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Helenek C, Krzysztoń R, Petreczky J, Wan Y, Cabral M, Coraci D, Balázsi G. Synthetic gene circuit evolution: Insights and opportunities at the mid-scale. Cell Chem Biol 2024:S2451-9456(24)00219-8. [PMID: 38925113 DOI: 10.1016/j.chembiol.2024.05.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/07/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024]
Abstract
Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.
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Affiliation(s)
- Christopher Helenek
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA
| | - Rafał Krzysztoń
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Julia Petreczky
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
| | - Yiming Wan
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Mariana Cabral
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA
| | - Damiano Coraci
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Gábor Balázsi
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA; Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY 11794, USA.
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2
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Guha M, Singh A, Butzin NC. Gram-positive bacteria are primed for surviving lethal doses of antibiotics and chemical stress. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.28.596288. [PMID: 38895422 PMCID: PMC11185512 DOI: 10.1101/2024.05.28.596288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Antibiotic resistance kills millions worldwide yearly. However, a major contributor to recurrent infections lies in a small fraction of bacterial cells, known as persisters. These cells are not inherently antibiotic-resistant, yet they lead to increased antibiotic usage, raising the risk of developing resistant progenies. In a bacterial population, individual cells exhibit considerable fluctuations in their gene expression levels despite being cultivated under identical, stable conditions. This variability in cell-to-cell characteristics (phenotypic diversity) within an isogenic population enables persister cells to withstand antibiotic exposure by entering a non-dividing state. We recently showed the existence of "primed cells" in E. coli. Primed cells are dividing cells prepared for antibiotic stress before encountering it and are more prone to form persisters. They also pass their "prepared state" down for several generations through epigenetic memory. Here, we show that primed cells are common among distant bacterial lineages, allowing for survival against antibiotics and other chemical stress, and form in different growth phases. They are also responsible for increased persister levels in transition and stationary phases compared to the log phase. We tested and showed that the Gram-positive bacterium Bacillus megaterium, evolutionarily very distant from E. coli, forms primed cells and has a transient epigenetic memory that is maintained for 7 generations or more. We showed this using ciprofloxacin and the non-antibiotic chemical stress fluoride. It is well established that persister levels are higher in the stationary phase than in the log phase, and B. megaterium persisters levels are nearly identical from the early to late-log phase but are ~2-fold and ~4-fold higher in the transition and stationary phase, respectively. It was previously proposed that there are two distinct types of persisters: Type II forms in the log phase, while Type I forms in the stationary phase. However, we show that primed cells lead to increased persisters in the transition and stationary phase and found no evidence of Type I or II persisters with distant phenotypes. Overall, we have provided substantial evidence of the importance of primed cells and their transitory epigenetic memories to surviving stress.
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Affiliation(s)
- Manisha Guha
- Department of Biology and Microbiology; South Dakota State University; Brookings, SD, 57006; USA
| | - Abhyudai Singh
- Electrical & Computer Engineering; University of Delaware; Newark, DE 19716; USA
| | - Nicholas C. Butzin
- Department of Biology and Microbiology; South Dakota State University; Brookings, SD, 57006; USA
- Department of Chemistry and Biochemistry; South Dakota State University; Brookings, SD, 57006; USA
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3
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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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4
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Ashokkumar M, Mei W, Peterson JJ, Harigaya Y, Murdoch DM, Margolis DM, Kornfein C, Oesterling A, Guo Z, Rudin CD, Jiang Y, Browne EP. Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae003. [PMID: 38902848 PMCID: PMC11189801 DOI: 10.1093/gpbjnl/qzae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 10/19/2023] [Indexed: 06/22/2024]
Abstract
Despite the success of antiretroviral therapy, human immunodeficiency virus (HIV) cannot be cured because of a reservoir of latently infected cells that evades therapy. To understand the mechanisms of HIV latency, we employed an integrated single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) approach to simultaneously profile the transcriptomic and epigenomic characteristics of ∼ 125,000 latently infected primary CD4+ T cells after reactivation using three different latency reversing agents. Differentially expressed genes and differentially accessible motifs were used to examine transcriptional pathways and transcription factor (TF) activities across the cell population. We identified cellular transcripts and TFs whose expression/activity was correlated with viral reactivation and demonstrated that a machine learning model trained on these data was 75%-79% accurate at predicting viral reactivation. Finally, we validated the role of two candidate HIV-regulating factors, FOXP1 and GATA3, in viral transcription. These data demonstrate the power of integrated multimodal single-cell analysis to uncover novel relationships between host cell factors and HIV latency.
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Affiliation(s)
- Manickam Ashokkumar
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- HIV Cure Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Wenwen Mei
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jackson J Peterson
- HIV Cure Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yuriko Harigaya
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - David M Murdoch
- Department of Medicine, Duke University, Durham, NC 27708, USA
| | - David M Margolis
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- HIV Cure Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Caleb Kornfein
- Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Alex Oesterling
- Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Zhicheng Guo
- Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Cynthia D Rudin
- Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Yuchao Jiang
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
- Department of Biology, Texas A&M University, College Station, TX 77843, USA
- Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Edward P Browne
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- HIV Cure Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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5
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Jiao F, Li J, Liu T, Zhu Y, Che W, Bleris L, Jia C. What can we learn when fitting a simple telegraph model to a complex gene expression model? PLoS Comput Biol 2024; 20:e1012118. [PMID: 38743803 PMCID: PMC11125521 DOI: 10.1371/journal.pcbi.1012118] [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: 02/06/2024] [Revised: 05/24/2024] [Accepted: 04/27/2024] [Indexed: 05/16/2024] Open
Abstract
In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.
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Affiliation(s)
- Feng Jiao
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Jing Li
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Ting Liu
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Yifeng Zhu
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Wenhao Che
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, China
| | - Leonidas Bleris
- Bioengineering Department, The University of Texas at Dallas, Richardson, Texas, United States of America
- Center for Systems Biology, The University of Texas at Dallas, Richardson, Texas, United States of America
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas, United States of America
| | - Chen Jia
- Applied and Computational Mathematics Division, Beijing Computational Science Research Center, Beijing, China
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6
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Izquierdo-Pujol J, Puertas MC, Martinez-Picado J, Morón-López S. Targeting Viral Transcription for HIV Cure Strategies. Microorganisms 2024; 12:752. [PMID: 38674696 PMCID: PMC11052381 DOI: 10.3390/microorganisms12040752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/05/2024] [Accepted: 04/05/2024] [Indexed: 04/28/2024] Open
Abstract
Combination antiretroviral therapy (ART) suppresses viral replication to undetectable levels, reduces mortality and morbidity, and improves the quality of life of people living with HIV (PWH). However, ART cannot cure HIV infection because it is unable to eliminate latently infected cells. HIV latency may be regulated by different HIV transcription mechanisms, such as blocks to initiation, elongation, and post-transcriptional processes. Several latency-reversing (LRA) and -promoting agents (LPA) have been investigated in clinical trials aiming to eliminate or reduce the HIV reservoir. However, none of these trials has shown a conclusive impact on the HIV reservoir. Here, we review the cellular and viral factors that regulate HIV-1 transcription, the potential pharmacological targets and genetic and epigenetic editing techniques that have been or might be evaluated to disrupt HIV-1 latency, the role of miRNA in post-transcriptional regulation of HIV-1, and the differences between the mechanisms regulating HIV-1 and HIV-2 expression.
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Affiliation(s)
- Jon Izquierdo-Pujol
- IrsiCaixa, 08916 Badalona, Spain; (J.I.-P.); (M.C.P.); (J.M.-P.)
- Germans Trias i Pujol Research Institute (IGTP), 08916 Badalona, Spain
| | - Maria C. Puertas
- IrsiCaixa, 08916 Badalona, Spain; (J.I.-P.); (M.C.P.); (J.M.-P.)
- Germans Trias i Pujol Research Institute (IGTP), 08916 Badalona, Spain
- CIBERINFEC, 28029 Madrid, Spain
| | - Javier Martinez-Picado
- IrsiCaixa, 08916 Badalona, Spain; (J.I.-P.); (M.C.P.); (J.M.-P.)
- Germans Trias i Pujol Research Institute (IGTP), 08916 Badalona, Spain
- CIBERINFEC, 28029 Madrid, Spain
- Department of Infectious Diseases and Immunity, School of Medicine, University of Vic-Central University of Catalonia (UVic-UCC), 08500 Vic, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), 08010 Barcelona, Spain
| | - Sara Morón-López
- IrsiCaixa, 08916 Badalona, Spain; (J.I.-P.); (M.C.P.); (J.M.-P.)
