Search bioRxiv⌕ Search

bioRxiv · 10.1101/2023.12.15.569064

Bioactive coatings on 3D printed polycaprolactone scaffolds for bone regeneration: a novel murine femur defect model for examination of the biomaterial capacity for repair.

Abstract

Bone tissue engineering is a rapidly advancing field that seeks to develop efficacious approaches for treating non-healing fractures and large bone defects. Healing complications arise due to trauma, disease, infection, aseptic loosening of orthopaedic implants or iatrogenic causes. An ideal biodegradable scaffold would induce and support bone formation until the bone matrix is sufficiently stable to facilitate healing. The current study has examined bone augmentation, using functionalised coated scaffolds, with the hypothesised potential to induce skeletal cell differentiation for the repair of critical-sized bone defects. However, challenges in clinical translation arise from the alterations in cellular microenvironment that are present in the translation from in vitro to in vivo with the application of animal models of progressively increasing size and complexity of the implantation site. 3D printed, porous poly(caprolactone) trimethacrylate (denoted PCL-TMA900) scaffolds were applied within a murine femur defect, stabilised by a polyimide intramedullary pin, to assess the efficacy of select coatings in inducing bone formation. The PCL-TMA900 scaffolds were coated with i) elastin-like polypeptide (ELP), ii) poly(ethyl acrylate)/fibronectin/bone morphogenetic protein-2 (PEA/FN/BMP-2), iii) both ELP and PEA/FN/BMP-2 concurrently, or iv) Laponite nanoclay binding BMP-2, as bioactive coatings. The murine femur defect model was refined to assess the coated PCL-TMA900 scaffolds in an osseous defect, with sequential microcomputed tomography ({micro}CT) and histological analysis of the new bone tissue. Overall, PCL-TMA900 was found to be an optimal robust, biocompatible, 3D printable scaffold material. All PCL-TMA900 scaffolds, uncoated and coated, showed integration with the femur. The PCL-TMA900 scaffold coated with the nanoclay material Laponite and BMP-2 induced consistent, significant bone formation compared to the uncoated PCL-TMA900 scaffold. Bone formation was observed within the pores of the Laponite/BMP-2 coated scaffold. Critically, no heterotopic bone formation was observed as the BMP-2 was retained around the scaffold and not released into the tissues, producing bone around the scaffold in the desired shape and volume, compared to bone formation observed with the positive control (collagen sponge/BMP-2 construct). In comparison, the ELP coated and PEA/FN/BMP-2 scaffolds did not demonstrate significant or consistent bone formation compared to uncoated PCL-TMA900 control scaffolds. In summary, nanoclay Laponite/BMP-2 coated PCL-TMA900 scaffolds offer a biodegradable, osteogenic construct for bone augmentation with potential for development into a large scale polymer scaffold for translation to the clinic.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Marshall, K. M., Wojciechowski, J. P., Jayawarna, V., Hasan, A., Echalier, C., Callens, S. J. P., Yang, T., Kanczler, J. M., Dawson, J. I., Mata, A., Salmeron-Sanchez, M., Stevens, M. M., Oreffo, R. O. C.. 2023-12-15. Bioactive coatings on 3D printed polycaprolactone scaffolds for bone regeneration: a novel murine femur defect model for examination of the biomaterial capacity for repair.. https://doi.org/10.1101/2023.12.15.569064

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↗