Specific sensory neurons and insulin-like peptides modulate food type-dependent oogenesis and fertilization in Caenorhabditis elegans.

An animal's responses to environmental cues are critical for its reproductive program. Thus, a mechanism that allows the animal to sense and adjust to its environment should make for a more efficient reproductive physiology. Here, we demonstrate that in Caenorhabditis elegans specific sensory neurons influence onset of oogenesis through insulin signaling in response to food-derived cues. The chemosensory neurons ASJ modulate oogenesis onset through the insulin-like peptide (ILP) INS-6. In contrast, other sensory neurons, the olfactory neurons AWA, regulate food type-dependent differences in C. elegans fertilization rates, but not onset of oogenesis. AWA modulates fertilization rates at least partly in parallel to insulin receptor signaling, since the insulin receptor DAF-2 regulates fertilization independently of food type, which requires ILPs other than INS-6. Together our findings suggest that optimal reproduction requires the integration of diverse food-derived inputs through multiple neuronal signals acting on the C. elegans germline.

3D Structure From 2D Microscopy Images Using Deep Learning.

Understanding the structure of a protein complex is crucial in determining its function. However, retrieving accurate 3D structures from microscopy images is highly challenging, particularly as many imaging modalities are two-dimensional. Recent advances in Artificial Intelligence have been applied to this problem, primarily using voxel based approaches to analyse sets of electron microscopy images. Here we present a deep learning solution for reconstructing the protein complexes from a number of 2D single molecule localization microscopy images, with the solution being completely unconstrained. Our convolutional neural network coupled with a differentiable renderer predicts pose and derives a single structure. After training, the network is discarded, with the output of this method being a structural model which fits the data-set. We demonstrate the performance of our system on two protein complexes: CEP152 (which comprises part of the proximal toroid of the centriole) and centrioles.

A Multicellular Network Mechanism for Temperature-Robust Food Sensing.

Responsiveness to external cues is a hallmark of biological systems. In complex environments, it is crucial for organisms to remain responsive to specific inputs even as other internal or external factors fluctuate. Here, we show how the nematode Caenorhabditis elegans can discriminate between different food levels to modulate its lifespan despite temperature perturbations. This end-to-end robustness from environment to physiology is mediated by food-sensing neurons that communicate via transforming growth factor ß (TGF-ß) and serotonin signals to form a multicellular gene network. Specific regulations in this network change sign with temperature to maintain similar food responsiveness in the lifespan output. In contrast to robustness of stereotyped outputs, our findings uncover a more complex robustness process involving the higher order function of discrimination in food responsiveness. This process involves rewiring a multicellular network to compensate for temperature and provides a basis for understanding gene-environment interactions. Together, our findings unveil sensory computations that integrate environmental cues to govern physiology.

Pheromones Modulate Learning by Regulating the Balanced Signals of Two Insulin-like Peptides.

Social environment modulates learning through unknown mechanisms. Here, we report that a pheromone mixture that signals overcrowding inhibits C. elegans from learning to avoid pathogenic bacteria. We find that learning depends on the balanced signaling of two insulin-like peptides (ILPs), INS-16 and INS-4, which act respectively in the pheromone-sensing neuron ADL and the bacteria-sensing neuron AWA. Pheromone exposure inhibits learning by disrupting this balance: it activates ADL and increases expression of ins-16, and this cellular effect reduces AWA activity and AWA-expressed ins-4. The activities of the sensory neurons are required for learning and the expression of the ILPs. Interestingly, pheromones also promote the ingestion of pathogenic bacteria while increasing resistance to the pathogen. Thus, the balance of the ILP signals integrates social information into the learning process as part of a coordinated adaptive response that allows consumption of harmful food during times of high population density.

Genetic dissection of neuropeptide cell biology at high and low activity in a defined sensory neuron.