- Germans Trias i Pujol Research Institute (IGTP), 08916 Badalona, Spain
- CIBERINFEC, 28029 Madrid, Spain
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7
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Zhang C, Jiao F. Using steady-state formula to estimate time-dependent parameters of stochastic gene transcription models. Biosystems 2024; 236:105128. [PMID: 38280446 DOI: 10.1016/j.biosystems.2024.105128] [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: 11/01/2023] [Revised: 01/20/2024] [Accepted: 01/21/2024] [Indexed: 01/29/2024]
Abstract
When studying stochastic gene transcription, it is important to understand how system parameters are temporally modulated in response to varying environments. Experimentally, the dynamic distribution data of RNA copy numbers measured at multiple time points are often fitted to stochastic transcription models to estimate time-dependent parameters. However, current methods require determining which parameters are time-dependent, as well as their analytical formulas, before the optimal fit. In this study, we developed a method to estimate time-dependent parameters in a classical two-state model without prior assumptions regarding the system parameters. At each measured time point, the method fitted the dynamic distribution data using a steady-state distribution formula, in which the estimated constant parameters were approximated as time-dependent parameter values at the measured time point. The accuracy of this method can be guaranteed for RNA molecules with relatively high degradation rates and genes with relatively slow responses to induction. We quantify the accuracy of the method and implemented this method on two sets of dynamic distribution data from prokaryotic and eukaryotic cells, and revealed the temporal modulation of transcription burst size in response to environmental changes.
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Affiliation(s)
- Congrun Zhang
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, 510006, PR China; College of Mathematics and Information Sciences, Guangzhou University, Guangzhou 51006, China
| | - Feng Jiao
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, 510006, PR China; College of Mathematics and Information Sciences, Guangzhou University, Guangzhou 51006, China.
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8
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Damour A, Slaninova V, Radulescu O, Bertrand E, Basyuk E. Transcriptional Stochasticity as a Key Aspect of HIV-1 Latency. Viruses 2023; 15:1969. [PMID: 37766375 PMCID: PMC10535884 DOI: 10.3390/v15091969] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/16/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
Abstract
This review summarizes current advances in the role of transcriptional stochasticity in HIV-1 latency, which were possible in a large part due to the development of single-cell approaches. HIV-1 transcription proceeds in bursts of RNA production, which stem from the stochastic switching of the viral promoter between ON and OFF states. This switching is caused by random binding dynamics of transcription factors and nucleosomes to the viral promoter and occurs at several time scales from minutes to hours. Transcriptional bursts are mainly controlled by the core transcription factors TBP, SP1 and NF-κb, the chromatin status of the viral promoter and RNA polymerase II pausing. In particular, spontaneous variability in the promoter chromatin creates heterogeneity in the response to activators such as TNF-α, which is then amplified by the Tat feedback loop to generate high and low viral transcriptional states. This phenomenon is likely at the basis of the partial and stochastic response of latent T cells from HIV-1 patients to latency-reversing agents, which is a barrier for the development of shock-and-kill strategies of viral eradication. A detailed understanding of the transcriptional stochasticity of HIV-1 and the possibility to precisely model this phenomenon will be important assets to develop more effective therapeutic strategies.
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Affiliation(s)
- Alexia Damour
- MFP UMR 5234 CNRS, Université de Bordeaux, 33076 Bordeaux, France;
| | - Vera Slaninova
- IGH UMR 9002 CNRS, Université de Montpellier, 34094 Montpellier, France;
| | - Ovidiu Radulescu
- LPHI, UMR 5294 CNRS, University of Montpellier, 34095 Montpellier, France;
| | - Edouard Bertrand
- IGH UMR 9002 CNRS, Université de Montpellier, 34094 Montpellier, France;
| | - Eugenia Basyuk
- MFP UMR 5234 CNRS, Université de Bordeaux, 33076 Bordeaux, France;
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9
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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: 4] [Impact Index Per Article: 4.0] [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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10
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Nowak CM, Quarton T, Bleris L. Impact of variability in cell cycle periodicity on cell population dynamics. PLoS Comput Biol 2023; 19:e1011080. [PMID: 37339124 DOI: 10.1371/journal.pcbi.1011080] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 04/06/2023] [Indexed: 06/22/2023] Open
Abstract
The cell cycle consists of a series of orchestrated events controlled by molecular sensing and feedback networks that ultimately drive the duplication of total DNA and the subsequent division of a single parent cell into two daughter cells. The ability to block the cell cycle and synchronize cells within the same phase has helped understand factors that control cell cycle progression and the properties of each individual phase. Intriguingly, when cells are released from a synchronized state, they do not maintain synchronized cell division and rapidly become asynchronous. The rate and factors that control cellular desynchronization remain largely unknown. In this study, using a combination of experiments and simulations, we investigate the desynchronization properties in cervical cancer cells (HeLa) starting from the G1/S boundary following double-thymidine block. Propidium iodide (PI) DNA staining was used to perform flow cytometry cell cycle analysis at regular 8 hour intervals, and a custom auto-similarity function to assess the desynchronization and quantify the convergence to an asynchronous state. In parallel, we developed a single-cell phenomenological model the returns the DNA amount across the cell cycle stages and fitted the parameters using experimental data. Simulations of population of cells reveal that the cell cycle desynchronization rate is primarily sensitive to the variability of cell cycle duration within a population. To validate the model prediction, we introduced lipopolysaccharide (LPS) to increase cell cycle noise. Indeed, we observed an increase in cell cycle variability under LPS stimulation in HeLa cells, accompanied with an enhanced rate of cell cycle desynchronization. Our results show that the desynchronization rate of artificially synchronized in-phase cell populations can be used a proxy of the degree of variance in cell cycle periodicity, an underexplored axis in cell cycle research.
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Affiliation(s)
- Chance M Nowak
- Bioengineering Department, The University of Texas at Dallas, Richardson, Texas, United States of America
- Center for Systems Biology, The University of Texas at Dallas, Richardson, Texas, United States of America
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas, United States of America
| | - Tyler Quarton
- Bioengineering Department, The University of Texas at Dallas, Richardson, Texas, United States of America
- Center for Systems Biology, The University of Texas at Dallas, Richardson, Texas, United States of America
| | - Leonidas Bleris
- Bioengineering Department, The University of Texas at Dallas, Richardson, Texas, United States of America
- Center for Systems Biology, The University of Texas at Dallas, Richardson, Texas, United States of America
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, Texas, United States of America
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11
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Fajiculay E, Hsu C. Noise response in monomolecular closed systems. J CHIN CHEM SOC-TAIP 2023. [DOI: 10.1002/jccs.202200526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2023]
Affiliation(s)
- Erickson Fajiculay
- Institute of Chemistry Academia Sinica Taipei Taiwan
- Bioinformatics Program, Institute of Statistical Science, Taiwan International Graduate Program Academia Sinica Taipei Taiwan
- Institute of Bioinformatics and Structure Biology National Tsinghua University Hsinchu City Taiwan
| | - Chao‐Ping Hsu
- Institute of Chemistry Academia Sinica Taipei Taiwan
- Bioinformatics Program, Institute of Statistical Science, Taiwan International Graduate Program Academia Sinica Taipei Taiwan
- Physics Division National Center for Theoretical Sciences Taipei Taiwan
- Genome and Systems Biology Degree Program National Taiwan University Taipei Taiwan
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12
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Calia GP, Chen X, Zuckerman B, Weinberger LS. Comparative analysis between single-cell RNA-seq and single-molecule RNA FISH indicates that the pyrimidine nucleobase idoxuridine (IdU) globally amplifies transcriptional noise. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.14.532632. [PMID: 36993609 PMCID: PMC10055090 DOI: 10.1101/2023.03.14.532632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability, but the physiological roles of noise have remained difficult to determine in the absence of generalized noise-modulation approaches. Previous single-cell RNA-sequencing (scRNA-seq) suggested that the pyrimidine-base analog (5'-iodo-2'-deoxyuridine, IdU) could generally amplify noise without substantially altering mean-expression levels but scRNA-seq technical drawbacks potentially obscured the penetrance of IdU-induced transcriptional noise amplification. Here we quantify global-vs.-partial penetrance of IdU-induced noise amplification by assessing scRNA-seq data using numerous normalization algorithms and directly quantifying noise using single-molecule RNA FISH (smFISH) for a panel of genes from across the transcriptome. Alternate scRNA-seq analyses indicate IdU-induced noise amplification for ~90% of genes, and smFISH data verified noise amplification for ~90% of tested genes. Collectively, this analysis indicates which scRNA-seq algorithms are appropriate for quantifying noise and argues that IdU is a globally penetrant noise-enhancer molecule that could enable investigations of the physiological impacts of transcriptional noise.
