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Record W4386697651 · doi:10.1101/2023.09.12.557388

Hyperexcitability in human <i>MECP2</i> null neuronal networks manifests as calcium-dependent reverberating super bursts

2023· preprint· en· W4386697651 on OpenAlexafffund
Kartik S. Pradeepan, Fraser P. McCready, Wei Wei, Milad Khaki, Wenbo Zhang, Michael W. Salter, James Ellis, Julio Martínez-Trujillo

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoRobarts Clinical TrialsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundSimons Foundation Autism Research InitiativeCanadian Institutes of Health ResearchAutism Speaks
KeywordsNeuroscienceBurstingExcitatory postsynaptic potentialNull (SQL)MECP2SomaNeuronBiologyComputer scienceInhibitory postsynaptic potentialPhenotype

Abstract

fetched live from OpenAlex

ABSTRACT Rett syndrome (RTT) patients show abnormal developmental trajectories including loss of language and repetitive hand movements but also have signs of cortical hyperexcitability such as seizures. RTT is predominantly caused by mutations in MECP2 and can be modelled in vitro using human stem cell-derived neurons. MECP2 null excitatory neurons are smaller in soma size and have reduced synaptic connectivity but are also hyperexcitable, due to higher input resistance, which increases the chance to evoke action potentials with a given depolarized current. Few studies examine how single neuron activity integrates into neuronal networks during human development. Paradoxically, networks of MECP2 null neurons show a decrease in the frequency of bursting patterns consistent with synaptic hypoconnectivity, but no hyperexcitable network events have been reported. Here, we show that MECP2 null neurons have an increase in the frequency of a network event described as reverberating super bursts (RSBs) relative to isogenic controls. RSBs can be mistakenly called as a single long duration burst by standard burst detection algorithms. However, close examination revealed an initial large amplitude network burst followed by high frequency repetitive low amplitude mini-bursts. Using a custom burst detection algorithm, we unfolded the multi-burst structure of RSBs revealing that MECP2 null networks increased the total number of bursts relative to isogenic controls. Application of the Ca 2+ chelator EGTA-AM selectively eliminated RSBs and rescued the network burst phenotype relative to the isogenic controls. Our results indicate that during early development, MECP2 null neurons are hyperexcitable and produce hyperexcitable networks. This may predispose them to the emergence of hyper-synchronic states that potentially translate into seizures. Network hyperexcitability is dependent on asynchronous neurotransmitter release driven by pre-synaptic Ca 2+ and can be rescued by EGTA-AM to restore typical network dynamics. HIGHLIGHTS Reverberating super-bursts (RSBs) follow a stereotypic form of a large initial network burst followed by several smaller amplitude high-frequency mini-bursts. RSBs occur more often in MECP2 null excitatory networks. MECP2 null excitatory networks with increased RSBs show a hyperexcitable network burst phenotype relative to isogenic controls. The calcium chelator, EGTA-AM, decreases RSBs and rescues the dynamics of MECP2 null hyperexcitable networks.

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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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

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 head0.018
GPT teacher head0.241
Teacher spread0.223 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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