021; 13:127. [PMID: 34372935 PMCID: PMC8351364 DOI: 10.1186/s13073-021-00938-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 07/09/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Cancer evolution depends on epigenetic and genetic diversity. Historically, in multiple myeloma (MM), subclonal diversity and tumor evolution have been investigated mostly from a genetic perspective. METHODS Here, we performed an analysis of 42 MM samples from 21 patients by using enhanced reduced representation bisulfite sequencing (eRRBS). We combined several metrics of epigenetic heterogeneity to analyze DNA methylation heterogeneity in MM patients. RESULTS We show that MM is characterized by the continuous accumulation of stochastic methylation at the promoters of development-related genes. High combinatorial entropy change is associated with poor outcomes in our pilot study and depends predominantly on partially methylated domains (PMDs). These PMDs, which represent the major source of inter- and intrapatient DNA methylation heterogeneity in MM, are linked to other key epigenetic aberrations, such as CpG island (CGI)/transcription start site (TSS) hypermethylation and H3K27me3 redistribution as well as 3D organization alterations. In addition, transcriptome analysis revealed that intratumor methylation heterogeneity was associated with low-level expression and high variability. CONCLUSIONS We propose that disrupted DNA methylation in MM is responsible for high epigenetic and transcriptomic instability allowing tumor cells to adapt to environmental changes by tapping into a pool of evolutionary trajectories.
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Affiliation(s)
- Jennifer Derrien
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
| | - Catherine Guérin-Charbonnel
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- Institut de Cancérologie de l'Ouest, Nantes-Saint Herblain, France
| | - Victor Gaborit
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- LS2N, CNRS, Université de Nantes, Nantes, France
| | - Loïc Campion
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- Institut de Cancérologie de l'Ouest, Nantes-Saint Herblain, France
| | - Magali Devic
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- Centre Hospitalier Universitaire, Nantes, France
| | - Elise Douillard
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- Centre Hospitalier Universitaire, Nantes, France
| | - Nathalie Roi
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- Centre Hospitalier Universitaire, Nantes, France
| | - Hervé Avet-Loiseau
- Institut Universitaire du Cancer, CHU, Centre de Recherche en Cancérologie de Toulouse, INSERM 1037, Toulouse, France
| | | | | | - Jan-Philipp Mallm
- Research Group Genome Organization & Function, DKFZ, and BioQuant Heidelberg, Heidelberg, 69120, Germany
| | - Roland Eils
- Health Data Science Unit, Medical Faculty Heidelberg and BioQuant, Heidelberg, 69120, Germany
- Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt- Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany
- Berlin Institute of Health (BIH), Center for Digital Health, Anna-Louisa-Karsch-Strasse 2, Berlin, 10178, Germany
| | - Nikhil C Munshi
- Dana-Farber Cancer Institute, Harvard Medical School, LeBow Institute for Myeloma Therapeutics and Jerome Lipper Center for Multiple Myeloma Research, Boston, MA, United States
| | - Philippe Moreau
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- Centre Hospitalier Universitaire, Nantes, France
| | - Carl Herrmann
- Health Data Science Unit, Medical Faculty Heidelberg and BioQuant, Heidelberg, 69120, Germany
| | - Florence Magrangeas
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France
- Centre Hospitalier Universitaire, Nantes, France
| | - Stéphane Minvielle
- Université de Nantes, CNRS, INSERM, CRCINA, Nantes, F-44000, France.
- Centre Hospitalier Universitaire, Nantes, France.
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31
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Sahoo S, Mishra A, Kaur H, Hari K, Muralidharan S, Mandal S, Jolly MK. A mechanistic model captures the emergence and implications of non-genetic heterogeneity and reversible drug resistance in ER+ breast cancer cells. NAR Cancer 2021; 3:zcab027. [PMID: 34316714 PMCID: PMC8271219 DOI: 10.1093/narcan/zcab027] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 06/02/2021] [Accepted: 06/18/2021] [Indexed: 02/07/2023] Open
Abstract
Resistance to anti-estrogen therapy is an unsolved clinical challenge in successfully treating ER+ breast cancer patients. Recent studies have demonstrated the role of non-genetic (i.e. phenotypic) adaptations in tolerating drug treatments; however, the mechanisms and dynamics of such non-genetic adaptation remain elusive. Here, we investigate coupled dynamics of epithelial–mesenchymal transition (EMT) in breast cancer cells and emergence of reversible drug resistance. Our mechanism-based model for underlying regulatory network reveals that these two axes can drive one another, thus enabling non-genetic heterogeneity in a cell population by allowing for six co-existing phenotypes: epithelial-sensitive, mesenchymal-resistant, hybrid E/M-sensitive, hybrid E/M-resistant, mesenchymal-sensitive and epithelial-resistant, with the first two ones being most dominant. Next, in a population dynamics framework, we exemplify the implications of phenotypic plasticity (both drug-induced and intrinsic stochastic switching) and/or non-genetic heterogeneity in promoting population survival in a mixture of sensitive and resistant cells, even in the absence of any cell–cell cooperation. Finally, we propose the potential therapeutic use of mesenchymal–epithelial transition inducers besides canonical anti-estrogen therapy to limit the emergence of reversible drug resistance. Our results offer mechanistic insights into empirical observations on EMT and drug resistance and illustrate how such dynamical insights can be exploited for better therapeutic designs.
