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Professor David Broadhurst

Director, Centre for Integrative Metabolomics & Computational Biology

Staff Member Details
Telephone: +61 8 6304 2705
Mobile: 0403 707 305
Email: d.broadhurst@ecu.edu.au
Campus: Joondalup  
Room: JO19.332  
ORCID iD: https://orcid.org/0000-0003-0775-9581

David is the Professor of Data Science & Biostatistics in the School of Science, and director of the Centre for Integrative Metabolomics & Computational Biology (CiMCB).

Background

David holds a first class honours degree in Electronic Engineering, an MSc in Medical Informatics, and a PhD in the ‘‘Application of Artificial Neural Networks and Evolutionary Algorithms to Metabolic Profiling’’. He has been an active member of the Systems Biology community for over 20 years, where he has recognised expertise in design of experiments, signal processing, biostatistics, and machine learning. He started his professional career at University of Wales, Aberystwyth, witnessing the birth of Metabolomics (the quantitative study of low-molecular weight chemicals in a biological system related to genetic or environmental perturbations) as an independent post-genomic science, under the mentorship of Professor Douglas Kell C.B.E. After leaving Wales, David worked for an extended period as a post-doctoral research fellow at the University of Manchester developing large-scale clinical metabolomics protocols in collaboration with GSK and AstraZeneca. In 2009 he moved to Cork University Maternity Hospital, Ireland, to work as part of a team investigating pre-symptomatic metabolite biomarkers predictive of major pregnancy disorders. In 2011 David was appointed Associate Professor of Biostatistics at the University of Alberta, Canada, where he was scientific lead for a range of basic/clinical metabolomics projects, and continued his pregnancy related research. More recently he has expanded his research portfolio to a diverse range of post-genomic translational/precision medicine projects. In March 2016 he was appointed to his current position as Professor at Edith Cowan University. David’s current research focuses on integrative metabolomics and computational biology. With particular emphasis on multi-system/multi-omic data integration, population stratification via personalised trajectories, model optimization by evolutionary computation, artificial neural networks, and data visualisation.

Professional Associations

Director of the International Metabolomics Society.

Qualifications

  • Doctor of Philosophy, Wales, 1998.
  • Master of Science, England, 1993.
  • Bachelor of Engineering, England, 1992.

Research Outputs

Journal Articles

  • O’boyle, D., Dunn, W., O'Neill, D., Kirwan, J., Broadhurst, D., Hallberg, B., Boylan, G., Murray, D. (2021). Improvement in the prediction of neonatal hypoxic-ischemic encephalopathy with the integration of umbilical cord metabolites and current clinical makers. Journal of Pediatrics, 229(NA), 175-181. https://doi.org/10.1016/j.jpeds.2020.09.065.
  • Lette, E., Burnham, Q., Lawler, N., Horwitz, P., Boyce, M., Broadhurst, D., Duffy, R., Koenders, A. (2021). Detecting sex-related changes to the metabolome of a critically endangered freshwater crayfish during the mating season. Frontiers in Molecular Biosciences, 8(NA), 252. https://doi.org/10.3389/fmolb.2021.650839.
  • Shafaei Darestani, A., Rees, J., Christophersen, C., Devine, A., Broadhurst, D., Boyce, M. (2021). Extraction and quantitative determination of bile acids in feces. Analytica Chimica Acta, 1150(March), Article 338224. https://doi.org/10.1016/j.aca.2021.338224.
  • Litton, E., Anstey, M., Broadhurst, D., Chapman, A., Currie, A., Ferrier, J., Gummer, J., Higgins, A., Lim, J., Manning, L., Myers, E., Orr, K., Palermo, A., Paparini, A., Pellicano, S., Raby, E., Rammohan, A., Regli, A., Richter, B., Salman, S., Strunk, T., Waterson, S., Weight, D., Wibrow, B., Wood, F. (2021). Early and sustained Lactobacillus plantarum probiotic therapy in critical illness: the randomised, placebo-controlled, restoration of gut microfora in critical illness trial (ROCIT). Intensive Care Medicine, 2021(Online ahead of print), Online ahead of print. https://doi.org/10.1007/s00134-020-06322-w.