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Affiliation(s)
- Giuliana P. Calia
- Gladstone|UCSF Center for Cell Circuitry, University of California, San Francisco, CA 94158
| | - Xinyue Chen
- Gladstone|UCSF Center for Cell Circuitry, University of California, San Francisco, CA 94158
| | - Binyamin Zuckerman
- Gladstone|UCSF Center for Cell Circuitry, University of California, San Francisco, CA 94158
| | - Leor S. Weinberger
- Gladstone|UCSF Center for Cell Circuitry, University of California, San Francisco, CA 94158
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
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13
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Ilan Y. Making use of noise in biological systems. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 178:83-90. [PMID: 36640927 DOI: 10.1016/j.pbiomolbio.2023.01.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/07/2022] [Accepted: 01/09/2023] [Indexed: 01/12/2023]
Abstract
Disorder and noise are inherent in biological systems. They are required to provide systems with the advantages required for proper functioning. Noise is a part of the flexibility and plasticity of biological systems. It provides systems with increased routes, improves information transfer, and assists in response triggers. This paper reviews recent studies on noise at the genome, cellular, and whole organ levels. We focus on the need to use noise in system engineering. We present some of the challenges faced in studying noise. Optimizing the efficiency of complex systems requires a degree of variability in their functions within certain limits. Constrained noise can be considered a method for improving system robustness by regulating noise levels in continuously dynamic settings. The digital pill-based artificial intelligence (AI)-based platform is the first to implement second-generation AI comprising variability-based signatures. This platform enhances the efficacy of the therapeutic regimens. Systems requiring variability and mechanisms regulating noise are mandatory for understanding biological functions.
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Affiliation(s)
- Yaron Ilan
- Hebrew University, Faculty of Medicine, Department of Medicine, Hadassah Medical Center, POB 1200, IL91120, Jerusalem, Israel.
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14
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Singh A, Saint-Antoine M. Probing transient memory of cellular states using single-cell lineages. Front Microbiol 2023; 13:1050516. [PMID: 36824587 PMCID: PMC9942930 DOI: 10.3389/fmicb.2022.1050516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 12/22/2022] [Indexed: 02/10/2023] Open
Abstract
The inherent stochasticity in the gene product levels can drive single cells within an isoclonal population to different phenotypic states. The dynamic nature of this intercellular variation, where individual cells can transition between different states over time, makes it a particularly hard phenomenon to characterize. We reviewed recent progress in leveraging the classical Luria-Delbrück experiment to infer the transient heritability of the cellular states. Similar to the original experiment, individual cells were first grown into cell colonies, and then, the fraction of cells residing in different states was assayed for each colony. We discuss modeling approaches for capturing dynamic state transitions in a growing cell population and highlight formulas that identify the kinetics of state switching from the extent of colony-to-colony fluctuations. The utility of this method in identifying multi-generational memory of the both expression and phenotypic states is illustrated across diverse biological systems from cancer drug resistance, reactivation of human viruses, and cellular immune responses. In summary, this fluctuation-based methodology provides a powerful approach for elucidating cell-state transitions from a single time point measurement, which is particularly relevant in situations where measurements lead to cell death (as in single-cell RNA-seq or drug treatment) or cause an irreversible change in cell physiology.
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Affiliation(s)
- Abhyudai Singh
- Departments of Electrical and Computer Engineering, Biomedical Engineering, Mathematical Sciences University of Delaware, Newark, DE, United States
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15
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Fajiculay E, Hsu CP. Localization of Noise in Biochemical Networks. ACS OMEGA 2023; 8:3043-3056. [PMID: 36713703 PMCID: PMC9878546 DOI: 10.1021/acsomega.2c06113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 12/27/2022] [Indexed: 06/18/2023]
Abstract
Noise, or uncertainty in biochemical networks, has become an important aspect of many biological problems. Noise can arise and propagate from external factors and probabilistic chemical reactions occurring in small cellular compartments. For species survival, it is important to regulate such uncertainties in executing vital cell functions. Regulated noise can improve adaptability, whereas uncontrolled noise can cause diseases. Simulation can provide a detailed analysis of uncertainties, but parameters such as rate constants and initial conditions are usually unknown. A general understanding of noise dynamics from the perspective of network structure is highly desirable. In this study, we extended the previously developed law of localization for characterizing noise in terms of (co)variances and developed noise localization theory. With linear noise approximation, we can expand a biochemical network into an extended set of differential equations representing a fictitious network for pseudo-components consisting of variances and covariances, together with chemical species. Through localization analysis, perturbation responses at the steady state of pseudo-components can be summarized into a sensitivity matrix that only requires knowledge of network topology. Our work allows identification of buffering structures at the level of species, variances, and covariances and can provide insights into noise flow under non-steady-state conditions in the form of a pseudo-chemical reaction. We tested noise localization in various systems, and here we discuss its implications and potential applications. Results show that this theory is potentially applicable in discriminating models, scanning network topologies with interesting noise behavior, and designing and perturbing networks with the desired response.
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Affiliation(s)
- Erickson Fajiculay
- Institute
of Chemistry, Academia Sinica, Taipei115201, Taiwan
- Bioinformatics
Program, Institute of Information Science, Taiwan International Graduate
Program, Academia Sinica, Taipei115201, Taiwan
- Institute
of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu300044, Taiwan
| | - Chao-Ping Hsu
- Institute
of Chemistry, Academia Sinica, Taipei115201, Taiwan
- Bioinformatics
Program, Institute of Information Science, Taiwan International Graduate
Program, Academia Sinica, Taipei115201, Taiwan
- Physics
Division, National Center for Theoretical
Sciences, Taipei106319, Taiwan
- Genome
and Systems Biology Degree Program, National
Taiwan University, Taipei106319, Taiwan
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16
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Shipps C, Thrush KL, Reinhardt CR, Siwiecki SA, Claydon JL, Noble DB, O'Hern CS. "Student-led workshop strengthens perceived discussion skills and community in an interdisciplinary graduate program". FASEB Bioadv 2023; 5:1-12. [PMID: 36643898 PMCID: PMC9832528 DOI: 10.1096/fba.2021-00165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 09/30/2022] [Accepted: 10/05/2022] [Indexed: 01/18/2023] Open
Abstract
The Integrated Graduate Program in Physical and Engineering Biology (IGPPEB) at Yale University brings together Ph.D. students from the physical, engineering, and biological sciences. The main goals of this program are for students to become comfortable working in an interdisciplinary and collaborative research environment and adept at communicating with scientists and nonscientists. To fill a student-identified learning gap in engaging in inclusive discussions, IGPPEB students developed a communication workshop to improve skills in visual engagement, citing specific content, constructive conversation entrances, and encouragement of peers. Based on short- and long-term assessment of the workshop, 100% of students reported that it should be offered to future cohorts and 63% of students perceived it to be personally helpful. Additionally, 92% of participants reported using one or more of the core skills beyond the course, with skills in "Encouraging peers" and "Constructive conversation entrances" rated the highest in perceived improvement. Based on the highest average rating of 76 ± 24 (on a scale of 0-100), students agreed that the workshop made them feel more welcome in the IGPPEB community. With a rating of 68 ± 13, they also agreed that the workshop had a positive impact on their graduate school experience. Participants provided suggestions for future improvements, such as increasing student involvement in leading discussions of course material. This study demonstrates that a student-led workshop can improve perceived discussion skills and build community across an interdisciplinary program in the sciences.
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Affiliation(s)
- Catharine Shipps
- Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut USA
- Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut USA
| | - Kyra L Thrush
- Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut USA
- Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut USA
| | - Clorice R Reinhardt
- Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut USA
- Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut USA
| | - Sara A Siwiecki
- Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut USA
- Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut USA
| | - Jennifer L Claydon
- Poorvu Center for Teaching and Learning Yale University New Haven Connecticut USA
- Combined Graduate Program in Biological and Biomedical Sciences Yale University New Haven Connecticut USA
| | - Dorottya B Noble
- Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut USA
- Program in Physics, Engineering, and Biology Yale University New Haven Connecticut USA
| | - Corey S O'Hern
- Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut USA
- Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut USA
- Program in Physics, Engineering, and Biology Yale University New Haven Connecticut USA
- Department of Mechanical Engineering & Materials Science Yale University New Haven Connecticut USA
- Department of Physics Yale University New Haven Connecticut USA
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17
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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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18
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Loell K, Wu Y, Staller MV, Cohen B. Activation domains can decouple the mean and noise of gene expression. Cell Rep 2022; 40:111118. [PMID: 35858548 PMCID: PMC9912357 DOI: 10.1016/j.celrep.2022.111118] [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: 09/08/2021] [Revised: 01/18/2022] [Accepted: 06/28/2022] [Indexed: 11/03/2022] Open
Abstract
Regulatory mechanisms set a gene's average level of expression, but a gene's expression constantly fluctuates around that average. These stochastic fluctuations, or expression noise, play a role in cell-fate transitions, bet hedging in microbes, and the development of chemotherapeutic resistance in cancer. An outstanding question is what regulatory mechanisms contribute to noise. Here, we demonstrate that, for a fixed mean level of expression, strong activation domains (ADs) at low abundance produce high expression noise, while weak ADs at high abundance generate lower expression noise. We conclude that differences in noise can be explained by the interplay between a TF's nuclear concentration and the strength of its AD's effect on mean expression, without invoking differences between classes of ADs. These results raise the possibility of engineering gene expression noise independently of mean levels in synthetic biology contexts and provide a potential mechanism for natural selection to tune the noisiness of gene expression.
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Affiliation(s)
- Kaiser Loell
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA,The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - Yawei Wu
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA,The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - Max V. Staller
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Barak Cohen
- Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA; The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA.
