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Record W2762891745 · doi:10.24870/cjb.2017-a166

miRNA-mRNA integrative expression mapping during mouse embryonic stem cell to Neuron progenitor differentiation

2017· article· en· W2762891745 on OpenAlexvenueno aff
Rakhee Nayak, Vanita Uppada, Sreenivasulu Kurukuti

Bibliographic record

VenueCanadian Journal of Biotechnology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsnot available
Fundersnot available
KeywordsEmbryonic stem cellProgenitor cellmicroRNABiologyProgenitorStem cellCell biologyCellular differentiationMessenger RNAGeneticsGene

Abstract

fetched live from OpenAlex

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.

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 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 score0.010
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.234
Teacher spread0.222 · 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.

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
Published2017
Admission routes1
Has abstractyes

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