Journal Articles

  • Klåvus, A., Kokla, M., Noerman, S., Koistinen, V., Tuomainen, M., Zarei, I., Meuronen, T., Häkkinen, M., Rummukainen, S., Farizah Babu, A., Sallinen, T., Kärkkäinen, O., Paananen, J., Broadhurst, D., Brunius, C., Hanhineva, K. (2020). “Notame”: Workflow for Non-Targeted LC–MS Metabolic Profiling. Metabolites, 10(4), Article number 135. https://doi.org/10.3390/metabo10040135.
  • Mendez, K., Broadhurst, D., Reinke, S. (2020). Migrating from partial least squares discriminant analysis to artificial neural networks: a comparison of functionally equivalent visualisation and feature contribution tools using jupyter notebooks. Metabolomics, 16(2), Article number 17. https://doi.org/https://doi.org/10.1007/s11306-020-1640-0.
  • Lette, E., Lawler, N., Burnham, Q., Boyce, M., Duffy, R., Koenders, A., Broadhurst, D. (2020). Metabolomic Profiling of Crayfish Haemolymph Distinguishes Sister Species and Sex: Implications for Conservation, Aquaculture and Physiological Studies. Freshwater Crayfish, 25(1), 89-101. https://doi.org/https://doi.org/10.5869/fc.2020.v25-1.089.
  • Evans, A., O’donovan, C., Playdon, M., Beecher, C., Beger, R., Bowden, J., Broadhurst, D., Clish, C., Dasari, S., Dunn, W., Griffin, J., Hartung, T., Hsu, P., Huan, T., Jans, J., Jones, C., Kachman, M., Kleensang, A., Lewis, M., Eugenia Monge, M., Mosley, J., Taylor, E., Tayyari, F., Theodoridis, G., Torta, F., Ubhi, B., Vuckovic, D. (2020). Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC–MS based untargeted metabolomics practitioners. Metabolomics, 16(10), Article number 113. https://doi.org/10.1007/s11306-020-01728-5.
  • Litton, E., Anstey, M., Broadhurst, D., Chapman, A., Currie, A., Ferrier, J., Gummer, J., Higgins, A., Lim, J., Manning, L., Myers, E., Orr, K., Palermo, A., Paparini, A., Pellicano, S., Raby, E., Rammohan, A., Regli, A., Richter, B., Salman, S., Strunk, T., Waterson, S., Wibrow, B., Wood, F. (2020). Study protocol for the safety and efficacy of probiotic therapy on days alive and out of hospital in adult ICU patients: the multicentre, randomised, placebo-controlled Restoration Of gut microflora in Critical Illness Trial (ROCIT). BMJ Open, 10(6), Article number e035930. https://doi.org/10.1136/bmjopen-2019-035930.