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19
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Das AK. Stochastic gene transcription with non-competitive transcription regulatory architecture. THE EUROPEAN PHYSICAL JOURNAL. E, SOFT MATTER 2022; 45:61. [PMID: 35831727 DOI: 10.1140/epje/s10189-022-00213-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
The transcription factors, such as activators and repressors, can interact with the promoter of gene either in a competitive or non-competitive way. In this paper, we construct a stochastic model with non-competitive transcriptional regulatory architecture and develop an analytical theory that re-establishes the experimental results with an improved data fitting. The analytical expressions in the theory allow us to study the nature of the system corresponding to any of its parameters and hence, enable us to find out the factors that govern the regulation of gene expression for that architecture. We notice that, along with transcriptional reinitiation and repressors, there are other parameters that can control the noisiness of this network. We also observe that, the Fano factor (at mRNA level) varies from sub-Poissonian regime to super-Poissonian regime. In addition to the aforementioned properties, we observe some anomalous characteristics of the Fano factor (at mRNA level) and that of the variance of protein at lower activator concentrations in the presence of repressor molecules. This model is useful to understand the architecture of interactions which may buffer the stochasticity inherent to gene transcription.
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20
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Guo X, Tang T, Duan M, Zhang L, Ge H. The nonequilibrium mechanism of noise-enhanced drug synergy in HIV latency reactivation. iScience 2022; 25:104358. [PMID: 35620426 PMCID: PMC9127169 DOI: 10.1016/j.isci.2022.104358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 03/04/2022] [Accepted: 04/29/2022] [Indexed: 11/29/2022] Open
Abstract
Noise-modulating chemicals can synergize with transcriptional activators in reactivating latent HIV to eliminate latent HIV reservoirs. To understand the underlying biomolecular mechanism, we investigate a previous two-gene-state model and identify two necessary conditions for the synergy: an assumption of the inhibition effect of transcription activators on noise enhancers; and frequent transitions to the gene non-transcription-permissive state. We then develop a loop-four-gene-state model with Tat transcription/translation and find that drug synergy is mainly determined by the magnitude and direction of energy input into the genetic regulatory kinetics of the HIV promoter. The inhibition effect of transcription activators is actually a phenomenon of energy dissipation in the nonequilibrium gene transition system. Overall, the loop-four-state model demonstrates that energy dissipation plays a crucial role in HIV latency reactivation, which might be useful for improving drug effects and identifying other synergies on lentivirus latency reactivation. The inhibition of Activator on Noise enhancer is necessary for their synergy in reactivating HIV The drug synergy is a nonequilibrium phenomenon in the gene regulatory system The magnitude and direction of energy input determine the drug synergy This nonequilibrium mechanism is general without regarding molecular details
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21
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Fajiculay E, Hsu CP. BioSANS: A software package for symbolic and numeric biological simulation. PLoS One 2022; 17:e0256409. [PMID: 35436294 PMCID: PMC9015124 DOI: 10.1371/journal.pone.0256409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 03/15/2022] [Indexed: 12/03/2022] Open
Abstract
Modeling biochemical systems can provide insights into behaviors that are difficult to observe or understand. It requires software, programming, and understanding of the system to build a model and study it. Softwares exist for systems biology modeling, but most support only certain types of modeling tasks. Desirable features including ease in preparing input, symbolic or analytical computation, parameter estimation, graphical user interface, and systems biology markup language (SBML) support are not seen concurrently in one software package. In this study, we developed a python-based software that supports these features, with both deterministic and stochastic propagations. The software can be used by graphical user interface, command line, or as a python import. We also developed a semi-programmable and intuitively easy topology input method for the biochemical reactions. We tested the software with semantic and stochastic SBML test cases. Tests on symbolic solution and parameter estimation were also included. The software we developed is reliable, well performing, convenient to use, and compliant with most of the SBML tests. So far it is the only systems biology software that supports symbolic, deterministic, and stochastic modeling in one package that also features parameter estimation and SBML support. This work offers a comprehensive set of tools and allows for better availability and accessibility for studying kinetics and dynamics in biochemical systems.
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Affiliation(s)
- Erickson Fajiculay
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Bioinformatics Program, Institute of Information Science, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan
- Institute of Bioinformatics and Structure Biology, National Tsinghua University, Hsinchu, Taiwan
| | - Chao-Ping Hsu
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Physics Division, National Center for Theoretical Sciences, Taipei, Hsinchu, Taiwan
- Genome and Systems Biology Degree program, National Taiwan University, Taipei, Taiwan
- * E-mail:
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22
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Jiao F, Tang M. Quantification of transcription noise’s impact on cell fate commitment with digital resolutions. Bioinformatics 2022; 38:3062-3069. [DOI: 10.1093/bioinformatics/btac277] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 02/18/2022] [Accepted: 04/12/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Motivation
Gene transcription is a random and noisy process. Tremendous efforts in single cell studies have been mapping transcription noises to phenotypic variabilities between isogenic cells. However, the exact role of the noise in cell fate commitment remains largely descriptive or even controversial.
Results
For a specified cell fate, we define the jumping digit I of a critical gene as a statistical threshold that a single cell has approximately an equal chance to commit the fate as to have at least I transcripts of the gene. When the transcription is perturbed by a noise enhancer without changing the basal transcription level E 0, we find a crossing digit k such that the noise catalyzes cell fate change when I > k while stabilizes the current state when I < k; k remains stable against enormous variations of kinetic rates. We further test the reactivation of latent HIV in 22 integration sites by noise enhancers paired with transcriptional activators. Strong synergistic actions are observed when the activators increase transcription burst frequency, whereas no synergism, but antagonism, is often observed if activators increase burst size. The synergistic efficiency can be predicted accurately by the ratio I / E0. When the noise enhancers double the noise, the activators double the burst frequency, and I / E0 ≥ 7, their combination is 10 times more effective than their additive effects across all 22 sites.
Availability and implementation
The jumping digit I may provide a novel probe to explore the phenotypic consequences of transcription noise in cell functions. Code is freely available at http://cam.gzhu.edu.cn/info/1014/1223.htm.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Feng Jiao
- Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, 510006, P. R. China
- College of Mathematics and Information Sciences, Guangzhou University, Guangzhou, 510006, P. R. China
| | - Moxun Tang
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
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23
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Emergent phenomena in living systems: A statistical mechanical perspective. J Biosci 2022. [DOI: 10.1007/s12038-021-00247-2] [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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24
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Bose I. Tipping the Balance: A Criticality Perspective. ENTROPY 2022; 24:e24030405. [PMID: 35327916 PMCID: PMC8947304 DOI: 10.3390/e24030405] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/10/2022] [Accepted: 03/12/2022] [Indexed: 01/02/2023]
Abstract
Cell populations are often characterised by phenotypic heterogeneity in the form of two distinct subpopulations. We consider a model of tumour cells consisting of two subpopulations: non-cancer promoting (NCP) and cancer-promoting (CP). Under steady state conditions, the model has similarities with a well-known model of population genetics which exhibits a purely noise-induced transition from unimodality to bimodality at a critical value of the noise intensity σ2. The noise is associated with the parameter λ representing the system-environment coupling. In the case of the tumour model, λ has a natural interpretation in terms of the tissue microenvironment which has considerable influence on the phenotypic composition of the tumour. Oncogenic transformations give rise to considerable fluctuations in the parameter. We compute the λ−σ2 phase diagram in a stochastic setting, drawing analogies between bifurcations and phase transitions. In the region of bimodality, a transition from a state of balance to a state of dominance, in terms of the competing subpopulations, occurs at λ = 0. Away from this point, the NCP (CP) subpopulation becomes dominant as λ changes towards positive (negative) values. The variance of the steady state probability density function as well as two entropic measures provide characteristic signatures at the transition point.
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Affiliation(s)
- Indrani Bose
- Department of Physics, Bose Institute, 93/1, A. P. C. Road, Kolkata 700009, India
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25
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Aydin O, Passaro AP, Raman R, Spellicy SE, Weinberg RP, Kamm RD, Sample M, Truskey GA, Zartman J, Dar RD, Palacios S, Wang J, Tordoff J, Montserrat N, Bashir R, Saif MTA, Weiss R. Principles for the design of multicellular engineered living systems. APL Bioeng 2022; 6:010903. [PMID: 35274072 PMCID: PMC8893975 DOI: 10.1063/5.0076635] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 02/02/2022] [Indexed: 12/14/2022] Open
Abstract
Remarkable progress in bioengineering over the past two decades has enabled the formulation of fundamental design principles for a variety of medical and non-medical applications. These advancements have laid the foundation for building multicellular engineered living systems (M-CELS) from biological parts, forming functional modules integrated into living machines. These cognizant design principles for living systems encompass novel genetic circuit manipulation, self-assembly, cell-cell/matrix communication, and artificial tissues/organs enabled through systems biology, bioinformatics, computational biology, genetic engineering, and microfluidics. Here, we introduce design principles and a blueprint for forward production of robust and standardized M-CELS, which may undergo variable reiterations through the classic design-build-test-debug cycle. This Review provides practical and theoretical frameworks to forward-design, control, and optimize novel M-CELS. Potential applications include biopharmaceuticals, bioreactor factories, biofuels, environmental bioremediation, cellular computing, biohybrid digital technology, and experimental investigations into mechanisms of multicellular organisms normally hidden inside the "black box" of living cells.
