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Eigenfeld M, Kerpes R, Whitehead I, Becker T. Autofluorescence prediction model for fluorescence unmixing and age determination. Biotechnol J 2022; 17:e2200091. [DOI: 10.1002/biot.202200091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 10/11/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Marco Eigenfeld
- Technical University of Munich, School of Life Science Institute of Brewing and Beverage Technology Freising Germany
| | - Roland Kerpes
- Technical University of Munich, School of Life Science Institute of Brewing and Beverage Technology Freising Germany
| | - Iain Whitehead
- Technical University of Munich, School of Life Science Institute of Brewing and Beverage Technology Freising Germany
| | - Thomas Becker
- Technical University of Munich, School of Life Science Institute of Brewing and Beverage Technology Freising Germany
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2
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Pavlou A, Cinquemani E, Geiselmann J, de Jong H. Maturation models of fluorescent proteins are necessary for unbiased estimates of promoter activity. Biophys J 2022; 121:4179-4188. [PMID: 36146937 PMCID: PMC9675035 DOI: 10.1016/j.bpj.2022.09.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 06/13/2022] [Accepted: 09/19/2022] [Indexed: 11/27/2022] Open
Abstract
Fluorescent proteins (FPs) are a powerful tool to quantitatively monitor gene expression. The dynamics of a promoter and its regulation can be inferred from fluorescence data. The interpretation of fluorescent data, however, is strongly dependent on the maturation of FPs since different proteins mature in distinct ways. We propose a novel approach for analyzing fluorescent reporter data by incorporating maturation dynamics in the reconstruction of promoter activities. Our approach consists of developing and calibrating mechanistic maturation models for distinct FPs. These models are then used alongside a Bayesian approach to estimate promoter activities from fluorescence data. We demonstrate by means of targeted experiments in Escherichia coli that our approach provides robust estimates and that accounting for maturation is, in many cases, essential for the interpretation of gene expression data.
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Affiliation(s)
- Antrea Pavlou
- University Grenoble Alpes, Inria, Grenoble, France; University Grenoble Alpes, CNRS, LIPhy, Grenoble, France
| | | | - Johannes Geiselmann
- University Grenoble Alpes, Inria, Grenoble, France; University Grenoble Alpes, CNRS, LIPhy, Grenoble, France.
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Vidal G, Vitalis C, Muñoz Silva M, Castillo-Passi C, Yáñez Feliú G, Federici F, Rudge TJ. Accurate characterization of dynamic microbial gene expression and growth rate profiles. Synth Biol (Oxf) 2022; 7:ysac020. [PMID: 36267953 PMCID: PMC9569155 DOI: 10.1093/synbio/ysac020] [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: 12/09/2021] [Revised: 07/16/2022] [Accepted: 09/29/2022] [Indexed: 11/16/2022] Open
Abstract
Genetic circuits are subject to variability due to cellular and compositional contexts. Cells face changing internal states and environments, the cellular context, to which they sense and respond by changing their gene expression and growth rates. Furthermore, each gene in a genetic circuit operates in a compositional context of genes which may interact with each other and the host cell in complex ways. The context of genetic circuits can, therefore, change gene expression and growth rates, and measuring their dynamics is essential to understanding natural and synthetic regulatory networks that give rise to functional phenotypes. However, reconstruction of microbial gene expression and growth rate profiles from typical noisy measurements of cell populations is difficult due to the effects of noise at low cell densities among other factors. We present here a method for the estimation of dynamic microbial gene expression rates and growth rates from noisy measurement data. Compared to the current state-of-the-art, our method significantly reduced the mean squared error of reconstructions from simulated data of growth and gene expression rates, improving the estimation of timing and magnitude of relevant shapes of profiles. We applied our method to characterize a triple-reporter plasmid library combining multiple transcription units in different compositional and cellular contexts in Escherichia coli. Our analysis reveals cellular and compositional context effects on microbial growth and gene expression rate dynamics and suggests a method for the dynamic ratiometric characterization of constitutive promoters relative to an in vivo reference.
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Affiliation(s)
- Gonzalo Vidal
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK
| | - Carolus Vitalis
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Macarena Muñoz Silva
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Carlos Castillo-Passi
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ Hospital, London, UK
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH), Santiago, Chile
| | - Guillermo Yáñez Feliú
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Fernán Federici
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- ANID – Millennium Science Initiative Program, Millennium Institute for Integrative Biology (iBio) & FONDAP Center for Genome Regulation, Santiago, Chile
| | - Timothy J Rudge
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK
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Midani FS, Collins J, Britton RA. AMiGA: Software for Automated Analysis of Microbial Growth Assays. mSystems 2021; 6:e0050821. [PMID: 34254821 PMCID: PMC8409736 DOI: 10.1128/msystems.00508-21] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 06/16/2021] [Indexed: 12/18/2022] Open
Abstract
The analysis of microbial growth is one of the central methods in the field of microbiology. Microbial growth dynamics can be characterized by meaningful parameters, including carrying capacity, exponential growth rate, and growth lag. However, microbial assays with clinical isolates, fastidious organisms, or microbes under stress often produce atypical growth shapes that do not follow the classical microbial growth pattern. Here, we introduce the analysis of microbial growth assays (AMiGA) software, which streamlines the analysis of growth curves without any assumptions about their shapes. AMiGA can pool replicates of growth curves and infer summary statistics for biologically meaningful growth parameters. In addition, AMiGA can quantify death phases and characterize diauxic shifts. It can also statistically test for differential growth under distinct experimental conditions. Altogether, AMiGA streamlines the organization, analysis, and visualization of microbial growth assays. IMPORTANCE Our current understanding of microbial physiology relies on the simple method of measuring microbial populations' sizes over time and under different conditions. Many advances have increased the throughput of those assays and enabled the study of nonlab-adapted microbes under diverse conditions that widely affect their growth dynamics. Our software provides an all-in-one tool for estimating the growth parameters of microbial cultures and testing for differential growth in a high-throughput and user-friendly fashion without any underlying assumptions about how microbes respond to their growth conditions.