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Affiliation(s)
- Sarthak Sahoo
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Ashutosh Mishra
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Harsimran Kaur
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Kishore Hari
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Srinath Muralidharan
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, India
| | - Susmita Mandal
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
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32
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Yang J, Lee J, Land MA, Lai S, Igoshin OA, St-Pierre F. A synthetic circuit for buffering gene dosage variation between individual mammalian cells. Nat Commun 2021; 12:4132. [PMID: 34226556 PMCID: PMC8257781 DOI: 10.1038/s41467-021-23889-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 05/19/2021] [Indexed: 02/06/2023] Open
Abstract
Precise control of gene expression is critical for biological research and biotechnology. However, transient plasmid transfections in mammalian cells produce a wide distribution of copy numbers per cell, and consequently, high expression heterogeneity. Here, we report plasmid-based synthetic circuits - Equalizers - that buffer copy-number variation at the single-cell level. Equalizers couple a transcriptional negative feedback loop with post-transcriptional incoherent feedforward control. Computational modeling suggests that the combination of these two topologies enables Equalizers to operate over a wide range of plasmid copy numbers. We demonstrate experimentally that Equalizers outperform other gene dosage compensation topologies and produce as low cell-to-cell variation as chromosomally integrated genes. We also show that episome-encoded Equalizers enable the rapid generation of extrachromosomal cell lines with stable and uniform expression. Overall, Equalizers are simple and versatile devices for homogeneous gene expression and can facilitate the engineering of synthetic circuits that function reliably in every cell.
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Affiliation(s)
- Jin Yang
- Department of Bioengineering, Rice University, Houston, TX, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jihwan Lee
- Systems, Synthetic, and Physical Biology Program, Rice University, Houston, TX, USA
| | - Michelle A Land
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Shujuan Lai
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Oleg A Igoshin
- Department of Bioengineering, Rice University, Houston, TX, USA
- Systems, Synthetic, and Physical Biology Program, Rice University, Houston, TX, USA
- Department of Biosciences, Rice University, Houston, TX, USA
- Department of Chemistry, Rice University, Houston, TX, USA
| | - François St-Pierre
- Systems, Synthetic, and Physical Biology Program, Rice University, Houston, TX, USA.
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA.
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33
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Farquhar KS, Rasouli Koohi S, Charlebois DA. Does transcriptional heterogeneity facilitate the development of genetic drug resistance? Bioessays 2021; 43:e2100043. [PMID: 34160842 DOI: 10.1002/bies.202100043] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 05/30/2021] [Accepted: 06/07/2021] [Indexed: 12/24/2022]
Abstract
Non-genetic forms of antimicrobial (drug) resistance can result from cell-to-cell variability that is not encoded in the genetic material. Data from recent studies also suggest that non-genetic mechanisms can facilitate the development of genetic drug resistance. We speculate on how the interplay between non-genetic and genetic mechanisms may affect microbial adaptation and evolution during drug treatment. We argue that cellular heterogeneity arising from fluctuations in gene expression, epigenetic modifications, as well as genetic changes contribute to drug resistance at different timescales, and that the interplay between these mechanisms enhance pathogen resistance. Accordingly, developing a better understanding of the role of non-genetic mechanisms in drug resistance and how they interact with genetic mechanisms will enhance our ability to combat antimicrobial resistance. Also see the video abstract here: https://youtu.be/aefGpdh-bgU.
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Affiliation(s)
| | - Samira Rasouli Koohi
- Department of Physics, University of Alberta, Edmonton, Alberta, T6G-2E1, Canada
| | - Daniel A Charlebois
- Department of Physics, University of Alberta, Edmonton, Alberta, T6G-2E1, Canada.,Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
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34
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Pfeuty B, Courtade E, Thommen Q. Fine-tuned control of stress priming and thermotolerance. Phys Biol 2021; 18. [PMID: 34156353 DOI: 10.1088/1478-3975/ac02a8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 05/18/2021] [Indexed: 11/11/2022]
Abstract
A common signature of cell adaptation to stress is the improved resistance upon priming by prior stress exposure. In the context of hyperthermia, priming or preconditioning with sublethal heat shock can be a useful tool to confer thermotolerance and competitive advantage to cells. In the present study, we develop a data-driven modeling framework that is simple and generic enough to capture a broad set of adaptation behaviors to heat stress at both molecular and cellular levels. The model recovers the main features of thermotolerance and clarifies the tradeoff principles which maximize the thermotolerance effect. It therefore provides an effective predictive tool to design preconditioning and fractionation hyperthermia protocols for therapeutic purpose.
