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    The chemical and computational biology of inflammation

    Small, Benjamin Gavin

    [Thesis]. Manchester, UK: The University of Manchester; 2012.

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    Abstract

    Non-communicable diseases (NCD) such as cancer, heart disease and cerebrovascularinjury are dependent on or aggravated by inflammation. Their prevention and treatment isarguably one of the greatest challenges to medicine in the 21st century. The pleiotropic,proinflammatory cytokine; interleukin-1 beta (IL-1b) is a primary, causative messenger ofinflammation. Lipopolysaccharide (LPS) induction of IL-1b expression via toll-likereceptor 4 (TLR4) in myeloid cells is a robust experimental model of inflammation and isdriven in large part via p38-MAPK and NF-kB signaling networks. The control ofsignaling networks involved in IL-1b expression is distributed and highly complex, so toperturb intracellular networks effectively it is often necessary to modulate several stepssimultaneously. However, the number of possible permutations for intervention leads to acombinatorial explosion in the experiments that would have to be performed in a completeanalysis. We used a multi-objective evolutionary algorithm (EA) to optimise reagentcombinations from a dynamic chemical library of 33 compounds with established orpredicted targets in the regulatory network controlling IL-1β expression. The EAconverged on excellent solutions within 11 generations during which we studied just 550combinations out of the potential search space of ~ 9 billion. The top five reagents with thegreatest contribution to combinatorial effects throughout the EA were then optimised pairwisewith respect to their concentrations, using an adaptive, dose matrix search protocol.A p38a MAPK inhibitor (30 ± 10% inhibition alone) with either an inhibitor of IkB kinase(12 ± 9 % inhibition alone) or a chelator of poorly liganded iron (19 ± 8 % inhibitionalone) yielded synergistic inhibition (59 ± 5 % and 59 ± 4 % respectively, n=7, p≤0.04 forboth combinations, tested by one way ANOVA with Tukey’s multiple test correction) ofmacrophage IL-1b expression.Utilising the above data, in conjunction with the literature, an LPS-directed transcriptionalmap of IL-1b expression was constructed. Transcription factors (TF) targeted by thesignaling networks coalesce at precise nucleotide binding elements within the IL-1bregulatory DNA. Constitutive binding of PU.1 and C/EBP-b TF’s are obligate for IL-1bexpression. The findings in this thesis suggest that PU.1 and C/EBP-b TF’s form scaffoldsfacilitating dynamic control exerted by other TF’s, as exemplified by c-Jun. Similarly,evidence is emerging that epigenetic factors, such as the hetero-euchromatin balance, arealso important in the relative transcriptional efficacy in different cell types.Evolutionary searches provide a powerful and general approach to the discovery of novelcombinations of pharmacological agents with potentially greater therapeutic indices thanthose of single drugs. Similarly, construction of signaling network maps aid theelucidation of pharmacological mechanism and are mandatory precursors to thedevelopment of dynamic models. The symbiosis of both approaches has provided furtherinsight into the mechanisms responsible for IL-1b expression, and reported here provide aplatform for further developments in understanding NCD’s dependent on or aggravated byinflammation

    Layman's Abstract

    A computer program which can select effective drug combinations from a billion others isset to improve our understanding of cancer, heart disease, stroke and many other diseases.Inflammation-best known in arthritis, is common to many chronic diseases. Inflammationcauses the release of a whole array of molecules that help our bodies fight infection but canalso be very damaging in long term diseases. A key molecule called IL-1 is produced byblood cells called macrophages and these cells can be studied in the laboratory. Only 30drugs are required to generate over a billion possible combinations, so it would take over100 years to test all possible combinations even with the assistance of the lab robot!We bypassed this problem with a computer program: an evolutionary algorithm (EA) thatcompared the results of a few combinations that we tested in the lab, and selected thosethat were most effective in blocking IL-1 production. We repeated this cycle until the bestcombination was found, but in 10 weeks rather than 100 years! (see Figure)Although the promise of future remedies is a distant one, using this approach, of computerprogram with experiments will vastly speed up the search for remedies and provide a betterunderstanding of chronic diseases such as cancer, cerebrovascular injury and heart disease.

