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Moore JP, Kamino K, Kottou R, Shimizu TS, Emonet T. Signal integration and adaptive sensory diversity tuning in Escherichia coli chemotaxis. Cell Syst 2024; 15:628-638.e8. [PMID: 38981486 PMCID: PMC11307269 DOI: 10.1016/j.cels.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 04/01/2024] [Accepted: 06/17/2024] [Indexed: 07/11/2024]
Abstract
In uncertain environments, phenotypic diversity can be advantageous for survival. However, as the environmental uncertainty decreases, the relative advantage of having diverse phenotypes decreases. Here, we show how populations of E. coli integrate multiple chemical signals to adjust sensory diversity in response to changes in the prevalence of each ligand in the environment. Measuring kinase activity in single cells, we quantified the sensitivity distribution to various chemoattractants in different mixtures of background stimuli. We found that when ligands bind uncompetitively, the population tunes sensory diversity to each signal independently, decreasing diversity when the signal's ambient concentration increases. However, among competitive ligands, the population can only decrease sensory diversity one ligand at a time. Mathematical modeling suggests that sensory diversity tuning benefits E. coli populations by modulating how many cells are committed to tracking each signal proportionally as their prevalence changes.
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Affiliation(s)
- Jeremy Philippe Moore
- Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA; Quantitative Biology Institute, Yale University, New Haven, CT 06511, USA
| | - Keita Kamino
- Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan
| | - Rafaela Kottou
- Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA; Quantitative Biology Institute, Yale University, New Haven, CT 06511, USA
| | | | - Thierry Emonet
- Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA; Quantitative Biology Institute, Yale University, New Haven, CT 06511, USA; Department of Physics, Yale University, New Haven, CT 06511, USA.
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Moore JP, Kamino K, Kottou R, Shimizu TS, Emonet T. Signal Integration and Adaptive Sensory Diversity Tuning in Escherichia coli Chemotaxis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.02.08.527720. [PMID: 36798398 PMCID: PMC9934624 DOI: 10.1101/2023.02.08.527720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
In uncertain environments, phenotypic diversity can be advantageous for survival. However, as the environmental uncertainty decreases, the relative advantage of having diverse phenotypes decreases. Here, we show how populations of E. coli integrate multiple chemical signals to adjust sensory diversity in response to changes in the prevalence of each ligand in the environment. Measuring kinase activity in single cells, we quantified the sensitivity distribution to various chemoattractants in different mixtures of background stimuli. We found that when ligands bind uncompetitively, the population tunes sensory diversity to each signal independently, decreasing diversity when the signal ambient concentration increases. However, amongst competitive ligands the population can only decrease sensory diversity one ligand at a time. Mathematical modeling suggests that sensory diversity tuning benefits E. coli populations by modulating how many cells are committed to tracking each signal proportionally as their prevalence changes.
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Kamino K, Kadakia N, Avgidis F, Liu ZX, Aoki K, Shimizu T, Emonet T. Optimal inference of molecular interaction dynamics in FRET microscopy. Proc Natl Acad Sci U S A 2023; 120:e2211807120. [PMID: 37014867 PMCID: PMC10104582 DOI: 10.1073/pnas.2211807120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 02/10/2023] [Indexed: 04/05/2023] Open
Abstract
Intensity-based time-lapse fluorescence resonance energy transfer (FRET) microscopy has been a major tool for investigating cellular processes, converting otherwise unobservable molecular interactions into fluorescence time series. However, inferring the molecular interaction dynamics from the observables remains a challenging inverse problem, particularly when measurement noise and photobleaching are nonnegligible-a common situation in single-cell analysis. The conventional approach is to process the time-series data algebraically, but such methods inevitably accumulate the measurement noise and reduce the signal-to-noise ratio (SNR), limiting the scope of FRET microscopy. Here, we introduce an alternative probabilistic approach, B-FRET, generally applicable to standard 3-cube FRET-imaging data. Based on Bayesian filtering theory, B-FRET implements a statistically optimal way to infer molecular interactions and thus drastically improves the SNR. We validate B-FRET using simulated data and then apply it to real data, including the notoriously noisy in vivo FRET time series from individual bacterial cells to reveal signaling dynamics otherwise hidden in the noise.
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Affiliation(s)
- Keita Kamino
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT06511
- Quantitative Biology Institute, Yale University, New Haven, CT06511
- Institute of Molecular Biology, Academia Sinica, Taipei115, Taiwan
- PRESTO, Japan Science and Technology Agency, Kawaguchi-shi, Saitama332-0012, Japan
| | - Nirag Kadakia
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT06511
- Quantitative Biology Institute, Yale University, New Haven, CT06511
- Swartz Foundation for Theoretical Neuroscience, Yale University, New Haven, CT06511
| | | | - Zhe-Xuan Liu
- Institute of Physics, National Yang Ming Chiao Tung University, Hsinchu30010, Taiwan
| | - Kazuhiro Aoki
- Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, Okazaki, Aichi444-8787, Japan
- National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Aichi444-8585, Japan
- Department of Basic Biology, School of Life Science, SOKENDAI (The Graduate University for Advanced Studies), Okazaki, Aichi240-0193, Japan
| | | | - Thierry Emonet
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT06511
- Quantitative Biology Institute, Yale University, New Haven, CT06511
- Department of Physics, Yale University, New Haven, CT06511
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Karin O, Alon U. The dopamine circuit as a reward-taxis navigation system. PLoS Comput Biol 2022; 18:e1010340. [PMID: 35877694 PMCID: PMC9352198 DOI: 10.1371/journal.pcbi.1010340] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 08/04/2022] [Accepted: 06/29/2022] [Indexed: 01/29/2023] Open
Abstract
Studying the brain circuits that control behavior is challenging, since in addition to their structural complexity there are continuous feedback interactions between actions and sensed inputs from the environment. It is therefore important to identify mathematical principles that can be used to develop testable hypotheses. In this study, we use ideas and concepts from systems biology to study the dopamine system, which controls learning, motivation, and movement. Using data from neuronal recordings in behavioral experiments, we developed a mathematical model for dopamine responses and the effect of dopamine on movement. We show that the dopamine system shares core functional analogies with bacterial chemotaxis. Just as chemotaxis robustly climbs chemical attractant gradients, the dopamine circuit performs ‘reward-taxis’ where the attractant is the expected value of reward. The reward-taxis mechanism provides a simple explanation for scale-invariant dopaminergic responses and for matching in free operant settings, and makes testable quantitative predictions. We propose that reward-taxis is a simple and robust navigation strategy that complements other, more goal-directed navigation mechanisms.
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Affiliation(s)
- Omer Karin
- Dept. of Molecular Cell Biology, Weizmann Institute of Science, Rehovot Israel
- Dept. of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, University of Cambridge, Cambridge, United Kingdom
- Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom
- * E-mail: (OK); (UA)
| | - Uri Alon
- Dept. of Molecular Cell Biology, Weizmann Institute of Science, Rehovot Israel
- * E-mail: (OK); (UA)
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