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Abstract 4140151: Network Analysis of Differentially Expressed Genes after Silencing Dynamin 2 in Pulmonary Arterial Hypertension

2024· article· en· W4404246373 on OpenAlexaff
Danchen Wu, Asish Das Gupta, Kuang‐Hueih Chen, Charles C.T. Hindmarch, Ting Hu, Stephen L. Archer

Bibliographic record

VenueCirculation · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineGene silencingDynaminGeneCardiologyInternal medicineGeneticsEndocytosisReceptorBiology

Abstract

fetched live from OpenAlex

Rationale: Pulmonary arterial hypertension (PAH) is a rare cardiopulmonary disease that can cause right heart failure and even death. In PAH, excessive mitochondrial fission is responsible for the hyperproliferation of pulmonary artery smooth muscle cells (PASMC), which is incompletely mediated by the large GTPase dynamin-related protein 1 (Drp1). We hypothesize that another GTPase, Dynamin 2 (DNM2), elevated in PASMC of PAH patients, is responsible for the final stage of mitochondrial fission. Objectives: 1. Identify the differentially expressed gene (DEG) signature in PAH PASMC after silencing DNM2 using small interfering RNA (siRNA) by means of RNA sequencing (RNA-seq); 2. Identify the biological processes or pathways associated with these DEGs. Methods: Human PAH PASMCs (n=5 cell line) were transfected with control siRNA or siRNA targeting DNM2. 3’RNA-seq was used to create libraries which we sequenced on the Illumina Nextseq550. Bioinformatic analysis revealed DEGs which we placed into context using the STRING: protein query database and visualized using Cytoscape 3.10.2. Network functional enrichment analysis was performed using STRING. Results: Inhibiting DNM2 significantly reduced mitochondrial fission and slowed PAH PASMC proliferation. RNA-seq identified 156 DEGs (80 down-regulated, 56 up-regulated). The top 5 down-regulated DEGs were DNM2 , RGCC , TICAM1 , RASSF3 , MRPL39 and the top 5 up-regulated DEGs were ITIF1 , ARL6IP1 , POLR3G , CBX1 , CMPK2 . We tested the biologic relevance of RGCC which encodes the Regulator of Cell Cycle using siRNA and showed inhibition of cell proliferation in human PAH PASMC. Network analysis identified 152 edges amongst all DEGs. The largest subnetwork contains 77 nodes and 151 edges. PXDN , which encodes Peroxidasin, a heme-containing peroxidase that is secreted into the extracellular matrix, has the highest degree of connectivity (15 connections), the highest betweenness centrality (0.266), and the highest closeness centrality (0.441). Enrichment analysis found that the p53 signaling pathway and the cytokine signaling in the immune system are the top KEGG and Reactome pathways affected by siDNM2, respectively. Conclusions: DNM2 appears to be an important regulator of the increased mitochondrial fission in PAH. Silencing DNM2 not only inhibits fission but also slows cell proliferation, apparently by reducing the expression of RGCC. Silencing DNM2 in human PAH PASMC results in the disruption of 136 genes.

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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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