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Record W1563618164 · doi:10.5772/20381

Transcriptional Regulation of Acute Phase Protein Genes

2011· book-chapter· en· W1563618164 on OpenAlexafffund
Claude Asselin, Mylène Blais

Bibliographic record

VenueInTech eBooks · 2011
Typebook-chapter
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsUniversité de Sherbrooke
FundersCrohn's and Colitis Foundation of CanadaCrohn's and Colitis Foundation
KeywordsGeneComputational biologyPhase (matter)BiologyGeneticsChemistry

Abstract

fetched live from OpenAlex

Acute Phase Proteins -Regulation and Functions of Acute Phase Proteins 2 response genes, secondary response genes need new protein synthesis to establish full expression patterns. This complex regulation depends on an array of transcription factors that may be divided in four classes The first two classes of transcription factors are ubiquitous stress sensors that respond to external stress signals. Class I includes constitutively expressed transcription factors, such as NF-B and IRF3, activated by signaldependent post-translational modifications that affect their activation properties and nuclear localization. For example, cytoplasmic NF-B is rapidly translocated to the nucleus after LPS stimulation, and is involved in the induction of primary genes. Other transcription factors of this class include latent nuclear AP-1 transcription factors, such as c-Jun phosphorylated rapidly after LPS stimulation. Class II transcription factors, including C/EBP and AP-1 transcription factor family members, need new protein synthesis for LPSdependent stimulation. In addition to inducing secondary late gene expression, these transcription factors play a role in determining waves of time-dependent levels of gene expression. In macrophages, CCAAT/enhancer-binding protein (C/EBP ) expression is increased late after LPS induction (see below). The two last classes comprise tissue-restricted and cell-lineage transcription factors. The third category includes the macrophage-differentiation transcription factors PU.1 and C/EBP . Transcription factors of this class establish inducible cell-specific responses to stress and inflammation, by generating macrophage-specific chromatin domain modifications. The fourth category includes metabolic sensors of the nuclear receptor family, such as peroxisome proliferator-activated receptors (PPARs) and liver X receptors (LXR), activated respectively by fatty acids and cholesterol metabolites (Glass and Saijo, 2010). These ligand-dependent transcription factors are anti-inflammatory and link metabolism and tissue inflammation. Recent findings have uncovered a general view of the various regulatory mechanisms establishing differential gene-specific patterns of primary and secondary gene expression after LPS stimulation in macrophages. These studies have determined the role of transcription factors, chromatin modifications and structure in gene regulation from transcription start sites and proximal promoter elements, or from enhancers, with microarray data generating genome-wide expression patterns, global chromatin immunoprecipitation experiments (ChIPon-ChIP), real-time PCR analysis and massively parallel sequencing.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.103
GPT teacher head0.372
Teacher spread0.269 · 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.

Study designBench or experimental
Domainnot available
GenreOther

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

Citations3
Published2011
Admission routes2
Has abstractyes

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