Journal Articles

  • Lawler, N., Abbiss, C., Gummer, J., Broadhurst, D., Govus, A., Fairchild, T., Thompson, K., Garvican-Lewis, L., Gore, C., Maker, G., Trengove, R., Peiffer, J. (2019). Characterizing the plasma metabolome during 14 days of live-high, train-low simulated altitude: A metabolomic approach. Experimental Physiology, 104(1), 81-92. https://doi.org/10.1113/EP087159.
  • Mendez, K., Reinke, S., Broadhurst, D. (2019). A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification. Metabolomics, 15(12), Article number: 150. https://doi.org/https://doi.org/10.1007/s11306-019-1612-4.
  • Beger, R., Dunn, W., Bandukwala, A., Bethan, B., Broadhurst, D., Clish, C., Dasari, S., Derr, L., Evans, A., Fischer, S., Flynn, T., Hartung, T., Herrington, D., Higashi, R., Hsu, P., Jones, C., Kachman, M., Karuso, H., Kruppa, G., Lippa, K., Maruvada, P., Mosley, J., Ntai, I., O’donovan, C., Playdon, M., Raftery, D., Shaughnessy, D., Souza, A., Spaeder, T., Spalholz, B., Tayyari, F., Ubhi, B., Verma, M., Walk, T., Wilson, I., Witkin, K., Bearden, D., Zanetti, K. (2019). Towards quality assurance and quality control in untargeted metabolomics studies. Metabolomics, 15(1), article number 4. https://doi.org/10.1007/s11306-018-1460-7.
  • Mendez, K., Broadhurst, D., Reinke, S. (2019). The application of artificial neural networks in metabolomics: a historical perspective. Metabolomics, 15(11), Article Number 142. https://doi.org/10.1007/s11306-019-1608-0.
  • Mendez, K., Pritchard, L., Reinke, S., Broadhurst, D. (2019). Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing. Metabolomics, 15(10), Article no.125. https://doi.org/10.1007/s11306-019-1588-0.
  • Newton, R., Christophersen, C., Fairman, C., Hart, N., Taaffe, D., Broadhurst, D., Devine, A., Chee, R., Tang, C., Spry, N., Galvao, D. (2019). Does exercise impact gut microbiota composition in men receiving androgen deprivation therapy for prostate cancer? A single-blinded, two-armed, randomised controlled trial. BMJ Open, 9(4), article no. e024872. https://doi.org/10.1136/bmjopen-2018-024872.
  • Denihan, NM., Kirwan, JA., Walsh, BH., Dunn, WB., Broadhurst, D., Boylan, GB., Murray, DM. (2019). Untargeted metabolomic analysis and pathway discovery in perinatal asphyxia and hypoxic-ischaemic encephalopathy. Journal of Cerebral Blood Flow and Metabolism, 39(1), 147-162. https://doi.org/10.1177/0271678X17726502.

Journal Articles

  • Reinke, S., Galindo-Prieto, B., Skotare, T., Broadhurst, D., Singhania, A., Horowitz, D., Djukanovic, R., Hinks, T., Geladi, P., Trygg, J., Wheelock, C. (2018). OnPLS-Based Multi-Block Data Integration: A Multivariate Approach to Interrogating Biological Interactions in Asthma. Analytical Chemistry, 90(22), 13400-13408. https://doi.org/10.1021/acs.analchem.8b03205.
  • Cassiède, M., Mercier, P., Shipley, P., Dueck, M., Kamravaei, S., Nair, S., Mino, J., Pei, L., Broadhurst, D., Lacy, P., Quémerais, B. (2018). Comparison of computational approaches for identification and quantification of urinary metabolites in 1H NMR spectra. Analytical Methods: advancing methods and applications, 10(18), 2129-2137. https://doi.org/10.1039/c8ay00830b.
  • Kirwan, J., Brennan, L., Broadhurst, D., Fiehn, O., Cascante, M., Dunn, W., Schmidt, M., Velagapudi, V. (2018). Preanalytical processing and biobanking procedures of biological samples for metabolomics research: A white paper, community perspective (for “Precision medicine and pharmacometabolomics task group”—The metabolomics society initiative). Clinical Chemistry (Washington, DC): international journal of molecular diagnostics and laboratory medicine, 64(8), 1158-1182. https://doi.org/10.1373/clinchem.2018.287045.
  • Broadhurst, D., Goodacre, R., Reinke, S., Kuligowski, J., Wilson, I., Lewis, M., Dunn, W. (2018). Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics, 14(6), article no.72. https://doi.org/10.1007/s11306-018-1367-3.
  • Manaf, F., Lawler, N., Peiffer, J., Maker, G., Boyce, M., Fairchild, T., Broadhurst, D. (2018). Characterizing the plasma metabolome during and following a maximal exercise cycling test. Journal of Applied Physiology, 125(4), 1193-1203. https://doi.org/10.1152/japplphysiol.00499.2018.

Journal Articles

  • Dunn, W., Broadhurst, D., Edison, A., Guillou, C., Viant, M., Bearden, D., Beger, R. (2017). Quality assurance and quality control processes: summary of a metabolomics community questionnaire. Metabolomics, 13(5), article no.50. https://doi.org/10.1007/s11306-017-1188-9.