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Affiliation(s)
| | - Austin P. Passaro
- Regenerative Bioscience Center, University of Georgia, Athens, Georgia 30602, USA
| | - Ritu Raman
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | | | - Robert P. Weinberg
- School of Pharmacy, Massachusetts College of Pharmacy and Health Sciences, Boston, Massachusetts 02115, USA
| | | | - Matthew Sample
- Center for Ethics and Law in the Life Sciences, Leibniz Universität Hannover, 30167 Hannover, Germany
| | - George A. Truskey
- Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA
| | - Jeremiah Zartman
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, USA
| | - Roy D. Dar
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Sebastian Palacios
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA
| | - Jason Wang
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Jesse Tordoff
- Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Nuria Montserrat
- Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain
| | | | - M. Taher A. Saif
- Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - Ron Weiss
- Author to whom correspondence should be addressed:
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26
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Yang X, Luo S, Zhang Z, Wang Z, Zhou T, Zhang J. Silent transcription intervals and translational bursting lead to diverse phenotypic switching. Phys Chem Chem Phys 2022; 24:26600-26608. [DOI: 10.1039/d2cp03703c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
For complex process of gene expression, we use theoretical analysis and stochastic simulations to study the phenotypic diversity induced by silent transcription intervals and translational bursting.
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Affiliation(s)
- Xiyan Yang
- School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, P. R. China
| | - Songhao Luo
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Zhenquan Zhang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Zihao Wang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Tianshou Zhou
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
| | - Jiajun Zhang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, P. R. China
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, P. R. China
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27
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Chen L, Zhu C, Jiao F. A generalized moment-based method for estimating parameters of stochastic gene transcription. Math Biosci 2022; 345:108780. [DOI: 10.1016/j.mbs.2022.108780] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/27/2021] [Accepted: 01/13/2022] [Indexed: 12/22/2022]
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28
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A Novel Approach for Calculating Exact Forms of mRNA Distribution in Single-Cell Measurements. MATHEMATICS 2021. [DOI: 10.3390/math10010027] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Gene transcription is a stochastic process manifested by fluctuations in mRNA copy numbers in individual isogenic cells. Together with mathematical models of stochastic transcription, the massive mRNA distribution data that can be used to quantify fluctuations in mRNA levels can be fitted by Pm(t), which is the probability of producing m mRNA molecules at time t in a single cell. Tremendous efforts have been made to derive analytical forms of Pm(t), which rely on solving infinite arrays of the master equations of models. However, current approaches focus on the steady-state (t→∞) or require several parameters to be zero or infinity. Here, we present an approach for calculating Pm(t) with time, where all parameters are positive and finite. Our approach was successfully implemented for the classical two-state model and the widely used three-state model and may be further developed for different models with constant kinetic rates of transcription. Furthermore, the direct computations of Pm(t) for the two-state model and three-state model showed that the different regulations of gene activation can generate discriminated dynamical bimodal features of mRNA distribution under the same kinetic rates and similar steady-state mRNA distribution.
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29
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Wei L, Li S, Zhang P, Hu T, Zhang MQ, Xie Z, Wang X. Characterizing microRNA-mediated modulation of gene expression noise and its effect on synthetic gene circuits. Cell Rep 2021; 36:109573. [PMID: 34433047 DOI: 10.1016/j.celrep.2021.109573] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 07/13/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022] Open
Abstract
MicroRNAs (miRNAs) have been shown to modulate gene expression noise, but less is known about how miRNAs with different properties may regulate noise differently. Here, we investigate the role of competing RNAs and the composition of miRNA response elements (MREs) in modulating noise. We find that weak competing RNAs could introduce lower noise than strong competing RNAs. In comparison with a single MRE, both repetitive and composite MREs can reduce the noise at low expression, but repetitive MREs can elevate the noise remarkably at high expression. We further observed the behavior of a synthetic cell-type classifier with miRNAs as inputs and find that miRNAs and MREs that could introduce higher noise tend to enhance cell state transition. These results provide a systematic and quantitative understanding of the function of miRNAs in controlling gene expression noise and the utilization of miRNAs to modulate the behavior of synthetic gene circuits.
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Affiliation(s)
- Lei Wei
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shuailin Li
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Pengcheng Zhang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Tao Hu
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Michael Q Zhang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China; Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China; Department of Molecular and Cell Biology, Center for Systems Biology, The University of Texas, Richardson, TX 75080-3021, USA
| | - Zhen Xie
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaowo Wang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing 100084, China.
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30
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Jiao F, Lin G, Yu J. Approximating gene transcription dynamics using steady-state formulas. Phys Rev E 2021; 104:014401. [PMID: 34412315 DOI: 10.1103/physreve.104.014401] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/02/2021] [Indexed: 01/01/2023]
Abstract
Understanding how genes in a single cell respond to dynamically changing signals has been a central question in stochastic gene transcription research. Recent studies have generated massive steady-state or snapshot mRNA distribution data of individual cells, and inferred a large spectrum of kinetic transcription parameters under varying conditions. However, there have been few algorithms to convert these static data into the temporal variation of kinetic rates. Real-time imaging has been developed to monitor stochastic transcription processes at the single-cell level, but the immense technicality has prevented its application to most endogenous loci in mammalian cells. In this article, we introduced a stochastic gene transcription model with variable kinetic rates induced by unstable cellular conditions. We approximated the transcription dynamics using easily obtained steady-state formulas in the model. We tested the approximation against experimental data in both prokaryotic and eukaryotic cells and further solidified the conditions that guarantee the robustness of the method. The method can be easily implemented to provide convenient tools for quantifying dynamic kinetics and mechanisms underlying the widespread static transcription data, and may shed a light on circumventing the limitation of current bursting data on transcriptional real-time imaging.
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Affiliation(s)
- Feng Jiao
- Center for Applied Mathematics, Guangzhou University, Guangzhou 510006, People's Republic of China.,College of Mathematics and Information Sciences, Guangzhou University, Guangzhou 51006, People's Republic of China
| | - Genghong Lin
- Center for Applied Mathematics, Guangzhou University, Guangzhou 510006, People's Republic of China.,College of Mathematics and Information Sciences, Guangzhou University, Guangzhou 51006, People's Republic of China
| | - Jianshe Yu
- Center for Applied Mathematics, Guangzhou University, Guangzhou 510006, People's Republic of China
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31
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Monitoring reactivation of latent HIV by label-free gradient light interference microscopy. iScience 2021; 24:102940. [PMID: 34430819 PMCID: PMC8367845 DOI: 10.1016/j.isci.2021.102940] [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: 01/12/2021] [Revised: 05/24/2021] [Accepted: 07/30/2021] [Indexed: 11/23/2022] Open
Abstract
Human immunodeficiency virus (HIV) can infect cells and take a quiescent and nonexpressive state called latency. In this study, we report insights provided by label-free, gradient light interference microscopy (GLIM) about the changes in dry mass, diameter, and dry mass density associated with infected cells that occur upon reactivation. We discovered that the mean cell dry mass and mean diameter of latently infected cells treated with reactivating drug, TNF-α, are higher for latent cells that reactivate than those of the cells that did not reactivate. Cells with mean dry mass and diameter less than approximately 10 pg and 8 μm, respectively, remain exclusively in the latent state. Also, cells with mean dry mass greater than approximately 28-30 pg and mean diameter greater than 11–12 μm have a higher probability of reactivating. This study is significant as it presents a new label-free approach to quantify latent reactivation of a virus in single cells. GLIM imaging reveals differences between latent and reactivated HIV in JLat cells Cells with reactivated HIV have higher dry mass and diameter
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32
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The switch of DNA states filtering the extrinsic noise in the system of frequency modulation. Sci Rep 2021; 11:16309. [PMID: 34381062 PMCID: PMC8357933 DOI: 10.1038/s41598-021-95365-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 07/20/2021] [Indexed: 02/07/2023] Open
Abstract
There is a special node, which the large noise of the upstream element may not always lead to a broad distribution of downstream elements. This node is DNA, with upstream element TF and downstream elements mRNA and proteins. By applying the stochastic simulation algorithm (SSA) on gene circuits inspired by the fim operon in Escherichia coli, we found that cells exchanged the distribution of the upstream transcription factor (TF) for the transitional frequency of DNA. Then cells do an inverse transform, which exchanges the transitional frequency of DNA for the distribution of downstream products. Due to this special feature, DNA in the system of frequency modulation is able to reset the noise. By probability generating function, we know the ranges of parameter values that grant such an interesting phenomenon.
