1
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Ilker E, Hinczewski M. Bioenergetic costs and the evolution of noise regulation by microRNAs. Proc Natl Acad Sci U S A 2024; 121:e2308796121. [PMID: 38386708 PMCID: PMC10907262 DOI: 10.1073/pnas.2308796121] [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: 05/27/2023] [Accepted: 01/14/2024] [Indexed: 02/24/2024] Open
Abstract
Noise control, together with other regulatory functions facilitated by microRNAs (miRNAs), is believed to have played important roles in the evolution of multicellular eukaryotic organisms. miRNAs can dampen protein fluctuations via enhanced degradation of messenger RNA (mRNA), but this requires compensation by increased mRNA transcription to maintain the same expression levels. The overall mechanism is metabolically expensive, leading to questions about how it might have evolved in the first place. We develop a stochastic model of miRNA noise regulation, coupled with a detailed analysis of the associated metabolic costs. Additionally, we calculate binding free energies for a range of miRNA seeds, the short sequences which govern target recognition. We argue that natural selection may have fine-tuned the Michaelis-Menten constant [Formula: see text] describing miRNA-mRNA affinity and show supporting evidence from analysis of experimental data. [Formula: see text] is constrained by seed length, and optimal noise control (minimum protein variance at a given energy cost) is achievable for seeds of 6 to 7 nucleotides in length, the most commonly observed types. Moreover, at optimality, the degree of noise reduction approaches the theoretical bound set by the Wiener-Kolmogorov linear filter. The results illustrate how selective pressure toward energy efficiency has potentially shaped a crucial regulatory pathway in eukaryotes.
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Affiliation(s)
- Efe Ilker
- Max Planck Institute for the Physics of Complex Systems, Dresden01187, Germany
| | - Michael Hinczewski
- Department of Physics, Case Western Reserve University, Cleveland, OH44106
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2
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Adhikary R, Roy A, Jolly MK, Das D. Effects of microRNA-mediated negative feedback on gene expression noise. Biophys J 2023; 122:4220-4240. [PMID: 37803829 PMCID: PMC10645566 DOI: 10.1016/j.bpj.2023.09.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 07/19/2023] [Accepted: 09/28/2023] [Indexed: 10/08/2023] Open
Abstract
MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression post-transcriptionally in eukaryotes by binding with target mRNAs and preventing translation. miRNA-mediated feedback motifs are ubiquitous in various genetic networks that control cellular decision making. A key question is how such a feedback mechanism may affect gene expression noise. To answer this, we have developed a mathematical model to study the effects of a miRNA-dependent negative-feedback loop on mean expression and noise in target mRNAs. Combining analytics and simulations, we show the existence of an expression threshold demarcating repressed and expressed regimes in agreement with earlier studies. The steady-state mRNA distributions are bimodal near the threshold, where copy numbers of mRNAs and miRNAs exhibit enhanced anticorrelated fluctuations. Moreover, variation of negative-feedback strength shifts the threshold locations and modulates the noise profiles. Notably, the miRNA-mRNA binding affinity and feedback strength collectively shape the bimodality. We also compare our model with a direct auto-repression motif, where a gene produces its own repressor. Auto-repression fails to produce bimodal mRNA distributions as found in miRNA-based indirect repression, suggesting the crucial role of miRNAs in creating phenotypic diversity. Together, we demonstrate how miRNA-dependent negative feedback modifies the expression threshold and leads to a broader parameter regime of bimodality compared to the no-feedback case.
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Affiliation(s)
- Raunak Adhikary
- Department of Biological Sciences, Indian Institute of Science Education And Research Kolkata Mohanpur, Nadia, West Bengal, India
| | - Arnab Roy
- Department of Biological Sciences, Indian Institute of Science Education And Research Kolkata Mohanpur, Nadia, West Bengal, India
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bengaluru, India
| | - Dipjyoti Das
- Department of Biological Sciences, Indian Institute of Science Education And Research Kolkata Mohanpur, Nadia, West Bengal, India.
