Department of Chemistry   University of Oxford

 

Metabolomics Research in the McCullagh Group

The McCullagh group studies cellular chemistry and metabolism. In particular it focusses on the development and application of metabolomics and associated methodologies to help understand environmental, genetic and proteomic influences on metabolism in plants, microorganisms and humans. The group focusses on understanding disease aetiology in a range of contexts, biomarker discovery, elucidating therapeutic targets and the metabolic impact of genetic mutations.

We are a highly collaborative group and have worked in a range of disease related areas including cancer, diabetes and heart disease and immune-metabolism. We work on a range of sample types including cells, tissues and bio-fluids, bone and plant structures and microorganisms. We specialise in identifying changes in central metabolism including glycolysis, gluconeogensesis, citric acid cycle, pentose phosphate pathways, amino acid and pathways associated with nucleic acid metabolism. If you are interested in working with us please contact james McCullagh in the first instance (james.mccullagh@chem.ox.ac.uk).

 

Sample preparation: We spend a lot of time working on sample preparation as it is a key element in the production of high quality data associated with metabolism. No one sample preparation method is suitable for all metabolites so we have a number of protocols depending on the sample type and quantity or material. Please email to request a protocol.

workflow iimage

Metabolite identification: We use a number of in house bespoke metabolite databases for identification purposes which include retention time matching, isotope pattern matching, fragmentation pattern and accurate mass matching. These currently contain over 800 metabolites. For putative identification we also use publically available databases such as HMDB, NIST, KEGG and Metlin.

A list of metabolites in our three in-house databases is available on request.

Bioinformatics: We use Progenesis QI for small molecules to process and analyse our data and to identify compounds. We use a Umetrics bioinformatics package containing multivariate statistical methods for supervised and unsupervised modelling of data including PCA, OPLS-DA and other biomarker discovery tools.

Data visualisation: We can provide statistical analysis and an in-house developed pathways mapping heat-map to visualise how compounds change between experimental groups in relation to the metabolic pathways they are a part of.

 

 

Heat map shows 31 antimicrobial and anthelmintic compounds identified across 17 grassland plants. Significant differences in plant species and their antimicrobial components is apparent. 

 


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