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Record W2761115149

Abstract 15641: Profiling the Altered Cellular and Secreted Proteome of Cardiac Fibroblasts in Hypoxia Through Label-free Quantitation

2016· article· en· W2761115149 on OpenAlexaff
Jake Cosme, Hongbo Guo, Andrew Emili, Anthony O. Gramolini

Bibliographic record

VenueCirculation · 2016
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProteomeCardiac fibrosisCell biologyHypoxia (environmental)SecretionMedicineParacrine signallingPathophysiologyFibrosisMolecular biologyBiologyBiochemistryChemistryPathologyInternal medicineReceptor
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Cardiac fibroblasts (CF) are key contributers to pathophysiology of the heart after ischemic injury and disease progression, such as fibrosis, which ultimately lead to heart failure Cardiomyocyte (CM) and CF intercellular communication can occur through paracrine interactions and modulate to myocyte stress response. In addition to soluble factors, cardiac cells secrete exosomes (EXO), with evidence suggesting CF EXO modulate pathophysiology in vitro . Detailed proteomic analysis of the fibroblast secretome in normal and stressed conditions will offer insights into the role of CF in heart disease. Methods: Primary mouse CF were cultured for 24h in 21% (normoxic) or 2% (hypoxic) O 2 for 24h in serum-free media. Conditioned media was differentially centrifuged and ultracentrifuged to obtain EXO and EXO-depleted secretome (SEC) fractions. Successful EXO isolation was verified biochemically and via electron microscopy. 6-step MuDPIT was performed on four biological replicates in duplicate. Whole cell lysate data (WCL) was also generated to provide subcelluar context to the CF secretome. Data was searched using multi-search algorithm platform. Protein relative abundance was compared using QSpec. Results: Proteomic analysis identified 6163 unique proteins in total, with 5655, 1752, and 715 in normoxic WCL, EXO, and SEC, respectively and 5158, 1616, and 1042 in hypoxic WCL, EXO, and SEC, respectively. QSpec analysis identified 494 proteins differentially expressed between normoxic fractions, 430 proteins between hypoxic fractions, 122 proteins between normoxic and hypoxic SEC, and 144 proteins between normoxic and hypoxic EXO. Gene Ontology revealed hypoxic conditions increase expression of ECM and signalling annotations, suggesting an activated secretory phenotype. Proteins enriched in EXO and in SEC were associated with cytoskeleton and glycoprotein annotations, respectively. For functional assessment, we subjected cardiomyocytes pretreated with either CF EXO or SEC for 24h, to 60 μM H 2 O 2 for 24h to mimic oxidative stress. Viability assays suggest reduced viability due to CF-derived secreted factors. Conclusions: CF secretome proteomics reveal differential expression based on mode of secretion and oxygen-levels in vitro .

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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.271
Teacher spread0.247 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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