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Record W4317888643 · doi:10.1002/1873-3468.14584

The emerging landscape of eukaryotic polyphosphatases

2023· review· en· W4317888643 on OpenAlexafffund
Liam McCarthy, Michael Downey

Bibliographic record

VenueFEBS Letters · 2023
Typereview
Languageen
FieldMedicine
TopicCoagulation, Bradykinin, Polyphosphates, and Angioedema
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsContext (archaeology)BiologyEnzymePolyphosphateYeastSignallingSaccharomyces cerevisiaeCell biologyComputational biologyBiochemistryPhosphate

Abstract

fetched live from OpenAlex

Polyphosphate (polyP) is a conserved polymer of inorganic phosphate residues that can reach thousands of moieties in length. PolyP has been implicated in cellular functions ranging from energy and phosphate homeostasis to cell signalling in eukaryotes from yeast to humans. Despite the interest in the role of polyP as a signalling molecule, the spatiotemporal regulation of polyP itself remains poorly understood. This knowledge gap limits our ability to understand how polyP impacts the physiology of normal and diseased cells and how this might be exploited in a therapeutic context. Polyphosphatases, enzymes that degrade polyP to generate shorter chains and free inorganic phosphate are ideally positioned to mediate polyP dynamics. However, little is known about how the activities of these enzymes are linked to specific cellular functions and how they might be regulated. Here, we provide an in-depth overview of polyphosphatase enzymes in budding yeast, which has served as a workhorse for polyP research, and in mammalian cells where the enzymes that make and degrade polyP have remained elusive. We identify critical open questions in both systems and propose strategies to guide future work.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.051
GPT teacher head0.333
Teacher spread0.282 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations21
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueFEBS LettersSame topicCoagulation, Bradykinin, Polyphosphates, and AngioedemaFrench-language works237,207