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A gene-expression-based neural code for food abundance that modulates lifespan.

Entchev EV, Patel DS, Zhan M, Steele AJ, Lu H, Ch'ng Q - Elife (2015)

Bottom Line: These intricate regulatory features provide distinct mechanisms for TGFβ and serotonin signaling to tune the accuracy of this multi-neuron code: daf-7 primarily regulates gene-expression variability, while tph-1 primarily regulates the dynamic range of gene-expression responses.This code is functional because daf-7 and tph-1 mutations bidirectionally attenuate food level-dependent changes in lifespan.Our results reveal a neural code for food abundance and demonstrate that gene expression serves as an additional layer of information processing in the nervous system to control long-term physiology.

View Article: PubMed Central - PubMed

Affiliation: MRC Centre for Developmental Neurobiology, King's College London, London, United Kingdom.

ABSTRACT
How the nervous system internally represents environmental food availability is poorly understood. Here, we show that quantitative information about food abundance is encoded by combinatorial neuron-specific gene-expression of conserved TGFβ and serotonin pathway components in Caenorhabditis elegans. Crosstalk and auto-regulation between these pathways alters the shape, dynamic range, and population variance of the gene-expression responses of daf-7 (TGFβ) and tph-1 (tryptophan hydroxylase) to food availability. These intricate regulatory features provide distinct mechanisms for TGFβ and serotonin signaling to tune the accuracy of this multi-neuron code: daf-7 primarily regulates gene-expression variability, while tph-1 primarily regulates the dynamic range of gene-expression responses. This code is functional because daf-7 and tph-1 mutations bidirectionally attenuate food level-dependent changes in lifespan. Our results reveal a neural code for food abundance and demonstrate that gene expression serves as an additional layer of information processing in the nervous system to control long-term physiology.

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Overview of the high-throughput quantitative imaging system for single-cell fluorescent intensity measurements in C. elegans.(A) The system incorporates a microfluidic chip with pressure controlled valves for worm handling, a custom-built pneumatic control system to control on-chip valves, a standard wide-field epifluorescent microscope and camera system and custom LabVIEW software for integration and control. (B) The microfluidic chip consists of worm and fluid inlets and outlets, two control channels for valve activation and a second layer cooling channel for immobilization. Sequential activation of the valves, worm and fluid inlets permit sequential loading of individual worms for imaging. (C) The LabVIEW software interacts with the pneumatic valve control system (green blocks) and camera (blue blocks) to automate the imaging process by sequential loading and imaging. (D) Image of the LabVIEW user interface. (E) Block diagram of steps used to calculate single-cell fluorescent intensities from raw z-stacks. Cell identifications and locations are calculated from 2-D maximum projections. Locations are then mapped onto the z-stacks for 3-D volume integration. (F) Maximum projections and binary images from a thresholding procedure are used to ascertain cellular locations.DOI:http://dx.doi.org/10.7554/eLife.06259.011
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fig3s1: Overview of the high-throughput quantitative imaging system for single-cell fluorescent intensity measurements in C. elegans.(A) The system incorporates a microfluidic chip with pressure controlled valves for worm handling, a custom-built pneumatic control system to control on-chip valves, a standard wide-field epifluorescent microscope and camera system and custom LabVIEW software for integration and control. (B) The microfluidic chip consists of worm and fluid inlets and outlets, two control channels for valve activation and a second layer cooling channel for immobilization. Sequential activation of the valves, worm and fluid inlets permit sequential loading of individual worms for imaging. (C) The LabVIEW software interacts with the pneumatic valve control system (green blocks) and camera (blue blocks) to automate the imaging process by sequential loading and imaging. (D) Image of the LabVIEW user interface. (E) Block diagram of steps used to calculate single-cell fluorescent intensities from raw z-stacks. Cell identifications and locations are calculated from 2-D maximum projections. Locations are then mapped onto the z-stacks for 3-D volume integration. (F) Maximum projections and binary images from a thresholding procedure are used to ascertain cellular locations.DOI:http://dx.doi.org/10.7554/eLife.06259.011

