Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.07.05.731787

Intramuscular Delivery of BMP-2 and Increasing Doses of LECT-1 Using Keratin-PEG Gels for Ectopic Tissue Differentiation

Abstract

Producing bone and cartilage in a controlled and localized manner remains a significant challenge in regenerative medicine. This study investigated the ability of keratin- and polyethylene glycol (PEG)-based degradable hydrogels to deliver bone morphogenetic protein 2 (BMP-2) and leukocyte cell-derived chemotaxin 1 (LECT-1; also known as chondromodulin-1) intramuscularly to induce ectopic tissue formation. Adult male CD-1 mice received intramuscular implants of keratin-PEG gels containing a fixed dose of BMP-2 and increasing amounts of LECT-1. After two weeks, implants and surrounding muscle were analyzed using computed tomography (CT) and histology. The results showed that BMP-2 is necessary for forming new bone and cartilage, whereas LECT-1 alone appeared to trigger muscle dedifferentiation without ossification or chondrogenesis. Co-delivery of BMP-2 and LECT-1 enhanced bone and cartilage formation in a dose-dependent manner: higher LECT-1 doses led to proportionally more ectopic cartilage (linear correlation, r2 {approx} 90%), while bone formation peaked at the third LECT-1 dose at approximately twice the volume of the BMP-2-only group. These findings indicate that muscle-resident cells may be capable of reverting and switching to mesenchymal lineages, recapitulating endochondral ossification. The platform offers a promising strategy for growing bone and cartilage autografts within skeletal muscle bundles. Impact StatementThis study presents a novel strategy for inducing ectopic bone and cartilage formation by delivering BMP-2 and LECT-1 from keratin-PEG hydrogels into skeletal muscle. The findings suggest that muscle-resident cells can dedifferentiate and transdifferentiate into mesenchymal lineages, enabling controlled ectopic tissue regeneration. This approach may inform future tissue engineering therapies in which autografts are harvested from within the body, using skeletal muscle as an in vivo tissue incubator.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mathews, A., Fisher, L., Saparova, D., Cevahir, A., Meer, A., Radecker, N., de Guzman, R. C.. 2026-07-06. Intramuscular Delivery of BMP-2 and Increasing Doses of LECT-1 Using Keratin-PEG Gels for Ectopic Tissue Differentiation. https://doi.org/10.64898/2026.07.05.731787

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

KEEP EXPLORING

Related preprints

RNASeek: A Cross-Phyla Generative Foundation Model for Multipurpose RNA Modeling and Reinforcement Learning-Based Design

RNA plays central roles in regulating information flow and provides a versatile substrate for engineering biological functions. While large language models (LLMs) have transformed natural language processing and protein design, a general framework connecting RNA foundation models to functional sequence design remains limited. Here, we present RNASeek, a 1.6-billion-parameter generative foundation model built on a DeepSeek architecture and trained on a cross-phyla transcriptomic corpus for RNA sequence representation and generation. Natural-language tokens enable flexible conditional prediction and sequence design using a unified backbone. RNASeek captures species-specific transcript features and intron-exon boundaries in a zero-shot setting. We then fine-tune RNASeek to predict ribozyme self-cleavage activity and viral mRNA stability, revealing interpretable sequence features associated with function, including ribozyme loop flexibility and stem stability, as well as AU-rich motifs associated with mRNA stability. We use these functional predictors as reward models and apply Group Relative Policy Optimization (GRPO) to update the generation policy of RNASeek toward sequences with desired properties. GRPO-guided generation produces faster-cleaving ribozymes and stability-enhancing 3' UTRs while satisfying user-specified IUPAC constraints. Experimentally validated RNASeek-generated ribozymes achieve wild-type levels of activity, while RNASeek-generated 3' UTR sequences exceed the performance of the training data and benchmarked AI-generated 3' UTRs. Together, RNASeek establishes a unified pretrain-predict-optimize framework that connects learned RNA function to controllable de novo sequence design and provides a general strategy for engineering regulatory RNAs with desired properties.

bioengineering↗

Joint Vector Flow Mapping and Segmentation: Ill-Posedness,Differentiable Bayesian Inference, and Synthetic Vortex-FlowBenchmarks

Vector flow mapping (VFM) reconstructs left-ventricular (LV) blood velocity from color-Doppler echocardiography by combining the measured beamwise component with physical and regularizing constraints. Analysis of the discrete VFM formulation shows that the inverse problem is intrinsically ill posed: the occurrence of singular modes can be predicted from the geometry of the segmented blood-pool domain, the imposed boundary conditions, and the degree of smoothing. These modes can propagate uncertainty along entire transverse bands of the reconstructed velocity field, yet conventional VFM neither quantifies this uncertainty nor allows for correcting the blood-pool segmentation. We introduce Bayesian VFM (B--VFM), a hierarchical framework that jointly infers radial and transverse velocities, a probabilistic blood-pool mask, their spatially resolved uncertainties, and hyperparameters weighting Doppler and segmentation fidelity, mass conservation, boundary conditions, and smoothness. The discretized posterior admits a closed-form gradient and exact Hessian, enabling computationally efficient, gradient-based MAP estimation, sampling, and direct analysis of ill-posed modes. Posterior inference combines Gibbs sampling of conjugate Gamma-distributed hyperparameters with conditional maximum-a-posteriori estimation and a Laplace approximation for the high-dimensional velocity and mask fields. To accommodate systematic departures from planar mass conservation, B-VFM can learn the covariance of the planar divergence residual from an ensemble of flows and incorporate it as a structured model-discrepancy prior. Independent chains converged reproducibly, while covariance priors learned from flow ensembles illustrated how model discrepancies can be incorporated into the inference. B--VFM was evaluated using Lamb-Chaplygin dipoles under ideal conditions and with Doppler corruption, Doppler voids, and segmentation defects, and using the Hicks-Moffatt family of spherical vortices to assess violations of planar mass conservation. The method produced smooth reconstructions, localized uncertainty near unreliable measurements and regions of model inconsistency, and used flow information to correct segmentation errors. Within the tested vortex family, the data-informed planar divergence prior reduced velocity bias and mask distortion. B--VFM thus provides an uncertainty-aware reconstruction method and a flexible foundation for future VFM formulations incorporating additional priors, observations, and physical models. Future work will evaluate the method using clinical data and more complex three-dimensional benchmark flows.

bioengineering↗

Computational design of a versatile, zero-radius proximity labeling enzyme

The ability to map protein interactomes and organelle proteomes is foundational for achieving a molecular understanding of living cells. Proximity labeling (PL) provides a powerful strategy for this, but existing enzymes and photocatalysts are limited by their spatial resolution, reliance on biotin, and/or in vivo compatibility. Here we report FlexID, an engineered promiscuous ligase that catalyzes the rapid attachment of diverse small-molecule probes to proximal endogenous proteins. Critically, FlexID operates through a zero-radius, direct-contact mechanism, offering superior spatial precision compared to existing PL tools. We engineered FlexID by combining the strengths of sequence- and structure-trained computational models to enhance its catalytic activity and structural stability. Biophysical analysis revealed that specific conformational changes in FlexID improve its ability to recognize diverse target proteins while simultaneously preventing the premature release of the reactive intermediate. We demonstrate FlexID's versatility through in vivo proximity labeling, comprehensive organelle proteome mapping, and a high-throughput, fluorescence-based screen for molecular glues. Our work shows that computational methods can be harnessed to create mechanistically distinct PL enzymes and establishes FlexID as a flexible, high-resolution tool for mapping protein interactions and proteomes in living cells.

bioengineering↗