Neuropeptides are ubiquitous modulators of behavior and physiology. They are packaged in specialized secretory organelles called dense core vesicles (DCVs) that are released upon neural stimulation. Unlike synaptic vesicles, which can be recycled and refilled close to release sites, DCVs must be replenished by de novo synthesis in the cell body. Here, we dissect DCV cell biology in vivo in a Caenorhabditis elegans sensory neuron whose tonic activity we can control using a natural stimulus. We express fluorescently tagged neuropeptides in the neuron and define parameters that describe their subcellular distribution. We measure these parameters at high and low neural activity in 187 mutants defective in proteins implicated in membrane traffic, neuroendocrine secretion, and neuronal or synaptic activity. Using unsupervised hierarchical clustering methods, we analyze these data and identify 62 groups of genes with similar mutant phenotypes. We explore the function of a subset of these groups. We recapitulate many previous findings, validating our paradigm. We uncover a large battery of proteins involved in recycling DCV membrane proteins, something hitherto poorly explored. We show that the unfolded protein response promotes DCV production, which may contribute to intertissue communication of stress. We also find evidence that different mechanisms of priming and exocytosis may operate at high and low neural activity. Our work provides a defined framework to study DCV biology at different neural activity levels.

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans.

Sensory systems allow animals to detect, process, and respond to their environment. Food abundance is an environmental cue that has profound effects on animal physiology and behavior. Recently, we showed that modulation of longevity in the nematode Caenorhabditis elegans by food abundance is more complex than previously recognized. The responsiveness of the lifespan to changes in food level is determined by specific genes that act by controlling information processing within a neural circuit. Our framework combines genetic analysis, high-throughput quantitative imaging and information theory. Here, we describe how these techniques can be used to characterize any gene that has a physiological relevance to broad-range dietary restriction. Specifically, this workflow is designed to reveal how a gene of interest regulates lifespan under broad-range dietary restriction; then to establish how the expression of the gene varies with food level; and finally, to provide an unbiased quantification of the amount of information conveyed by gene expression about food abundance in the environment. When several genes are examined simultaneously under the context of a neural circuit, this workflow can uncover the coding strategy employed by the circuit.

Genetic control of encoding strategy in a food-sensing neural circuit.

Neuroendocrine circuits encode environmental information via changes in gene expression and other biochemical activities to regulate physiological responses. Previously, we showed that daf-7 TGFß and tph-1 tryptophan hydroxylase expression in specific neurons encode food abundance to modulate lifespan in Caenorhabditis elegans, and uncovered cross- and self-regulation among these genes (Entchev et al., 2015). Here, we now extend these findings by showing that these interactions between daf-7 and tph-1 regulate redundancy and synergy among neurons in food encoding through coordinated control of circuit-level signal and noise properties. Our analysis further shows that daf-7 and tph-1 contribute to most of the food-responsiveness in the modulation of lifespan. We applied a computational model to capture the general coding features of this system. This model agrees with our previous genetic analysis and highlights the consequences of redundancy and synergy during information transmission, suggesting a rationale for the regulation of these information processing features.

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

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 TGFß 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 (TGFß) and tph-1 (tryptophan hydroxylase) to food availability. These intricate regulatory features provide distinct mechanisms for TGFß 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.

Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans.

Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM). These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a guide, we envision the broad utility of the framework for diverse problems across different length scales and imaging methods.

An insulin-to-insulin regulatory network orchestrates phenotypic specificity in development and physiology.

Insulin-like peptides (ILPs) play highly conserved roles in development and physiology. Most animal genomes encode multiple ILPs. Here we identify mechanisms for how the forty Caenorhabditis elegans ILPs coordinate diverse processes, including development, reproduction, longevity and several specific stress responses. Our systematic studies identify an ILP-based combinatorial code for these phenotypes characterized by substantial functional specificity and diversity rather than global redundancy. Notably, we show that ILPs regulate each other transcriptionally, uncovering an ILP-to-ILP regulatory network that underlies the combinatorial phenotypic coding by the ILP family. Extensive analyses of genetic interactions among ILPs reveal how their signals are integrated. A combined analysis of these functional and regulatory ILP interactions identifies local genetic circuits that act in parallel and interact by crosstalk, feedback and compensation. This organization provides emergent mechanisms for phenotypic specificity and graded regulation for the combinatorial phenotypic coding we observe. Our findings also provide insights into how large hormonal networks regulate diverse traits.

Selecting synaptic partners: GRASPing the role of UNC-6/netrin.

Forming synaptic connections of the appropriate strength between specific neurons is crucial for constructing neural circuits to control behavior. A recent paper in Neural Development describes the use of a synapse-specific label in Caenorhabditis elegans to implicate local UNC-6/netrin signaling in this developmental process. Thus, as well as their well known roles in cell migration and axon guidance, UNC-6/netrin signals distinguish an appropriate synaptic partner from other potential targets.

