Meta‐Analysis of Transcriptome Regulation During Induction to Cardiac Myocyte Fate From Mouse and Human Fibroblasts
Bibliographic record
Abstract
Ectopic expression of a defined set of transcription factors (TFs) can directly convert fibroblasts into a cardiac myocyte cell fate. Beside inefficiency in generating induced cardiomyocytes (iCMs), the molecular mechanisms that regulate this process remained to be well defined. The main purpose of this study was to provide better insight on the transcriptome regulation and to introduce a new strategy for candidating TFs for the transdifferentiation process. Eight mouse and three human high quality microarray data sets were analyzed to find differentially expressed genes (DEGs), which we integrated with TF-binding sites and protein-protein interactions to construct gene regulatory and protein-protein interaction networks. Topological and biological analyses of constructed gene networks revealed the main regulators and most affected biological processes. The DEGs could be categorized into two distinct groups, first, up-regulated genes that are mainly involved in cardiac-specific processes and second, down-regulated genes that are mainly involved in fibroblast-specific functions. Gata4, Mef2a, Tbx5, Tead4 TFs were identified as main regulators of cardiac-specific gene expression program; and Trp53, E2f1, Myc, Sfpi1, Lmo2, and Meis1 were identified as TFs which mainly regulate the expression of fibroblast-specific genes. Furthermore, we compared gene expression profiles and identified TFs between mouse and human to find the similarities and differences. In summary, our strategy of meta-analyzing the data of high-throughput techniques by computational approaches, besides revealing the mechanisms involved in the regulation of the gene expression program, also suggests a new approach for increasing the efficiency of the direct reprogramming of fibroblasts into iCMs. J. Cell. Physiol. 232: 2053-2062, 2017. © 2016 Wiley Periodicals, Inc.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".