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Record W3166499510 · doi:10.1101/2021.06.17.448756

Presynaptic Mitochondrial Volume and Density Scale with Presynaptic Power Demand

2021· preprint· en· W3166499510 on OpenAlexaff
Karlis A. Justs, Zhongmin Lu, Amit K. Chouhan, J Borycz, Zhiyuan Lu, Ian A. Meinertzhagen, Gregory T. Macleod

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsDalhousie University
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of Health
KeywordsOxidative phosphorylationBioenergeticsMitochondrionGlycolysisBiophysicsPower densityCell biologyBiochemistryBiologyChemistryPower (physics)MetabolismPhysics

Abstract

fetched live from OpenAlex

Abstract S table neural function requires an energy supply that can meet the intense episodic power demands of neuronal activity. The bioenergetic machinery of glycolysis and oxidative phosphorylation is highly responsive to such demands, but it must occupy a minimum volume if it is to accommodate these demands. We examined the trade-off between presynaptic power demands and the volume available to the bioenergetic machinery. We quantified the energy demands of six Drosophila motor nerve terminals through direct measurements of neurotransmitter release and Ca 2+ entry, and via theoretical estimates of Na + entry and power demands at rest. Electron microscopy revealed that terminals with the highest power demands contained the greatest volume of mitochondria, indicating that mitochondria are allocated according to presynaptic power demands. In addition, terminals with the greatest power demand-to-volume ratio (∼66 nmol·min -1 ·μL -1 ) harbor the largest mitochondria packed at the greatest density. If we assume sequential and complete oxidation of glucose by glycolysis and oxidative phosphorylation, then these mitochondria are required to produce ATP at a rate of 52 nmol·min -1 ·μL -1 at rest, rising to 963 during activity. Glycolysis would contribute ATP at 0.24 nmol·min -1 ·μL -1 of cytosol at rest, rising to 4.36. These data provide a quantitative framework for presynaptic bioenergetics in situ , and reveal that, beyond an immediate capacity to accelerate ATP output from glycolysis and oxidative phosphorylation, over longer time periods presynaptic terminals optimize mitochondrial volume and density to meet power demand. Significance Statement The remarkable energy demands of the brain are supported by the complete oxidation of its fuel but debate continues regarding a division of labor between glycolysis and oxidative phosphorylation across different cell types. Here we leverage the neuromuscular synapse, a model for studying neurophysiology, to elucidate fundamental aspects of neuronal energy metabolism that ultimately constrain rates of neural processing. We quantified energy production rates required to sustain activity at individual nerve terminals and compared these data with the volume capable of oxidative phosphorylation (mitochondria) and glycolysis (cytosol). We find strong support for oxidative phosphorylation playing a primary role in presynaptic terminals and provide the first in vivo estimates of energy production rates per unit volume of presynaptic mitochondria and cytosol.

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)0.001
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

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