A microarray analysis of full depth knee cartilage of ovariectomized rats

<p>Abstract</p> <p>Background</p> <p>This short communication focuses the on articular cartilage and the subchondral bone, both of which play important roles in the development of osteoarthritis (OA). There are indications that estrogen-deficiency, as the post-menopausal state, accelerate the development of OA.</p> <p>Findings</p> <p>We investigated, which extracellular matrix (ECM) protein, proteases and different pro-inflammatory factors was up- or down-regulated in the knee joint tissue in response to estrogen-deficiency in rats induced by ovariectomy. These data support previous findings that several metalloproteinases (MMPs) and cysteine proteases are co-regulated with numerous collagens and proteoglycans that are important for cartilage integrity. Furthermore quite a few pro-inflammatory cytokines were regulated by estrogen deprivation.</p> <p>Conclusion</p> <p>We found multiple genes where regulated in the joint by estrogen-deficiency, many of which correspond well with our current knowledge of the pathogenesis of OA. It supports that estrogen-deficiency (e.g. OVX) may accelerate joint deterioration. However, there are also data that draw attention the need for better understanding of the synergy between proteases and tissue turnover.</p

The Inhibitory Effect of Salmon Calcitonin on Tri-Iodothyronine Induction of Early Hypertrophy in Articular Cartilage

Salmon calcitonin has chondroprotective effect both in vitro and in vivo, and is therefore being tested as a candidate drug for cartilage degenerative diseases. Recent studies have indicated that different chondrocyte phenotypes may express the calcitonin receptor (CTR) differentially. We tested for the presence of the CTR in chondrocytes from tri-iodothyronin (T3)-induced bovine articular cartilage explants. Moreover, investigated the effects of human and salmon calcitonin on the explants.Early chondrocyte hypertrophy was induced in bovine articular cartilage explants by stimulation over four days with 20 ng/mL T3. The degree of hypertrophy was investigated by molecular markers of hypertrophy (ALP, IHH, COLX and MMP13), by biochemical markers of cartilage turnover (C2M, P2NP and AGNxII) and histology. The expression of the CTR was detected by qPCR and immunohistochemistry. T3-induced explants were treated with salmon or human calcitonin. Calcitonin down-stream signaling was measured by levels of cAMP, and by the molecular markers.Compared with untreated control explants, T3 induction increased expression of the hypertrophic markers (p<0.05), of cartilage turnover (p<0.05), and of CTR (p<0.01). Salmon, but not human, calcitonin induced cAMP release (p<0.001). Salmon calcitonin also inhibited expression of markers of hypertrophy and cartilage turnover (p<0.05).T3 induced early hypertrophy of chondrocytes, which showed an elevated expression of the CTR and was thus a target for salmon calcitonin. Molecular marker levels indicated salmon, but not human, calcitonin protected the cartilage from hypertrophy. These results confirm that salmon calcitonin is able to modulate the CTR and thus have chondroprotective effects

Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning

Background: Osteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions. Methods: This study describes the bioinformatic analysis of data generated from high throughput proteomics for identification of potential biomarkers of OA. The mass spectrometry data was generated using a canine explant model of articular cartilage treated with the pro-inflammatory cytokine interleukin 1 β (IL-1β). The bioinformatics analysis involved the application of machine learning and network analysis to the proteomic mass spectrometry data. A rule based machine learning technique, BioHEL, was used to create a model that classified the samples into their relevant treatment groups by identifying those proteins that separated samples into their respective groups. The proteins identified were considered to be potential biomarkers. Protein networks were also generated; from these networks, proteins pivotal to the classification were identified. Results: BioHEL correctly classified eighteen out of twenty-three samples, giving a classification accuracy of 78.3% for the dataset. The dataset included the four classes of control, IL-1β, carprofen, and IL-1β and carprofen together. This exceeded the other machine learners that were used for a comparison, on the same dataset, with the exception of another rule-based method, JRip, which performed equally well. The proteins that were most frequently used in rules generated by BioHEL were found to include a number of relevant proteins including matrix metalloproteinase 3, interleukin 8 and matrix gla protein. Conclusions: Using this protocol, combining an in vitro model of OA with bioinformatics analysis, a number of relevant extracellular matrix proteins were identified, thereby supporting the application of these bioinformatics tools for analysis of proteomic data from in vitro models of cartilage degradation

Transglutaminase 2 in cartilage homoeostasis: novel links with inflammatory osteoarthritis.

Transglutaminase 2 (TG2) is highly expressed during chondrocyte maturation and contributes to the formation of a mineralised scaffold by introducing crosslinks between extracellular matrix (ECM) proteins. In healthy cartilage, TG2 stabilises integrity of ECM and likely influences cartilage stiffness and mechanistic properties. At the same time, the abnormal accumulation of TG2 in the ECM promotes chondrocyte hypertrophy and cartilage calcification, which might be an important aspect of osteoarthritis (OA) initiation. Although excessive joint loading and injuries are one of the main causes leading to OA development, it is now being recognised that the presence of inflammatory mediators accelerates OA progression. Inflammatory signalling is known to stimulate the extracellular TG2 activity in cartilage and promote TG2-catalysed crosslinking of molecules that promote chondrocyte osteoarthritic differentiation. It is, however, unclear whether TG2 activity aims to resolve or aggravate damages within the arthritic joint. Better understanding of the complex signalling pathways linking inflammation with TG2 activities is needed to identify the role of TG2 in OA and to define possible avenues for therapeutic interventions

Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta