Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies

  1. Mario Pannunzi
  2. Thomas Nowotny

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Abstract

When flies explore their environment, they encounter odors in complex, highly intermittent plumes. To navigate a plume and, for example, find food, they must solve several challenges, including reliably identifying mixtures of odorants and their intensities, and discriminating odorant mixtures emanating from a single source from odorants emitted from separate sources and just mixing in the air. Lateral inhibition in the antennal lobe is commonly understood to help solving these challenges. With a computational model of the Drosophila olfactory system, we analyze the utility of an alternative mechanism for solving them: Non-synaptic (“ephaptic”) interactions (NSIs) between olfactory receptor neurons that are stereotypically co-housed in the same sensilla.

We found that NSIs improve mixture ratio detection and plume structure sensing and they do so more efficiently than the traditionally considered mechanism of lateral inhibition in the antennal lobe. However, we also found that NSIs decrease the dynamic range of co-housed ORNs, especially when they have similar sensitivity to an odorant. These results shed light, from a functional perspective, on the role of NSIs, which are normally avoided between neurons, for instance by myelination.

Author summary

Myelin is important to isolate neurons and avoid disruptive electrical interference between them; it can be found in almost any neural assembly. However, there are a few exceptions to this rule and it remains unclear why. One particularly interesting case is the electrical interaction between olfactory sensory neurons co-housed in the sensilla of insects. Here, we created a computational model of the early stages of the Drosophila olfactory system and observed that the electrical interference between olfactory receptor neurons can be a useful trait that can help flies, and other insects, to navigate the complex plumes of odorants in their natural environment.

With the model we were able to shed new light on the trade-off of adopting this mechanism: We found that the non-synaptic interactions (NSIs) improve both the identification of the concentration ratio in mixtures of odorants and the discrimination of odorant mixtures emanating from a single source from odorants emitted from separate sources – both highly advantageous. However, they also decrease the dynamic range of the olfactory sensory neurons – a clear disadvantage.

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  1. eLife

    ###Reviewer #3:

    The authors touch upon a highly relevant issue. Non-synaptic peripheral interactions (NSIs) are of interest to the broader neuroscience community as they are typically left in the shadow of the more prominent network studies. The authors compare a simple computational model of pure NSI with the established model of lateral network inhibition, concluding that NSIs perform better in odour mixture identification and source separation. To achieve a comprehensive model study that would become a definitive reference in the field, I identified a number of required improvements with respect to clarity, validity, and interpretation of the model.

    1. Model approach

    The model mixes different methodological approaches and model description overall lacks clarity. The model could be severely streamlined by omitting unnecessary/unwanted simplifications and complications.

    The ORN binding rate model (Eqns.2+3) and ORN-ORN interaction (Eqn.5) are clear (see also #2) and generate activation variables x with adaptation y.

    The authors then claim to use a "biophysical spike generator", which in my eyes is not true. Rather, transfer fcn (4) generates a firing rate nu, subsequently used as intensity for stochastic point process realizations (non-homogenous Poisson, see minor #1). The Poisson assumption is surprising and ref. Kaissling et al. (2014) incomplete. Nagel & Wilson (2011) argue for Poisson-like transduction process and subsequent adaptation in the spike generating mechanism, which in a biophysical conductance/current based model generates beneficial non-renewal properties (Farkhooi et al., 2013). Omitting Eqn.4 and adaptation variable y in Eqn.3+5, using x plus noise (Poisson transduction events?) as input to a biophysical spike-generator model would elegantly separate transduction and spike generation, and naturally implement spike frequency adaptation.

    The next step is confusing: each ORN spike is transformed into a binary signal of a certain duration and amplitude (it took me quite a while to figure out what is actually meant with spike height and width). This seems an unnecessary and unwanted complication, reminiscent of simpler binary models. The biophysical voltage model of the PN includes short synaptic (tau_s) and long adaptation (tau_x) time constants that ensure the temporally extended effect of each incoming spike and synaptic amplitude is encoded as alpha_ORN. Thus, omitting the 'spike block' of height and width should be feasible and render the model more biologically realistic and transparent.