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Affiliation(s)
- Benjamin Pfeuty
- Univ. Lille, CNRS, UMR 8523-PhLAM-Physique des Lasers Atomes et Molécules, F-59000 Lille, France
| | - Emmanuel Courtade
- Univ. Lille, CNRS, UMR 8523-PhLAM-Physique des Lasers Atomes et Molécules, F-59000 Lille, France
| | - Quentin Thommen
- Univ. Lille, CNRS, UMR 8523-PhLAM-Physique des Lasers Atomes et Molécules, F-59000 Lille, France
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35
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Capp J. Interplay between genetic, epigenetic, and gene expression variability: Considering complexity in evolvability. Evol Appl 2021; 14:893-901. [PMID: 33897810 PMCID: PMC8061278 DOI: 10.1111/eva.13204] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 01/27/2021] [Accepted: 01/30/2021] [Indexed: 12/11/2022] Open
Abstract
Genetic variability, epigenetic variability, and gene expression variability (noise) are generally considered independently in their relationship with phenotypic variation. However, they appear to be intrinsically interconnected and influence it in combination. The study of the interplay between genetic and epigenetic variability has the longest history. This article rather considers the introduction of gene expression variability in its relationships with the two others and reviews for the first time experimental evidences over the four relationships connected to gene expression noise. They show how introducing this third source of variability complicates the way of thinking evolvability and the emergence of biological novelty. Finally, cancer cells are proposed to be an ideal model to decipher the dynamic interplay between genetic, epigenetic, and gene expression variability when one of them is either experimentally increased or therapeutically targeted. This interplay is also discussed in an evolutionary perspective in the context of cancer cell drug resistance.
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Affiliation(s)
- Jean‐Pascal Capp
- Toulouse Biotechnology InstituteINSACNRSINRAEUniversity of ToulouseToulouseFrance
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36
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Li R, Chen Z, Dai Z, Yu Y. Nanotechnology assisted photo- and sonodynamic therapy for overcoming drug resistance. Cancer Biol Med 2021; 18:j.issn.2095-3941.2020.0328. [PMID: 33755377 PMCID: PMC8185853 DOI: 10.20892/j.issn.2095-3941.2020.0328] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Accepted: 07/21/2020] [Indexed: 12/29/2022] Open
Abstract
Drug resistance is considered the most important reason for the clinical failure of cancer chemotherapy. Circumventing drug resistance and improving the efficacy of anticancer agents remains a major challenge. Over the past several decades, photodynamic therapy (PDT) and sonodynamic therapy (SDT) have attracted substantial attention for their efficacy in cancer treatment, and have been combined with chemotherapy to overcome drug resistance. However, simultaneously delivering sensitizers and chemotherapy drugs to same tumor cell remains challenging, thus greatly limiting this combinational therapy. The rapid development of nanotechnology provides a new approach to solve this problem. Nano-based drug delivery systems can not only improve the targeted delivery of agents but also co-deliver multiple drug components in single nanoparticles to achieve optimal synergistic effects. In this review, we briefly summarize the mechanisms of drug resistance, discuss the advantages and disadvantages of PDT and SDT in reversing drug resistance, and describe state-of-the-art research using nano-mediated PDT and SDT to solve these refractory problems. This review also highlights the clinical translational potential for this combinational therapy.
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Affiliation(s)
- Rui Li
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Zhimin Chen
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Zhifei Dai
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Yingjie Yu
- Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen 518039, China
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37
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Lasri A, Sturrock M. The influence of methylation status on a stochastic model of MGMT dynamics in glioblastoma: Phenotypic selection can occur with and without a downshift in promoter methylation status. J Theor Biol 2021; 521:110662. [PMID: 33684406 DOI: 10.1016/j.jtbi.2021.110662] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 02/26/2021] [Accepted: 03/01/2021] [Indexed: 01/02/2023]
Abstract
Glioblastoma originates in the brain and is one of the most aggressive cancer types. Glioblastoma represents 15% of all brain tumours, with a median survival of 15 months. Although the current standard of care for such a tumour (the Stupp protocol) has shown positive results for the prognosis of patients, O-6-methylguanine-DNA methyltransferase (MGMT) driven drug resistance has been an issue of increasing concern and hence requires innovative approaches. In addition to the well established drug resistance factors such as tumour location and blood brain barriers, it is also important to understand how the genetic and epigenetic dynamics of the glioblastoma cells can play a role. One important aspect of this is the study of methylation status of MGMT following administration of temozolomide. In this paper, we extend our previously published model that simulated MGMT expression in glioblastoma cells to incorporate the promoter methylation status of MGMT. This methylation status has clinical significance and is used as a marker for patient outcomes. Using this model, we investigate the causative relationship between temozolomide treatment and the methylation status of the MGMT promoter in a population of cells. In addition by constraining the model to relevant biological data using Approximate Bayesian Computation, we were able to identify parameter regimes that yield different possible modes of resistances, namely, phenotypic selection of MGMT, a downshift in the methylation status of the MGMT promoter or both simultaneously. We analysed each of the parameter sets associated with the different modes of resistance, presenting representative solutions as well as discovering some similarities between them as well as unique requirements for each of them. Finally, we used them to devise optimal strategies for inhibiting MGMT expression with the aim of minimising live glioblastoma cell numbers.
