miRNA-mRNA integrative expression mapping during mouse embryonic stem cell to Neuron progenitor differentiation
Bibliographic record
Abstract
Genome-wide transcriptome profiling of mammalian cells has given more insight into complexity of RNA world. Vast majority of RNAs are falling under non-coding category, among which ~22 nucleotides RNA is known as microRNA. It regulates gene expression by repressing translationally or cleaving mRNAs. 6-8 nucleotides region at 5’ end of miRNAs bind to 3’ UTR region of mRNA called ‘seed region’. This binding is either completely or incompletely complementary. Complete complementarity associates with RNA Induced Silencing Complex (RISC) that able to degrade mRNA with the help of Argonaute protein (Ago). But, incomplete complementarity is believed to be in a type of poised state that can be removed during requirement of that particular protein synthesis. mRNA translational repression by miRNA leads to subsequent mRNA destabilization. These dynamics of miRNA and mRNA integrative expression was further studied in embryonic stem cell to Neuron Progenitor differentiation system. Upon retinoic acid treatment to mouse embryonic stem cell line (R1), without LIF and 2i inhibitors that maintain undifferentiating state in ESC, cells were differentiated to neuronal cell lineage. To study, the dynamic changes in expression during differentiation we performed both microRNA and mRNA sequencing separately in these two states with biologically duplicated samples. Interestingly, we came up with 82 differentially regulated miRNAs, 9 lncRNAs and 4336 mRNA genes during this phage. Along with these 31 novel miRNAs were identified, among them 17 were identified that specific to ESC and 14 were in NP. Interaction maps of miRNA-mRNA was confirmed the down regulation of targeted mRNAs due to upregulated miRNAs during differentiation. KEGG pathways analysis of these genes showed downregulation of signaling pathways regulating pluripotency of stem cells (by miR-466m-3p, miR-466k, miR-1198 and miR344e) and upregulation of axon guidance and Neurotrophin signaling pathways that involves in differentiation of neuronal cells. These studies reveal genome wide miRNAs and mRNAs interaction map in ESC to NP differentiation. That will help in understanding of controlled gene expression patterns during neuronal differentiation due to miRNAs and lncRNAs parallel expressions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".