Odour representations supporting ethology-relevant categorisation and discrimination in the Drosophila mushroom body

  1. Ivy Chi Wai Chan
  2. Felipe Yaroslav Kalle Kossio
  3. Gaia Tavosanis

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Abstract

Neural representations of sensory stimuli serve multiple distinct purposes, from the rapid recognition of familiar environments, to the precise identification of individual salient cues. In the insect mushroom body (MB), odours are encoded by the activity of Kenyon cells (KCs). The random wiring of olfactory projection neurons (PNs) and KCs in the MB calyx is thought to enhance odour discrimination. Here, we examined the impact of deviations from random wiring and demonstrated their significant roles in shaping odour representations. We confirm that different KC types have distinct PN input biases correlated with the contextual relevance of the odour information delivered by the PNs. By recording the functional responses of different KC types to ethologically defined odour categories, we found that the αβ and α’β’ KCs produce segregated representations of relevant odour groups, potentially enhancing the categorisation of odours based on ethological relevance. Simultaneously, these same KC types displayed distinct representations for food-related odours, supporting precise discrimination. In contrast, γ KCs lacked significant segregation of odour representations by ethological category. Computational simulations refined with our functional data indicated that the specific PN input connection pattern of individual KC types largely accounts for the observed representations. Taken together, we propose that individual KC types process odour information with distinct objectives, supporting both ethological categorisation and discrimination.

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  1. PREreview

    This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17925524.

    This study effectively combines connectomic analysis, in vivo calcium imaging, and computational modeling to investigate how the connectivity between the 2nd-order olfactory projection neurons (PNs) and 3rd-order Kenyon cell (KC) of the mushroom body supports odor categorization and discrimination in Drosophila. While the prevailing view has long held that mushroom body input is random, recent connectomics data indicates that PN-to-KC connectivity may actually deviate from chance. Bridging the gap between these anatomical findings and their functional roles, the authors provide evidence that αβ and α'β' KCs have biased connectivity toward food-related projection neurons (PNs), facilitating the categorization and potential discrimination of food odors. In contrast, γ KCs process odors more randomly without category preferences. Below are suggestions to further strengthen the conclusions.

    Major Points

    1.       The central conclusion is that αβ and α'β' KCs segregate odors based on "ethological relevance" (e.g., food vs. non-food). However, the "food" odor panel consists primarily of esters, whereas the "reproduction" and "danger" panels are chemically diverse. It is difficult to determine if the observed clustering in αβ KCs results from ethological categorization or simply reflects the ability to distinguish esters. Including a more diverse range of "food" odors (e.g., ethanol, acetic acid) would strengthen the conclusion regarding ethological relevance.

    2.       For Figure 4, the model parameters (spiking threshold, excitability) were optimized so that vector lengths matched experimental data. There is a risk of overfitting, which might force the model to replicate the separation observed biologically regardless of the underlying connectivity structure. Consider testing a variety of parameters to demonstrate that the connection structure, rather than parameter tuning alone, drives category separation.

    3.       The network models make strong predictions about discrimination and categorization performance, but these currently lack behavioral validation. Although feasibility may be a concern, consider performing an assay where αβ/α'β' silencing is expected to reduce such categorization between food vs non-food odors, whereas γ silencing should have smaller effects. This would further strengthen the conclusion that αβ/α'β' KC connectivity is important for food odor discrimination.

    Minor Points

    1.       In Figure 1B, consider including 3D orientation axes to clearly indicate the dorsal-ventral and anterior-posterior directions.

    2.       Although explained in the Methods, it would be beneficial for the authors to briefly elaborate in the main text on why Euclidean distance and Cosine distance were used in specific contexts and how they should be interpreted to improve reader comprehension.

    Competing interests

    The authors declare that they have no competing interests.

    Use of Artificial Intelligence (AI)

    The authors declare that they did not use generative AI to come up with new ideas for their review.

  2. PREreview

    This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17926322.

    In this study, the authors investigate how specific wiring patterns between projection neurons (PNs) and Kenyon cells (KCs) influence olfactory processing, a question with broad significance as it reveals the general principles by which neural circuits convert sensory inputs into behaviorally relevant codes. Although PN–KC connectivity has long been assumed to be random, recent detailed connectome analyses reveal clear deviations from randomness as suggested by preferential co-arborization of certain PN types and biased connections between PNs and KCs. However, the functional significance of these newly identified deviations has remained unclear. The authors propose that deviations from random wiring enhance ethological categorization, a conclusion strongly supported by convergent evidence from connectomes, olfactory responses of KCs (new experimental data from the current study), and computational modeling. This work reconciles the classic hypothesis that random connectivity supports odor discrimination with recent discoveries of structured PN–KC connections. It provides a mechanistic explanation for how dissimilar odors can be recognized as categories, an aspect of olfactory coding that has been largely unknown. Overall, this study greatly advances our understanding of odor representation in the fly olfactory system. Below are minor comments that may help further strengthen the manuscript:

    (1)     In figure 1(d) and similar plots in supplementary figures, it would be helpful for readers to also see the absolute numbers in addition to percentages, given that KC subtype abundances vary (as shown in supplementary data figure 1(c)).

    (2)     The random non-uniform connectivity schematic in Figure 1(e) may give a misleading first impression of altered spatial distribution because most boutons appear on the right side. It would be clearer to emphasize the counts rather than the spatial layout and explicitly show on the schematic that the bouton numbers are proportional to the fractions observed in the hemibrain dataset.

    (3)     Although the authors perform activity imaging on danger-related odors, the connectome analysis includes comparatively little information about danger-responsive PNs (DPNs), in contrast to FPNs and RPNs. It would be informative to also compare the wiring patterns of DPNs with FPNs/RPNs, and explore whether their connectivity differences relate to the physiological properties observed.

    (4)     It would be helpful to add a brief explanation, either in the supplementary figure legends or in the Methods, about why Euclidean distances are used in Figure 3(b) whereas cosine distances are used in Supplementary Figures 3–6, and why these methods lead to the different results. This clarification would help readers more fully appreciate the conclusions presented in the main figure.

    Competing interests

    The authors declare that they have no competing interests.

    Use of Artificial Intelligence (AI)

    The authors declare that they did not use generative AI to come up with new ideas for their review.

  3. Version published to 10.1101/2025.01.25.634657 on bioRxiv