    The authors further introduce a post-hoc model for precise ORN-ORN correlations. Considering the other model simplifications (list in Discussion) this seems a rather unmotivated complication and its effect is not explored. The experimentally observed correlation could stem from either competition of co-housed ORNs or from antennal lobe network interactions affecting ORN axons. The former was explicitly excluded from the model and the latter is not captured.

    1. Model interpretation

    One major concern is the model reduction to two ORN types with exclusive odour sensitivity, which might overemphasize the NSI effect. Tuning of receptor types can be rather broad (e.g. Wilson et al., 2004). Related is the reduction to only two glomeruli. How would the picture change with increasing number of receptor types and glomeruli with a broader receptor tuning model?

    A second major concern is the restricted comparison to the pure NSI and pure LI model. If we assume that LI is present in the AL, the 3rd choice of the combined model should ideally show synergistic effects.

    The conclusion ”information about input correlations is contained in the first part of the response before adaptation takes place" in the NSI model is based on the surplus spike count within a window of 50-150ms of estimated rates above 150Hz (Fig. 8d). The 'encoding' of temporal whiff correlation was seen in the average rate for the LN but not the NSI model (Fig.8c). This looks like an ad-hoc implementation of a new measure to achieve a wanted effect of the NSI model. The authors must motivate this unusual measure with biological plausibility.

    The AL model assumes LN activation by PNs. It has been argued for different species (Galizia 2014) including D. melanogaster (Seki et al., 2010) that LNs receive direct input from ORNs. Previous computational models have used either type of implementation. What is the author's rationale behind their choice and would ORN->LN activation change their conclusions?

    What are the crucial experiments to be conducted for testing model predictions? E.g. transient (temperature-sensitive) genetic suppression of a specific OR type? Optogenetic activation of a specific OR type?

    1. Evolutionary perspective

    The abstract promises that "... results shed light, from an evolutionary perspective, on the role of NSIs, which are normally avoided between neurons..." and I was looking forward to a knowledgeable discussion. The MS would gain relevance on a broader scope if the authors could provide (comparative) arguments. Do some (older) families within the class of insects or other arthropod classes (e.g. crustaceans) lack co-housing of different ORN types? Is there known variation within groups, e.g. between different bee species? Can this be linked to ecological demands?

  2. eLife

    ###Reviewer #2:

    In this manuscript, the authors postulate that the observed phenomena of stereotyped colocalization of OSNs in insect antenna coupled with evidence of "non-synaptic interactions" (NSI) can serve an important role in parsing mixture ratios. Parsing these ratios accurately has been of key interest both for the understanding of pheromone recognition, as well as the proposed concept of "concentration invariance".

    The authors perform a nice series of calculations showing that NSI can improve the resolution of synchronous inputs, and conversely, improve the separation between asynchronous inputs. Both aspects are important features of resolving stochastic and intermittent plume information in nature.

    Although I have collaborated in a number of computational studies, my main expertise is in the neuroethology of olfaction, and therefore my comments will be concentrated on this aspect. However, in general the computation performed appears reasonable for the concept to be tackled.

    However, I have a few questions on the rationale for the study, as well as it's interpretation I would like the authors to address. I will separate my concerns into three categories for simplicity:

    1. BIOLOGY: The choice of Drosophila for the calculations is understood and likely necessary as it is the only system for which we have sufficient neurophysiological data at both the periphery and central levels to address this question. However, the concept of co-localization itself is known across the Arthropoda, and varies widely among species. For example, while moths and flies generally have 1-4 colocalized OSNs per sensilla (and these are the two systems that the authors reference), other systems like beetles, ants, and bees have up to 20-30 colocalized sensilla. Locusts, for which Gilles Laurent performed foundational research on blend encoding, have up to 50 OSNs in the same sensilla. Further, while it is true that pheromone blend neurons are often colocalized, this is not always the case.

