MétaCan
Menu
Back to cohort
Record W4229014754 · doi:10.1101/2022.05.06.490918

Orai3 and Orai1 are essential for CRAC channel function and metabolic reprogramming in B cells

2022· preprint· en· W4229014754 on OpenAlexfundno aff
Scott M. Emrich, Ryan E. Yoast, Xuexin Zhang, Adam J. Fike, Yin‐Hu Wang, Kristen N Bricker, Anthony Tao, Ping Xin, Vonn Walter, Martin Johnson, Trayambak Pathak, Adam C. Straub, Stefan Feske, Ziaur S. M. Rahman, Mohamed Trebak

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldNeuroscience
TopicIon Channels and Receptors
Canadian institutionsnot available
FundersPenn State College of MedicineNational Institutes of HealthYork UniversityPennsylvania State University
KeywordsORAI1NFATCell biologyReprogrammingBiologySTIM1ChemistryCellBiochemistryTranscription factorEndoplasmic reticulumGene

Abstract

fetched live from OpenAlex

Abstract The essential role of store-operated Ca 2+ entry (SOCE) through Ca 2+ release-activated Ca 2+ (CRAC) channels in T cells is well established. In contrast, the contribution of individual Orai isoforms to SOCE and their downstream signaling functions in B cells are poorly understood. Here, we demonstrate changes in expression of Orai isoforms in response to B cell activation. We show that Orai3 and Orai1 are essential components of native CRAC channels in B cells and are critical for primary B cell proliferation and survival. The combined loss of Orai1 and Orai3 strongly impairs SOCE, nuclear factor for activated T cells (NFAT) activation, mitochondrial respiration, glycolysis, and the metabolic reprogramming of B cells in response to antigenic stimulation. Our results clarify the molecular composition and cellular functions of SOCE in B lymphocytes.

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 score0.008

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.236
Teacher spread0.214 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicIon Channels and ReceptorsFrench-language works237,207