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3
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The distributed delay rearranges the bimodal distribution at protein level. J Taiwan Inst Chem Eng 2022. [DOI: 10.1016/j.jtice.2022.104436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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4
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Kovalev RA, Fedorova ND, Pantina RA, Semenova EV, Filatov MV, Varfolomeeva EY. Stochasticity of p53 Protein Expression in Cells of Primary and Transferable Human Lines. Biophysics (Nagoya-shi) 2022. [DOI: 10.1134/s0006350922030101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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5
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Justino JR, Reis CFD, Fonseca AL, Souza SJD, Stransky B. An integrated approach to identify bimodal genes associated with prognosis in câncer. Genet Mol Biol 2021; 44:e20210109. [PMID: 34617951 PMCID: PMC8495773 DOI: 10.1590/1678-4685-gmb-2021-0109] [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: 04/15/2021] [Accepted: 07/08/2021] [Indexed: 02/08/2023] Open
Abstract
Bimodal gene expression (where a gene expression distribution has two maxima) is
associated with phenotypic diversity in different biological systems. A critical
issue, thus, is the integration of expression and phenotype data to identify
genuine associations. Here, we developed tools that allow both: i) the
identification of genes with bimodal gene expression and ii) their association
with prognosis in cancer patients from The Cancer Genome Atlas (TCGA).
Bimodality was observed for 554 genes in expression data from 25 tumor types.
Furthermore, 96 of these genes presented different prognosis when patients
belonging to the two expression peaks were compared. The software to execute the
method and the corresponding documentation are available at the Data access
section.
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Affiliation(s)
- Josivan Ribeiro Justino
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil.,Universidade Federal de Rondônia, Departamento de Matemática e Estatística, Ji-Parana, RO, Brazil
| | - Clovis Ferreira Dos Reis
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil
| | - Andre Luis Fonseca
- Universidade de São Paulo, Departamento de Genética e Biologia Evolutiva, São Paulo, SP, Brazil
| | - Sandro Jose de Souza
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil.,Universidade Federal do Rio Grande do Norte (UFRN), Instituto do Cérebro, Natal, RN, Brazil.,Sichuan University, West China Hospital, Institutes for Systems Genetics, Chengdu, China
| | - Beatriz Stransky
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil.,Universidade Federal do Rio Grande do Norte (UFRN), Centro de Tecnologia, Departamento de Engenharia Biomédica, Natal, RN, Brazil
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6
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Hu T, Wei L, Li S, Cheng T, Zhang X, Wang X. Single-cell Transcriptomes Reveal Characteristics of MicroRNA in Gene Expression Noise Reduction. GENOMICS PROTEOMICS & BIOINFORMATICS 2021; 19:394-407. [PMID: 34606979 PMCID: PMC8864250 DOI: 10.1016/j.gpb.2021.05.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 04/29/2021] [Accepted: 08/01/2021] [Indexed: 11/30/2022]
Abstract
Isogenic cells growing in identical environments show cell-to-cell variations because of the stochasticity in gene expression. High levels of variation or noise can disrupt robust gene expression and result in tremendous consequences for cell behaviors. In this work, we showed evidence from single-cell RNA sequencing data analysis that microRNAs (miRNAs) can reduce gene expression noise at the mRNA level in mouse cells. We identified that the miRNA expression level, number of targets, target pool abundance, and miRNA–target interaction strength are the key features contributing to noise repression. miRNAs tend to work together in cooperative subnetworks to repress target noise synergistically in a cell type-specific manner. By building a physical model of post-transcriptional regulation and observing in synthetic gene circuits, we demonstrated that accelerated degradation with elevated transcriptional activation of the miRNA target provides resistance to extrinsic fluctuations. Together, through the integrated analysis of single-cell RNA and miRNA expression profiles, we demonstrated that miRNAs are important post-transcriptional regulators for reducing gene expression noise and conferring robustness to biological processes.