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Affiliation(s)
- Ayoub Lasri
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, York house, Dublin, Ireland.
| | - Marc Sturrock
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, York house, Dublin, Ireland
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38
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Singh D, Bocci F, Kulkarni P, Jolly MK. Coupled Feedback Loops Involving PAGE4, EMT and Notch Signaling Can Give Rise to Non-genetic Heterogeneity in Prostate Cancer Cells. ENTROPY (BASEL, SWITZERLAND) 2021; 23:288. [PMID: 33652914 PMCID: PMC7996788 DOI: 10.3390/e23030288] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/18/2021] [Accepted: 02/23/2021] [Indexed: 12/12/2022]
Abstract
Non-genetic heterogeneity is emerging as a crucial factor underlying therapy resistance in multiple cancers. However, the design principles of regulatory networks underlying non-genetic heterogeneity in cancer remain poorly understood. Here, we investigate the coupled dynamics of feedback loops involving (a) oscillations in androgen receptor (AR) signaling mediated through an intrinsically disordered protein PAGE4, (b) multistability in epithelial-mesenchymal transition (EMT), and c) Notch-Delta-Jagged signaling mediated cell-cell communication, each of which can generate non-genetic heterogeneity through multistability and/or oscillations. Our results show how different coupling strengths between AR and EMT signaling can lead to monostability, bistability, or oscillations in the levels of AR, as well as propagation of oscillations to EMT dynamics. These results reveal the emergent dynamics of coupled oscillatory and multi-stable systems and unravel mechanisms by which non-genetic heterogeneity in AR levels can be generated, which can act as a barrier to most existing therapies for prostate cancer patients.
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Affiliation(s)
- Divyoj Singh
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India;
- Undergraduate Programme, Indian Institute of Science, Bangalore 560012, India
| | - Federico Bocci
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA;
- NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697, USA
| | - Prakash Kulkarni
- Department of Medical Oncology and Experimental Therapeutics, City of Hope National Medical Center, Duarte, CA 91010, USA;
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India;
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39
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Yin H, Liu S, Wen X. Optimal transition paths of phenotypic switching in a non-Markovian self-regulation gene expression. Phys Rev E 2021; 103:022409. [PMID: 33736096 DOI: 10.1103/physreve.103.022409] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 01/06/2021] [Indexed: 11/07/2022]
Abstract
Gene expression is a complex biochemical process involving multiple reaction steps, creating molecular memory because the probability of waiting time between consecutive reaction steps no longer follows exponential distributions. What effect the molecular memory has on metastable states in gene expression remains not fully understood. Here, we study transition paths of switching between bistable states for a non-Markovian model of gene expression equipped with a self-regulation. Employing the large deviation theory for this model, we analyze the optimal transition paths of switching between bistable states in gene expression, interestingly finding that dynamic behaviors in gene expression along the optimal transition paths significantly depend on the molecular memory. Moreover, we discover that the molecular memory can prolong the time of switching between bistable states in gene expression along the optimal transition paths. Our results imply that the molecular memory may be an unneglectable factor to affect switching between metastable states in gene expression.
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Affiliation(s)
- Hongwei Yin
- School of Mathematics and Statistics, Xuzhou University of Technology, Xuzhou 221111, People's Republic of China
- School of Science, Nanchang University, Nanchang 330031, People's Republic of China
| | - Shuqin Liu
- School of Science, Nanchang University, Nanchang 330031, People's Republic of China
| | - Xiaoqing Wen
- School of Mathematics and Statistics, Xuzhou University of Technology, Xuzhou 221111, People's Republic of China
- School of Science, Nanchang University, Nanchang 330031, People's Republic of China
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40
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Chakraborty P, George JT, Woodward WA, Levine H, Jolly MK. Gene expression profiles of inflammatory breast cancer reveal high heterogeneity across the epithelial-hybrid-mesenchymal spectrum. Transl Oncol 2021; 14:101026. [PMID: 33535154 PMCID: PMC7851345 DOI: 10.1016/j.tranon.2021.101026] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 01/09/2021] [Accepted: 01/18/2021] [Indexed: 01/10/2023] Open
Abstract
No unique genome signature or molecular therapy exists for inflammatory breast cancer (IBC), a highly aggressive breast cancer with a 5-year survival rate of less than 30%. We show that various gene lists proposed as molecular footprints of IBC have no overlap and thus very limited predictive accuracy in identifying IBC samples. We observed that single-sample gene set enrichment analysis (ssGSEA) of IBC samples along the epithelial-hybrid-mesenchymal spectrum can help IBC identification. IBC samples robustly displayed a higher coefficient of variation in terms of EMT scores, as compared to non-IBC samples. Higher heterogeneity along the epithelial-hybrid-mesenchymal spectrum can be regarded to be a hallmark of IBC and a possibly useful biomarker.
