Judith and David Coffey Seminar Series: Prof Elaine Holmes

Speaker: Dr.  Elaine Holmes (Health Futures Institute, Murdoch University) 

Abstract: The use of metabolic profiling to define metabolic phenotypes associated with
a wide range of pathologies is expanding and demand for sensitive, high quality disease
diagnostics has facilitated the development of new technological and statistical methods
for extracting biomarkers from spectroscopic data obtained from biofluids such as urine,
serum and stool extracts.  These metabolite signatures can subsequently modelled with
other ‘-omic’ data, including next generation sequencing data in order to establish
connections between the gut bacteria and human (patho)physiology.  Examples of urinary
or faecal metabolites that are products of the microbiota, or microbiota-host
interactions include phenols, indoles, bile acids, short chain fatty acids and choline
derivatives, all of which can be quantitatively profiled using spectroscopic
technology.  Thus the metabolic phenotype can provide a window onto dynamic biochemical
responses to physiological and pathological stimuli and also contains information
relating to the metabolic activity and function of the gut microbiome.  

In order to optimise information recovery from the spectra, analytical strategies for
spectral alignment, scaling, curve resolution and quantification, statistical
correlation and annotation are necessary.  Some exemplar analytical pipelines are
presented here with particular focus on a series of methods for enhancing biomarker
detection via a family of statistical correlation algorithms.  Cross-correlation of
multiplatform data allows further characterisation and extraction of improved molecular
descriptors of metabolites identified as candidate biomarkers, which in turn, can
provide new insights into perturbed pathways and aetiopathogenetic mechanisms through
correlation hierarchies of related metabolites.  This systems analysis framework extends
to encompass other datatypes such as metagenomic or metatranscriptomic data and can
identify new correlates between datasets and establish biological coherence across
metabolic pathways and networks.  

About the speaker: Elaine Holmes is an ARC Laureate Fellow at Murdoch University, where
she runs the Centre for Computational and Systems Medicine in the Health Futures
Institute.  She was elected as a Fellow of the Academy of Medical Sciences in 2018 and
the Australian Academy of Science in 2022.  Holmes is one of the pioneers in the
development and implementation of metabolic phenotyping in translational clinical
paradigms.  The analytical framework conceptualised for metabolic phenotyping and
biomarker discovery has been applied across several disease areas.  She also
co-developed the Metabolome-Wide Association Study concept and has shown that the
microbial component of the metabolic profile is associated with a wide range of
conditions including obesity, inflammatory bowel disease, allergies, and certain
cancers.  Her current focus is around computational modelling of metabolic and
metagenomic data to understand the role of the gut microbiome in healthy aging with
specific interest in the influence of nutrition on the microbiome.