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Record W4295874228 · doi:10.1101/2022.09.09.507035

Characterizing the differential distribution and targets of Sumo paralogs in the mouse brain

2022· preprint· en· W4295874228 on OpenAlexafffund
Terry R. Suk, Trina T. Nguyen, Zoe Fisk, Mišo Mitkovski, Haley Geertsma, Jean-Louis Parmasad, Meghan M. Heer, Steve Callaghan, Nils Brose, Marilyn Tirard, Maxime W.C. Rousseaux

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsUniversity of Ottawa
FundersHealth CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsFondation Brain CanadaALS Society of Canada
KeywordsSUMO proteinBiologyGene knockinImmunoprecipitationCell biologyGeneticsUbiquitinGene

Abstract

fetched live from OpenAlex

ABSTRACT SUMOylation is an evolutionarily conserved and essential mechanism whereby Small Ubiquitin Like Modifiers, or SUMO proteins (Sumo in mice), are covalently bound to protein substrates in a highly dynamic and reversible manner. SUMOylation is involved in a variety of basic neurological processes including learning and memory, and central nervous system development, but is also linked with neurological disorders. However, studying SUMOylation in vivo remains challenging due to limited tools to study Sumo proteins and their targets in their native context. More complexity arises from the fact that Sumo1 and Sumo2 are ∼50% homologous, whereas Sumo2 and Sumo3 are nearly identical and indistinguishable with antibodies. While Sumo paralogues can compensate for one another’s loss, Sumo2 is highest expressed and only paralog essential for embryonic development making it critical to uncover roles specific to Sumo2 in vivo . To further examine the roles of Sumo2, and to begin to tease apart the redundancy and similarity between key Sumo paralogs, we generated (His 6 -)HA epitope-tagged Sumo2 knock-in mouse alleles, expanding the current Sumo knock-in mouse tool-kit comprising of the previously generated His 6 -HA-Sumo1 knock-in model. Using these HA-Sumo mouse lines, we performed whole brain imaging and mapping to the Allen Brain Atlas to analyze the relative distribution of the Sumo1 and Sumo2 paralogues in the adult mouse brain. We observed differential staining patterns between Sumo1 and Sumo2, including a partial localization of Sumo2 in nerve cell synapses of the hippocampus. Combining immunoprecipitation with mass spectrometry, we identified native substrates targeted by Sumo1 or Sumo2 in the mouse brain. We validated select hits using proximity ligation assays, further providing insight into the subcellular distribution of neuronal Sumo2-conjugates. These mouse models thus serve as valuable tools to study the cellular and biochemical roles of SUMOylation in the central nervous system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.219
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

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