Inflammatory breast cancer (IBC) is a highly aggressive breast cancer that metastasizes largely via tumor emboli, and has a 5-year survival rate of less than 30%. No unique genomic signature has yet been identified for IBC nor has any specific molecular therapeutic been developed to manage the disease. Thus, identifying gene expression signatures specific to IBC remains crucial. Here, we compare various gene lists that have been proposed as molecular footprints of IBC using different clinical samples as training and validation sets and using independent training algorithms, and determine their accuracy in identifying IBC samples in three independent datasets. We show that these gene lists have little to no mutual overlap, and have limited predictive accuracy in identifying IBC samples. Despite this inconsistency, single-sample gene set enrichment analysis (ssGSEA) of IBC samples correlate with their position on the epithelial-hybrid-mesenchymal spectrum. This positioning, together with ssGSEA scores, improves the accuracy of IBC identification across the three independent datasets. Finally, we observed that IBC samples robustly displayed a higher coefficient of variation in terms of EMT scores, as compared to non-IBC samples. Pending verification that this patient-to-patient variability extends to intratumor heterogeneity within a single patient, these results suggest that higher heterogeneity along the epithelial-hybrid-mesenchymal spectrum can be regarded to be a hallmark of IBC and a possibly useful biomarker.
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Affiliation(s)
- Priyanka Chakraborty
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Jason T George
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA; Medical Scientist Training Program, Baylor College of Medicine, Houston, TX 77005, USA
| | - Wendy A Woodward
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; MD Anderson Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Herbert Levine
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA; Departments of Physics and Bioengineering, Northeastern University, Boston, MA 02115, USA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India.
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41
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Bonny AR, Fonseca JP, Park JE, El-Samad H. Orthogonal control of mean and variability of endogenous genes in a human cell line. Nat Commun 2021; 12:292. [PMID: 33436569 PMCID: PMC7804932 DOI: 10.1038/s41467-020-20467-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 11/25/2020] [Indexed: 12/11/2022] Open
Abstract
Stochastic fluctuations at the transcriptional level contribute to isogenic cell-to-cell heterogeneity in mammalian cell populations. However, we still have no clear understanding of the repercussions of this heterogeneity, given the lack of tools to independently control mean expression and variability of a gene. Here, we engineer a synthetic circuit to modulate mean expression and heterogeneity of transgenes and endogenous human genes. The circuit, a Tunable Noise Rheostat (TuNR), consists of a transcriptional cascade of two inducible transcriptional activators, where the output mean and variance can be modulated by two orthogonal small molecule inputs. In this fashion, different combinations of the inputs can achieve the same mean but with different population variability. With TuNR, we achieve low basal expression, over 1000-fold expression of a transgene product, and up to 7-fold induction of the endogenous gene NGFR. Importantly, for the same mean expression level, we are able to establish varying degrees of heterogeneity in expression within an isogenic population, thereby decoupling gene expression noise from its mean. TuNR is therefore a modular tool that can be used in mammalian cells to enable direct interrogation of the implications of cell-to-cell variability.
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Affiliation(s)
- Alain R Bonny
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - João Pedro Fonseca
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, 94158, USA
- Amyris Bio Products Portugal, Porto, Portugal
| | - Jesslyn E Park
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Hana El-Samad
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, 94158, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
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42
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Exploiting noise to engineer adaptability in synthetic multicellular systems. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2020. [DOI: 10.1016/j.cobme.2020.100251] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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43
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Xu YC, Guo YL. Less Is More, Natural Loss-of-Function Mutation Is a Strategy for Adaptation. PLANT COMMUNICATIONS 2020; 1:100103. [PMID: 33367264 PMCID: PMC7743898 DOI: 10.1016/j.xplc.2020.100103] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 07/08/2020] [Accepted: 08/12/2020] [Indexed: 05/12/2023]
Abstract
Gene gain and loss are crucial factors that shape the evolutionary success of diverse organisms. In the past two decades, more attention has been paid to the significance of gene gain through gene duplication or de novo genes. However, gene loss through natural loss-of-function (LoF) mutations, which is prevalent in the genomes of diverse organisms, has been largely ignored. With the development of sequencing techniques, many genomes have been sequenced across diverse species and can be used to study the evolutionary patterns of gene loss. In this review, we summarize recent advances in research on various aspects of LoF mutations, including their identification, evolutionary dynamics in natural populations, and functional effects. In particular, we discuss how LoF mutations can provide insights into the minimum gene set (or the essential gene set) of an organism. Furthermore, we emphasize their potential impact on adaptation. At the genome level, although most LoF mutations are neutral or deleterious, at least some of them are under positive selection and may contribute to biodiversity and adaptation. Overall, we highlight the importance of natural LoF mutations as a robust framework for understanding biological questions in general.