    Thus, I would like the authors to take some time to consider: If NSIs are important for mixture processing, why do insects like bees (who, as shown by Giovanni Galizia and Paul Szyszka referenced in the manuscript can process mixtures at high speeds) have 20-30 OSNs together? How would this work?

    1. ENVIRONMENT: While concentration invariance and ratio processing has been shown to be important for pheromone processing in moths and some other cases, the true complexity of odor detection is just beginning to be appreciated. See (https://doi.org/10.3389/fphys.2019.00972) for a nice recent review. First, odors are not always presented as point sources, they are not often without a chemical background, and insects themselves might not always have need for such strict attention to ratio. In the case of Drosophila, one can easily argue that when locating a rotting fruit for oviposition, the exact composition of the fruit odor might be less important, although the flies have specific OSNs to detect it.

    So, I would like the authors to address - If NSIs are important for mixture processing, what happens when they are not needed, meaning when concentration ratios are not essential for identification? Would they limit the processing otherwise? If the authors disagree with this line of thinking, I would also like them to comment on the evidence that insects always need such fine tuning of ratios in their odor detection.

    3.) OTHER EXPLANATIONS: The authors, as well as others like Tim Pearce and Christiane Linster, have spent considerable time providing computational evidence regarding mixture processing (not just monomolecular odors). While there is time spent on comparing the NSI model to other models ("Comparison with related modelling works"), it mainly focuses on how the current model incorporates more information, rather than on why it performs better in detecting ratios.

    I would like the authors to take more time here to compare the NSI to other mixture processing models (several of which are not referenced) and explain why their model is better, just like they do in comparing how NSI improves ratio processing over LN/PN activity alone. Further, they mention myelination - so can the authors explain how mammals that would need similar attention to ratios accomplish this without NSIs - are there any similarities expected?

    These explanations and additions will greatly improve the relevance of this study to insect science and future research on this interesting topic.

  3. eLife

    ###Reviewer #1:

    This is an admirably clear account of how non-synaptic interactions (NSIs) in the ORNs in the insect sensillum might improve processing of odor mixtures with complex temporal structures. The paper methodically goes through the initial constraining to data, comparison with other models, and predictions of the improved signal representation by a model incorporating NSIs.

    The fundamental computational concept here is that the NSIs can carry out highly specific high time-resolution mutual inhibition operations. All else follows directly from this.

    General comments:

    1. My major critique of the paper is that I don't think it adds much conceptually. Higher time-resolution in responses follows directly from the biophysics of ORN interactions in a sensillum. My reading is that the improvements in coding follow directly from this improved time-resolution.

    2. While the authors discuss various limitations of the model by way of simplifications, I would like to point out another by way of network structure: the only pairwise interactions possible here are those encoded by the co-expression of ORNs in a sensillum. Thus the LN network will potentially support a wider range of lateral inhibition interactions than NSIs. There should be some data on this, and certainly the authors should comment on it.

    3. I think that perhaps the authors are missing a possible additional value of NSIs, which is that if the odor filaments are fine enough to excite only a small fraction of sensilla at a time, the NSI computation might be more effective than converging multiple homotypic ORNs into the PNs and then doing lateral inhibition. I don't know if odor filaments on this scale have been demonstrated.

    In summary, I think the paper does a very good job of presenting this model and exploring its implications. However, I found the coding implications to be obvious outcomes of the higher temporal resolution of the NSIs as compared to synaptically mediated lateral inhibition. The well described model of early insect olfaction will be of value to specialists in the field.

  4. eLife

    ##Preprint Review

    This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 2 of the manuscript.

    ###Summary:

    The study is a lucid analysis of non-synaptic interactions between ORNs in insect sensilla, with predictions on how these interactions could improve processing of odor mixtures with complex temporal structures. However, the reviewers and I had a number of major concerns with the study.

  5. Version published to 10.1101/2020.07.23.217216 on bioRxiv