Effects of insulin-like growth factor-1 and dexamethasone on cytokine-challenged cartilage: relevance to post-traumatic osteoarthritis.

OBJECTIVE: Interleukin-1 is one of the inflammatory cytokines elevated after traumatic joint injury that plays a critical role in mediating cartilage tissue degradation, suppressing matrix biosynthesis, and inducing chondrocyte apoptosis, events associated with progression to post-traumatic osteoarthritis (PTOA). We studied the combined use of insulin-like growth factor-1 (IGF-1) and dexamethasone (Dex) to block these multiple degradative effects of cytokine challenge to articular cartilage. METHODS: Young bovine and adult human articular cartilage explants were treated with IL-1α in the presence or absence of IGF-1, Dex, or their combination. Loss of sulfated glycosaminoglycans (sGAG) and collagen were evaluated by the DMMB and hydroxyproline assays, respectively. Matrix biosynthesis was measured via radiolabel incorporation, chondrocyte gene expression by qRT-PCR, and cell viability by fluorescence staining. RESULTS: In young bovine cartilage, the combination of IGF-1 and Dex significantly inhibited the loss of sGAG and collagen, rescued the suppression of matrix biosynthesis, and inhibited the loss of chondrocyte viability caused by IL-1α treatment. In adult human cartilage, only IGF-1 rescued matrix biosynthesis and only Dex inhibited sGAG loss and improved cell viability. Thus, the combination of IGF-1 + Dex together showed combined beneficial effects in human cartilage. CONCLUSIONS: Our findings suggest that the combination of IGF-1 and Dex has greater beneficial effects than either molecule alone in preventing cytokine-mediated cartilage degradation in adult human and young bovine cartilage. Our results support the use of such a combined approach as a potential treatment relevant to early cartilage degradative changes associated with joint injury.

Single timepoint models of dynamic systems.

Many interesting studies aimed at elucidating the connectivity structure of biomolecular pathways make use of abundance measurements, and employ statistical and information theoretic approaches to assess connectivities. These studies often do not address the effects of the dynamics of the underlying biological system, yet dynamics give rise to impactful issues such as timepoint selection and its effect on structure recovery. In this work, we study conditions for reliable retrieval of the connectivity structure of a dynamic system, and the impact of dynamics on structure-learning efforts. We encounter an unexpected problem not previously described in elucidating connectivity structure from dynamic systems, show how this confounds structure learning of the system and discuss possible approaches to overcome the confounding effect. Finally, we test our hypotheses on an accurate dynamic model of the IGF signalling pathway. We use two structure-learning methods at four time points to contrast the performance and robustness of those methods in terms of recovering correct connectivity.

Learning cyclic signaling pathway structures while minimizing data requirements.

Bayesian network structure learning is a useful tool for elucidation of regulatory structures of biomolecular pathways. The approach however is limited by its acyclicity constraint, a problematic one in the cycle-containing biological domain. Here, we introduce a novel method for modeling cyclic pathways in biology, by employing our newly introduced Generalized Bayesian Networks (GBNs). Our novel algorithm enables cyclic structure learning while employing biologically relevant data, as it extends our cycle-learning algorithm to permit learning with singly perturbed samples. We present theoretical arguments as well as structure learning results from realistic, simulated data of a biological system. We also present results from a real world dataset, involving signaling pathways in T-cells.