Encyclopedia of molecular mechanisms of disease
Professor McCarthy and his team have made major contributions in the human molecular genetics field to understanding the molecular basis of the life threatening inherited disorder Malignant Hyperthermia MH and the associated myopathy Central Core Disease CCD. Susan Walsh. Professor McCarthy also has a keen interest in molecular genetic technologies.
Encyclopedia of Molecular Mechanisms of Disease
His fundamental and applied research on DNA repair enzymes led to the development of a novel platform for detection of mutations and polymorphisms in DNA. His recent work on gene reporters has led to the development of a single secreted luciferase reporter assay for promoter analysis. Alongside Dr. Professor McCarthy has more than 55 publications in international peer reviewed journals and several reviews and book chapters. His research has led to five international patent applications three granted to date in the DNA characterisation field, some of which have been licensed to third parties for commercial exploitation.
Professor McCarthy has over 25 years of experience in competing successfully for research grant funding across several national and international research funding agencies. Professor McCarthy has extensive experience as a peer reviewer for scientific papers and research grants at the national, EU and international level.
Professor McCarthy has significant experience in research strategy and planning and technology transfer. Professor McCarthy also has significant experience in mentoring and supervision of PhD students and has directly supervised over PhD students. Professor McCarthy also has significant experience in career mentoring of undergraduates, postgraduates, postdoctoral fellows and pre-tenure career track fellows.
Professor McCarthy has a keen interest in teaching and teaching methodologies that enhance education, and learning. He has over 25 years of experience in curriculum and course development, course and degree management and course assessment at the undergraduate and postgraduate level. Close Search UCC. Website People Courses. Biochemical Society Transactions , 33 Pt 5 [DOI] [Details] 'The kDa glutathione transferase of Yarrowia lipolytica is encoded by a homologue of the TEF3 gene from Saccharomyces cerevisiae: cloning, expression, and homology modeling of the recombinant protein' McGoldrick S, McCarthy TV, Sheehan D; 'The kDa glutathione transferase of Yarrowia lipolytica is encoded by a homologue of the TEF3 gene from Saccharomyces cerevisiae: cloning, expression, and homology modeling of the recombinant protein'.
Types of pathogenesis include microbial infection , inflammation , malignancy and tissue breakdown. For example, bacterial pathogenesis is the mechanism by which bacteria cause infectious illness. Most diseases are caused by multiple processes. For example, certain cancers arise from dysfunction of the immune system skin tumors and lymphoma after a renal transplant , which requires immunosuppression. The pathogenic mechanisms of a disease or condition are set in motion by the underlying causes, which if controlled would allow the disease to be prevented.
The pathological perspective can be directly integrated into an epidemiological approach in the interdisciplinary field of molecular pathological epidemiology. From Wikipedia, the free encyclopedia. The schema formalism permits telescoping of sets of steps into a single MM, so that well-established submechanisms need not be spelled out in detail—e. At each stage there is a small number of likely classes of perturbation. Similarly, within a stage or between a pair of stages, there are a small number of likely MM classes.
A key feature is the representation of ignorance, alternatives, ambiguities, and uncertainty, linked to supporting evidence Fig 3. Where there is uncertainty as to whether such a link exists, the black MM contains a question mark. Unknown mechanism components are represented by black ovals; ambiguity by branching in the schema; and uncertainty of evidence by element color green for high confidence, orange for medium confidence, and red for low confidence.
Blue octagons represent sites of possible therapeutic intervention. For each symbol, pop-up boxes give access to more detailed information and link to fuller entries that provide a brief commentary on the mechanism feature, a summary of the evidence and the evidence sources, links to the relevant literature and data, and a confidence value 1 to 5.
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For complex trait disease and for cancer, multiple genetic perturbations at multiple loci contribute to a disease, and each such relationship is represented by a mechanism schema. For a particular disease, there are also schemas representing how each drug affects disease phenotypes. There may also be schemas for environmental effects. For example, in Crohn disease, there are contributions to disease risk and other disease-related phenotypes from variations in microbiome composition [ 17 ].
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Thus, the full set of schemas for a disease form a disease-mechanism graph. As discussed below, these graphs enable a number of applications. Eventually, we anticipate that mechanism schemas will be populated directly from mining the literature and by identification of subject-object-predicate triplets corresponding to the SSP-MM-SSP triplets of schemas. The current state of the art for this kind of text mining, as reflected by objective testing in the BioCreative community challenges [ 20 ], is such that this is not yet practical.
Therefore, we have optimized the MecCog resource for human interaction, with an intuitive graphical language, and extensive tools to facilitate construction, including pull-down menus of possible classes for each mechanism stage.
Three primary means of evaluating and improving schema quality are used: i schema curators: each schema is assigned a curator, with responsibility for reviewing content and soliciting additional input if needed. MecCog is implemented on Node. The web application is built using Sails. Sails also has an object-relational mapping ORM , Waterline, providing a simple data access layer for different types of backend database. The MecCog implementation uses the MySQL relational database management system to store data on users and mechanism schemas.
The aesthetics of the website is supported by the open-source front-end library Bootstrap. As noted earlier, the mechanism schema framework can be used to describe and analyze the relationship between genetic variation and disease phenotypes for all types of genetic disease. Examples are available on the MecCog website www. Here, we describe one case of a locus implicated in risk of a complex trait disease.
GWASs have now revealed over loci scattered throughout the genome where the presence of an SNP is associated with increased risk of Crohn disease [ 21 ]. For some loci, the corresponding mechanisms have been extensively studied, for example [ 18 , 19 ]. For others, little or nothing is yet known. Fig 4 shows an example of a mechanism schema for a moderately well understood Crohn disease locus—the relationship between the presence of a GWAS marker SNP rs associated with increased risk of the disease [ 22 ] via a mechanism affecting the activity of Macrophage Stimulating Protein MSP. This schema contains two unknown MMs black ovals.
For one of these, experiment has shown a lower serum abundance of the MSP in the presence of the disease risk marker SNP, but the mechanism for that is unknown is it decreased expression, lower protein stability, altered degradation properties, for instance.
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In the other, altered macrophage activation results in an increased inflammatory response, but the exact mechanism is unclear. Confidence in the schema steps varies—high green , medium orange , and low red. There are also two major mechanism ambiguities, represented by the two branched sections of the schema. The second branch in the schema the parallel paths in the right-hand segment represents uncertainty as to whether the lower intracellular signaling resulting from reduced abundance of the MSP-RON complex most affects macrophage activity and, consequently, innate immunity, or whether it affects wound healing activities of epithelial cells, thus primarily altering barrier integrity, or both.
Many schemas, for example, that of variants in NOD2 [ 18 ] accessible on the MecCog site , are substantially more complicated. All schemas examined exhibit knowledge gaps and ambiguities. In addition to providing an integrative framework for describing what is and is not known about a disease mechanism, the schemas have a number of further applications, described below. Identification of gaps and uncertainty in knowledge allows a more objective evaluation of which experiments are most critical. But the second ambiguity—whether it is innate immune cell activation or wound healing, or both, that is affected—is important for understanding how this risk factor fits into the overall picture of the disease and suggests that further experiments to resolve the ambiguity are warranted.
Schemas also allow identification of potential sites of therapeutic intervention. For example, in spite of the uncertainties and ambiguities in the MSP schema, the central role of the MSP-RON complex is clear, and that suggests a possible therapeutic intervention: an appropriate compound a conventional small-molecule drug or an antibody that bridges the structural interface between RON and MSP [ 23 ] could restore wild-type signaling strength.
Of course, there are many reasons why this may turn out not to be a useful target, but the mechanism schemas do provide a means of systematically identifying such possibilities. Nonadditive contributions from pairs or higher-order combinations of variants contributing to complex trait disease are widely expected to play a major role in disease mechanisms [ 25 ]. In cancer, identification of such interactions has provided the basis of a treatment strategy [ 26 ], and a similar strategy may be possible for complex-trait disease if the interactions can be found.
In principle, GWAS data can be analyzed to discover nonadditive relationships between genetic variants. In practice, the large number of possible combinations drowns any potential signal a study looking at N variants implies N 2 statistical tests , and very large studies will be needed to overcome that. The mechanism graph for a disease, formed by combining all relevant schemas, facilitates the generation of specific hypotheses that can be tested against GWAS data with minimal multitesting issues.
For complex trait diseases and for cancer, each affected individual has contributing variants that affect only a subset of disease-mechanism-related genes. For instance, an individual diagnosed with Crohn disease typically has no risk alleles in about a third of the relevant loci [ 30 ]. Therefore, for each patient, only part of the full disease-mechanism graph is relevant, and the extent and nature of the overlap of that subgraph with the schemas for available drugs provides a potential means of prioritizing drug choice.
In principle, association studies of the relationship between drug efficacy and marker SNPs can also provide this information, for instance [ 32 ], but because of the large number of possible associations that must be tested, there are often insufficient data to provide robust associations e. Historically, diseases have been classified on a number of criteria: the location of the disease anatomical , the cause of the disease etiology , or the symptoms of the disease [ 34 ].
The disease ontology [ 35 ] utilizes these criteria as well as others. It has also been proposed that a disease taxonomy should be based on molecular mechanisms [ 36 ] so that patient subgroups can be characterized by their shared molecular etiology. The set of classes for each stage of mechanism schemas provides a basis for a mechanism-based comparison of genetic diseases. For example, which disease mechanisms involve lower abundance of a protein? Where that occurs, is it produced by lower expression, shorter half-life, or impaired folding? Where the latter is the case, is it mediated by altered ubiquitination rate?