Full Schedule

Hideaki Nagase, Ph.D.
Kennedy Institute of Rheumatology-Centre for Degenerative Diseases, University of Oxford, UK

Osteoarthritis: diagnosis, treatment and challenges

Hideaki Nagase¹, Ngee Han Lim¹, George Bou-Gharios¹, Ernst Meinjohanns²  and Morten Meldal³

1. Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, London, W6 8LH  UK
2. Carlsberg Laboratory, Copenhagen, Denmark,
3. Nano-Science Center, Department of Chemistry, University of Copenhagen, Denmark

Osteoarthritis (OA) is the most prevalent age-related degenerative joint disease. With the expanding ageing population, it imposes a major socio-economic burden on society.  A key feature of OA is a gradual loss of articular cartilage and deformation of bone, resulting in the impairment of joint function. Currently, there is no effective disease-modifying treatment except joint replacement surgery. There are many possible causes of cartilage loss (e.g. mechanical load, injury, reactive oxygen species, aging, etc.) and etiological factors (obesity, genetics), but the degradation of cartilage is primarily caused by elevated levels of active metalloproteinases.  It is therefore attractive to consider proteinase inhibitors as potential therapeutics. However, there are several hurdles to overcome, namely early diagnosis and continuous monitoring of the efficacy of inhibitor therapeutics. We are therefore aiming at developing non-invasive probes to detect cartilage degrading metalloproteinase activities.

We have designed in vivo imaging probes to detect MMP-13 (collagenase 3) activity that participates in OA by degrade cartilage collagen II and MMP-12 (macrophage elastase) activity involved in inflammatory arthritis. These activity-based probes consist of a peptide that is selectively cleaved by the target proteinase, a near-infrared fluorophore and a quencher. The probe’s signal multiplies upon proteolysis.  They were first used to follow the respective enzyme activity in vivo in the mouse model of collagen-induced arthritis and we found MMP-12 activity probe (MMP12AP) activation peaked at 5 days after onset of the disease, whereas MMP13AP activation was observed at 10-15 days. The in vivo activation of these probes was inhibited by specific low molecule inhibitors.  We proceeded to test both probes in the mouse model of OA induced by the surgical destabilization of medial meniscus of the knee joints.  In this model, degradation of knee cartilage is first detected histologically 6 weeks after surgery with significant erosion detectable at 8 weeks. Little activation of MMP12AP was detected, which was expected, as macrophage migration is not obvious in OA. MMP13AP, on the other hand, was significantly activated in the operated knee at 6 weeks compared with the non-operated contralateral knee, but there were no significant differences between the operated and sham-operated knees.  At 8 weeks, however, the signals in the operated knees were significantly higher than both the contralateral and sham-operated controls. Activation of aggrecanases and MMP-13 are observed before structural changes of cartilage. We are therefore currently improving the MMP-13 probe for earlier detection by attaching it to polymers that are retained in  cartilage.

Claudia AM Wheeler-Kingshott, Ph.D.
University Reader in Magnetic Resonance Physics, Department of Neuroinflammation, UCL Institute of Neurology, London, UK

Abstract

Detecting neuronal activity in vivo non-invasively is possible with a number of techniques. Amongst these, in 1990 functional magnetic resonance imaging (fMRI) was proposed as a technique that has a great ability to spatially map brain activity by exploiting the blood oxygenation level dependent (BOLD) contrast mechanism [1, 2]. In fact, neuronal activation triggers a demand for oxygen and induces a localised increase in blood flow and blood volume, which actually exceeds the metabolic needs. This in turns causes an increase of oxyhaemoglobin in the venous compartment, which is a transient phenomenon and is accompanied by a transient change (decrease) in the concentration of deoxyhaemoglobin. Due to its paramagnetic properties, the amount of deoxyhaemoglobin present in the venous blood affects the local magnetic field seen by the spins (protons) and determines the local properties of the MR signal. A decrease in deoxyhaemoglobin during neuronal activity, therefore, induces local variations of this magnetic field that increases the average transverse relaxation time of tissue, measured via the T2* parameter [3]. This means that there is an increase of the MR signal (of the order of a few %, typically <5%) linked to metabolic changes happening during brain function. Activation can be inferred at different brain locations by performing tasks while acquiring the MR signal and comparing periods of rest to periods of activity.

The macroscopic changes of the BOLD signal are well characterised, while the reason for the increased blood supply, exceeding demands, needs further thoughts. Here we will discuss two approaches for explaining the BOLD phenomenon, one that links it to adenosine triphosphate production [4] and enzyme saturation, the other that relates it to the very slow diffusion of oxygen through the blood-brain-barrier with a consequent compensatory high demand of oxygen [5]. Some evidence of restricted oxygen diffusion has been shown by means of hypercapnia [6], although it is not excluded that both mechanisms may be present.

Overall, the BOLD signal changes theory and its physiological basis will be presented and discussed.

References

1. Ogawa, S., et al., Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A, 1990. 87(24): p. 9868-72.
2. Kwong, K.K., et al., Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci U S A, 1992. 89(12): p. 5675-9.
3. Bandettini PA, et al. Spin-echo and gradient-echo EPI of human brain activation using BOLD contrast: a comparative study at 1.5 T. NMR Biomed. 1994 Mar;7(1-2):12-20
4.  Fox, P.T., et al., Nonoxidative glucose consumption during focal physiologic neural activity. Science, 1988. 241(4864): p. 462-4.
5. Gjedde, A., et al. Reduction of functional capillary density in human brain after stroke. J Cereb Blood Flow Metab, 1990. 10(3): p. 317-26.
6. Hoge, R.D., et al., Linear coupling between cerebral blood flow and oxygen consumption in activated human cortex. Proc Natl Acad Sci U S A, 1999. 96(16): p. 9403-8.

Egidio D’Angelo, MD, Ph.D.
Full Professor of Physiology & Director, Brain Connectivity Center, University of Pavia, Italy

Realistic modeling: new insight into the functions of the cerebellar network

Realistic modeling is an approach based on the careful reconstruction of neurons synapses starting from biological details at the molecular and cellular level. This technique, combined with the connection topologies derived from histological measurements, allows the reconstruction of precise neuronal networks. Finally, the advent of specific software platforms (PYTHON-NEURON) and of super-computers allows large-scale network simulation to be performed in reasonable time. This approach inverts the logics of older theoretical models, which anticipated an intuition on how the network might work.  In realistic modeling, network properties “emerge” from the numerous biological properties embedded into the model.

This approach is illustrated here through an outstanding application of realistic modeling to the cerebellar cortex network. The neurons (over 105) are reproduced at a high level of detail generating non-linear network effects like population oscillations and resonance, phase-reset, bursting, rebounds, short-term and long-term plasticity, spatiotemporal redistrbution of input patterns. The model is currently being used in the context of he HUMAN BRAIN PROJECT to investigate the cerebellar network function.

Correspondence should be addressed to

Dr. EgidioD’Angelo,
Laboratory of Neurophysiology
Via Forlanini 6, 27100 Pavia, Italy
Phone: 0039 (0) 382 987606
Fax: 0039 (0) 382 987527
dangelo@unipv.it

Acknowledgments

This work was supported by grants from European Union to ED (CEREBNET FP7-ITN238686, REALNET FP7-ICT270434) and by grants from the Italian Ministry of Health to ED (RF-2009-1475845).