Mentions: Previous studies showed that daf-7 and tph-1 expression are regulated by environmental cues (Ren et al., 1996; Schackwitz et al., 1996; Sze et al., 2000; Zhang et al., 2005; Chang et al., 2006; Liang et al., 2006; Greer et al., 2008; Pocock and Hobert, 2010). However, their expression profiles over a broad range of inputs remain unknown because manual studies limit the number of animals and environmental conditions that can be feasibly studied in a consistent way. To overcome these limitations, we used an automated, high-throughput microfluidic-based platform (Figure 3A and Figure 3—figure supplement 1) (Chung et al., 2008; Crane et al., 2012) for quantitative large-scale imaging of individual worms bearing single-copy fluorescent transcriptional reporters for both tph-1 and daf-7 (Ptph-1::mCherry and Pdaf-7::Venus) across different food levels (Figure 3B). For brevity, we refer to these reporter activities as tph-1 and daf-7 expression. Our reporters contain the same regulatory regions as published reporters that have been well validated, and show identical expression patterns (Ren et al., 1996; Schackwitz et al., 1996; Sze et al., 2000; Zhang et al., 2005; Chang et al., 2006; Liang et al., 2006; Greer et al., 2008; Pocock and Hobert, 2010) (Figure 3B). Starvation, hypoxia, or pathogenic bacteria alter both tph-1 reporter expression and serotonin levels (Zhang et al., 2005; Liang et al., 2006; Pocock and Hobert, 2010), while corresponding changes occur in daf-7 RNA levels and daf-7 reporter expression (Ren et al., 1996). These published results indicate that tph-1 and daf-7 reporters are faithful readouts for the expression of their respective genes (see ‘Materials and methods’ for additional details on reporter validation).10.7554/eLife.06259.010Figure 3.High-throughput quantitative imaging of tph-1 and daf-7 fluorescent reporters reveals neuron-specific, graded expression responses to food level.


A gene-expression-based neural code for food abundance that modulates lifespan.

Entchev EV, Patel DS, Zhan M, Steele AJ, Lu H, Ch'ng Q - Elife (2015)

Overview of the high-throughput quantitative imaging system for single-cell fluorescent intensity measurements in C. elegans.(A) The system incorporates a microfluidic chip with pressure controlled valves for worm handling, a custom-built pneumatic control system to control on-chip valves, a standard wide-field epifluorescent microscope and camera system and custom LabVIEW software for integration and control. (B) The microfluidic chip consists of worm and fluid inlets and outlets, two control channels for valve activation and a second layer cooling channel for immobilization. Sequential activation of the valves, worm and fluid inlets permit sequential loading of individual worms for imaging. (C) The LabVIEW software interacts with the pneumatic valve control system (green blocks) and camera (blue blocks) to automate the imaging process by sequential loading and imaging. (D) Image of the LabVIEW user interface. (E) Block diagram of steps used to calculate single-cell fluorescent intensities from raw z-stacks. Cell identifications and locations are calculated from 2-D maximum projections. Locations are then mapped onto the z-stacks for 3-D volume integration. (F) Maximum projections and binary images from a thresholding procedure are used to ascertain cellular locations.DOI:http://dx.doi.org/10.7554/eLife.06259.011
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4417936&req=5

