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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 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] [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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2
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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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3
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Lakshminarasimhan KJ, Xie M, Cohen JD, Sauerbrei BA, Hantman AW, Litwin-Kumar A, Escola S. Specific connectivity optimizes learning in thalamocortical loops. Cell Rep 2024; 43:114059. [PMID: 38602873 PMCID: PMC11104520 DOI: 10.1016/j.celrep.2024.114059] [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: 05/22/2023] [Revised: 01/04/2024] [Accepted: 03/20/2024] [Indexed: 04/13/2024] Open
Abstract
Thalamocortical loops have a central role in cognition and motor control, but precisely how they contribute to these processes is unclear. Recent studies showing evidence of plasticity in thalamocortical synapses indicate a role for the thalamus in shaping cortical dynamics through learning. Since signals undergo a compression from the cortex to the thalamus, we hypothesized that the computational role of the thalamus depends critically on the structure of corticothalamic connectivity. To test this, we identified the optimal corticothalamic structure that promotes biologically plausible learning in thalamocortical synapses. We found that corticothalamic projections specialized to communicate an efference copy of the cortical output benefit motor control, while communicating the modes of highest variance is optimal for working memory tasks. We analyzed neural recordings from mice performing grasping and delayed discrimination tasks and found corticothalamic communication consistent with these predictions. These results suggest that the thalamus orchestrates cortical dynamics in a functionally precise manner through structured connectivity.
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Affiliation(s)
| | - Marjorie Xie
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Jeremy D Cohen
- Neuroscience Center, University of North Carolina, Chapel Hill, NC 27559, USA
| | - Britton A Sauerbrei
- Department of Neurosciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Adam W Hantman
- Neuroscience Center, University of North Carolina, Chapel Hill, NC 27559, USA
| | - Ashok Litwin-Kumar
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
| | - Sean Escola
- Department of Psychiatry, Columbia University, New York, NY 10032, USA.
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Zavatone-Veth JA, Masset P, Tong WL, Zak JD, Murthy VN, Pehlevan C. Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.21.545947. [PMID: 37961548 PMCID: PMC10634677 DOI: 10.1101/2023.06.21.545947] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Within a single sniff, the mammalian olfactory system can decode the identity and concentration of odorants wafted on turbulent plumes of air. Yet, it must do so given access only to the noisy, dimensionally-reduced representation of the odor world provided by olfactory receptor neurons. As a result, the olfactory system must solve a compressed sensing problem, relying on the fact that only a handful of the millions of possible odorants are present in a given scene. Inspired by this principle, past works have proposed normative compressed sensing models for olfactory decoding. However, these models have not captured the unique anatomy and physiology of the olfactory bulb, nor have they shown that sensing can be achieved within the 100-millisecond timescale of a single sniff. Here, we propose a rate-based Poisson compressed sensing circuit model for the olfactory bulb. This model maps onto the neuron classes of the olfactory bulb, and recapitulates salient features of their connectivity and physiology. For circuit sizes comparable to the human olfactory bulb, we show that this model can accurately detect tens of odors within the timescale of a single sniff. We also show that this model can perform Bayesian posterior sampling for accurate uncertainty estimation. Fast inference is possible only if the geometry of the neural code is chosen to match receptor properties, yielding a distributed neural code that is not axis-aligned to individual odor identities. Our results illustrate how normative modeling can help us map function onto specific neural circuits to generate new hypotheses.