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33
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Desai RV, Chen X, Martin B, Chaturvedi S, Hwang DW, Li W, Yu C, Ding S, Thomson M, Singer RH, Coleman RA, Hansen MMK, Weinberger LS. A DNA repair pathway can regulate transcriptional noise to promote cell fate transitions. Science 2021; 373:science.abc6506. [PMID: 34301855 DOI: 10.1126/science.abc6506] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 07/08/2021] [Indexed: 12/13/2022]
Abstract
Stochastic fluctuations in gene expression ("noise") are often considered detrimental, but fluctuations can also be exploited for benefit (e.g., dither). We show here that DNA base excision repair amplifies transcriptional noise to facilitate cellular reprogramming. Specifically, the DNA repair protein Apex1, which recognizes both naturally occurring and unnatural base modifications, amplifies expression noise while homeostatically maintaining mean expression levels. This amplified expression noise originates from shorter-duration, higher-intensity transcriptional bursts generated by Apex1-mediated DNA supercoiling. The remodeling of DNA topology first impedes and then accelerates transcription to maintain mean levels. This mechanism, which we refer to as "discordant transcription through repair" ("DiThR," which is pronounced "dither"), potentiates cellular reprogramming and differentiation. Our study reveals a potential functional role for transcriptional fluctuations mediated by DNA base modifications in embryonic development and disease.
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Affiliation(s)
- Ravi V Desai
- Gladstone/UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA.,Medical Scientist Training Program and Tetrad Graduate Program, University of California, San Francisco, CA 94158, USA
| | - Xinyue Chen
- Gladstone/UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Benjamin Martin
- Gladstone/UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA.,Institute for Molecules and Materials, Radboud University, 6525 AJ Nijmegen, the Netherlands
| | - Sonali Chaturvedi
- Gladstone/UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Dong Woo Hwang
- Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Weihan Li
- Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Chen Yu
- Gladstone Institute of Cardiovascular Disease, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Sheng Ding
- Gladstone Institute of Cardiovascular Disease, Gladstone Institutes, San Francisco, CA 94158, USA.,School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Matt Thomson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Robert H Singer
- Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Robert A Coleman
- Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Maike M K Hansen
- Institute for Molecules and Materials, Radboud University, 6525 AJ Nijmegen, the Netherlands
| | - Leor S Weinberger
- Gladstone/UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA. .,Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA.,Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA
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34
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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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35
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Zhao S, Tsibris A. Leveraging Novel Integrated Single-Cell Analyses to Define HIV-1 Latency Reversal. Viruses 2021; 13:1197. [PMID: 34206546 PMCID: PMC8310207 DOI: 10.3390/v13071197] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/11/2021] [Accepted: 06/16/2021] [Indexed: 01/24/2023] Open
Abstract
While suppressive antiretroviral therapy can effectively limit HIV-1 replication and evolution, it leaves behind a residual pool of integrated viral genomes that persist in a state of reversible nonproductive infection, referred to as the HIV-1 reservoir. HIV-1 infection models were established to investigate HIV-1 latency and its reversal; recent work began to probe the dynamics of HIV-1 latency reversal at single-cell resolution. Signals that establish HIV-1 latency and govern its reactivation are complex and may not be completely resolved at the cellular and regulatory levels by the aggregated measurements of bulk cellular-sequencing methods. High-throughput single-cell technologies that characterize and quantify changes to the epigenome, transcriptome, and proteome continue to rapidly evolve. Combinations of single-cell techniques, in conjunction with novel computational approaches to analyze these data, were developed and provide an opportunity to improve the resolution of the heterogeneity that may exist in HIV-1 reactivation. In this review, we summarize the published single-cell HIV-1 transcriptomic work and explore how cutting-edge advances in single-cell techniques and integrative data-analysis tools may be leveraged to define the mechanisms that control the reversal of HIV-1 latency.
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Affiliation(s)
| | - Athe Tsibris
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02139, USA;
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36
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Blanco A, Mahajan T, Coronado RA, Ma K, Demma DR, Dar RD. Synergistic Chromatin-Modifying Treatments Reactivate Latent HIV and Decrease Migration of Multiple Host-Cell Types. Viruses 2021; 13:v13061097. [PMID: 34201394 PMCID: PMC8228244 DOI: 10.3390/v13061097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/29/2021] [Accepted: 06/02/2021] [Indexed: 11/29/2022] Open
Abstract
Upon infection of its host cell, human immunodeficiency virus (HIV) establishes a quiescent and non-productive state capable of spontaneous reactivation. Diverse cell types harboring the provirus form a latent reservoir, constituting a major obstacle to curing HIV. Here, we investigate the effects of latency reversal agents (LRAs) in an HIV-infected THP-1 monocyte cell line in vitro. We demonstrate that leading drug treatments synergize activation of the HIV long terminal repeat (LTR) promoter. We establish a latency model in THP-1 monocytes using a replication incompetent HIV reporter vector with functional Tat, and show that chromatin modifiers synergize with a potent transcriptional activator to enhance HIV reactivation, similar to T-cells. Furthermore, leading reactivation cocktails are shown to differentially affect latency reactivation and surface expression of chemokine receptor type 4 (CXCR4), leading to altered host cell migration. This study investigates the effect of chromatin-modifying LRA treatments on HIV latent reactivation and cell migration in monocytes. As previously reported in T-cells, epigenetic mechanisms in monocytes contribute to controlling the relationship between latent reactivation and cell migration. Ultimately, advanced “Shock and Kill” therapy needs to successfully target and account for all host cell types represented in a complex and composite latency milieu.
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Affiliation(s)
- Alexandra Blanco
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; (A.B.); (T.M.); (R.A.C.); (K.M.); (D.R.D.)
| | - Tarun Mahajan
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; (A.B.); (T.M.); (R.A.C.); (K.M.); (D.R.D.)
| | - Robert A. Coronado
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; (A.B.); (T.M.); (R.A.C.); (K.M.); (D.R.D.)
| | - Kelly Ma
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; (A.B.); (T.M.); (R.A.C.); (K.M.); (D.R.D.)
| | - Dominic R. Demma
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; (A.B.); (T.M.); (R.A.C.); (K.M.); (D.R.D.)
| | - Roy D. Dar
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; (A.B.); (T.M.); (R.A.C.); (K.M.); (D.R.D.)
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Correspondence: ; Tel.: +1-(217)-265-0708
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37
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Chen M, Zhou T, Zhang J. Correlation between external regulators governs the mean-noise relationship in stochastic gene expression. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:4713-4730. [PMID: 34198461 DOI: 10.3934/mbe.2021239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Gene transcription in single cells is inherently a probabilistic process. The relationship between variance ($ \sigma^{2} $) and mean expression ($ \mu $) is of paramount importance for investigations into the evolutionary origins and consequences of noise in gene expression. It is often formulated as $ \log \left({{{\sigma}^{2}}}/{{{\mu}^{2}}}\; \right) = \beta\log\mu+\log\alpha $, where $ \beta $ is a key parameter since its sign determines the qualitative dependence of noise on mean. We reveal that the sign of $ \beta $ is controlled completely by external regulation, but independent of promoter structure. Specifically, it is negative if regulators as stochastic variables are independent but positive if they are correlated. The essential mechanism revealed here can well interpret diverse experimental phenomena underlying expression noise. Our results imply that external regulation rather than promoter sequence governs the mean-noise relationship.
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Affiliation(s)
- Meiling Chen
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Tianshou Zhou
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Jiajun Zhang
- Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, China
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
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38
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Shock-and-kill versus block-and-lock: Targeting the fluctuating and heterogeneous HIV-1 gene expression. Proc Natl Acad Sci U S A 2021; 118:2103692118. [PMID: 33758027 DOI: 10.1073/pnas.2103692118] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
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39
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Conway JM, Meily P, Li JZ, Perelson AS. Unified model of short- and long-term HIV viral rebound for clinical trial planning. J R Soc Interface 2021; 18:20201015. [PMID: 33849338 PMCID: PMC8086917 DOI: 10.1098/rsif.2020.1015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 03/23/2021] [Indexed: 11/12/2022] Open
Abstract
Antiretroviral therapy (ART) effectively controls HIV infection, suppressing HIV viral loads. Typically suspension of therapy is rapidly followed by rebound of viral loads to high, pre-therapy levels. Indeed, a recent study showed that approximately 90% of treatment interruption study participants show viral rebound within at most a few months of therapy suspension, but the remaining 10%, showed viral rebound some months, or years, after ART suspension. Some may even never rebound. We investigate and compare branching process models aimed at gaining insight into these viral dynamics. Specifically, we provide a theory that explains both short- and long-term viral rebounds, and post-treatment control, via a multitype branching process with time-inhomogeneous rates, validated with data from Li et al. (Li et al. 2016 AIDS30, 343-353. (doi:10.1097/QAD.0000000000000953)). We discuss the associated biological interpretation and implications of our best-fit model. To test the effectiveness of an experimental intervention in delaying or preventing rebound, the standard practice is to suspend therapy and monitor the study participants for rebound. We close with a discussion of an important application of our modelling in the design of such clinical trials.
