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

por Turquoise Dog del HHMI Transparent and Accountable Peer Review Training Program
Publicado
DOI
10.5281/zenodo.17926322
Licencia
CC BY 4.0

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.

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  1. por Gaia Tavosanis
    Publicado
    Licencia
    CC BY 4.0

    Thank you for recognizing the importance of the topic we address here and for your input on our manuscript. While we are preparing a fully revised version of this manuscript, we gladly respond below to each of your comments:

    1. We agree and we will add the absolute number of KCs in the figure legend. In the main text, we will keep the distribution if inputs (as fraction of total) to reflect the variability in number of cells and number of formed connections for instance from FPNs or RPNs to individual KC types in Figure 1(d).

    2. In the revised figure, we have distributed the boutons uniformly along the axons: thank you for pointing to this. Additionally, we improved the clarity of the figure by showing a different number of PNs per PN type. The information “the bouton numbers are proportional to the fractions observed in the hemibrain dataset” is included in the main text.

    3. While we analysed all the PNs included in the text, for the sake of clarity we decided to focus primarily on FPNs in the revised manuscript, since these show the most significant wiring bias.

    4. To evaluate the nature of the differences between food and non-food odour representations in the different KC types, we utilized two different measures of distance (Euclidean distance and cosine distance). The cosine distance between two odour representation vectors measures the angular distance between the two vectors. We found that the cosine distances between the food-odour representations were not significantly different from those between the other-odour representations, while the Euclidean distances were. These results suggested that the observed enhanced categorization might rely primarily upon different response amplitudes of KCs, Figure 3D. We agree with the Reviewer that the original manuscript lacked an explicit explanation in the main text (but see lines 648-678 in the methods explaining how we analysed similarity among the functional responses of the different KC types). To improve the clarity, in the revised text, we will explain the rationale for using the cosine distance in the results section and discuss more explicitly the implications in the discussion.

    Competing interests

    I am one of the authors of the manuscript and I respond in the name of all authors.