A connectomics-based taxonomy of mammals
- McGill University, Canada
- Tel Aviv University, Israel
- Vrije Universiteit Amsterdam, Netherlands
- Indiana University, United States
- Mila - Quebec Artificial Intelligence Institute, Canada
Abstract
Mammalian taxonomies are conventionally defined by morphological traits and genetics. How species differ in terms of neural circuits and whether inter-species differences in neural circuit organization conform to these taxonomies is unknown. The main obstacle for the comparison of neural architectures have been differences in network reconstruction techniques, yielding species-specific connectomes that are not directly comparable to one another. Here we comprehensively chart connectome organization across the mammalian phylogenetic spectrum using a common reconstruction protocol. We analyze the mammalian MRI (MaMI) data set, a database that encompasses high-resolution ex vivo structural and diffusion magnetic resonance imaging (MRI) scans of 124 species across 12 taxonomic orders and 5 superorders, collected using a unified MRI protocol. We assess similarity between species connectomes using two methods: similarity of Laplacian eigenspectra and similarity of multiscale topological features. We find greater inter-species similarities among species within the same taxonomic order, suggesting that connectome organization reflects established taxonomic relationships defined by morphology and genetics. While all connectomes retain hallmark global features and relative proportions of connection classes, inter-species variation is driven by local regional connectivity profiles. By encoding connectomes into a common frame of reference, these findings establish a foundation for investigating how neural circuits change over phylogeny, forging a link from genes to circuits to behaviour.
Data availability
The MaMI data set was originally collected and analyzed by Assaf and colleagues in Assaf, Y. et al., 2020 , Nat. Neurosci. (doi: https://doi.org/10.1038/s41593-020-0641-7). We have included the connectivity matrices used in this study in a public repository available at \url{https://doi.org/10.5281/zenodo.7143143}.
Article and author information
Author details
Funding
Natural Sciences and Engineering Research Council of Canada
- Bratislav Misic
National Science Foundation - BSF
- Yaniv Assaf
Canadian Institutes of Health Research
- Bratislav Misic
Fondation Brain Canada (Future Leaders Fund)
- Bratislav Misic
Canada Research Chairs
- Bratislav Misic
Michael J. Fox Foundation for Parkinson's Research
- Bratislav Misic
Healthy Brains for Healthy Lives
- Bratislav Misic
Natural Sciences and Engineering Research Council of Canada
- Guillaume Lajoie
Canada Research Chairs
- Guillaume Lajoie
Canadian Institute for Advanced Research
- Guillaume Lajoie
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Chris I Baker, National Institute of Mental Health, National Institutes of Health, United States
Version history
- Preprint posted: March 12, 2022 (view preprint)
- Received: March 14, 2022
- Accepted: November 4, 2022
- Accepted Manuscript published: November 7, 2022 (version 1)
- Version of Record published: November 22, 2022 (version 2)
Copyright
© 2022, Suarez et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,633
- views
-
- 264
- downloads
-
- 14
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
A connectomics-based taxonomy of mammals
eLife 11:e78635.
https://doi.org/10.7554/eLife.78635
Further reading
-
- Neuroscience
Abnormal activity in the cerebellar nuclei can be used to predict motor symptoms and induce them experimentally, pointing to potential therapeutic strategies.
-
- Cell Biology
- Neuroscience
One of the most extensively studied members of the Ras superfamily of small GTPases, Rac1 is an intracellular signal transducer that remodels actin and phosphorylation signaling networks. Previous studies have shown that Rac1-mediated signaling is associated with hippocampal-dependent working memory and longer-term forms of learning and memory and that Rac1 can modulate forms of both pre- and postsynaptic plasticity. How these different cognitive functions and forms of plasticity mediated by Rac1 are linked, however, is unclear. Here, we show that spatial working memory in mice is selectively impaired following the expression of a genetically encoded Rac1 inhibitor at presynaptic terminals, while longer-term cognitive processes are affected by Rac1 inhibition at postsynaptic sites. To investigate the regulatory mechanisms of this presynaptic process, we leveraged new advances in mass spectrometry to identify the proteomic and post-translational landscape of presynaptic Rac1 signaling. We identified serine/threonine kinases and phosphorylated cytoskeletal signaling and synaptic vesicle proteins enriched with active Rac1. The phosphorylated sites in these proteins are at positions likely to have regulatory effects on synaptic vesicles. Consistent with this, we also report changes in the distribution and morphology of synaptic vesicles and in postsynaptic ultrastructure following presynaptic Rac1 inhibition. Overall, this study reveals a previously unrecognized presynaptic role of Rac1 signaling in cognitive processes and provides insights into its potential regulatory mechanisms.
