The University of Chicago

Musc Foundation for Research Development

Richard J Quigg

Stephen Tomlinson

Stephen J Wright

Stuart A Kurtz

Terry Clark

Ridgway Scott

Chris (Hristem) Dyanov

Systemic suppression of the complement system has been shown to be effective to treat inflammatory disease, yet at the potential cost of compromising host defense and immune homeostasis. Herein disclosed are methods for antigen-specific targeting of complement inhibitors that show that complement inhibitors targeted to the proximal tubular epithelium protect against tubulointerstitial injury and renal dysfunction in a rat model of nephrosis. It is shown that appropriate targeting of a systemically administered complement inhibitor to a site of disease markedy enhances efficacy and obviates the need to systemically inhibit complement. Additionally, it is shown by specifically inhibiting the terminal pathway of complement, that the membrane attack complex (MAC) plays a key role in proteinuria-induced tubulointerstitial injury, thus establishing the MAC as a valid target for pharmacological intervention in proteinuric disorders. The disclosed are compositions can be used in methods of treating pathogenic diseases and inflammatory conditions by modulating the complement system.

Tissue targeted complement modulators

A method for determining a matrix of expression levels corresponding to a set of biological targets (e.g., genes or gene fragments) and a set of biological samples, including obtaining a matrix of signal values corresponding to the set of biological targets; computing a vector of expression levels for a sample in the set of biological samples using the matrix of signal values; storing the vector of computed expression levels in a storage matrix; repeating the computing and storing steps for each sample in the set of biological samples; and outputting the storage matrix as the matrix of expression levels. The method, based on a linear programming formulation of the problem, works for both 'promiscuous' probe array data, in which there may be multiple targets indicated by a single probe, and the 'polygamous' case, in which there are multiple probes for a single target. The preferred method can also process data obtained from multiple SAGE analyses using multiple markers. A second embodiment of the method determines optimal expression levels when the available probe data is noisy or uncertain.

Method for determining biological expression levels by linear programming

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Hinsdale, IL, United States of America

Richard J Quigg