Sensory influence on homeostasis and lifespan: molecules and circuits.

The animal's ability to maintain homeostasis in response to different environments can influence its survival. This chapter will discuss the mechanisms by which environmental cues act through sensory pathways to influence hormone secretion and homeostasis. Interestingly, recent studies also show that there is a sensory influence on lifespan that requires the modulation of hormonal signaling activities. Thus, this raises the possibility that the sensory influence on homeostasis underlies the sensory influence on lifespan.

Profiling synaptic proteins identifies regulators of insulin secretion and lifespan.

Cells are organized into distinct compartments to perform specific tasks with spatial precision. In neurons, presynaptic specializations are biochemically complex subcellular structures dedicated to neurotransmitter secretion. Activity-dependent changes in the abundance of presynaptic proteins are thought to endow synapses with different functional states; however, relatively little is known about the rules that govern changes in the composition of presynaptic terminals. We describe a genetic strategy to systematically analyze protein localization at Caenorhabditis elegans presynaptic specializations. Nine presynaptic proteins were GFP-tagged, allowing visualization of multiple presynaptic structures. Changes in the distribution and abundance of these proteins were quantified in 25 mutants that alter different aspects of neurotransmission. Global analysis of these data identified novel relationships between particular presynaptic components and provides a new method to compare gene functions by identifying shared protein localization phenotypes. Using this strategy, we identified several genes that regulate secretion of insulin-like growth factors (IGFs) and influence lifespan in a manner dependent on insulin/IGF signaling.

An RNAi screen identifies genes that regulate GABA synapses.

GABA synapses play a critical role in many aspects of circuit development and function. For example, conditions that perturb GABA transmission have been implicated in epilepsy. To identify genes that regulate GABA transmission, we performed an RNAi screen for genes whose inactivation increases the activity of C. elegans body muscles, which receive direct input from GABAergic motor neurons. We identified 90 genes, 21 of which were previously implicated in seizure syndromes, suggesting that this screen has effectively identified candidate genes for epilepsy. Electrophysiological recordings and imaging of excitatory and inhibitory synapses indicate that several genes alter muscle activity by selectively regulating GABA transmission. In particular, we identify two humoral pathways and several protein kinases that modulate GABA transmission but have little effect on excitatory transmission at cholinergic neuromuscular junctions. Our data suggest these conserved genes are components of signaling pathways that regulate GABA transmission and consequently may play a role in epilepsy and other cognitive or psychiatric disorders.

Systematic analysis of genes required for synapse structure and function.

Chemical synapses are complex structures that mediate rapid intercellular signalling in the nervous system. Proteomic studies suggest that several hundred proteins will be found at synaptic specializations. Here we describe a systematic screen to identify genes required for the function or development of Caenorhabditis elegans neuromuscular junctions. A total of 185 genes were identified in an RNA interference screen for decreased acetylcholine secretion; 132 of these genes had not previously been implicated in synaptic transmission. Functional profiles for these genes were determined by comparing secretion defects observed after RNA interference under a variety of conditions. Hierarchical clustering identified groups of functionally related genes, including those involved in the synaptic vesicle cycle, neuropeptide signalling and responsiveness to phorbol esters. Twenty-four genes encoded proteins that were localized to presynaptic specializations. Loss-of-function mutations in 12 genes caused defects in presynaptic structure.

Identification of genes that regulate a left-right asymmetric neuronal migration in Caenorhabditis elegans.

In C. elegans, cells of the QL and QR neuroblast lineages migrate with left-right asymmetry; QL and its descendants migrate posteriorly whereas QR and its descendants migrate anteriorly. One key step in generating this asymmetry is the expression of the Hox gene mab-5 in the QL descendants but not in the QR descendants. This asymmetry appears to be coupled to the asymmetric polarizations and movements of QL and QR as they migrate and relies on an asymmetric response to an EGL-20/Wnt signal. To identify genes involved in these complex layers of regulation and to isolate targets of mab-5 that direct posterior migrations, we screened visually for mutants with cell migration defects in the QL and QR lineages. Here, we describe a set of new mutants (qid-5, qid-6, qid-7, and qid-8) that primarily disrupt the migrations of the QL descendants. Most of these mutants were defective in mab-5 expression in the QL lineage and might identify genes that interact directly or indirectly with the EGL-20/Wnt signaling pathway.