In the search for new therapeutic and/or biological functions, we first applied various biological tests to our protein collection. We thus work on high content rather than high throughput assays, meaning that rath-
17.5 Moving from the Protein to the Biological Activity: The Post-Genome Era 179
Fig. 17.5. Identification of osteopontin as a nerve remy-elination factor (Selvaraju et al. 2004). Osteopontin was shown to be up-regulated in EAE samples in areas of active remyelination, and the protein was shown to be the most active cytokine from our collection in terms of nerve remyelination in co-culture models. We have since been able to demonstrate that as well as reversing the pathology, osteopon-tin is able to restore function in rodent models of nerve crush
er than miniaturizing assays to increase throughput and reduce costs, we have transformed more and more complex biology to 96-well formats. For example, in our field of multiple sclerosis, we have pioneered a way to study remyelination of neurons in 96-well assays - a technology involving several cell types, a 21-day incubation period, and complex image analysis. It was this technology that led to the identification of several new proteins involved in neuronal repair - of which osteopontin is the forerunner (Fig. 17.5). In the immunology field we were the first to link the FACS cell sorter to our robotics system - and now routinely examine the cytokine production profile of cells (Besson et al. 2003). Whenever possible, we have used human cell or cell-based assays that reflect as closely as possible the mechanism of human physiology and pathology 'in vitro'. Out of our collection of 2,000 proteins and using approximately 10 assays, a large quantity of data points need to be collected.
Proteins showing activity are moved in vivo using either of the three following approaches. First is the classical production and purification of the milligrams of protein for direct injection into animal models. Some of these models involve simple inflammatory stimuli such as LPS, concanavalin A or TNF to activate the immune system. Second, more rapid progress can be made with our technology for direct expression of the cDNA 'in vivo' (see the example for IL18 bp in Mallat et al. 2001). This allows a rapid read-out of the biological activity, bypassing the need to express and purify the protein.
Third, in vivo data can also be obtained with a knock-out of the murine protein. Replacement of the gene of interest with LacZ allows us to understand the expression pattern of the protein, which can shed light on further disease models to be tested. The validity of this high throughput screening approach is underlined by the results obtained when known drug targets have been knocked out (Bolon and Galbreath 2002; Zambrovicz and Sands 2003,2004). Given the amount of information produced by the above approaches, one ofourkeystrate-gic advantages has been in investment in data processing, including treatment and analysis, so that we can easily distinguish real activities from false positives.
Although we set out to find novel therapeutic proteins, we also identify in many cases new activities for known proteins. Often the first biological activity found has tended to bias a research activity. This has always been an issue - interferons for instance were originally identified based on their antiviral activity but interferon gamma is today used to treat lymphoma following autologous peripheral blood stem cell transplantation (Ohno et al. 2005), and interferon beta is an effective treatment for multiple sclerosis.
From our protein pipeline, we have a number of projects now progressing through early preclinical studies, which shows the power of such approaches to identify new biological activities. Our next stage is to test these proteins in more and more diverse types of cellular biology, for which we are currently building a wide range of collaborations.
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