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Affiliation(s)
- Jessica M. Conway
- Department of Mathematics and Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, USA
| | - Paige Meily
- University of Pennsylvania School of Arts and Sciences, Philadephia, PA, USA
| | - Jonathan Z. Li
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Alan S. Perelson
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
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40
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Lu Y, Singh H, Singh A, Dar RD. A transient heritable memory regulates HIV reactivation from latency. iScience 2021; 24:102291. [PMID: 33889814 PMCID: PMC8050369 DOI: 10.1016/j.isci.2021.102291] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 02/04/2021] [Accepted: 03/05/2021] [Indexed: 02/07/2023] Open
Abstract
Reactivation of human immunodeficiency virus 1 (HIV-1) from latently infected T cells is a critical barrier to cure patients. It remains unknown whether reactivation of individual latent cells occurs stochastically in response to latency reversal agents (LRAs) or is a deterministic outcome of an underlying cell state. To characterize these single-cell responses, we leverage the classical Luria-Delbrück fluctuation test where single cells are isolated from a clonal population and exposed to LRAs after colony expansion. Data show considerable colony-to-colony fluctuations with the fraction of reactivating cells following a skewed distribution. Modeling systematic measurements of fluctuations over time uncovers a transient heritable memory that regulates HIV-1 reactivation, where single cells are in an LRA-responsive state for a few weeks before switching back to an irresponsive state. These results have enormous implications for designing therapies to purge the latent reservoir and further utilize fluctuation-based assays to uncover hidden transient cellular states underlying phenotypic heterogeneity.
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Affiliation(s)
- Yiyang Lu
- Department of Bioengineering, University of Illinois at Urbana-Champaign, 321 Everitt Laboratory, 1406 West Green Street, Urbana, IL 61801, USA
| | - Harpal Singh
- Department of Bioengineering, University of Illinois at Urbana-Champaign, 321 Everitt Laboratory, 1406 West Green Street, Urbana, IL 61801, USA
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA
- Corresponding author
| | - Roy D. Dar
- Department of Bioengineering, University of Illinois at Urbana-Champaign, 321 Everitt Laboratory, 1406 West Green Street, Urbana, IL 61801, USA
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, 1206 West Gregory Drive, Urbana, IL 61801, USA
- Corresponding author
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41
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Abstract
Strategies to stabilize and suppress the latent cell reservoir of HIV have been proposed and need to be carefully examined. We demonstrate the use of time-lapse fluorescence microscopy to quantify HIV gene-expression dynamics and detect several latency-promoting agents (LPAs) that would be overlooked when screening for mean gene expression alone. These LPAs are structurally and functionally related to inhibitors of the thioredoxin/thioredoxin reductase redox pathway, which has been suggested as a promising HIV target. Some LPAs are Food and Drug Administration–approved and commercially available and can expand the currently limited LPA repertoire. This study provides a foundation to research suppression mechanisms of HIV gene expression, alternative latency-promoting therapies, and ultimately remove the need for antiretroviral therapy in patients. Upon treatment removal, spontaneous reactivation of latently infected T cells remains a major barrier toward curing HIV. Therapies that reactivate and clear the latent reservoir are only partially effective, while latency-promoting agents (LPAs) used to suppress reactivation and stabilize latency are understudied and lack diversity in their mechanisms of action. Here, we identify additional LPAs using a screen for gene-expression fluctuations (or “noise”) that drive cell-fate specification and control HIV reactivation from latency. Single-cell protein dynamics of a minimal HIV gene circuit were monitored with time-lapse fluorescence microscopy. We screened 1,806 drugs, out of which 279 modulate noise magnitude or half autocorrelation time. Next, we tested the strongest noise modulators in a Jurkat T cell latency model and discovered three LPAs that would be overlooked by quantifying their mean expression levels alone. The LPAs reduced reactivation of latency in both Jurkat and primary cell models when challenged by synergistic and potent combinations of HIV activators. The two strongest LPAs, NSC 401005 and NSC 400938, are structurally and functionally related to inhibitors of thioredoxin reductase, a protein involved in maintaining redox balance in host cells. Experiments with multiple functional analogs revealed two additional LPAs, PX12 and tiopronin, and suggest a potential LPA family, within which some are commercially available and Food and Drug Administration–approved. The LPAs presented here may provide new strategies to complement antiretroviral treatments. Screening for gene expression noise holds the potential for drug discovery in other diseases.
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42
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Extensive proteomic and transcriptomic changes quench the TCR/CD3 activation signal of latently HIV-1 infected T cells. PLoS Pathog 2021; 17:e1008748. [PMID: 33465149 PMCID: PMC7846126 DOI: 10.1371/journal.ppat.1008748] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 01/29/2021] [Accepted: 12/01/2020] [Indexed: 12/24/2022] Open
Abstract
The biomolecular mechanisms controlling latent HIV-1 infection, despite their importance for the development of a cure for HIV-1 infection, are only partially understood. For example, ex vivo studies have recently shown that T cell activation only triggered HIV-1 reactivation in a fraction of the latently infected CD4+ T cell reservoir, but the molecular biology of this phenomenon is unclear. We demonstrate that HIV-1 infection of primary T cells and T cell lines indeed generates a substantial amount of T cell receptor (TCR)/CD3 activation-inert latently infected T cells. RNA-level analysis identified extensive transcriptomic differences between uninfected, TCR/CD3 activation-responsive and -inert T cells, but did not reveal a gene expression signature that could functionally explain TCR/CD3 signaling inertness. Network analysis suggested a largely stochastic nature of these gene expression changes (transcriptomic noise), raising the possibility that widespread gene dysregulation could provide a reactivation threshold by impairing overall signal transduction efficacy. Indeed, compounds that are known to induce genetic noise, such as HDAC inhibitors impeded the ability of TCR/CD3 activation to trigger HIV-1 reactivation. Unlike for transcriptomic data, pathway enrichment analysis based on phospho-proteomic data directly identified an altered TCR signaling motif. Network analysis of this data set identified drug targets that would promote TCR/CD3-mediated HIV-1 reactivation in the fraction of otherwise TCR/CD3-reactivation inert latently HIV-1 infected T cells, regardless of whether the latency models were based on T cell lines or primary T cells. The data emphasize that latent HIV-1 infection is largely the result of extensive, stable biomolecular changes to the signaling network of the host T cells harboring latent HIV-1 infection events. In extension, the data imply that therapeutic restoration of host cell responsiveness prior to the use of any activating stimulus will likely have to be an element of future HIV-1 cure therapies. A curative therapy for HIV-1 infection will at least require the eradication of a small pool of CD4+ helper T cells in which the virus can persist in an inactive, latent state, even after years of successful antiretroviral therapy. It has been assumed that activation of these viral reservoir T cells will also reactivate the latent virus, which is a prerequisite for the destruction of these cells. Remarkably, this is not always the case and following application of even the most potent stimuli that activate normal T cells through their T cell receptor, a large portion of the latent virus pool remains in a dormant state. Herein we demonstrate that a large part of latent HIV-1 infection events reside in T cells that have been rendered activation inert. We provide a systemwide, biomolecular description of the changes that render latently HIV-1 infected T cells activation inert and using this description, devise pharmacologic interference strategies that render initially activation inert T cells responsive to stimulation. This in turn allows for efficient triggering of HIV-1 reactivation in a large part of the otherwise unresponsive latently HIV-1 infected T cell reservoir.
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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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Gnann C, Cesnik AJ, Lundberg E. Illuminating Non-genetic Cellular Heterogeneity with Imaging-Based Spatial Proteomics. Trends Cancer 2021; 7:278-282. [PMID: 33436349 DOI: 10.1016/j.trecan.2020.12.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 12/07/2020] [Accepted: 12/08/2020] [Indexed: 10/22/2022]
Abstract
Cellular heterogeneity is an important biological phenomenon observed across space and time in human tissues. Imaging-based spatial proteomic technologies can provide fruitful new readouts of phenotypic states for individual cells at subcellular resolution, which may help unravel the roles of non-genetic cellular heterogeneity in tumorigenesis and drug resistance.
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Affiliation(s)
- Christian Gnann
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, 17121, Sweden
| | - Anthony J Cesnik
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, 17121, Sweden
| | - Emma Lundberg
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, 17121, Sweden.
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45
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Guillemin A, Stumpf MPH. Noise and the molecular processes underlying cell fate decision-making. Phys Biol 2021; 18:011002. [PMID: 33181489 DOI: 10.1088/1478-3975/abc9d1] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Cell fate decision-making events involve the interplay of many molecular processes, ranging from signal transduction to genetic regulation, as well as a set of molecular and physiological feedback loops. Each aspect offers a rich field of investigation in its own right, but to understand the whole process, even in simple terms, we need to consider them together. Here we attempt to characterise this process by focussing on the roles of noise during cell fate decisions. We use a range of recent results to develop a view of the sequence of events by which a cell progresses from a pluripotent or multipotent to a differentiated state: chromatin organisation, transcription factor stoichiometry, and cellular signalling all change during this progression, and all shape cellular variability, which becomes maximal at the transition state.
