MétaCan
Menu
Back to cohort
Record W3211323914 · doi:10.1371/journal.pgen.1009871

Metabolic flux from the Krebs cycle to glutamate transmission tunes a neural brake on seizure onset

2021· article· en· W3211323914 on OpenAlexfundno aff
Jiwon Jeong, Jongbin Lee, Ji-hyung Kim, Chunghun Lim

Bibliographic record

VenuePLoS Genetics · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsnot available
FundersInstitute of GeneticsMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaChungnam National UniversityNational Research Foundation
KeywordsGlutamatergicGlutamate receptorIonotropic effectBiologyNeuroscienceCitric acid cycleOptogeneticsNeurotransmissionBiochemistryMetabolismReceptor

Abstract

fetched live from OpenAlex

Kohlschütter-Tönz syndrome (KTS) manifests as neurological dysfunctions, including early-onset seizures. Mutations in the citrate transporter SLC13A5 are associated with KTS, yet their underlying mechanisms remain elusive. Here, we report that a Drosophila SLC13A5 homolog, I'm not dead yet (Indy), constitutes a neurometabolic pathway that suppresses seizure. Loss of Indy function in glutamatergic neurons caused "bang-induced" seizure-like behaviors. In fact, glutamate biosynthesis from the citric acid cycle was limiting in Indy mutants for seizure-suppressing glutamate transmission. Oral administration of the rate-limiting α-ketoglutarate in the metabolic pathway rescued low glutamate levels in Indy mutants and ameliorated their seizure-like behaviors. This metabolic control of the seizure susceptibility was mapped to a pair of glutamatergic neurons, reversible by optogenetic controls of their activity, and further relayed onto fan-shaped body neurons via the ionotropic glutamate receptors. Accordingly, our findings reveal a micro-circuit that links neural metabolism to seizure, providing important clues to KTS-associated neurodevelopmental deficits.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.027
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.066
GPT teacher head0.330
Teacher spread0.264 · 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 teacher head, 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

Citations10
Published2021
Admission routes1
Has abstractyes

Explore more

Same venuePLoS GeneticsSame topicNeuroscience and Neuropharmacology ResearchFrench-language works237,207