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Affiliation(s)
- 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
| | - 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
- 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
| | - Tianrun Cheng
- 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
| | - Xuegong 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
| | - 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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7
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Thomas P. Stochastic Modeling Approaches for Single-Cell Analyses. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11539-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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8
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Huff K, Suárez-Trujillo A, Kuang S, Plaut K, Casey T. One-to-one relationships between milk miRNA content and protein abundance in neonate duodenum support the potential for milk miRNAs regulating neonate development. Funct Integr Genomics 2020; 20:645-656. [PMID: 32458191 DOI: 10.1007/s10142-020-00743-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 05/08/2020] [Accepted: 05/13/2020] [Indexed: 10/24/2022]
Abstract
Breast milk plays an essential role for offspring development; however, there lacks evidence of how specific milk components like nucleic acids mechanistically function to regulate neonate development. Previously, we found that maternal high-fat diet (HFD) not only significantly affected mRNA and miRNA content of the secreted milk transcriptome in mice but also affected the duodenal proteome of suckling pups. Here, we hypothesized that nucleic acids differentially expressed in milk of HFD fed dams are related to differentially abundant proteins in offspring duodenum nursed by HFD dams. We tested this hypothesis by analyzing one-to-one relationships in RNA-seq data of milk transcriptomes from control (10% kcal fat) and HFD (60% kcal fat) fed mice and liquid chromatography-tandem mass spectrometry (LC-MS/MS) duodenal proteome data from pups exposed to milk. Ten percent of differentially abundant duodenal proteins between controls and HFD-exposed pups had predicted upregulation or downregulation based on differential milk RNA content. Of these, 76% were targets of upregulated miRNA, and linear regression analysis indicated relationships (p < 0.05) between multiple milk miRNA counts and duodenal protein abundance. Duodenal proteins that were potential targets of milk miRNA enriched Gene Ontology (GO) terms and KEGG pathways related to cytoskeletal structure and neural development, suggesting potential regulation of pup enteric nervous system. One-to-one relationships between milk miRNA content and protein abundance in neonate duodenum support the potential for milk miRNAs regulating neonate development. Identification of milk miRNAs that changed in response to maternal diet will enable design of mechanistic studies that test effects on neonate.
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Affiliation(s)
- Katelyn Huff
- Biological & Biomedical Sciences Program, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Aridany Suárez-Trujillo
- Department of Animal Sciences, Purdue University, 175 South University Street, West Lafayette, IN, 47907-2063, USA
| | - Shihuan Kuang
- Department of Animal Sciences, Purdue University, 175 South University Street, West Lafayette, IN, 47907-2063, USA
| | - Karen Plaut
- Department of Animal Sciences, Purdue University, 175 South University Street, West Lafayette, IN, 47907-2063, USA
| | - Theresa Casey
- Department of Animal Sciences, Purdue University, 175 South University Street, West Lafayette, IN, 47907-2063, USA.
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9
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Ferro E, Enrico Bena C, Grigolon S, Bosia C. microRNA-mediated noise processing in cells: A fight or a game? Comput Struct Biotechnol J 2020; 18:642-649. [PMID: 32257047 PMCID: PMC7103774 DOI: 10.1016/j.csbj.2020.02.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 02/27/2020] [Accepted: 02/27/2020] [Indexed: 12/17/2022] Open
Abstract
In the past decades, microRNAs (miRNA) have much attracted the attention of researchers at the interface between life and theoretical sciences for their involvement in post-transcriptional regulation and related diseases. Thanks to the always more sophisticated experimental techniques, the role of miRNAs as "noise processing units" has been further elucidated and two main ways of miRNA noise-control have emerged by combinations of theoretical and experimental studies. While on one side miRNAs were thought to buffer gene expression noise, it has recently been suggested that miRNAs could also increase the cell-to-cell variability of their targets. In this Mini Review, we focus on the role of miRNAs in molecular noise processing and on the advantages as well as current limitations of theoretical modelling.
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Affiliation(s)
- Elsi Ferro
- Italian Institute for Genomic Medicine, Italy
| | | | - Silvia Grigolon
- The Francis Crick Institute, 1, Midland Road, London NW1 1AT, UK
| | - Carla Bosia
- Italian Institute for Genomic Medicine, Italy
- Department of Applied Science and Technology, Politecnico di Torino, Italy
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10
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Abstract
Understanding how mammalian organisms achieve the full diversity of cell types in the adult organism is a central goal of developmental cell biology. Recent work has shown that some embryonic precursor cells can self-organize into developmental structures but the mechanisms of gene regulation that contribute to this process remain unknown. Here we show embryonic stem cells self-organize into distinct gene expression states that resemble developmental gene programs. We find that microRNAs, small noncoding regulators of gene expression, play a critical role in organizing fluctuations across gene networks to help achieve this organization into distinct expression states. Pluripotent embryonic stem cells (ESCs) contain the potential to form a diverse array of cells with distinct gene expression states, namely the cells of the adult vertebrate. Classically, diversity has been attributed to cells sensing their position with respect to external morphogen gradients. However, an alternative is that diversity arises in part from cooption of fluctuations in the gene regulatory network. Here we find ESCs exhibit intrinsic heterogeneity in the absence of external gradients by forming interconverting cell states. States vary in developmental gene expression programs and display distinct activity of microRNAs (miRNAs). Notably, miRNAs act on neighborhoods of pluripotency genes to increase variation of target genes and cell states. Loss of miRNAs that vary across states reduces target variation and delays state transitions, suggesting variable miRNAs organize and propagate variation to promote state transitions. Together these findings provide insight into how a gene regulatory network can coopt variation intrinsic to cell systems to form robust gene expression states. Interactions between intrinsic heterogeneity and environmental signals may help achieve developmental outcomes.