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Affiliation(s)
- Yong-Chao Xu
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ya-Long Guo
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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44
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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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45
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Quarton T, Kang T, Papakis V, Nguyen K, Nowak C, Li Y, Bleris L. Uncoupling gene expression noise along the central dogma using genome engineered human cell lines. Nucleic Acids Res 2020; 48:9406-9413. [PMID: 32810265 PMCID: PMC7498316 DOI: 10.1093/nar/gkaa668] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 07/29/2020] [Accepted: 08/03/2020] [Indexed: 01/22/2023] Open
Abstract
Eukaryotic protein synthesis is an inherently stochastic process. This stochasticity stems not only from variations in cell content between cells but also from thermodynamic fluctuations in a single cell. Ultimately, these inherently stochastic processes manifest as noise in gene expression, where even genetically identical cells in the same environment exhibit variation in their protein abundances. In order to elucidate the underlying sources that contribute to gene expression noise, we quantify the contribution of each step within the process of protein synthesis along the central dogma. We uncouple gene expression at the transcriptional, translational, and post-translational level using custom engineered circuits stably integrated in human cells using CRISPR. We provide a generalized framework to approximate intrinsic and extrinsic noise in a population of cells expressing an unbalanced two-reporter system. Our decomposition shows that the majority of intrinsic fluctuations stem from transcription and that coupling the two genes along the central dogma forces the fluctuations to propagate and accumulate along the same path, resulting in increased observed global correlation between the products.
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Affiliation(s)
- Tyler Quarton
- Bioengineering Department, University of Texas at Dallas, Richardson, TX, USA.,Center for Systems Biology, University of Texas at Dallas, Richardson, TX, USA
| | - Taek Kang
- Bioengineering Department, University of Texas at Dallas, Richardson, TX, USA.,Center for Systems Biology, University of Texas at Dallas, Richardson, TX, USA
| | - Vasileios Papakis
- Center for Systems Biology, University of Texas at Dallas, Richardson, TX, USA.,Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Khai Nguyen
- Center for Systems Biology, University of Texas at Dallas, Richardson, TX, USA.,Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Chance Nowak
- Bioengineering Department, University of Texas at Dallas, Richardson, TX, USA.,Center for Systems Biology, University of Texas at Dallas, Richardson, TX, USA.,Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Yi Li
- Bioengineering Department, University of Texas at Dallas, Richardson, TX, USA.,Center for Systems Biology, University of Texas at Dallas, Richardson, TX, USA
| | - Leonidas Bleris
- Bioengineering Department, University of Texas at Dallas, Richardson, TX, USA.,Center for Systems Biology, University of Texas at Dallas, Richardson, TX, USA.,Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, USA
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46
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Mohanty A, Nam A, Pozhitkov A, Yang L, Srivastava S, Nathan A, Wu X, Mambetsariev I, Nelson M, Subbalakshmi A, Guo L, Nasser MW, Batra SK, Orban J, Jolly MK, Massarelli E, Kulkarni P, Salgia R. A Non-genetic Mechanism Involving the Integrin β4/Paxillin Axis Contributes to Chemoresistance in Lung Cancer. iScience 2020; 23:101496. [PMID: 32947124 PMCID: PMC7502350 DOI: 10.1016/j.isci.2020.101496] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 06/08/2020] [Accepted: 08/20/2020] [Indexed: 02/07/2023] Open
Abstract
Tumor heterogeneity and cisplatin resistance are major causes of tumor relapse and poor survival. Here, we show that in lung cancer, interaction between paxillin (PXN) and integrin β4 (ITGB4), components of the focal adhesion (FA) complex, contributes to cisplatin resistance. Knocking down PXN and ITGB4 attenuated cell growth and improved cisplatin sensitivity, both in 2D and 3D cultures. PXN and ITGB4 independently regulated expression of several genes. In addition, they also regulated expression of common genes including USP1 and VDAC1, which are required for maintaining genomic stability and mitochondrial function, respectively. Mathematical modeling suggested that bistability could lead to stochastic phenotypic switching between cisplatin-sensitive and resistant states in these cells. Consistently, purified subpopulations of sensitive and resistant cells re-created the mixed parental population when cultured separately. Altogether, these data point to an unexpected role of the FA complex in cisplatin resistance and highlight a novel non-genetic mechanism.