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Affiliation(s)
- Firas S. Midani
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas, USA
- Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, Texas, USA
| | - James Collins
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas, USA
- Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, Texas, USA
| | - Robert A. Britton
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas, USA
- Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, Texas, USA
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Yáñez Feliú G, Earle Gómez B, Codoceo Berrocal V, Muñoz Silva M, Nuñez IN, Matute TF, Arce Medina A, Vidal G, Vitalis C, Dahlin J, Federici F, Rudge TJ. Flapjack: Data Management and Analysis for Genetic Circuit Characterization. ACS Synth Biol 2021; 10:183-191. [PMID: 33382586 DOI: 10.1021/acssynbio.0c00554] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Characterization is fundamental to the design, build, test, learn (DBTL) cycle for engineering synthetic genetic circuits. Components must be described in such a way as to account for their behavior in a range of contexts. Measurements and associated metadata, including part composition, constitute the test phase of the DBTL cycle. These data may consist of measurements of thousands of circuits, measured in hundreds of conditions, in multiple assays potentially performed in different laboratories and using different techniques. In order to inform the learn phase this large volume of data must be filtered, collated, and analyzed. Characterization consists of using this data to parametrize models of component function in different contexts, and combining them to predict behaviors of novel circuits. Tools to store, organize, share, and analyze large volumes of measurement and metadata are therefore essential to linking the test phase to the build and learn phases, closing the loop of the DBTL cycle. Here we present such a system, implemented as a web app with a backend data registry and analysis engine. An interactive frontend provides powerful querying, plotting, and analysis tools, and we provide a REST API and Python package for full integration with external build and learn software. All measurements are associated with circuit part composition via SBOL (Synthetic Biology Open Language). We demonstrate our tool by characterizing a range of genetic components and circuits according to composition and context.
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Affiliation(s)
- Guillermo Yáñez Feliú
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Benjamín Earle Gómez
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Verner Codoceo Berrocal
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Macarena Muñoz Silva
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Isaac N Nuñez
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Tamara F Matute
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Anibal Arce Medina
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
- Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Gonzalo Vidal
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Carlos Vitalis
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Jonathan Dahlin
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Fernán Federici
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
- FONDAP, Center for Genome Regulation, Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Timothy J Rudge
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
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A sensitive mNeonGreen reporter system to measure transcriptional dynamics in Drosophila development. Commun Biol 2020; 3:663. [PMID: 33184447 PMCID: PMC7665215 DOI: 10.1038/s42003-020-01375-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 10/08/2020] [Indexed: 12/20/2022] Open
Abstract
The gene regulatory network governing anterior–posterior axis formation in Drosophila is a well-established paradigm to study transcription in developmental biology. The rapid temporal dynamics of gene expression during early stages of development, however, are difficult to track with standard techniques. We optimized the bright and fast-maturing fluorescent protein mNeonGreen as a real-time, quantitative reporter of enhancer expression. We derive enhancer activity from the reporter fluorescence dynamics with high spatial and temporal resolution, using a robust reconstruction algorithm. By comparing our results with data obtained with the established MS2-MCP system, we demonstrate the higher detection sensitivity of our reporter. We used the reporter to quantify the activity of variants of a simple synthetic enhancer, and observe increased activity upon reduction of enhancer–promoter distance or addition of binding sites for the pioneer transcription factor Zelda. Our reporter system constitutes a powerful tool to study spatio-temporal gene expression dynamics in live embryos. Ceolin et al. optimise the fluorescent protein mNeonGreen to generate a reporter to measure enhancer expression in living Drosophila embryos. They also find that reduced enhancer–promoter distance or addition of binding sites for the pioneer transcription factor Zelda increases enhancer activity.
