Search bioRxiv⌕ Search

bioRxiv · 10.1101/318931

Bone remodeling: A tissue-level process emerging from cell-level molecular algorithms

Abstract

Human skeleton undergoes constant remodeling during the whole life. By means of such process, which occurs at a microscopic scale, worn out bone is replaced by new, fully functional one. Multiple bone remodeling events occur simultaneously and independently throughout the body, so the whole skeleton is completely renewed about every ten years.\n\nBone remodeling is performed by groups of cells called Bone Multicellular Units (BMU). BMUs consist of different cell types; some are specialized in destroying old bone, whereas others produce new bone to replace the former. The whole process is tightly regulated so that the amount of new bone produced exactly balances that of old one removed and bone microscopic structure is maintained.\n\nTo date, many regulatory molecules involved in bone remodeling have been identified, but the precise mechanism of BMU operation remains to be fully elucidated. Given the complexity of the signaling pathways already known, the question arises of ascertaining whether such complexity is an inherent requirement of the process, or a consequence of operational redundancy.\n\nIn this work we propose a minimal model of BMU function which involves a small number of signals and accounts for fully functional BMU operation. Our main assumptions are i) at any given time, any cell within a BMU can select only one among a reduced choice of decisions: divide, die, migrate or differentiate, ii) such decision is irreversibly determined by depletion of an appropriate internal inhibitor and iii) the dynamics of any such inhibitor is coupled to that of a few external mediators. It is shown that efficient BMU operation then unfolds as an emergent property, which results from individual decisions taken by cells in the BMU unit in the absence of any external planning.\n\nAuthor summaryOur skeleton is a living organ that is being renewed throughout our life. This task is accomplished by teams of bone cells termed as Bone Multicellular Units (BMUs) that are recruited when and where needed, to operate at places where bone has lost functionality either for an excess of mechanical stress or because loss of activity. Once assembled, BMU remove old bone and replace it by new one, and disband as soon as their mission has been accomplished. No single bone evades BMU screening, so that the whole human skeleton is completely renewed approximately every ten years.\n\nIt is natural to wonder how such robust and fascinating process is regulated. Many signaling pathways involved in bone remodeling have been identified so far, but whether all of them are necessary for BMU operation remains unclear. In this work we show that just a reduced number of such signals could suffice for that purpose. This suggests that a large degree of redundancy might have been kept in place, perhaps as a consequence of different convergent strategies developed in the course of evolution.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Arias, C. F., Herrero, M. A., Echeverri, L. F., Oleaga, G. E., Lopez, J. M.. 2018-05-10. Bone remodeling: A tissue-level process emerging from cell-level molecular algorithms. https://doi.org/10.1101/318931

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

KEEP EXPLORING

Related preprints

Uncovering disease progression trajectories across multiple timescales with hybrid ODE-mixture models

Biological systems are capable of evolving across multiple timescales, yet most modeling approaches capture either short-term physiological responses or long-term disease progression. Additionally, methods that combine these time scales either struggle with inter-individual variation, or require large volumes of data to work. We present Latent Class Parameter Trajectory Analysis (LCPTA), a hybrid framework that combines mechanistic ordinary differential equation (ODE) modeling of short-term dynamics with latent class trajectory modeling of long-term parameter evolution. To support this approach, we developed TemporalMixtureModels.jl, a Julia package for univariate and multivariate temporal mixture modeling. We evaluated LCPTA in two synthetic longitudinal studies, covering a simulation of a heterogeneous population developing hypertension, as well as a synthetic longitudinal study of 75 virtual individuals followed over 30 years, representing muscle insulin resistance, liver insulin resistance, and healthy trajectories. We compared LCPTA with a conditional neural ODE and a conditional neural network for reconstruction of long-term parameter trajectories and subgroup separation. In both studies LCPTA recovered relevant subgroup structure and trajectory patterns. In the cardiovascular study, the neural approaches were able to distinguish inter-individual differences, but in the insulin sensitivity study, neural approaches failed to represent parameter trajectories faithfully, primarily separating individuals along a single dominant parameter and showing weaker multivariate trajectory reconstruction. In additional experiments with sparse and irregularly sampled unseen individuals, LCPTA maintained strong classification performance. We show that LCPTA is an efficient and interpretable strategy for multiscale modeling of heterogeneous metabolic disease progression, with potential utility for data-efficient digital twin applications.

systems biology↗

The Global Phosphorylation Landscape of Acute Heat Stress in Reef-Building Corals

Warming oceans disrupt the coral-algal symbiosis that sustains reefs, yet the signaling mechanisms initiating bleaching remain poorly understood. Integrating global phosphoproteomics across three reef-building corals with protein structure modeling revealed conservation between coral and human kinases, enabling inference of coral kinase activities and uncovering a shared signaling program induced by acute heat stress. Heat rapidly activated ERK, JNK, and p38 MAP kinases, suppressed cell cycle signaling, and remodeled pathways controlling chromatin, RNA processing, and metabolism. Most strikingly, thermal stress rewired immune receptor signaling networks associated with non-self recognition, suggesting that heat shifts the coral host from tolerance to immune recognition of its algal symbionts. These findings define conserved signaling mechanisms that precede bleaching, revealing actionable molecular targets for enhancing coral resilience.

systems biology↗

Estimating glioblastoma migration-to-proliferation ratio from single time-point MRI data via radial density distribution

Glioblastoma (GBM) tumors with a high migration-to-proliferation ratio (D/k) are associated with treatment resistance, motivating the estimation of D/k from routine pre-treatment magnetic resonance imaging (MRI) data and the investigation of mechanisms underlying its heterogeneity across patients. Unlike previous approaches based on nested spherical approximations of enhancing and non-enhancing tumor volumes, we incorporate the spatial arrangement of the enhancing and non-enhancing tumor regions by deriving a 1D tumor density distribution from their segmentations and fitting a 1D reaction-diffusion model to estimate D/k. We achieved an error of under 5% when comparing our method to synthetic density distributions and found excellent correlation with synthetic tumor segmentations (Pearson correlation coefficient (PCC) = 0.996). We then evaluated associations between patient-specific D/k values and their clinical, transcriptomic, and radiomic features. When applied to 102 pre-treatment MRI scans from the Brain Tumor Segmentation (BraTS) 2017 dataset, elevated D/k significantly correlated with reduced overall survival (log-rank p = 0.01), older age (p = 0.04), and a trend toward lower Karnofsky Performance Status (p = 0.059). In a subset of 91 patients with available microarray data, mesenchymal-like phenotypes consistently exhibited elevated D/k across three established molecular subtype classifications (p = 0.013, p = 0.055, and p = 0.127). Elastic Net regularization followed by overrepresentation analysis revealed an enrichment of glycosylation-related pathways among genes associated with D/k variability (p = 0.0123 and p = 0.0368). Furthermore, Multi-Omic Factor Analysis identified a radiomic factor correlated with D/k (r = 0.57, p = 4x10-9), with the highest-weighted features corresponding to smoothness of the non-enhancing tumor region on T2 and FLAIR.

systems biology↗