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Affiliation(s)
- Jacob A Zavatone-Veth
- Center for Brain Science, Harvard University Cambridge, MA 02138
- Department of Physics, Harvard University Cambridge, MA 02138
| | - Paul Masset
- Center for Brain Science, Harvard University Cambridge, MA 02138
- Department of Molecular and Cellular Biology, Harvard University Cambridge, MA 02138
| | - William L Tong
- Center for Brain Science, Harvard University Cambridge, MA 02138
- John A. Paulson School of Engineering and Applied Sciences, Harvard University Cambridge, MA 02138
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University Cambridge, MA 02138
| | - Joseph D Zak
- Department of Biological Sciences, University of Illinois at Chicago Chicago, IL 60607
| | - Venkatesh N Murthy
- Center for Brain Science, Harvard University Cambridge, MA 02138
- Department of Molecular and Cellular Biology, Harvard University Cambridge, MA 02138
| | - Cengiz Pehlevan
- Center for Brain Science, Harvard University Cambridge, MA 02138
- John A. Paulson School of Engineering and Applied Sciences, Harvard University Cambridge, MA 02138
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University Cambridge, MA 02138
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5
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Lin A, Qin S, Casademunt H, Wu M, Hung W, Cain G, Tan NZ, Valenzuela R, Lesanpezeshki L, Venkatachalam V, Pehlevan C, Zhen M, Samuel AD. Functional imaging and quantification of multineuronal olfactory responses in C. elegans. SCIENCE ADVANCES 2023; 9:eade1249. [PMID: 36857454 PMCID: PMC9977185 DOI: 10.1126/sciadv.ade1249] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 02/01/2023] [Indexed: 05/21/2023]
Abstract
Many animals perceive odorant molecules by collecting information from ensembles of olfactory neurons, where each neuron uses receptors that are tuned to recognize certain odorant molecules with different binding affinity. Olfactory systems are able, in principle, to detect and discriminate diverse odorants using combinatorial coding strategies. We have combined microfluidics and multineuronal imaging to study the ensemble-level olfactory representations at the sensory periphery of the nematode Caenorhabditis elegans. The collective activity of C. elegans chemosensory neurons reveals high-dimensional representations of olfactory information across a broad space of odorant molecules. We reveal diverse tuning properties and dose-response curves across chemosensory neurons and across odorants. We describe the unique contribution of each sensory neuron to an ensemble-level code for volatile odorants. We show that a natural stimuli, a set of nematode pheromones, are also encoded by the sensory ensemble. The integrated activity of the C. elegans chemosensory neurons contains sufficient information to robustly encode the intensity and identity of diverse chemical stimuli.
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Affiliation(s)
- Albert Lin
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Shanshan Qin
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Helena Casademunt
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Min Wu
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Wesley Hung
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Gregory Cain
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Nicolas Z. Tan
- Department of Physics, Northeastern University, Boston, MA, USA
| | | | - Leila Lesanpezeshki
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | | | - Cengiz Pehlevan
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Mei Zhen
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Aravinthan D.T. Samuel
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
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Abstract
Enzymes are represented across a vast space of protein sequences and structural forms and have activities that far exceed the best chemical catalysts; however, engineering them to have novel or enhanced activity is limited by technologies for sensing product formation. Here, we describe a general and scalable approach for characterizing enzyme activity that uses the metabolism of the host cell as a biosensor by which to infer product formation. Since different products consume different molecules in their synthesis, they perturb host metabolism in unique ways that can be measured by mass spectrometry. This provides a general way by which to sense product formation, to discover unexpected products and map the effects of mutagenesis. The testing of engineered enzymes represents a bottleneck. Here the authors report a screening method combining microfluidics and mass spectrometry, to map the catalysis of a mutated enzyme, characterise the range of products generated and recover the sequences of variants with desired activities.
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Singh V, Tchernookov M, Balasubramanian V. What the odor is not: Estimation by elimination. Phys Rev E 2021; 104:024415. [PMID: 34525542 PMCID: PMC8892575 DOI: 10.1103/physreve.104.024415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 08/02/2021] [Indexed: 11/07/2022]
Abstract
Olfactory systems use a small number of broadly sensitive receptors to combinatorially encode a vast number of odors. We propose a method of decoding such distributed representations by exploiting a statistical fact: Receptors that do not respond to an odor carry more information than receptors that do because they signal the absence of all odorants that bind to them. Thus, it is easier to identify what the odor is not rather than what the odor is. For realistic numbers of receptors, response functions, and odor complexity, this method of elimination turns an underconstrained decoding problem into a solvable one, allowing accurate determination of odorants in a mixture and their concentrations. We construct a neural network realization of our algorithm based on the structure of the olfactory pathway.
