Characterization of clinical data for patient stratification in moderate osteoarthritis with support vector machines, regulatory network models, and verification against osteoarthritis Initiative data.

Knee osteoarthritis (OA) diagnosis is based on symptoms, assessed through questionnaires such as the WOMAC. However, the inconsistency of pain recording and the discrepancy between joint phenotype and symptoms highlight the need for objective biomarkers in knee OA diagnosis. To this end, we study relationships among clinical and molecular data in a cohort of women (n = 51) with Kellgren-Lawrence grade 2-3 knee OA through a Support Vector Machine (SVM) and a regulation network model. Clinical descriptors (i.e., pain catastrophism, depression, functionality, joint pain, rigidity, sensitization and synovitis) are used to classify patients. A Youden's test is performed for each classifier to determine optimal binarization thresholds for the descriptors. Thresholds are tested against patient stratification according to baseline WOMAC data from the Osteoarthritis Initiative, and the mean accuracy is 0.97. For our cohort, the data used as SVM inputs are knee OA descriptors, synovial fluid proteomic measurements (n = 25), and transcription factor activation obtained from regulatory network model stimulated with the synovial fluid measurements. The relative weights after classification reflect input importance. The performance of each classifier is evaluated through ROC-AUC analysis. The best classifier with clinical data is pain catastrophism (AUC = 0.9), highly influenced by funcionality and pain sensetization, suggesting that kinesophobia is involved in pain perception. With synovial fluid proteins used as input, leptin strongly influences every classifier, suggesting the importance of low-grade inflammation. When transcription factors are used, the mean AUC is limited to 0.608, which can be related to the pleomorphic behaviour of osteoarthritic chondrocytes. Nevertheless, funcionality has an AUC of 0.7 with a decisive importance of FOXO downregulation. Though larger and longitudinal cohorts are needed, this unique combination of SVM and regulatory network model shall help to stratify knee OA patients more objectively.

The interplay between biochemical mediators and mechanotransduction in chondrocytes: Unravelling the differential responses in primary knee osteoarthritis.

In primary or idiopathic osteoarthritis (OA), it is unclear which factors trigger the shift of articular chondrocyte activity from pro-anabolic to pro-catabolic. In fact, there is a controversy about the aetiology of primary OA, either mechanical or inflammatory. Chondrocytes are mechanosensitive cells, that integrate mechanical stimuli into cellular responses in a process known as mechanotransduction. Mechanotransduction occurs thanks to the activation of mechanosensors, a set of specialized proteins that convert physical cues into intracellular signalling cascades. Moderate levels of mechanical loads maintain normal tissue function and have anti-inflammatory effects. In contrast, mechanical over- or under-loading might lead to cartilage destruction and increased expression of pro-inflammatory cytokines. Simultaneously, mechanotransduction processes can regulate and be regulated by pro- and anti-inflammatory soluble mediators, both local (cells of the same joint, i.e., the chondrocytes themselves, infiltrating macrophages, fibroblasts or osteoclasts) and systemic (from other tissues, e.g., adipokines). Thus, the complex process of mechanotransduction might be altered in OA, so that cartilage-preserving chondrocytes adopt a different sensitivity to mechanical signals, and mechanic stimuli positively transduced in the healthy cartilage may become deleterious under OA conditions. This review aims to provide an overview of how the biochemical exposome of chondrocytes can alter important mechanotransduction processes in these cells. Four principal mechanosensors, i.e., integrins, Ca2+ channels, primary cilium and Wnt signalling (canonical and non-canonical) were targeted. For each of these mechanosensors, a brief summary of the response to mechanical loads under healthy or OA conditions is followed by a concise overview of published works that focus on the further regulation of the mechanotransduction pathways by biochemical factors. In conclusion, this paper discusses and explores how biological mediators influence the differential behaviour of chondrocytes under mechanical loads in healthy and primary OA.

African Swine Fever Virus Gene B117L Encodes a Small Protein Endowed with Low-pH-Dependent Membrane Permeabilizing Activity.

