Anti-Inflammatory Tension-Activated Repair Patches Improve Repair After Intervertebral Disc Herniation

Peredo, A. P.; Gullbrand, S. E.; Friday, C. S.; Orozco, B. S.; Bonnevie, E. D.; Hilliard, R. L.; Zlotnick, H. M.; Dodge, G. R.; Lee, D.; Hast, M. W.; Schaer, T. P.; Smith, H. E.; Mauck, R.

doi:10.1101/2022.10.22.513360

bioRxiv · 10.1101/2022.10.22.513360

Anti-Inflammatory Tension-Activated Repair Patches Improve Repair After Intervertebral Disc Herniation

Peredo, A. P.·Gullbrand, S. E.·Friday, C. S.·Orozco, B. S.·Bonnevie, E. D.·Hilliard, R. L.·Zlotnick, H. M.·Dodge, G. R.·Lee, D.·Hast, M. W.·Schaer, T. P.·Smith, H. E.·Mauck, R.

View original source ↗Export metadata ↓

Abstract

Conventional treatment for intervertebral disc herniation alleviates pain but does not repair the annulus fibrosus (AF), resulting in a high incidence of recurrent herniation and persistent disfunction. The lack of repair and the acute inflammation that arise after injury further compromises the disc and can result in disc-wide degeneration in the long term. To address this clinical need, we developed tension-activated repair patches (TARPs) for annular repair and the local delivery of bioactive anti-inflammatory factors. TARPs transmit physiologic strains to mechanically-activated microcapsules (MAMCs) embedded within, which activate and release encapsulated biomolecules in response to physiologic loading. Here, we demonstrate that the TARP design modulates implant biomechanical properties and regulates MAMC mechano-activation. Next, the FDA-approved anti-inflammatory molecule, interleukin 1 receptor antagonist, Anakinra, was loaded in TARPs and the effects of TARP-mediated annular repair and Anakinra delivery was evaluated in a model of annular injury in the goat cervical spine. TARPs showed robust integration with the native tissue and provided structural reinforcement at the injury site that prevented disc-wide aberrant remodeling resulting from AF detensioning. The delivery of Anakinra via TARP implantation improved the retention of disc biochemical composition through increased matrix deposition and retention at the site of annular injury. Anakinra delivery additionally attenuated the inflammatory response associated by scaffold implantation, decreasing osteolysis in adjacent vertebrae and preserving disc cellularity and matrix organization throughout the AF. These results demonstrate the translational and therapeutic potential of this novel TARP system for the treatment of intervertebral disc herniations. One Sentence SummaryTension-activated repair patches delivering bioactive anti-inflammatory factors improve healing in an in vivo goat cervical disc injury model.

Explore related subjects

bioengineering Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Peredo, A. P., Gullbrand, S. E., Friday, C. S., Orozco, B. S., Bonnevie, E. D., Hilliard, R. L., Zlotnick, H. M., Dodge, G. R., Lee, D., Hast, M. W., Schaer, T. P., Smith, H. E., Mauck, R.. 2022-10-23. Anti-Inflammatory Tension-Activated Repair Patches Improve Repair After Intervertebral Disc Herniation. https://doi.org/10.1101/2022.10.22.513360

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

KEEP EXPLORING

Related preprints

bioRxiv2026

AtomWeaver: Multi-Component Flow Matching with a Structured Geometric Prior Facilitates Non-Canonical Peptide Design

Fixed-backbone sequence discovery, or inverse folding, is a critical recurring task in the development of new polypeptide therapeutics. Once promising backbones are established for a target pocket, computational inverse folding methods greatly help accelerate generation of candidate sequences. Such methods are mature for the traditional case of limiting to the fixed twenty-letter canonical vocabulary; however, they cannot access the broader space of non-canonical amino acids (NCAAs). This design constraint is exacerbated for peptide binders, a fast-growing modality that readily incorporates NCAAs, though in practice non-canonical design frequently depends on laborious medicinal-chemistry campaigns. An extension of inverse folding to NCAAs is thus critical to accelerating design of novel therapeutic peptides. AtomWeaver uses a joint all-site, atom-level generative scheme that does not restrict side-chain categorical assignment by either predetermined or co-resolving residue identity. Conditioned only on a fixed peptide backbone and its target protein, its multi-component flow guides side-chain atoms as unlabeled points in R3 from a nested shell prior to a variable-count final atom cloud. Identity is then read by matching each predicted cloud against a reference library of canonical and non-canonical templates. Since identity is decided only at decode time, the addressable vocabulary is a property of the library rather than of the trained weights: a new NCAA costs one reference structure and no retraining, and the model can select residues it was never prompted for and never saw in training. On a deep mutational scan of two peptide-target systems, AtomWeaver's canonical readout shows high observed mean agreement with experimental values among the compared inverse-folding methods. In the mixed canonical-noncanonical setting that canonical-only baselines cannot support at all, it likewise retains ranking signal across both systems. AtomWeaver also displayed self-consistent designs on de novo binder backbones, with the highest interface confidence among compared methods. Notably, it reached these metrics while achieving broad empirical coverage of our 300-residue vocabulary, including four non-canonical types never visible in training. AtomWeaver thus serves canonical and non-canonical peptide design alike, while transforming residue vocabulary to an expandable inference-time choice.

bioengineering↗

bioRxiv2026

The Influence of Obesity and Body Shape on Sagittal Plane Knee Kinematics and Kinetics during Obstacle Crossing

Altered walking mechanics in individuals with obesity can contribute to knee osteoarthritis. The gait deviations may become more pronounced during obstacle crossing. In women, body fat distribution may further influence knee load, especially when excess fat accumulates in the thighs and hips. However, relatively little is known about how regional fat distribution affects gait in women with obesity. This study investigated how obesity and, among women, different fat distributions (Apple: more abdominal fat; Pear: more lower-limb fat) influence knee biomechanics during walking with and without obstacle crossing. Participants were 15 controls without obesity (NB) and 27 with obesity (OB). Within female participants, 10 without obesity (fNB) were compared with 20 with obesity, stratified by waist-hip ratio (Apple:10, Pear:10). Speed-adjusted statistical parametric mapping applied a general linear model (NB vs. OB) and an analysis of covariance (fNB vs. Apple vs. Pear). OB exhibited a significantly greater late-stance knee extension moment than NB across all tasks, and this difference persisted among fNB, Apple, and Pear in obstacle tasks (p<0.05). OB walked with reduced knee flexion during the early-stance leading limb after crossing a medium-height obstacle (p=0.048) and a high-height obstacle (p=0.008) compared to NB. There were significant body-shape effects (p<0.05), and post-hoc comparisons confirmed that Pear had lower knee angles than fNB in both leading-limb conditions after crossing medium- and high-height obstacles (p=0.008 and p=0.001, respectively). These findings suggest that obstacle crossing helps illuminate how excess weight influences knee biomechanics, and how regional fat distribution modulates the degree of this alteration.

bioengineering↗

bioRxiv2026

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↗