fig3s1: Overview of the high-throughput quantitative imaging system for single-cell fluorescent intensity measurements in C. elegans.(A) The system incorporates a microfluidic chip with pressure controlled valves for worm handling, a custom-built pneumatic control system to control on-chip valves, a standard wide-field epifluorescent microscope and camera system and custom LabVIEW software for integration and control. (B) The microfluidic chip consists of worm and fluid inlets and outlets, two control channels for valve activation and a second layer cooling channel for immobilization. Sequential activation of the valves, worm and fluid inlets permit sequential loading of individual worms for imaging. (C) The LabVIEW software interacts with the pneumatic valve control system (green blocks) and camera (blue blocks) to automate the imaging process by sequential loading and imaging. (D) Image of the LabVIEW user interface. (E) Block diagram of steps used to calculate single-cell fluorescent intensities from raw z-stacks. Cell identifications and locations are calculated from 2-D maximum projections. Locations are then mapped onto the z-stacks for 3-D volume integration. (F) Maximum projections and binary images from a thresholding procedure are used to ascertain cellular locations.DOI:http://dx.doi.org/10.7554/eLife.06259.011
Mentions: Previous studies showed that daf-7 and tph-1 expression are regulated by environmental cues (Ren et al., 1996; Schackwitz et al., 1996; Sze et al., 2000; Zhang et al., 2005; Chang et al., 2006; Liang et al., 2006; Greer et al., 2008; Pocock and Hobert, 2010). However, their expression profiles over a broad range of inputs remain unknown because manual studies limit the number of animals and environmental conditions that can be feasibly studied in a consistent way. To overcome these limitations, we used an automated, high-throughput microfluidic-based platform (Figure 3A and Figure 3—figure supplement 1) (Chung et al., 2008; Crane et al., 2012) for quantitative large-scale imaging of individual worms bearing single-copy fluorescent transcriptional reporters for both tph-1 and daf-7 (Ptph-1::mCherry and Pdaf-7::Venus) across different food levels (Figure 3B). For brevity, we refer to these reporter activities as tph-1 and daf-7 expression. Our reporters contain the same regulatory regions as published reporters that have been well validated, and show identical expression patterns (Ren et al., 1996; Schackwitz et al., 1996; Sze et al., 2000; Zhang et al., 2005; Chang et al., 2006; Liang et al., 2006; Greer et al., 2008; Pocock and Hobert, 2010) (Figure 3B). Starvation, hypoxia, or pathogenic bacteria alter both tph-1 reporter expression and serotonin levels (Zhang et al., 2005; Liang et al., 2006; Pocock and Hobert, 2010), while corresponding changes occur in daf-7 RNA levels and daf-7 reporter expression (Ren et al., 1996). These published results indicate that tph-1 and daf-7 reporters are faithful readouts for the expression of their respective genes (see ‘Materials and methods’ for additional details on reporter validation).10.7554/eLife.06259.010Figure 3.High-throughput quantitative imaging of tph-1 and daf-7 fluorescent reporters reveals neuron-specific, graded expression responses to food level.

Bottom Line: These intricate regulatory features provide distinct mechanisms for TGFβ and serotonin signaling to tune the accuracy of this multi-neuron code: daf-7 primarily regulates gene-expression variability, while tph-1 primarily regulates the dynamic range of gene-expression responses.This code is functional because daf-7 and tph-1 mutations bidirectionally attenuate food level-dependent changes in lifespan.Our results reveal a neural code for food abundance and demonstrate that gene expression serves as an additional layer of information processing in the nervous system to control long-term physiology.

View Article: PubMed Central - PubMed

Affiliation: MRC Centre for Developmental Neurobiology, King's College London, London, United Kingdom.

ABSTRACT
How the nervous system internally represents environmental food availability is poorly understood. Here, we show that quantitative information about food abundance is encoded by combinatorial neuron-specific gene-expression of conserved TGFβ and serotonin pathway components in Caenorhabditis elegans. Crosstalk and auto-regulation between these pathways alters the shape, dynamic range, and population variance of the gene-expression responses of daf-7 (TGFβ) and tph-1 (tryptophan hydroxylase) to food availability. These intricate regulatory features provide distinct mechanisms for TGFβ and serotonin signaling to tune the accuracy of this multi-neuron code: daf-7 primarily regulates gene-expression variability, while tph-1 primarily regulates the dynamic range of gene-expression responses. This code is functional because daf-7 and tph-1 mutations bidirectionally attenuate food level-dependent changes in lifespan. Our results reveal a neural code for food abundance and demonstrate that gene expression serves as an additional layer of information processing in the nervous system to control long-term physiology.

Show MeSH
Related in: MedlinePlus