PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 1010155

Morphogenesis of lines and nets.

H Meinhardt
View original source ↗Export metadata ↓

Abstract

Complex linear appearing structures and networks (e.g. blood vessels, leaf veins, nerves) are formed reproducibly during the development of nearly every organism, but the molecular mechanism leading to such patterns is still unknown. A model is proposed in which a few simple coupled biochemical reactions are able to generate such structures. Among undifferentiated cells, a local peak of differentiation-inducing substance (activator) is formed by autocatalysis and lateral inhibition. The activator peak triggers the differentiation of the cell at that location. Due to changes in metabolism, the differentiated cell repels the activator peak and drives it to a neighbouring cell which then also differentiates. The repulsion between the activator peak and the already differentiated cells forces the activator peak to move ahead of the tip of the extending filament. Long filaments of differentiated cells may be formed, which can split, branch laterally, reconnect with each other and grow towards specific target cells. Partial differential equations describing the mutual interaction of the substances involved were presented and solved with a computer. The resulting patterns show self-regulating properties and other features found in the leaf vascular system, the pattern of tracheae in insect epidermis, and other biological networks.

Explore related subjects

Cell DifferentiationCell MovementCellsGrowth SubstancesMathematicsModels, BiologicalMorphogenesisPlant DevelopmentJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

H Meinhardt. 1976-08-03. Morphogenesis of lines and nets.. https://doi.org/10.1111/j.1432-0436.1976.tb01478.x

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↗