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Affiliation(s)
- Atish Mohanty
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Arin Nam
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Alex Pozhitkov
- Department of Computational and Quantitative Medicine, City of Hope, 1500 East Duarte Road, Duarte, CA, USA
| | - Lu Yang
- Department of Systems Biology, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, USA
| | - Saumya Srivastava
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Anusha Nathan
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Xiwei Wu
- Genomics Core Facility, Beckman Research Institute, City of Hope, Duarte, CA, USA
| | - Isa Mambetsariev
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Michael Nelson
- Department of Molecular Imaging and Therapy, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, USA
| | - A.R. Subbalakshmi
- Center for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Linlin Guo
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Mohd W. Nasser
- Department of Biochemistry and Molecular Biology, Division of Thoracic Surgery, University of Nebraska College of Medicine, Omaha, NE, USA
| | - Surinder K. Batra
- Department of Biochemistry and Molecular Biology, Division of Thoracic Surgery, University of Nebraska College of Medicine, Omaha, NE, USA
| | - John Orban
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD 20850, USA
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA
| | - Mohit Kumar Jolly
- Center for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Erminia Massarelli
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Prakash Kulkarni
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
| | - Ravi Salgia
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010-3000, USA
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47
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Farquhar KS, Flohr H, Charlebois DA. Advancing Antimicrobial Resistance Research Through Quantitative Modeling and Synthetic Biology. Front Bioeng Biotechnol 2020; 8:583415. [PMID: 33072732 PMCID: PMC7530828 DOI: 10.3389/fbioe.2020.583415] [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: 07/14/2020] [Accepted: 09/02/2020] [Indexed: 11/13/2022] Open
Abstract
Antimicrobial resistance (AMR) is an emerging global health crisis that is undermining advances in modern medicine and, if unmitigated, threatens to kill 10 million people per year worldwide by 2050. Research over the last decade has demonstrated that the differences between genetically identical cells in the same environment can lead to drug resistance. Fluctuations in gene expression, modulated by gene regulatory networks, can lead to non-genetic heterogeneity that results in the fractional killing of microbial populations causing drug therapies to fail; this non-genetic drug resistance can enhance the probability of acquiring genetic drug resistance mutations. Mathematical models of gene networks can elucidate general principles underlying drug resistance, predict the evolution of resistance, and guide drug resistance experiments in the laboratory. Cells genetically engineered to carry synthetic gene networks regulating drug resistance genes allow for controlled, quantitative experiments on the role of non-genetic heterogeneity in the development of drug resistance. In this perspective article, we emphasize the contributions that mathematical, computational, and synthetic gene network models play in advancing our understanding of AMR to discover effective therapies against drug-resistant infections.
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Affiliation(s)
| | - Harold Flohr
- Department of Physics, University of Alberta, Edmonton, AB, Canada
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48
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Frei T, Cella F, Tedeschi F, Gutiérrez J, Stan GB, Khammash M, Siciliano V. Characterization and mitigation of gene expression burden in mammalian cells. Nat Commun 2020; 11:4641. [PMID: 32934213 PMCID: PMC7492461 DOI: 10.1038/s41467-020-18392-x] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 08/18/2020] [Indexed: 12/19/2022] Open
Abstract
Despite recent advances in circuit engineering, the design of genetic networks in mammalian cells is still painstakingly slow and fraught with inexplicable failures. Here, we demonstrate that transiently expressed genes in mammalian cells compete for limited transcriptional and translational resources. This competition results in the coupling of otherwise independent exogenous and endogenous genes, creating a divergence between intended and actual function. Guided by a resource-aware mathematical model, we identify and engineer natural and synthetic miRNA-based incoherent feedforward loop (iFFL) circuits that mitigate gene expression burden. The implementation of these circuits features the use of endogenous miRNAs as elementary components of the engineered iFFL device, a versatile hybrid design that allows burden mitigation to be achieved across different cell-lines with minimal resource requirements. This study establishes the foundations for context-aware prediction and improvement of in vivo synthetic circuit performance, paving the way towards more rational synthetic construct design in mammalian cells.
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Affiliation(s)
- Timothy Frei
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich, Mattenstrasse 26, Basel, 4058, Switzerland
| | - Federica Cella
- Istituto Italiano di Tecnologia-IIT, Largo Barsanti e Matteucci, Naples, 80125, Italy
- University of Genoa, Genoa, 16132, Italy
| | - Fabiana Tedeschi
- Istituto Italiano di Tecnologia-IIT, Largo Barsanti e Matteucci, Naples, 80125, Italy
| | - Joaquín Gutiérrez
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich, Mattenstrasse 26, Basel, 4058, Switzerland
| | - Guy-Bart Stan
- Department of Bioengineering and Centre for Synthetic Biology, Imperial College London, London, SW7 2AZ, UK
| | - Mustafa Khammash
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich, Mattenstrasse 26, Basel, 4058, Switzerland.
| | - Velia Siciliano
- Istituto Italiano di Tecnologia-IIT, Largo Barsanti e Matteucci, Naples, 80125, Italy.
