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Record W2836698146 · doi:10.1111/tra.12601

Evolution of protein trafficking in kinetoplastid parasites: Complexity and pathogenesis

2018· review· en· W2836698146 on OpenAlexfundno aff
Divya Venkatesh, Ning Zhang, Martin Zoltner, Ricardo Canavate Del Pino, Mark C. Field

Bibliographic record

VenueTraffic · 2018
Typereview
Languageen
FieldImmunology and Microbiology
TopicToxoplasma gondii Research Studies
Canadian institutionsnot available
FundersMedical Research CouncilMedical Research Council CanadaWellcome Trust
KeywordsBiologyCell biologyPathogenesisComputational biologyGeneticsImmunology

Abstract

fetched live from OpenAlex

The kinetoplastida and their close relatives are unicellular organisms prevalent within the biosphere and important for significant impacts on global health, economy and ecosystems. They are, under most models, an early branching lineage. Individual species adapted to highly diverse environments by adopting complex life styles; parasitic species can infect a wide range of eukaryotic hosts, while many relatives are free-living and some autotrophic from acquiring a plastid for photosynthesis. Adaptation is especially evident in the evolution of kinetoplastid cell surface architecture and is supported by endomembrane trafficking and serves as a platform for interaction with its environment. Here we summarize and discuss recent genomic and experimental studies of the protein trafficking system in kinetoplastids, with focus on the composition and function of the surface as well as mechanisms for constructing, maintaining and regulating the cell surface proteome. We hope this provides a broad view of how protein trafficking contributes to the intricate and dynamic host-parasite interfaces that are critical for successful environmental adaptation of this highly important lineage.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.070
GPT teacher head0.329
Teacher spread0.258 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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