PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 12844347

Thyroid hormone responsiveness in N-Tera-2 cells.

S Chan·C J McCabe·T J Visser·J A Franklyn·M D Kilby
View original source ↗Export metadata ↓

Abstract

N-TERA-2 cl/D1 (NT2) cells, a human embryonal cell line with characteristics of central nervous system precursor cells, were utilised to study thyroid hormone action during early neuronal growth and differentiation. Undifferentiated NT2 cells expressed mRNAs encoding thyroid hormone receptors (TRs) alpha1, alpha2 and beta1, iodothyronine deiodinases types 2 (D2) and 3 (D3) (which act as the pre-receptor regulators), and the thyroid hormone-responsive genes myelin basic protein (MBP) and neuroendocrine specific protein A (NSP-A). When terminally differentiated into post-mitotic neurons (hNT), TRalpha1 and TRbeta1 mRNA expression was decreased by 74% (P=0.05) and 95% (P<0.0001) respectively, while NSP-A mRNA increased 7-fold (P<0.05). However, mRNAs encoding TRalpha2, D2, D3 and MBP did not alter significantly upon neuronal differentiation and neither did activities of D2 and D3. With increasing 3,5,3'-triiodothyronine (T(3)) concentrations, TRbeta1 mRNA expression in cultured NT2 cells increased 2-fold at 10 nM T(3) and 1.3-fold at 100 nM T(3) (P<0.05) compared with that in T(3)-free media but no change was seen with T(3) treatment of hNT cells. D3 mRNA expression in NT2 cells also increased 3-fold at 10 nM T(3) (P=0.01) and 2.4-fold at 100 nM T(3) (P<0.05) compared with control, but there was no change in D3 enzyme activity. In contrast there was a 20% reduction in D3 mRNA expression in hNT cells at 10 nM T(3) (P<0.05) compared with control, with accompanying reductions in D3 activity with increasing T(3) concentrations (P<0.05). There was no significant change in the expression of the TRalpha isoforms, D2, MBP and NSP-A with increasing T(3) concentrations in either NT2 or hNT cells. Undifferentiated NT2 and differentiated hNT cells show differing patterns of T(3)-responsiveness, suggesting that there are different regulatory factors operating within these cell types.

Explore related subjects

Cell DifferentiationCell LineCentral Nervous SystemDose-Response Relationship, DrugGene ExpressionHomeostasisHumansIodide PeroxidaseIsoenzymesMyelin Basic ProteinNerve Tissue ProteinsNeuronsPluripotent Stem CellsRNA, MessengerReverse Transcriptase Polymerase Chain ReactionStatistics, NonparametricThyroid HormonesTriiodothyronineJournal ArticleResearch Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

S Chan, C J McCabe, T J Visser, J A Franklyn, M D Kilby. 2003. Thyroid hormone responsiveness in N-Tera-2 cells.. https://doi.org/10.1677/joe.0.1780159

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

FIERCE: reconstructing dynamic trajectories from the differentiation potency of single cells.

MOTIVATION: Since the introduction of single-cell RNA sequencing (scRNA-seq), numerous computational approaches have been developed to reconstruct dynamic cellular processes from static transcriptional profiles. These methods order cells along continuous trajectories by assessing their similarity in the gene-expression space. However, they rely on several assumptions, such as prior knowledge of the structure and directionality of the expected genealogy. These assumptions can limit their application to complex cellular systems with poorly understood developmental paths. RESULTS: To address this challenge, we introduce FIERCE (Framework for InfERence of the veloCity of Entropy), a novel computational pipeline designed to predict the changes in the differentiation potency of single cells during dynamic processes. Through a fully unsupervised approach, FIERCE enables the inference of cell lineages directly on the differentiation landscape of the biological system, thus eliminating the need for prior specification of developmental parameters. We demonstrate the efficacy of FIERCE by reconstructing three well-known mouse differentiation systems and by quantifying its accuracy on simulated data. AVAILABILITY AND IMPLEMENTATION: The FIERCE R package is available on GitHub at https://github.com/bicciatolab/FIERCE.

Cell Differentiation↗
PubMed2025

mRNA turnover dynamics are affected by cell differentiation and loss of the cytosine methyltransferase Nsun2.

Nsun2 catalyzes 5-methylcytosine (m5C) formation in several types of RNA, including messenger RNAs (mRNAs), transfer RNAs, and other non-coding RNAs. In mRNA, m5C was reported to influence transcript stability. However, it is unclear if it has stabilizing or destabilizing effects. To address the role of Nsun2 in mRNA stability, we characterized the landscape of mRNA turnover dynamics during embryonic stem cell (ESC) differentiation in wild-type and Nsun2-mutant cells. By using an RNA labeling approach combined with thiouridine-to-cytidine-sequencing (TUC-seq), we demonstrate that mRNA synthesis and stability undergo extensive changes during normal cellular differentiation. Remarkably, a large proportion of these changes did not result in altered mRNA abundance, providing evidence for robust transcript buffering during ESC differentiation. Importantly, also the loss of Nsun2 affected mRNA turnover dynamics but not the steady-state levels of transcripts. Furthermore, our data indicate that the effect of Nsun2 on mRNA turnover was not mediated by m5C deposition in mRNA, which is corroborated by catalysis-independent effects of Nsun2 on translation in early ESC differentiation. In conclusion, this study demonstrates that differentiation as well as loss of Nsun2 can induce changes in mRNA turnover dynamics that are independent of mRNA methylation but consistent with a buffering mechanism to maintain constant RNA levels.

Cell Differentiation↗
PubMed2025

Glucose modulates IRF6 transcription factor dimerization to enable epidermal differentiation.

Non-energetic roles for glucose are largely unclear, as is the interplay between transcription factors (TFs) and ubiquitous biomolecules. Metabolomic analyses uncovered elevation of intracellular glucose during differentiation of diverse cell types. Human and mouse tissue engineered with glucose sensors detected a glucose gradient that peaked in the outermost differentiated layers of the epidermis. Free glucose accumulation was essential for epidermal differentiation and required the SGLT1 glucose transporter. Glucose affinity chromatography uncovered glucose binding to diverse regulatory proteins, including the IRF6 TF. Direct glucose binding enabled IRF6 dimerization, DNA binding, genomic localization, and induction of IRF6 target genes, including essential pro-differentiation TFs GRHL1, GRHL3, HOPX, and PRDM1. These data identify a role for glucose as a gradient morphogen that modulates protein multimerization in cellular differentiation.

Cell Differentiation↗