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11
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Posner R, Laubenbacher R. The contribution of microRNA-mediated regulation to short- and long-term gene expression predictability. J Theor Biol 2020; 486:110055. [PMID: 31647935 DOI: 10.1016/j.jtbi.2019.110055] [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: 05/29/2019] [Revised: 10/14/2019] [Accepted: 10/20/2019] [Indexed: 11/28/2022]
Abstract
MicroRNAs are a class of short, noncoding RNAs which are essential for the coordination and timing of cell differentiation and embryonic development. However, despite their guiding role in development, microRNAs are dysregulated in many pathologies, including nearly all cases of cancer. While both development and oncogenesis can be thought of as extremes of phenotypic plasticity, they characteristically manifest on much different time scales: one taking place over a matter of weeks, the other typically requiring decades. Because microRNAs are believed to support this plasticity, a critically important question is how microRNAs affect phenotypic stability on different time scales, and what dynamical characteristics shift the balance between these two roles. To address this question, we extend a well-established mathematical model of transcriptional gene regulation to include translational regulation by microRNAs, and examine their effects on both short- and long-term gene expression predictability. Our findings show that microRNAs greatly improve short-term predictability for earlier, developmental phenotypes while causing a small decrease in long-term predictability, and that these effects are difficult to separate. In addition to providing a theoretical explanation for this seemingly duplicitous behavior, we describe some of the properties which determine the cost-benefit balance between short-term stabilization and long-term destabilization.
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Affiliation(s)
- Russell Posner
- Center for Quantitative Medicine, UConn Health, 263 Farmington Avenue Farmington, CT 06030, USA.
| | - Reinhard Laubenbacher
- Center for Quantitative Medicine, UConn Health, 263 Farmington Avenue Farmington, CT 06030, USA; The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr, Farmington, CT 06032, USA
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12
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Ferro E, Enrico Bena C, Grigolon S, Bosia C. From Endogenous to Synthetic microRNA-Mediated Regulatory Circuits: An Overview. Cells 2019; 8:E1540. [PMID: 31795372 PMCID: PMC6952906 DOI: 10.3390/cells8121540] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 11/22/2019] [Accepted: 11/25/2019] [Indexed: 12/13/2022] Open
Abstract
MicroRNAs are short non-coding RNAs that are evolutionarily conserved and are pivotal post-transcriptional mediators of gene regulation. Together with transcription factors and epigenetic regulators, they form a highly interconnected network whose building blocks can be classified depending on the number of molecular species involved and the type of interactions amongst them. Depending on their topology, these molecular circuits may carry out specific functions that years of studies have related to the processing of gene expression noise. In this review, we first present the different over-represented network motifs involving microRNAs and their specific role in implementing relevant biological functions, reviewing both theoretical and experimental studies. We then illustrate the recent advances in synthetic biology, such as the construction of artificially synthesised circuits, which provide a controlled tool to test experimentally the possible microRNA regulatory tasks and constitute a starting point for clinical applications.