African swine fever virus (ASFV) is causing a devastating pandemic in domestic and wild swine in Central Europe to East Asia, resulting in economic losses for the swine industry. The virus contains a large double-stranded DNA genome that contains more than 150 genes, most with no experimentally characterized function. In this study, we evaluate the potential function of the product of ASFV gene B117L, a 115-amino-acid integral membrane protein transcribed at late times during the virus replication cycle and showing no homology to any previously published protein. Hydrophobicity distribution along B117L confirmed the presence of a single transmembrane helix, which, in combination with flanking amphipathic sequences, composes a potential membrane-associated C-terminal domain of ca. 50 amino acids. Ectopic transient cell expression of the B117L gene as a green fluorescent protein (GFP) fusion protein revealed the colocalization with markers of the endoplasmic reticulum (ER). Intracellular localization of various B117L constructs also displayed a pattern for the formation of organized smooth ER (OSER) structures compatible with the presence of a single transmembrane helix with a cytoplasmic carboxy terminus. Using partially overlapping peptides, we further demonstrated that the B117L transmembrane helix has the capacity to establish spores and ion channels in membranes at low pH. Furthermore, our evolutionary analysis showed the high conservation of the transmembrane domain during the evolution of the B117L gene, indicating that the integrity of this domain is preserved by the action of the purifying selection. Collectively our data support a viroporin-like assistant role for the B117L gene-encoded product in ASFV entry. IMPORTANCE ASFV is responsible for an extensively distributed pandemic causing important economic losses in the pork industry in Eurasia. The development of countermeasures is partially limited by the insufficient knowledge regarding the function of the majority of the more than 150 genes present on the virus genome. Here, we provide data regarding the functional experimental evaluation of a previously uncharacterized ASFV gene, B117L. Our data suggest that the B117L gene encodes a small membrane protein that assists in the permeabilization of the ER-derived envelope during ASFV infection.

Network-based modelling of mechano-inflammatory chondrocyte regulation in early osteoarthritis.

Osteoarthritis (OA) is a debilitating joint disease characterized by articular cartilage degradation, inflammation and pain. An extensive range of in vivo and in vitro studies evidences that mechanical loads induce changes in chondrocyte gene expression, through a process known as mechanotransduction. It involves cascades of complex molecular interactions that convert physical signals into cellular response(s) that favor either chondroprotection or cartilage destruction. Systematic representations of those interactions can positively inform early strategies for OA management, and dynamic modelling allows semi-quantitative representations of the steady states of complex biological system according to imposed initial conditions. Yet, mechanotransduction is rarely integrated. Hence, a novel mechano-sensitive network-based model is proposed, in the form of a continuous dynamical system: an interactome of a set of 118 nodes, i.e., mechano-sensitive cellular receptors, second messengers, transcription factors and proteins, related among each other through a specific topology of 358 directed edges is developed. Results show that under physio-osmotic initial conditions, an anabolic state is reached, whereas initial perturbations caused by pro-inflammatory and injurious mechanical loads leads to a catabolic profile of node expression. More specifically, healthy chondrocyte markers (Sox9 and CITED2) are fully expressed under physio-osmotic conditions, and reduced under inflammation, or injurious loadings. In contrast, NF-κB and Runx2, characteristic of an osteoarthritic chondrocyte, become activated under inflammation or excessive loading regimes. A literature-based evaluation shows that the model can replicate 94% of the experiments tested. Sensitivity analysis based on a factorial design of a treatment shows that inflammation has the strongest influence on chondrocyte metabolism, along with a significant deleterious effect of static compressive loads. At the same time, anti-inflammatory therapies appear as the most promising ones, though the restoration of structural protein production seems to remain a major challenge even in beneficial mechanical environments. The newly developed mechano-sensitive network model for chondrocyte activity reveals a unique potential to reflect load-induced chondroprotection or articular cartilage degradation in different mechano-chemical-environments.

Regulatory network-based model to simulate the biochemical regulation of chondrocytes in healthy and osteoarthritic environments.

In osteoarthritis (OA), chondrocyte metabolism dysregulation increases relative catabolic activity, which leads to cartilage degradation. To enable the semiquantitative interpretation of the intricate mechanisms of OA progression, we propose a network-based model at the chondrocyte level that incorporates the complex ways in which inflammatory factors affect structural protein and protease expression and nociceptive signals. Understanding such interactions will leverage the identification of new potential therapeutic targets that could improve current pharmacological treatments. Our computational model arises from a combination of knowledge-based and data-driven approaches that includes in-depth analyses of evidence reported in the specialized literature and targeted network enrichment. We achieved a mechanistic network of molecular interactions that represent both biosynthetic, inflammatory and degradative chondrocyte activity. The network is calibrated against experimental data through a genetic algorithm, and 81% of the responses tested have a normalized root squared error lower than 0.15. The model captures chondrocyte-reported behaviors with 95% accuracy, and it correctly predicts the main outcomes of OA treatment based on blood-derived biologics. The proposed methodology allows us to model an optimal regulatory network that controls chondrocyte metabolism based on measurable soluble molecules. Further research should target the incorporation of mechanical signals.