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Affiliation(s)
- Vijay Singh
- Department of Physics, North Carolina A&T State University, Greensboro, NC, 27410, USA
- Department of Physics, & Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Martin Tchernookov
- Department of Physics, University of Wisconsin, Whitewater, WI, 53190, USA
| | - Vijay Balasubramanian
- Department of Physics, & Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, PA 19104, USA
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Kirby D, Rothschild J, Smart M, Zilman A. Pleiotropy enables specific and accurate signaling in the presence of ligand cross talk. Phys Rev E 2021; 103:042401. [PMID: 34005921 DOI: 10.1103/physreve.103.042401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 02/22/2021] [Indexed: 12/27/2022]
Abstract
Living cells sense their environment through the binding of extracellular molecular ligands to cell surface receptors. Puzzlingly, vast numbers of signaling pathways exhibit a high degree of cross talk between different signals whereby different ligands act through the same receptor or shared components downstream. It remains unclear how a cell can accurately process information from the environment in such cross-wired pathways. We show that a feature which commonly accompanies cross talk-signaling pleiotropy (the ability of a receptor to produce multiple outputs)-offers a solution to the cross-talk problem. In a minimal model we show that a single pleiotropic receptor can simultaneously identify and accurately sense the concentrations of arbitrary unknown ligands present individually or in a mixture. We calculate the fundamental limits of the signaling specificity and accuracy of such signaling schemes. The model serves as an elementary "building block" toward understanding more complex cross-wired receptor-ligand signaling networks.
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Affiliation(s)
- Duncan Kirby
- Department of Physics, University of Toronto, Toronto, Ontario M5S 1A7, Canada
| | - Jeremy Rothschild
- Department of Physics, University of Toronto, Toronto, Ontario M5S 1A7, Canada
| | - Matthew Smart
- Department of Physics, University of Toronto, Toronto, Ontario M5S 1A7, Canada
| | - Anton Zilman
- Department of Physics, University of Toronto, Toronto, Ontario M5S 1A7, Canada.,Institute for Bioengineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
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Gutierrez GJ, Rieke F, Shea-Brown ET. Nonlinear convergence boosts information coding in circuits with parallel outputs. Proc Natl Acad Sci U S A 2021; 118:e1921882118. [PMID: 33593894 PMCID: PMC7923546 DOI: 10.1073/pnas.1921882118] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Neural circuits are structured with layers of converging and diverging connectivity and selectivity-inducing nonlinearities at neurons and synapses. These components have the potential to hamper an accurate encoding of the circuit inputs. Past computational studies have optimized the nonlinearities of single neurons, or connection weights in networks, to maximize encoded information, but have not grappled with the simultaneous impact of convergent circuit structure and nonlinear response functions for efficient coding. Our approach is to compare model circuits with different combinations of convergence, divergence, and nonlinear neurons to discover how interactions between these components affect coding efficiency. We find that a convergent circuit with divergent parallel pathways can encode more information with nonlinear subunits than with linear subunits, despite the compressive loss induced by the convergence and the nonlinearities when considered separately.
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Affiliation(s)
- Gabrielle J Gutierrez
- Department of Applied Mathematics, University of Washington, Seattle, WA 98195;
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195
| | - Fred Rieke
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195
| | - Eric T Shea-Brown
- Department of Applied Mathematics, University of Washington, Seattle, WA 98195
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195
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Neurotoxicity of nanoparticles entering the brain via sensory nerve-to-brain pathways: injuries and mechanisms. Arch Toxicol 2020; 94:1479-1495. [DOI: 10.1007/s00204-020-02701-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 03/05/2020] [Indexed: 12/15/2022]
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