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Feedback from network states generates variability in a probabilistic olfactory circuit.

ABSTRACT: Variability is a prominent feature of behavior and is an active element of certain behavioral strategies. To understand how neuronal circuits control variability, we examined the propagation of sensory information in a chemotaxis circuit of C. elegans where discrete sensory inputs can drive a probabilistic behavioral response. Olfactory neurons respond to odor stimuli with rapid and reliable changes in activity, but downstream AIB interneurons respond with a probabilistic delay. The interneuron response to odor depends on the collective activity of multiple neurons-AIB, RIM, and AVA-when the odor stimulus arrives. Certain activity states of the network correlate with reliable responses to odor stimuli. Artificially generating these activity states by modifying neuronal activity increases the reliability of odor responses in interneurons and the reliability of the behavioral response to odor. The integration of sensory information with network states may represent a general mechanism for generating variability in behavior.

SUBMITTER: Gordus A

PROVIDER: S-EPMC4821011 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Feedback from network states generates variability in a probabilistic olfactory circuit.

Gordus Andrew A Pokala Navin N Levy Sagi S Flavell Steven W SW Bargmann Cornelia I CI

Cell 20150312 2

Variability is a prominent feature of behavior and is an active element of certain behavioral strategies. To understand how neuronal circuits control variability, we examined the propagation of sensory information in a chemotaxis circuit of C. elegans where discrete sensory inputs can drive a probabilistic behavioral response. Olfactory neurons respond to odor stimuli with rapid and reliable changes in activity, but downstream AIB interneurons respond with a probabilistic delay. The interneuron ...[more]

PMID: 25772698

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Project description:BackgroundOlfactory receptors (ORs), the largest mammalian gene superfamily (900-1400 genes), has >50% pseudogenes in humans. While most of these inactive genes are identified via coding frame (nonsense) disruptions, seemingly intact genes may also be inactive due to other deleterious (missense) mutations. An ultimate assessment of the actual size of the functional human OR repertoire thus requires an accurate distinction between genes and pseudogenes.ResultsTo characterize inactive ORs with intact open reading frame, we have developed a probabilistic Classifier for Olfactory Receptor Pseudogenes (CORP). This algorithm is based on deviations from a functionally crucial consensus, constituting sixty highly conserved positions identified by a comparison of two evolutionarily-constrained OR repertoires (mouse and dog) with a small pseudogene fraction. We used a logistic regression analysis to assign appropriate coefficients to the conserved position and thus achieving maximal separation between active and inactive ORs. Consequently, the algorithms identified only 5% of the mouse functional ORs as pseudogenes, setting an upper limit of 0.05 to the false positive detection. Finally we used this algorithm to classify the 384 purportedly intact human OR genes. Of these, 135 were predicted as likely encoding non-functional proteins, and 38 were segregating between active and inactive forms due to missense polymorphisms.ConclusionWe demonstrated that the CORP algorithm is capable to distinguish between functional and non-functional OR genes with high precision even when the encoded protein would differ by a single amino acid. Using the CORP algorithm, we predict that approximately 70% of human OR genes are likely non-functional pseudogenes, a much higher number than hitherto suspected. The method we present may be employed for better annotation of inactive members in other gene families as well. CORP algorithm is available at: http://bioportal.weizmann.ac.il/HORDE/CORP/

Dataset Information

Feedback from network states generates variability in a probabilistic olfactory circuit.

Publications

Feedback from network states generates variability in a probabilistic olfactory circuit.

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