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49
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Wu F, Shim J, Gong T, Tan C. Orthogonal tuning of gene expression noise using CRISPR-Cas. Nucleic Acids Res 2020; 48:e76. [PMID: 32479612 PMCID: PMC7367181 DOI: 10.1093/nar/gkaa451] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 04/23/2020] [Accepted: 05/14/2020] [Indexed: 01/07/2023] Open
Abstract
The control of gene expression noise is important for improving drug treatment and the performance of synthetic biological systems. Previous work has tuned gene expression noise by changing the rate of transcription initiation, mRNA degradation, and mRNA translation. However, these methods are invasive: they require changes to the target genetic components. Here, we create an orthogonal system based on CRISPR-dCas9 to tune gene expression noise. Specifically, we modulate the gene expression noise of a reporter gene in Escherichia coli by incorporating CRISPR activation and repression (CRISPRar) simultaneously in a single cell. The CRISPRar uses a single dCas9 that recognizes two different single guide RNAs (sgRNA). We build a library of sgRNA variants with different expression activation and repression strengths. We find that expression noise and mean of a reporter gene can be tuned independently by CRISPRar. Our results suggest that the expression noise is tuned by the competition between two sgRNAs that modulate the binding of RNA polymerase to promoters. The CRISPRar may change how we tune expression noise at the genomic level. Our work has broad impacts on the study of gene functions, phenotypical heterogeneity, and genetic circuit control.
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Affiliation(s)
- Fan Wu
- Department of Biomedical Engineering, University of California Davis, Davis, CA 95616, USA
| | - Jiyoung Shim
- Department of Biomedical Engineering, University of California Davis, Davis, CA 95616, USA
| | - Ting Gong
- Department of Biomedical Engineering, University of California Davis, Davis, CA 95616, USA
| | - Cheemeng Tan
- Department of Biomedical Engineering, University of California Davis, Davis, CA 95616, USA
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50
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Lasri A, Juric V, Verreault M, Bielle F, Idbaih A, Kel A, Murphy B, Sturrock M. Phenotypic selection through cell death: stochastic modelling of O-6-methylguanine-DNA methyltransferase dynamics. ROYAL SOCIETY OPEN SCIENCE 2020; 7:191243. [PMID: 32874597 PMCID: PMC7428254 DOI: 10.1098/rsos.191243] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 06/17/2020] [Indexed: 05/11/2023]
Abstract
Glioblastoma (GBM) is the most aggressive malignant primary brain tumour with a median overall survival of 15 months. To treat GBM, patients currently undergo a surgical resection followed by exposure to radiotherapy and concurrent and adjuvant temozolomide (TMZ) chemotherapy. However, this protocol often leads to treatment failure, with drug resistance being the main reason behind this. To date, many studies highlight the role of O-6-methylguanine-DNA methyltransferase (MGMT) in conferring drug resistance. The mechanism through which MGMT confers resistance is not well studied-particularly in terms of computational models. With only a few reasonable biological assumptions, we were able to show that even a minimal model of MGMT expression could robustly explain TMZ-mediated drug resistance. In particular, we showed that for a wide range of parameter values constrained by novel cell growth and viability assays, a model accounting for only stochastic gene expression of MGMT coupled with cell growth, division, partitioning and death was able to exhibit phenotypic selection of GBM cells expressing MGMT in response to TMZ. Furthermore, we found this selection allowed the cells to pass their acquired phenotypic resistance onto daughter cells in a stable manner (as long as TMZ is provided). This suggests that stochastic gene expression alone is enough to explain the development of chemotherapeutic resistance.
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Affiliation(s)
- Ayoub Lasri
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, York House, Dublin, Ireland
| | - Viktorija Juric
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, York House, Dublin, Ireland
| | - Maité Verreault
- Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Institut du Cerveau et de la Moelle épinière, ICM, 75013 Paris, France
| | - Franck Bielle
- Sorbonne Université, Inserm, CNRS, UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière – Charles Foix, Service de Neurologie 2-Mazarin, 75013 Paris, France
| | - Ahmed Idbaih
- Sorbonne Université, Inserm, CNRS, UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière – Charles Foix, Service de Neurologie 2-Mazarin, 75013 Paris, France
| | - Alexander Kel
- Department of Research and Development, geneXplain GmbH, Wolfenbüttel 38302, Germany
- Laboratory of Pharmacogenomics, Institute of Chemical Biology and Fundamental Medicine, Novosibirsk 630090, Russia
| | - Brona Murphy
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, York House, Dublin, Ireland
| | - Marc Sturrock
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, York House, Dublin, Ireland
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