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WellInverter: a web application for the analysis of fluorescent reporter gene data. BMC Bioinformatics 2019; 20:309. [PMID: 31185910 PMCID: PMC6558888 DOI: 10.1186/s12859-019-2920-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 05/29/2019] [Indexed: 12/20/2022] Open
Abstract
Background Fluorescent reporter genes have become widely used for monitoring gene expression in living cells. When a microbial strain carrying a reporter gene is grown in a microplate reader, the fluorescence and the absorbance (optical density) of the culture can be automatically measured every few minutes in a highly parallelized way. The extraction of useful information from the resulting large amounts of data is not easy to achieve, because the fluorescence and absorbance measurements are only indirectly related to promoter activities and protein concentrations, requiring mathematical models of the expression of reporter genes for their interpretation. Although the principles of the analysis of reporter gene data are well-established today, there is a lack of general-purpose bioinformatics tools based on generic measurement models and sound inference procedures. This has motivated the development of WellInverter, a web application based on well-known methods for regularized linear inversion. Results We present a new version of WellInverter that considerably improves the performance and usability of the original application. In particular, we have put in place a parallel computing architecture with a load balancer to distribute analysis queries over several back-end servers, we have completely redesigned the graphical user interface to better support the different analysis steps, and we have developed a plug-in system for the parsing of data files produced by microplate readers from different manufacturers. We illustrate the functioning of WellInverter by analyzing data of the expression of a fluorescent reporter gene controlled by a phage promoter in growing Escherichia coli populations. We show that the expression pattern in different growth media, supporting different growth rates, corresponds to the pattern expected for a constitutive gene. Conclusions The new version of WellInverter is a robust, easy-to-use and scalable web application, which has been deployed on two publicly accessible web servers and which can also be installed locally. A demo version of the application with two sample datasets is available on-line. Electronic supplementary material The online version of this article (10.1186/s12859-019-2920-4) contains supplementary material, which is available to authorized users.
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Cinquemani E, Laroute V, Cocaign-Bousquet M, de Jong H, Ropers D. Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data. Bioinformatics 2018; 33:i301-i310. [PMID: 28881984 PMCID: PMC5870603 DOI: 10.1093/bioinformatics/btx250] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Motivation Technological advances in metabolomics have made it possible to monitor the concentration of extracellular metabolites over time. From these data, it is possible to compute the rates of uptake and excretion of the metabolites by a growing cell population, providing precious information on the functioning of intracellular metabolism. The computation of the rate of these exchange reactions, however, is difficult to achieve in practice for a number of reasons, notably noisy measurements, correlations between the concentration profiles of the different extracellular metabolites, and discontinuties in the profiles due to sudden changes in metabolic regime. Results We present a method for precisely estimating time-varying uptake and excretion rates from time-series measurements of extracellular metabolite concentrations, specifically addressing all of the above issues. The estimation problem is formulated in a regularized Bayesian framework and solved by a combination of extended Kalman filtering and smoothing. The method is shown to improve upon methods based on spline smoothing of the data. Moreover, when applied to two actual datasets, the method recovers known features of overflow metabolism in Escherichia coli and Lactococcus lactis, and provides evidence for acetate uptake by L. lactis after glucose exhaustion. The results raise interesting perspectives for further work on rate estimation from measurements of intracellular metabolites. Availability and implementation The Matlab code for the estimation method is available for download at https://team.inria.fr/ibis/rate-estimation-software/, together with the datasets. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Valérie Laroute
- LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France
| | | | - Hidde de Jong
- Inria, Centre de Recherche Grenoble - Rhône-Alpes, Montbonnot, France
| | - Delphine Ropers
- Inria, Centre de Recherche Grenoble - Rhône-Alpes, Montbonnot, France
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Kannan S, Sams T, Maury J, Workman CT. Reconstructing Dynamic Promoter Activity Profiles from Reporter Gene Data. ACS Synth Biol 2018; 7:832-841. [PMID: 29457721 DOI: 10.1021/acssynbio.7b00223] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Accurate characterization of promoter activity is important when designing expression systems for systems biology and metabolic engineering applications. Promoters that respond to changes in the environment enable the dynamic control of gene expression without the necessity of inducer compounds, for example. However, the dynamic nature of these processes poses challenges for estimating promoter activity. Most experimental approaches utilize reporter gene expression to estimate promoter activity. Typically the reporter gene encodes a fluorescent protein that is used to infer a constant promoter activity despite the fact that the observed output may be dynamic and is a number of steps away from the transcription process. In fact, some promoters that are often thought of as constitutive can show changes in activity when growth conditions change. For these reasons, we have developed a system of ordinary differential equations for estimating dynamic promoter activity for promoters that change their activity in response to the environment that is robust to noise and changes in growth rate. Our approach, inference of dynamic promoter activity (PromAct), improves on existing methods by more accurately inferring known promoter activity profiles. This method is also capable of estimating the correct scale of promoter activity and can be applied to quantitative data sets to estimate quantitative rates.
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Affiliation(s)
- Soumya Kannan
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Thomas Sams
- Department of Electrical Engineering, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Jérôme Maury
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Christopher T. Workman
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800 Lyngby, Denmark
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de Jong H, Geiselmann J. Fluorescent Reporter Genes and the Analysis of Bacterial Regulatory Networks. HYBRID SYSTEMS BIOLOGY 2015. [DOI: 10.1007/978-3-319-27656-4_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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