Journal Articles

  • Ahearne, C., Denihan, N., Walsh, B., Reinke, S., Kenny, L., Boylan, G., Broadhurst, D., Murray, D. (2016). Early Cord Metabolite Index and Outcome in Perinatal Asphyxia and Hypoxic-Ischaemic Encephalopathy. Neonatology: foetal and neonatal research, 110(4), 296-302. https://doi.org/10.1159/000446556.
  • Currie, F., Broadhurst, D., Dunn, W., Sellick, C., Goodacre, R. (2016). Metabolomics reveals the physiological response of Pseudomonas putida KT2440 (UWC1) after pharmaceutical exposure. Molecular BioSystems, 12(4), 1367-1377. https://doi.org/10.1039/c5mb00889a.
  • Murphy, N., Khashan, A., Broadhurst, D., Gilligan, O., O''donoghue, K., Kenny, L. (2016). Perinatal determinants of D-dimer levels in a cross-sectional study of low risk pregnant women. Obstetric Medicine, 9(2), 78-82. https://doi.org/10.1177/1753495X15625547.
  • Beger, RD., Dunn, W., Schmidt, M., Gross, S., Kirwan, J., Cascante, M., Brennan, L., Wishart, D., Oresic, M., Hankemeier, T., Broadhurst, D., Lane, A., Suhre, K., Kastenmuller, G., Sumner, S., Thiele, I., Fiehn, O., Kaddurah-Daouk, R. (2016). Metabolomics enables precision medicine: ‘‘A White Paper, Community Perspective’’. Metabolomics, 12(10), Article number 149. https://doi.org/10.1007/s11306-016-1094-6.
  • Chan, A., Mercier, P., Schiller, D., Bailey, R., Robbins, S., Eurich, D., Sawyer, M., Broadhurst, D. (2016). 1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer. British Journal of Cancer, 114(1), 59-62. https://doi.org/10.1038/bjc.2015.414.
  • Landi, A., Broadhurst, D., Vernon, S., Tyrrell, DL., Houghton, M. (2016). Reductions in circulating levels of IL-16, IL-7 and VEGF-A in myalgic encephalomyelitis/chronic fatigue syndrome. Cytokine, 78(2016), 27-36. https://doi.org/10.1016/j.cyto.2015.11.018.

Journal Articles

  • Stanley, J., Sulek, K., Andersson, I., Davidge, S., Kenny, L., Sibley, C., Mandal , R., Wishart, D., Broadhurst, D., Baker, P. (2015). Sildenafil therapy normalizes the aberrant metabolomic profile in the comt -/- mouse model of preeclampsia/fetal growth restriction. Scientific Reports, 5(15 December), ART 18241. https://doi.org/10.1038/srep18241.
  • Murphy, N., Broadhurst, D., Khashan, A., Gilligan, O., Kenny, L., Donoghue, K. (2015). Gestation-specific D-dimer reference ranges: a cross-sectional study. BJOG: an International Journal of Obstetrics and Gynaecology, 122(3), 395-400. https://doi.org/10.1111/1471-0528.12855.
  • Denihan, N., Walsh, B., Reinke, S., Sykes, BD., Mandal, R., Wishart, D., Broadhurst, D., Boylan, G., Murray, D. (2015). The effect of haemolysis on the metabolomic profile of umbilical cord blood. Clinical Biochemistry, 48(7-8), 534-537. https://doi.org/10.1016/j.clinbiochem.2015.02.004.
  • Dunn, W., Lin, W., Broadhurst, D., Begley, P., Brown, M., Zelena, E., Vaughan, A., Halsall, A., Harding, N., Knowles, J., Francis-McIntyre, S., Tseng, A., Ellis, D., O'Hagan, S., Aarons, G., Benjamin , B., Chew-Graham, S., Moseley, C., Potter, P., Winder, C., Potts, C., Thornton, P., McWhirter, C., Zubair, M., Pan, M., Burns, A., Cruickshank, J., Jayson, G., Purandare, N., Wu, F., Finn, J., Haselden, J., Nicholls, A., Wilson, I., Goodacre, R., Kell, D. (2015). Molecular phenotyping of a UK population: defining the human serum metabolome. Metabolomics, 11(1), 9-26. https://doi.org/10.1007/s11306-014-0707-1.
  • Hollywood, K., Winder, C., Dunn, W., Xu, Y., Broadhurst, D., Griffiths, C., Goodacre, R. (2015). Exploring the mode of action of dithranol therapy for psoriasis: A metabolomic analysis using HaCaTcells. Molecular Biosystems, 11(8), 2198-2209. https://doi.org/10.1039/c4mb00739e.
  • Balikcioglu, P., Balikcioglu, M., Muehlbauer, M., Purnell , J., Broadhurst, D., Freemark, M., Haqq, A. (2015). Macronutrient regulation of ghrelin and peptide YY in pediatric obesity and prader-willi syndrome. Journal of Clinical Endocrinology and Metabolism, 100(10), 3822-3831. https://doi.org/10.1210/jc.2015-2503.

