A key cause of low back pain is the degeneration of the intervertebral disc (IVD). Causality between infection of the IVD and its degenerative process gained great interest over the last decade. Granville Smith et al. (2021) identified 36 articles from 34 research studies investigating bacteria in human IVDs. Bacteria was identified in 27 studies, whereas 9 attributed bacterial presence to contamination. Cutibacterium acnes was the most abundant, followed by coagulase-negative staphylococcus. However, whether bacteria identified were present in vivo or represent perioperative contamination remains unclear. This study investigated whether bacteria are present in IVDs and what potential effects they may have on native disc cells.

Immunohistochemical staining for Gram positive bacteria was performed on human IVD tissue to identify presence and characterise bacterial species. Nucleus pulposus (NP) cells in monolayer and 3D alginate were stimulated with LPS and Peptidoglycan (0.1-50 µg/ml) for 48hrs. Following stimulation qPCR for factors associated with disc degeneration including matrix genes, matrix degrading enzymes, cytokines, neurotrophic factors and angiogenic factors and conditioned media collected for ELISA and luminex analysis

Gram positive bacteria was detected within human IVD tissue. Internalisation of bacteria by NP cells influenced the cell and nuclei morphology. Preliminary results of exposure of NP cells to bacterial components indicate that LPS as well as Peptidoglycan increase IL-8 and ADAMTS-4 gene expression following 48 hours of stimulation with a dose response seen for IL-8 induction by peptidoglycan compared to the control group. Underlining these results, IL-8 protein release was increased for treated groups compared to non-treated control. Further analysis is underway investigating other output measures and additional biological repeats.

This study has demonstrated bacteria are present within IVD cells within IVD tissue removed from degenerate IVD and is determining the potential influence of these on disc degeneration.

Objectives

This study aims to investigate whether bacteria are present in intervertebral discs (IVDs) and their influence. Causality between chronic infection of the IVD and its degenerative process gained great interest recently. Granville Smith et al. (2021) identified 36 articles from 34 research studies investigating bacteria in IVDs, from these 27 studies found, Cutibacterium acnes being the most abundant. However, whether bacteria identified were present in vivo or if they represent contamination remains unclear.

During OA the homeostasis of healthy articular chondrocytes is dysregulated, which leads to a phenotypical transition of the cells, further influenced by external stimuli. Chondrocytes sense those stimuli, integrate them at the intracellular level and respond by modifying their secretory and molecular state. This process is controlled by a complex interplay of intracellular factors. Each factor is influenced by a myriad of feedback mechanisms, making the prediction of what will happen in case of external perturbation challenging. Hampering the hypertrophic phenotype has emerged as a potential therapeutic strategy to help OA patients (Ripmeester et al. 2018). Therefore, we developed a computational model of the chondrocyte's underlying regulatory network (RN) to identify key regulators as potential drug targets

A mechanistic mathematical model of articular chondrocyte differentiation was implemented with a semi-quantitative formalism. It is composed of a protein RN and a gene RN(GRN) and developed by combining two strategies. First, we established a mechanistic network based on accumulation of decades of biological knowledge. Second, we combined that mechanistic network with data-driven modelling by inferring an OA-GRN using an ensemble of machine learning methods. This required a large gene expression dataset, provided by distinct public microarrays merged through an in-house pipeline for cross-platform integration.

We successfully merged various micro-array experiments into one single dataset where the biological variance was predominant over the batch effect from the different technical platforms. The gain of information provided by this merge enabled us to reconstruct an OA-GRN which subsequently served to complete our mechanistic model. With this model, we studied the system's multi-stability, equating the model's stable states to chondrocyte phenotypes. The network structure explained the occurrence of two biologically relevant phenotypes: a hypertrophic-like and a healthy-like phenotype, recognized based on known cell state markers. Second, we tested several hypotheses that could trigger the onset of OA to validate the model with relevant biological phenomena. For instance, forced inflammation pushed the chondrocyte towards hypertrophy but this was partly rescued by higher levels of TGF-β. However, we could annihilate this rescue by concomitantly mimicking an increase in the ALK1/ALK5 balance. Finally, we performed a screening of in-silico (combinatorial) perturbations (inhibitions and/or over-activations) to identify key molecular factors involved in the stability of the chondrocyte state. More precisely, we looked for the most potent conditions for decreasing hypertrophy. Preliminary validation experiments have confirmed that PKA activation could decrease the hypertrophic phenotype in primary chondrocytes. Importantly the in-silico results highlighted that targeting two factors at the same time would greatly help reducing hypertrophic changes.

A priori testing of conditions with in-silico models may cut time and cost of experiments via target prioritization and opens new routes for OA combinatorial therapies.

3D-printed orthopedic implants have been gaining popularity in recent years due to the control this manufacturing technique gives the designer over the different design aspects of the implant. This technique allows us to manufacture implants with material properties similar to bone, giving the implant designer the opportunity to address one of the main complications experienced after total hip arthroplasty (THA), i.e. aseptic loosening of the implant. To restore proper function after implant loosening, the implant needs to be replaced. During these revision surgeries, some extra bone is removed along with the implant, further increasing the already present defects, and making it harder to achieve proper mechanical stability with the revision implant. A possible way to limit the increasing loss of bone is the use of biodegradable orthopedic implants that optimize long-term implant stability. These implants need to both optimize the implant such that stress shielding is minimized, and tune the implant degradation rate such that newly formed bone is able to replace the degrading metal in order to maintain a proper bone-implant contact. The hope is that such (partly) degradable implants will lead to a reduction in the size of the bone defects over time, making possible future revisions less likely and less complex.

We focused on improving the long-term implant stability of patient-specific acetabular implants for large bone defects and the modeling of their biodegradable behavior. To improve long-term implant stability we implemented a topology optimization approach. A patient-specific finite element model of the hip joint with and without implant was derived from CT-scans to evaluate the performance of the designs during the optimization routine. To evaluate the biodegradation behavior, a quantitative mathematical model was developed to assess the degradation rates of the biodegradable part of the implant. Currently, the biodegradation model has been implemented for magnesium (Mg) implants as a first proof of concept.

For a first test case, an optimized implant was found with stress shielding levels below 20% in most regions. The highest stress shielding levels were found at the bone implant interface. The biodegradation model has been validated using experimental data, which includes immersion tests of simple scaffolds created from Commercial Pure Mg. The mass loss of the scaffold is about 0.8 mg/cm² for the first day of immersion in simulated body fluid (SBF) solution. After the formation of a protective film on the surface of the simple scaffold, the degradation rate starts to slow down.

Initial results presented serve as a proof of concept of the developed computational framework for the implant optimization and the implant biodegradation behavior. Currently, timing calibration, benchmarking and validation are taking place.

Reducing implant-induced stress shielding, obtaining a better implant integration and reduction of bone defects, by allowing for bone to partially replace the implant over time, are crucial design factors for large bone defect implants. In this research, we have developed in-silico models to investigate these factors. Once validated and coupled, the models will serve as an important tool to find the appropriate biodegradable implant designs and biodegradable metal properties for THA applications, that improve current implant lifetime while ensuring proper mechanical functioning.