Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2311.15662

The basement membrane in epidermal polarity, stemness, and regeneration

Patricia Rousselle·Chloé Laigle·Gaelle Rousselet
View original source ↗Read source document ↗Export metadata ↓

Abstract

The epidermis is a specialized epithelium that constitutes the outermost layer of the skin, and it provides a protective barrier against environmental assaults. Primarily consisting of multilayered keratinocytes, the epidermis is continuously renewed by proliferation of stem cells and the differentiation of their progeny, which undergo terminal differentiation as they leave the basal layer and move upward toward the surface, where they die and slough off. Basal keratinocytes rest on a basement membrane at the dermal-epidermal junction that is composed of specific extracellular matrix proteins organized into interactive and mechanically supportive networks. Firm attachment of basal keratinocytes, and their dynamic regulation via focal adhesions and hemidesmosomes, is essential for maintaining major skin processes, such as self-renewal, barrier function, and resistance to physical and chemical stresses. The adhesive integrin receptors expressed by epidermal cells serve structural, signaling, and mechanosensory roles that are critical for epidermal cell anchorage and tissue homeostasis. More specifically, the basement membrane components play key roles in preserving the stem cell pool, and establishing cell polarity cues enabling asymmetric cell divisions, which result in the transition from a proliferative basal cell layer to suprabasal cells committed to terminal differentiation. Finally, through a well-regulated sequence of synthesis and remodeling, the components of the dermal-epidermal junction play an essential role in regeneration of the epidermis during skin healing. Here too, they provide biological and mechanical signals that are essential to the restoration of barrier function.

Explore related subjects

q-bio.BM

Keep this discovery

BibTeXRIS

Patricia Rousselle, Chloé Laigle, Gaelle Rousselet. 2023-11-27. The basement membrane in epidermal polarity, stemness, and regeneration. https://doi.org/10.1152/ajpcell.00069.2022

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

KEEP EXPLORING

Related papers

arXiv2026

Sequence-Informed Geometric Evaluation of RNA 3D Structures

Computational RNA structure pipelines generate many candidate conformations for the same sequence. Reliable evaluation therefore requires more than recognising plausible geometry, it requires determining whether that geometry is compatible with the sequence. We introduce SIRGE, a sequence-informed geometric evaluator that conditions structural representations on nucleotide embeddings from a pretrained RNA language model. Early results show that SIRGE outperforms established evaluators in Kendall--$\tau$ alignment, Top-1 selection, and Top-3 ranking. Controlled comparisons further show that sequence conditioning corrects errors made by an otherwise matched geometric model and improves target-level rank structure. These findings provide initial evidence that pretrained sequence representations supply ranking information that complements geometric reasoning.

q-bio.BM↗
arXiv2026

PocketVE: Stable and Property-Guided Structure-Based Drug Design with Variance-Exploding Diffusion

Protein-conditioned 3D molecule generation is a central challenge in structure-based drug design, requiring a balance between pocket compatibility, molecular properties, and physical geometry. We propose \textbf{PocketVE}, a protein-pocket-conditioned variance-exploding (VE) diffusion framework that couples stable coordinate denoising with inference-time property guidance. Specifically, PocketVE combines an EDM-style training and sampling setup for 3D denoising, classifier-free guidance for multi-property steering without external property classifiers, and adaptive protein perturbation as a training-time pocket regularizer. Evaluated on CrossDocked2020 under the GenBench3D protocol, PocketVE improves Valid$_{3\text{D}}$ from 58.6 to 80.6 and reduces strain energy from 457.4 to 127.9 relative to its TAGMol architectural baseline, while retaining competitive docking and molecular-property scores under moderate guidance. A guidance-scale study shows that moderate guidance gives a favorable balance between target-related objectives and geometric quality, whereas stronger guidance can degrade geometry and distributional fidelity. Pocket-permutation and PoseCheck diagnostics further support pocket-specific spatial compatibility with reduced steric conflicts. Overall, the results suggest that geometric stability and inference-time property guidance should be considered as coupled design objectives.

q-bio.BM↗
arXiv2026

Predicting directional flexibility in proteins

Predicting protein dynamics is a long-standing problem in computational structural biology. Often, protein function critically depends on local directed motions, such as hinge movements, catalytic loop rearrangements and domain reorientations, which can be characterized by directional flexibility and correlated structural motions of the protein backbone. While Molecular Dynamics (MD) simulations provide an established but often prohibitively expensive approach, recent deep generative models aim to reduce this cost by directly predicting conformational ensembles, emulating MD. However, due to their large size and the need to generate several states until the derived dynamical properties converge, these models remain expensive. In this work, we propose BackFlip-2: a fast SE(3)-equivariant graph neural network trained to directly predict dynamical descriptors, such as directional backbone flexibility and pairwise dynamic correlations, from an equilibrium structure. In a series of experiments, we show that our model matches the accuracy of substantially larger ensemble generation models while being orders of magnitude faster, and demonstrate that the proposed equivariant architecture is especially well-suited for capturing anisotropic motions in proteins. BackFlip-2 model weights, training and inference code are available at https://github.com/graeter-group/backflip.

q-bio.BM↗