Book Chapters

  • Pritchard, L., Broadhurst, D. (2014). On the statistics of identifying candidate pathogen effectors. Plant-Pathogen Interactions: Methods and Protocols (53-64). Springer. https://doi.org/10.1007/978-1-62703-986-4_4.

Journal Articles

  • Walsh, J., Reinke, S., Mamik, M., McKenzie, B., Maingat, F., Branton, W., Broadhurst, D., Power, C. (2014). Rapid inflammasome activation in microglia contributes to brain disease in HIV/AIDS. Retrovirology, 11(2014), 35. https://doi.org/10.1186/1742-4690-11-35.
  • Anderson, S., Dunn, W., Banerjee, M., Brown, M., Broadhurst, D., Goodacre, R., Cooper, G., Kell, D., Cruickshank, JK. (2014). Evidence that multiple defects in lipid regulation occur before hyperglycemia during the prodrome of type-2 diabetes.. PLoS One, 9(9), e103217. https://doi.org/10.1371/journal.pone.0103217.
  • Reinke, S., Broadhurst, D., Sykes , B., Baker, G., Catz, I., Warren, K., Power, C. (2014). Metabolomic profiling in multiple sclerosis: insights into biomarkers and pathogenesis. Multiple Sclerosis Journal, 20(10), 1396-1400. https://doi.org/10.1177/1352458513516528.
  • Kirwan , J., Weber, R., Broadhurst, D., Viant, M. (2014). Direct infusion mass spectrometry metabolomics dataset: A benchmark for data processing and quality control. Scientific Data, 1(10 June 2014), Article No.140012. https://doi.org/10.1038/sdata.2014.12.

Journal Articles

  • Reinke, S., Walsh, B., Boylan, G., Sykes, B., Kenny, L., Murray, D., Broadhurst, D. (2013). 1H NMR derived metabolomic profile of neonatal asphyxia in umbilical cord serum: Implications for hypoxic ischemic encephalopathy. Journal of Proteome Research, 12(9), 4230-4239. https://doi.org/10.1021/pr400617m.
  • Kirwan, J., Broadhurst, D., Davidson, R., Viant, M. (2013). Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow. Analytical and Bioanalytical Chemistry, 405(15), 5147-5157. https://doi.org/10.1007/s00216-013-6856-7.
  • Xia, J., Broadhurst, D., Wilson, M., Wishart, D. (2013). Translational biomarker discovery in clinical metabolomics: An introductory tutorial. Metabolomics, 9(2), 280-299. https://doi.org/10.1007/s11306-012-0482-9.

