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Uncovering Novel Substrates and Functions for the Calcineurin Phosphatase in Human Cells

2017· article· en· W2901679055 on OpenAlexaff
Callie P. Wigington, Jagoree Roy, Nikhil P. Damle, Shein Ei Cho, Norman E. Davey, Ylva Ivarsson, Cassandra J. Wong, Anne‐Claude Gingras, Martha Cyert

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSignaling Pathways in Disease
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsPhosphataseProteomeHuman proteome projectCalcineurinDephosphorylationComputational biologyBiologyCell biologyPhosphorylationBiochemistryChemistryProteomicsGene

Abstract

fetched live from OpenAlex

Protein phosphatases play essential roles in every signaling pathway; however, systems‐level understanding of phosphatase signaling networks is lacking due to the inherent challenges associated with proteome‐wide identification of their substrates. Calcineurin (CN) is the conserved Ca 2+ /calmodulin‐activated protein phosphatase and target of the widely prescribed immunosuppressant drugs, FK506 and Cyclosporin A. CN is ubiquitously expressed and plays critical roles in the immune, nervous, skeletal and cardiovascular systems, as well as during development. However, only 50 substrates are currently attributed to this phosphatase. CN utilizes conserved docking surfaces to interact with substrates via Short Linear Motifs (SLiMs) termed PxIxIT and LxVP, which occur preferentially in intrinsically disordered domains and are challenging to identify due to sequence degeneracy and low affinity for CN. We are applying novel experimental and computational approaches to systematically identify CN‐interacting SLiMs within the human proteome with the goal of ultimately establishing the human CN signaling network. We directly identified novel CN‐binding sequences by performing unbiased peptide phage display selections with human CN using a library containing all predicted disordered regions in the human proteome. These SLiM sequences directly identified many novel candidate CN substrates, including the nucleoporin, NUP153. We have shown that NUP153 is directly dephosphorylated by CN in vitro and contains a conserved PxIxIT sequence that is required for interaction with CN in vivo . Furthermore, a NUP153 PxIxIT mutant is dephosphorylated less efficiently by CN in vitro and shows altered dephosphorylation in vivo . To further expand our identification of novel CN substrates, we employed two additional approaches: 1. A Position‐Specific Scoring Matrix (PSSM) generated using the novel CN‐binding SLiMs identified by phage display and 2. Proximity‐dependent biotinylation (BioID) followed by MS analysis in HEK293 cells. Both of these methods identified several nuclear pore components in addition to NUP153 as high confidence CN interactors, indicating a previously uncharacterized role for CN in regulating nuclear pore structure and/or function. In addition to nuclear pore proteins, these combined experimental and computational approaches have identified a host of novel candidate substrates for CN including ion channels, kinases, transcription factors and receptors. The significant overlap between these two datasets underscores the strength of these independent approaches in identifying novel CN substrates in human cells. Together, these studies suggest new points of cross‐talk between CN and other signaling pathways in human cells and will ultimately allow us to establish the first comprehensive signaling network for this critical Ca 2+ ‐dependent regulator of human health. Support or Funding Information F32GM120916‐01 to CP WigingtonR01GM119336‐01 to MS Cyert

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 score0.004
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

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

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 head0.028
GPT teacher head0.282
Teacher spread0.254 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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