Professor Elaine Holmes

Elaine is the Professor of Molecular Phenomics at Murdoch University and Head of the Centre for Computational & Systems medicine. She undertook her PhD in molecular phenotyping using NMR spectroscopy and has since broadened her research capabilities to develop chemometric and other statistical methods for analysing large-scale metabolic data. Elaine has applied metabolic phenotyping in the areas of toxicology, molecular epidemiology and nutrition and health. Her aim is to strengthen existing analytical frameworks in order to illuminate biochemical pathways involved in the gut microbial control of human metabolism by chemical elucidation of microbially derived biomarkers associated with health and establish variations in the molecular phenome of individuals. She was awarded an ARC Laureate Fellowship in 2020 to study the molecular processes of aging and the contribution of the gut microbiome.

Her ongoing interest areas include:

Development of integrated systems approaches to map multi-tissue pathogenic processes:

The concept of systems medicine is not new and in fact is embedded in the philosophy behind traditional Chinese ad ayurvedic medicine. Rather than looking at a single organ or tissue in isolation, systems medicine aims to take a top-down view of how the body works on the highest level with contribution from multiple organs and tissues, acting to maintain homeostasis and health. The three-way association between the microbiome, human metabolism and the immune system is critical in controling physiological and pharmacological processes. Metabolic phenotyping of body fluids such as blood and urine allow us to gain a snapshot of the body’s current status and can identify disruption to molecular pathways that reflect disease or risk of disease. By studying these pathways we can identify new pharmacological and nutritional interventions.

Perturbation of the microbiome and metabolome in critically undergoing surgery for congenital heart failure.

• Elliott P, et al. Urinary metabolic signatures of human adiposity. Sci Transl Med. 2015 Apr 29;7(285):285ra62. doi: 10.1126/scitranslmed.aaa5680.
• Saric, J. et al. Integrated cytokine and metabolic analysis of pathological responses to parasite exposure in rodents. J Proteome Res, 2010. 9(5): p. 2255-64.
• Wijeyesekera A, et al. Multi-Compartment Profiling of Bacterial and Host Metabolites Identifies Intestinal Dysbiosis and Its Functional Consequences in the Critically Ill Child. Version 2. Crit Care Med. 2019 Sep;47(9):e727-e734. doi: 10.1097/CCM.0000000000003841.

Development of novel analytical frameworks for determining pathological mechanisms:

Understanding of biological processes at the systems level requires tools for profiling dynamic, multi-compartmental metabolic responses to toxicological and pharmacological events incorporating multivariate trajectory mapping, bidirectional fusion of ‘omics’ data and methods for enhanced biomarker extraction. We have created tools for pre-processing and modelling spectral data and for integrating multi-omics data in order to interpret and understand complex human responses to their environment.

• Loo RL, et al. Manuscript Strategy for improved characterisation of human metabolic phenotypes using a COmbined Multiblock Principal components Analysis with Statistical Spectroscopy (COMPASS). Bioinformatics. 2020:btaa649. doi: 10.1093/bioinformatics/btaa649.
• Holmes, E., et al. Probing latent biomarker signatures and in vivo pathway activity in experimental disease states via statistical total correlation spectroscopy (STOCSY) of biofluids: application to HgCl₂ toxicity. J Proteome Res, 2006. 5(6): p. 1313-20.
• Rantalainen, M et al. Statistically integrated metabonomic-proteomic studies on a human prostate cancer xenograft model in mice. J Proteome Res, 2006. 5(10): p. 2642-55.
• Bylesjö, M., et al. (2006). OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. Journal of Chemometrics, 20(8-10), 341–351. doi:10.1002/cem.1006

Implementation of metabolic phenotyping in translational clinical paradigms:

Critical clinical questions such as mortality prediction in decompensated cirrhosis, monitoring recovery from SARS-CoV-2 infection and pharmacometabonomic (the ability to predict an individual’s disease risk or response to drug treatment based on their baseline metabolism) prediction of response to biologics for treatment of Crohn’s disease can be improved by using metabolic phenotyping alongside traditional clinical practice. The concept that individuals have a unique combination of genes ad environmental exposures lends itself to the development of unique solutions to therapeutic management. Metabolic phenotyping can be used to augment clinical decision making in a number of ways.

• McPhail MJ, et al. Multivariate metabotyping of plasma predicts survival in patients with decompensated cirrhosis. J Hepatol. 2016 May;64(5):1058-67. doi: 10.1016/j.jhep.2016.01.003.
• Ding NS, et al. Metabonomics and the Gut Microbiome Associated With Primary Response to Anti-TNF Therapy in Crohn’s Disease. J Crohns Colitis. 2020 Sep 7;14(8):1090-1102. doi: 10.1093/ecco-jcc/jjaa039. PMID: 32119090
• Kimhofer T, et al. Integrative Modelling of Quantitative Plasma Lipoprotein, Metabolic and Amino Acid Data Reveals a Multi-organ Pathological Signature of SARS-CoV-2 Infection. J Proteome Res. 2020. doi: 10.1021/acs.jproteome.0c00519. Epub ahead of print. PMID: 32806897.

Molecular Epidemiology:

The Metabolome-Wide Association Study (MWAS) approach metabolome-wide association study approach and has been instrumental in leading and devising rigorous mathematical methods for validating candidate biomarkers in high-dimensionality data. This has contributed to biomarker identification, validation and understanding of the impact of complex gene-environmental interactions on the phenotype in multiple pathologies and led to new hypotheses relating to blood pressure ad obesity. Importantly, these new metabolic models and biomarkers can be developed into clinically-actionable models for patient management.

• Holmes, E., et al. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature, 2008. 453(7193): p. 396-400.
• Yap, I.K., et al. Metabolome-wide association study identifies multiple biomarkers that discriminate north and south Chinese populations at differing risks of cardiovascular disease: INTERMAP study. J Proteome Res, 2010. 9(12): p. 6647-54.
• Seow WJ, et al. Association of Untargeted Urinary Metabolomics and Lung Cancer Risk Among Never-Smoking Women in China. JAMA Netw Open. 2019 Sep 4;2(9):e1911970. doi: 10.1001/jamanetworkopen.2019.11970.

Elucidation of host-microbiome interactions and impact on human health:

Metabolic profiling has contributed to furthering the understanding of how the gut microbiome impacts on human health by mapping the chemical cross-talk between the microbiome and human metabolism, moving beyond next generation sequencing approaches to focus on the functionality of the microbiome. The spectroscopically-mapped microbial component of the metabolic profile is associated with a wide range of conditions including obesity, inflammatory bowel disease, allergies, certain cancers, neurodegenerative diseases and autism. The molecular phenomics team at Murdoch have developed assays for profiling specific classes of gut microbial metabolites including bile acids, short chain fatty acids, biogenic amines, indoles and bacterial products of choline degradation. The research has extended to developing patient stratification frameworks with a view to advancing precision medicine through targeting of the gut bacteria and has shaped the research programmes of healthcare companies such as Nestle (initiated research program in the area of gut microbiome-host-diet interactions One of the key discoveries in host-microbiome signalling is the identification of a switch in protein degradation towards protein putrefaction following bariatric surgery and the association with microbially-generated biogenic amines with bacterial genera and species such as Enterobacter. These same bacteria are an important part of the metabolic signature that is associated with obesity and metabolic syndrome.

• West KA, et al. Longitudinal metabolic and gut bacterial profiling of pregnant women with previous bariatric surgery. Gut. 2020: gutjnl-2019-319620. doi: 10.1136/gutjnl-2019-319620.
• Nicholson, J.K., et al. Host-gut microbiota metabolic interactions. Science, 2012. 336(6086): p. 1262-7.
• Kundu P, et al. Neurogenesis and prolongevity signaling in young germ-free mice transplanted with the gut microbiota of old mice. Sci Transl Med. 2019; 11(518):eaau4760. doi: 10.1126/scitranslmed.aau4760. PMID: 31723038.

Precision nutrition:

Growing evidence suggests that a “one-size-fits-all” approach to health is not optimal and points towards the benefits of introducing a personalised or precision approach to dietary management; that is, nutrition advice based on an individual’s baseline metabolic profile. Current dietary recommendations are derived from general advice based on population-level statistics. The use of objective measures of assessment of dietary markers has been limited and has been generally constrained to a few specific nutrients such as sodium, potassium and nitrogen, which are not suitable for the overall assessment of dietary patterns. Research has shown that individuals respond differently to diet and that it is possible to optimize diet to improve T2D risk factors such as obesity, hypertension, high cholesterol etc. Metabolic profiling of biofluids such as urine and plasma using high resolution spectroscopy can capture both the presence of dietary components, offering an objective measure of what an individual has eaten, and the impact of that diet on metabolism. Mapping the critical impact of diet on the co-dependent evolution of metabolism and microbial colonisation is key to understanding obesogenic mechanisms that are driving the increase in many chronic diseases. We are currently applying metabolic phenotyping approaches to strengthen understanding between biological variation, response to meals, food and diet patterns, and health outcomes.

NutriomeXplorer

The NutriomeXplorer v.0.9 can be obtained from different public repositories (Figshare: https://doi.org/10.35092/yhjc.12181938; Box: https://imperialcollegelondon.box.com/s/f1in5lsnoh1hej5b8bvqr14tt7uoaq2v) for Windows (35.4 MB executable), Mac (37.0 MB app) and Linux (42.7 MB executable).

• Garcia-Perez I, et al. Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial. Lancet Diabetes Endocrinol. 2017 Mar;5(3):184-195. doi: 10.1016/S2213-8587(16)30419-3.
• Posma JM, et al. Nutriome-metabolome relationships provide insights into dietary intake and metabolism. Nat Food. 2020 Jul;1(7):426-436. doi: 10.1038/s43016-020-0093-y.
• Loo RL, et al. Characterization of metabolic responses to healthy diets and association with blood pressure: application to the Optimal Macronutrient Intake Trial for Heart Health (OmniHeart), a randomized controlled study. Am. J. Clin. Nutr. 2018;107(3):323-334. doi: 10.1093/ajcn/nqx072.