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Affiliation(s)
- Elsi Ferro
- IIGM—Italian Institute for Genomic Medicine, c/o IRCCS, 10060 Candiolo (Torino), Italy
- Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo (Torino), Italy
| | - Chiara Enrico Bena
- IIGM—Italian Institute for Genomic Medicine, c/o IRCCS, 10060 Candiolo (Torino), Italy
- Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo (Torino), Italy
| | - Silvia Grigolon
- The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Carla Bosia
- IIGM—Italian Institute for Genomic Medicine, c/o IRCCS, 10060 Candiolo (Torino), Italy
- Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
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13
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Piras V, Chiow A, Selvarajoo K. Long‐range order and short‐range disorder in
Saccharomyces cerevisiae
biofilm. ENGINEERING BIOLOGY 2019. [DOI: 10.1049/enb.2018.5008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Vincent Piras
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS Université Paris‐Sud, Université Paris‐Saclay avenue de la Terrasse 91198 Gif‐sur‐Yvette Cedex France
| | - Adam Chiow
- Department of Pharmaceutical Engineering Singapore Institute of Technology 10 Dover Drive Singapore 138683 Singapore
| | - Kumar Selvarajoo
- Biotransformation Innovation Platform (BioTrans) Agency for Science, Technology & Research A∗STAR 61 Biopolis Drive, Proteos Singapore 138673 Singapore
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14
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Thomas P. Intrinsic and extrinsic noise of gene expression in lineage trees. Sci Rep 2019; 9:474. [PMID: 30679440 PMCID: PMC6345792 DOI: 10.1038/s41598-018-35927-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 11/08/2018] [Indexed: 12/30/2022] Open
Abstract
Cell-to-cell heterogeneity is driven by stochasticity in intracellular reactions and the population dynamics. While these sources are usually studied separately, we develop an agent-based framework that accounts for both factors while tracking every single cell of a growing population. Apart from the common intrinsic variability, the framework also predicts extrinsic noise without the need to introduce fluctuating rate constants. Instead, extrinsic fluctuations are explained by cell cycle fluctuations and differences in cell age. We provide explicit formulas to quantify mean molecule numbers, intrinsic and extrinsic noise statistics in two-colour experiments. We find that these statistics differ significantly depending on the experimental setup used to observe the cells. We illustrate this fact using (i) averages over an isolated cell lineage tracked over many generations as observed in the mother machine, (ii) population snapshots with known cell ages as recorded in time-lapse microscopy, and (iii) snapshots with unknown cell ages as measured from static images or flow cytometry. Applying the method to models of stochastic gene expression and feedback regulation elucidates that isolated lineages, as compared to snapshot data, can significantly overestimate the mean number of molecules, overestimate extrinsic noise but underestimate intrinsic noise and have qualitatively different sensitivities to cell cycle fluctuations.
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Affiliation(s)
- Philipp Thomas
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK.
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15
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Kinetic Modelling of Competition and Depletion of Shared miRNAs by Competing Endogenous RNAs. Methods Mol Biol 2019; 1912:367-409. [PMID: 30635902 DOI: 10.1007/978-1-4939-8982-9_15] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Non-coding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting, e.g., the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.
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16
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Del Giudice M, Bosia C, Grigolon S, Bo S. Stochastic sequestration dynamics: a minimal model with extrinsic noise for bimodal distributions and competitors correlation. Sci Rep 2018; 8:10387. [PMID: 29991682 PMCID: PMC6039506 DOI: 10.1038/s41598-018-28647-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 06/21/2018] [Indexed: 12/26/2022] Open
Abstract
Many biological processes are known to be based on molecular sequestration. This kind of dynamics involves two types of molecular species, namely targets and sequestrants, that bind to form a complex. In the simple framework of mass-action law, key features of these systems appear to be threshold-like profiles of the amounts of free molecules as a function of the parameters determining their possible maximum abundance. However, biochemical processes are probabilistic and take place in stochastically fluctuating environments. How these different sources of noise affect the final outcome of the network is not completely characterised yet. In this paper we specifically investigate the effects induced by a source of extrinsic noise onto a minimal stochastic model of molecular sequestration. We analytically show how bimodal distributions of the targets can appear and characterise them as a result of noise filtering mediated by the threshold response. We then address the correlations between target species induced by the sequestrant and discuss how extrinsic noise can turn the negative correlation caused by competition into a positive one. Finally, we consider the more complex scenario of competitive inhibition for enzymatic kinetics and discuss the relevance of our findings with respect to applications.
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Affiliation(s)
- Marco Del Giudice
- Department of Applied Science and Technology, Politecnico di Torino corso Duca degli Abruzzi 24, Turin, IT-10129, Italy
- Italian Institute for Genomic Medicine, via Nizza 52, I-10126, Torino, Italy
| | - Carla Bosia
- Department of Applied Science and Technology, Politecnico di Torino corso Duca degli Abruzzi 24, Turin, IT-10129, Italy
- Italian Institute for Genomic Medicine, via Nizza 52, I-10126, Torino, Italy
| | - Silvia Grigolon
- The Francis Crick Institute, 1, Midland Road, London, NW1 1AT, United Kingdom
| | - Stefano Bo
- Nordita, Royal Institute of Technology and Stockholm University, Roslagstullsbacken 23, SE-106 91, Stockholm, Sweden.
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