    Bibliographic metadata

    Type of resource:
    Content type:
    Form of thesis:
    Type of submission:
    Degree type:
    Doctor of Philosophy
    Degree programme:
    PhD Chemical Engineering and Analytical Science (48 months)
    Publication date:
    Location:
    Manchester, UK
    Total pages:
    188
    Abstract:
    Non-communicable diseases (NCD) such as cancer, heart disease and cerebrovascularinjury are dependent on or aggravated by inflammation. Their prevention and treatment isarguably one of the greatest challenges to medicine in the 21st century. The pleiotropic,proinflammatory cytokine; interleukin-1 beta (IL-1b) is a primary, causative messenger ofinflammation. Lipopolysaccharide (LPS) induction of IL-1b expression via toll-likereceptor 4 (TLR4) in myeloid cells is a robust experimental model of inflammation and isdriven in large part via p38-MAPK and NF-kB signaling networks. The control ofsignaling networks involved in IL-1b expression is distributed and highly complex, so toperturb intracellular networks effectively it is often necessary to modulate several stepssimultaneously. However, the number of possible permutations for intervention leads to acombinatorial explosion in the experiments that would have to be performed in a completeanalysis. We used a multi-objective evolutionary algorithm (EA) to optimise reagentcombinations from a dynamic chemical library of 33 compounds with established orpredicted targets in the regulatory network controlling IL-1β expression. The EAconverged on excellent solutions within 11 generations during which we studied just 550combinations out of the potential search space of ~ 9 billion. The top five reagents with thegreatest contribution to combinatorial effects throughout the EA were then optimised pairwisewith respect to their concentrations, using an adaptive, dose matrix search protocol.A p38a MAPK inhibitor (30 ± 10% inhibition alone) with either an inhibitor of IkB kinase(12 ± 9 % inhibition alone) or a chelator of poorly liganded iron (19 ± 8 % inhibitionalone) yielded synergistic inhibition (59 ± 5 % and 59 ± 4 % respectively, n=7, p≤0.04 forboth combinations, tested by one way ANOVA with Tukey’s multiple test correction) ofmacrophage IL-1b expression.Utilising the above data, in conjunction with the literature, an LPS-directed transcriptionalmap of IL-1b expression was constructed. Transcription factors (TF) targeted by thesignaling networks coalesce at precise nucleotide binding elements within the IL-1bregulatory DNA. Constitutive binding of PU.1 and C/EBP-b TF’s are obligate for IL-1bexpression. The findings in this thesis suggest that PU.1 and C/EBP-b TF’s form scaffoldsfacilitating dynamic control exerted by other TF’s, as exemplified by c-Jun. Similarly,evidence is emerging that epigenetic factors, such as the hetero-euchromatin balance, arealso important in the relative transcriptional efficacy in different cell types.Evolutionary searches provide a powerful and general approach to the discovery of novelcombinations of pharmacological agents with potentially greater therapeutic indices thanthose of single drugs. Similarly, construction of signaling network maps aid theelucidation of pharmacological mechanism and are mandatory precursors to thedevelopment of dynamic models. The symbiosis of both approaches has provided furtherinsight into the mechanisms responsible for IL-1b expression, and reported here provide aplatform for further developments in understanding NCD’s dependent on or aggravated byinflammation
    Layman's abstract:
    A computer program which can select effective drug combinations from a billion others isset to improve our understanding of cancer, heart disease, stroke and many other diseases.Inflammation-best known in arthritis, is common to many chronic diseases. Inflammationcauses the release of a whole array of molecules that help our bodies fight infection but canalso be very damaging in long term diseases. A key molecule called IL-1 is produced byblood cells called macrophages and these cells can be studied in the laboratory. Only 30drugs are required to generate over a billion possible combinations, so it would take over100 years to test all possible combinations even with the assistance of the lab robot!We bypassed this problem with a computer program: an evolutionary algorithm (EA) thatcompared the results of a few combinations that we tested in the lab, and selected thosethat were most effective in blocking IL-1 production. We repeated this cycle until the bestcombination was found, but in 10 weeks rather than 100 years! (see Figure)Although the promise of future remedies is a distant one, using this approach, of computerprogram with experiments will vastly speed up the search for remedies and provide a betterunderstanding of chronic diseases such as cancer, cerebrovascular injury and heart disease.
    Thesis main supervisor(s):
    Thesis co-supervisor(s):
    Funder(s):
    Language:
    en

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    Manchester eScholar ID:
    uk-ac-man-scw:153797
    Created by:
    Small, Benjamin
    Created:
    23rd January, 2012, 23:40:17
    Last modified by:
    Small, Benjamin
    Last modified:
    22nd February, 2012, 13:17:46