Journal Articles

  • Xia, J., Mandal, R., Sinelnikov, I., Broadhurst, D., Wishart, D. (2012). MetaboAnalyst 2.0-a comprehensive server for metabolomic data analysis. Nucleic Acids Research, 40(W1), W127-W133. https://doi.org/10.1093/nar/gks374.
  • Walsh, B., Broadhurst, D., Rupasri, M., Wishart, D., Boylan, G., Kenny, L., Murray, D. (2012). The metabolomic profile of umbilical cord blood in neonatal hypoxic ischaemic encephalopathy. PLoS One, 7(12), Article No. e50520. https://doi.org/10.1371/journal.pone.0050520.
  • Egan, N., Bartels, A., Khashan, A., Broadhurst, D., Joyce, C., Mullane, J., Donoghue, K. (2012). Reference standard for serum bile acids in pregnancy. BJOG: an International Journal of Obstetrics and Gynaecology, 119(4), 493-498. https://doi.org/10.1111/j.1471-0528.2011.03245.x.
  • Reyes, L., Garcia, R., Ruiz, S., Broadhurst, D., Aroca, G., Davidge, S., Lopez-Jaramillo, P. (2012). Angiogenic imbalance and plasma lipid alterations in women with preeclampsia from a developing country. Growth Factors, 30(3), 158-166. https://doi.org/10.3109/08977194.2012.674035.
  • Bartels, A., Egan, N., Broadhurst, D., Khashan, A., Joyce, C., Stapleton, M., O'Mullane, J., O'Donoghue, K. (2012). Maternal serum cholesterol levels are elevated from the 1st trimester of pregnancy: A cross-sectional study. Journal of Obstetrics and Gynaecology, 32(8), 747-752. https://doi.org/10.3109/01443615.2012.714017.
  • Dunn, W., Wilson, I., Nicholls, A., Broadhurst, D. (2012). The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis, 4(18), 2249-2264. https://doi.org/10.4155/bio.12.204.

Journal Articles

  • Dunn, WB., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., Brown, M., Knowles, JD., Halsall, A., Haselden, JN., Nicholls, AW., Wilson, ID., Kell, DB., Goodacre, R. (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6(7), 1060-1083. https://doi.org/10.1038/nprot.2011.335.
  • Dunn, W., Broadhurst, D., Atherton, H., Goodacre, R., Griffin, J. (2011). Systems level studies of mammalian metabolomes: The roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chemical Society Reviews, 40(1), 387-426. https://doi.org/10.1039/b906712b.
  • Horgan, R., Broadhurst, D., Walsh, S., Dunn, W., Brown, M., Roberts, C., North, R., McCowan, L., Kell, D., Baker, P., Kenny, L. (2011). Metabolic profiling uncovers a phenotypic signature of small for gestational age in early pregnancy. Journal of Proteome Research, 10(8), 3660-3673. https://doi.org/10.1021/pr2002897.

Research Projects

  • An exploratory study to determine if exercise can impact the gut microbiota composition of men receiving androgen suppression therapy for prostate cancer, Prostate Cancer Foundation of Australia, Grant, 2018 ‑ 2023, $98,875.
  • Securing health in Fiji through strengthened health systems and integrated water management to tackle the Three Plagues: typhoid, dengue and leptospirosis., National Health and Medical Research Council, DFAT - Stronger Systems For Health Security Call, 2018 ‑ 2022, $375,179.
  • Multi-centre, multi-disciplinary study using a systems biology approach to investigate immunomodulation in children with acute wheeze, National Health and Medical Research Council, Project Grants, 2018 ‑ 2021, $73,873.
  • Australian Metabolic Phenotyping Centre (AMPC), Australian Research Council, Grant - Linkage (Infrastructure), 2017 ‑ 2019, $33,130.
  • High Throughput NMR Facility, Australian Research Council, Grant - Linkage (Infrastructure), 2019, $415,000.
  • Investigating lipid synthesis pathways in genetically modified seeds, Edith Cowan University, Edith Cowan University, School of Science Early Career Grant Scheme Semester 1, 2017, 2017, $10,000.
  • Improving barley malting quality through a combined metabolomics and transcriptomics approach, Edith Cowan University, Edith Cowan University, School of Science Early Career Grant Scheme Semester 1, 2017, 2017, $10,000.

Research Student Supervision

Principal Supervisor

  • Master of Science (Chemistry), Deriving statistical inference from the application of artificial neural networks to clinical metabolomics data
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