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Affiliation(s)
- Anissa Guillemin
- School of BioSciences, University of Melbourne, Parkville, Australia
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46
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Torre EA, Arai E, Bayatpour S, Jiang CL, Beck LE, Emert BL, Shaffer SM, Mellis IA, Fane ME, Alicea GM, Budinich KA, Weeraratna AT, Shi J, Raj A. Genetic screening for single-cell variability modulators driving therapy resistance. Nat Genet 2021; 53:76-85. [PMID: 33398196 PMCID: PMC7796998 DOI: 10.1038/s41588-020-00749-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Accepted: 11/12/2020] [Indexed: 02/07/2023]
Abstract
Cellular plasticity describes the ability of cells to transition from one set of phenotypes to another. In melanoma, transient fluctuations in the molecular state of tumor cells mark the formation of rare cells primed to survive BRAF inhibition and reprogram into a stably drug-resistant fate. However, the biological processes governing cellular priming remain unknown. We used CRISPR-Cas9 genetic screens to identify genes that affect cell fate decisions by altering cellular plasticity. We found that many factors can independently affect cellular priming and fate decisions. We discovered a new plasticity-based mode of increasing resistance to BRAF inhibition that pushes cells towards a more differentiated state. Manipulating cellular plasticity through inhibition of DOT1L before the addition of the BRAF inhibitor resulted in more therapy resistance than concurrent administration. Our results indicate that modulating cellular plasticity can alter cell fate decisions and may prove useful for treating drug resistance in other cancers.
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Affiliation(s)
- Eduardo A Torre
- Biochemistry and Molecular Biophysics Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eri Arai
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sareh Bayatpour
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Connie L Jiang
- Genetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lauren E Beck
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin L Emert
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney M Shaffer
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ian A Mellis
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mitchell E Fane
- Department of Biochemistry and Molecular Biology, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Gretchen M Alicea
- Department of Biochemistry and Molecular Biology, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Krista A Budinich
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ashani T Weeraratna
- Department of Biochemistry and Molecular Biology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Junwei Shi
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Arjun Raj
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Chen BR, You CX, Shu CC. The common misuse of noise decomposition as applied to genetic systems. Biosystems 2020; 198:104269. [PMID: 33038463 DOI: 10.1016/j.biosystems.2020.104269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 09/22/2020] [Accepted: 10/02/2020] [Indexed: 10/23/2022]
Abstract
The noise-decomposition technique is applied in several fields, including genetic systems, optical images, recording, and navigation. In genetic systems, noise decomposition is usually achieved by using two reporters [Elowitz M.B., Levine A.J., Siggia E.D., Swain P·S., 2002. Stochastic gene expression in a single cell. Science 297, 1183-6.]. A reporter is a protein with fluorescence, an RNA hybridized with a fluorescent probe, or any other detectable intracellular component. If a reporter is constructed in addition to the original reporter, the system's stochasticity may change. Such phenomena became severe for genes in plasmids with a high copy number. By SSA (stochastic simulation algorithm), we observed an approximately 50% increment in the coefficient of variation while introducing additional reporters. Besides, if two reporters respond to the upstream element at a different time, the trunk noise (or extrinsic noise) cannot be accurately determined. This is because the "calculative trunk noise" changes along with the delay, though the real trunk noise does not. For RNA reporters, a 5-min transcriptional delay caused a calculative trunk noise that was 90% less than the real trunk noise. Fortunately, this problem is negligible when the degradation rate constant is low, and it is usually true in the case of the protein reporters. One can check the lifespan of the reporter before applying the noise-decomposition technique.
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Affiliation(s)
- Bo-Ren Chen
- Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taiwan
| | - Chao-Xuan You
- Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taiwan
| | - Che-Chi Shu
- Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taiwan.
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Saha A, Dixit NM. Pre-existing resistance in the latent reservoir can compromise VRC01 therapy during chronic HIV-1 infection. PLoS Comput Biol 2020; 16:e1008434. [PMID: 33253162 PMCID: PMC7728175 DOI: 10.1371/journal.pcbi.1008434] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 12/10/2020] [Accepted: 10/11/2020] [Indexed: 01/26/2023] Open
Abstract
Passive immunization with broadly neutralizing antibodies (bNAbs) of HIV-1 appears a promising strategy for eliciting long-term HIV-1 remission. When administered concomitantly with the cessation of antiretroviral therapy (ART) to patients with established viremic control, bNAb therapy is expected to prolong remission. Surprisingly, in clinical trials on chronic HIV-1 patients, the bNAb VRC01 failed to prolong remission substantially. Identifying the cause of this failure is important for improving VRC01-based therapies and unraveling potential vulnerabilities of other bNAbs. In the trials, viremia resurged rapidly in most patients despite suppressive VRC01 concentrations in circulation, suggesting that VRC01 resistance was the likely cause of failure. ART swiftly halts viral replication, precluding the development of resistance during ART. If resistance were to emerge post ART, virological breakthrough would have taken longer than without VRC01 therapy. We hypothesized therefore that VRC01-resistant strains must have been formed before ART initiation, survived ART in latently infected cells, and been activated during VRC01 therapy, causing treatment failure. Current assays preclude testing this hypothesis experimentally. We developed a mathematical model based on the hypothesis and challenged it with available clinical data. The model integrated within-host HIV-1 evolution, stochastic latency reactivation, and viral dynamics with multiple-dose VRC01 pharmacokinetics. The model predicted that single but not higher VRC01-resistant mutants would pre-exist in the latent reservoir. We constructed a virtual patient population that parsimoniously recapitulated inter-patient variations. Model predictions with this population quantitatively captured data of VRC01 failure from clinical trials, presenting strong evidence supporting the hypothesis. We attributed VRC01 failure to single-mutant VRC01-resistant proviruses in the latent reservoir triggering viral recrudescence, particularly when VRC01 was at trough levels. Pre-existing resistant proviruses in the latent reservoir may similarly compromise other bNAbs. Our study provides a framework for designing bNAb-based therapeutic protocols that would avert such failure and maximize HIV-1 remission. Antiretroviral therapy (ART) can control but not eradicate HIV-1. Stopping ART leads to rapid viral resurgence and progressive disease. ART is therefore administered lifelong. Tremendous efforts are ongoing to devise strategies that will enable stopping ART and yet prevent viral resurgence. One such strategy involves the administration of broadly neutralizing antibodies (bNAbs) of HIV-1 at the time of stopping ART. This strategy is expected to delay if not prevent viral resurgence. Surprisingly, treatment with VRC01, a potent bNAb, resulted in hardly any improvement in viral remission. In this study, we elucidate the cause of this failure. We hypothesized that VRC01-resistant strains may pre-exist in latently infected cells, which are unaffected by ART. They can thus outlast ART and get reactivated, triggering VRC01 failure. We built a detailed mathematical model based on this hypothesis and showed that it quantitatively captured observations of VRC01 failure in clinical trials on chronic HIV-1 patients. Our study thus identifies a potential vulnerability of bNAbs, namely, bNAb-resistant strains pre-existing in latently infected cells. Our model offers a framework for predicting bNAb-based treatment protocols that would preclude failure due to pre-existing resistance and maximally prolong remission.
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Affiliation(s)
- Ananya Saha
- Department of Chemical Engineering, Indian Institute of Science, Bengaluru, India
| | - Narendra M. Dixit
- Department of Chemical Engineering, Indian Institute of Science, Bengaluru, India
- Centre for Biosystems Science and Engineering, Indian Institute of Science, Bengaluru, India
- * E-mail:
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49
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Zhang T, Foreman R, Wollman R. Identifying chromatin features that regulate gene expression distribution. Sci Rep 2020; 10:20566. [PMID: 33239733 PMCID: PMC7688950 DOI: 10.1038/s41598-020-77638-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 11/10/2020] [Indexed: 12/17/2022] Open
Abstract
Gene expression variability, differences in the number of mRNA per cell across a population of cells, is ubiquitous across diverse organisms with broad impacts on cellular phenotypes. The role of chromatin in regulating average gene expression has been extensively studied. However, what aspects of the chromatin contribute to gene expression variability is still underexplored. Here we addressed this problem by leveraging chromatin diversity and using a systematic investigation of randomly integrated expression reporters to identify what aspects of chromatin microenvironment contribute to gene expression variability. Using DNA barcoding and split-pool decoding, we created a large library of isogenic reporter clones and identified reporter integration sites in a massive and parallel manner. By mapping our measurements of reporter expression at different genomic loci with multiple epigenetic profiles including the enrichment of transcription factors and the distance to different chromatin states, we identified new factors that impact the regulation of gene expression distributions.
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Affiliation(s)
- Thanutra Zhang
- Institute for Quantitative and Computational Biosciences, UCLA, Los Angeles, CA, USA
| | - Robert Foreman
- Institute for Quantitative and Computational Biosciences, UCLA, Los Angeles, CA, USA
| | - Roy Wollman
- Institute for Quantitative and Computational Biosciences, UCLA, Los Angeles, CA, USA.
- Departments of Integrative Biology and Physiology and Chemistry and Biochemistry, UCLA, Los Angeles, CA, USA.
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50
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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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