Developing standards for metabonomics as a clinical tool.
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Developing standards for metabonomics as a clinical tool.

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Talk presented at COMBIO in 2007 in Sydney.

Talk presented at COMBIO in 2007 in Sydney.

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  • * Disorders of fatty acid oxidation and mitochondrial metabolism o E.g., medium chain acyl dehydrogenase deficiency * Disorders of porphyrin metabolism o E.g., acute intermittent porphyria (E80.2) * Disorders of purine or pyrimidine metabolism o E.g., Lesch-Nyhan syndrome (E79.1) * Disorders of steroid metabolism o E.g., congenital adrenal hyperplasia (E25.0) * Disorders of mitochondrial function o E.g., Kearns-Sayre syndrome (H49.8) * Disorders of peroxisomal function o E.g., Zellweger syndrome (Q87.8) * Lysosomal storage disorders o E.g., Gaucher's disease (E75.22)
  • Time courses of appearance of ethanol, acetate, formate - microbes or organic chemistry.
  • NST - non-sterile NMR tube, ST - sterile NMR tube
  • Jeremy Nicholson Nature Biotechnology metabonomic recommendations Beakering about the standards Heparin preferred for blood over citrate or EDTA as there is no interference with the analysis. Time of collection will affect the profiles and thus it is especially important with animal sample collection . Avoiding stress markers by acclimatizing animals to cages is essential Diet information of healthy controls should be as detailed as possible and fasted samples should be collected as for general testing Alcohol swabs should not be used prior to blood collection Things that have to be included in analysis part should include: design of experiment, data scaling, data normalization, algorithm selection, unsupervised statistical methods, supervised models, models testing and model validation

Developing standards for metabonomics as a clinical tool. Developing standards for metabonomics as a clinical tool. Presentation Transcript

  • Developing standards for metabonomics as a clinical tool. Agnieszka M. Lichanska, Shaffinaz Abd Rahman and Horst J. Schirra School of Dentistry and IMB, University of Queensland, Australia
  • Introduction
  • Genomics and proteomics tells you what might happen, but metabolomics tells you what actually did happen. - Bill Lasley, University of California, Davis
  • Genomics Transcriptomics Proteomics Metabolomics Metabonomics DNA RNA Protein Metabolites 25,000 genes 100,000 mRNAs 1,000,000 proteins 2,500 metabolites Metabonomics in context
  • Introduction Metabonomics •'The quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification' (Nicholson et. al.) •Use of statistical methods to detect changes in metabolites over time or between groups. Metabolomics •The characterisation of all metabolites in a sample/organism. … a lot of people use both terms interchangeably…
  • Metabolic changes Genetic changes (mutations) Drugs, diet Changes in metabolite concentration Blood Urine other biofluids NMR, MS spectra Disease Identification of individual metabolites Detailed analysis Metabolic changes and their analysis Metabolite Database
  • Methods • Bruker 500MHz spectrometer with sample changer • Urine from fasted male mice – WT controls – Mutant mice – Age 2-12 months • Samples frozen upon collection • 1M Phosphate buffer • TSP, D2O
  • Nuclear Magnetic Resonance (NMR) - based metabonomics • Advantages: – Fully quantitative – Non-destructive – Minimal sample preparation – High throughput • What can be analyzed? – Analysis of all types of biofluids (urine, plasma, saliva, cerebrospinal fluid, sperm, synovial fluid, amniotic fluid) – Solid samples (biopsies of organs and cell cultures)
  • Mouse studies - physiology of growth hormone WT 569 391 GHR -/- Rowland, Lichanska et al 2005, MCB 25:66-77 Model - GHR KI mice
  • Mouse studies - physiology of growth hormone Schirra et al 2007, under review
  • Mouse studies - physiology of growth hormone Schirra et al 2007, under review
  • Clinical Applications
  • Applications • Health management – Non-invasive monitoring of asthma in children - University Hospital Padua • Drug intervention - evaluation of drug treatment • Risk management - exposure to toxic substances • Lifestyle/diet studies – Nestle (age, gender, smoking, alcohol, menopause, sport, BMI) • Nutritional applications - Nestle studies (chocolate, coffee) • Diagnostics – CARDIUM project - Varese Hospital – Tumor markers - epithelial ovarian cancer (serum) – Inborn metabolic disorders – Meningitis diagnosis (cerebrospinal fluid)
  • Diagnosis of inborn errors of metabolism Disorders of: • carbohydrate metabolism – E.g. glycogen storage disease • amino acid metabolism – E.g. phenylketonuria • organic acid metabolism - E.g. alcaptonuria • fatty acid oxidation and mitochondrial metabolism • porphyrin metabolism • purine or pyrimidine metabolism – E.g. Lesch-Nyhan syndrome • steroid metabolism – E.g. congenital adrenal hyperplasia • mitochondrial function • peroxisomal function – Zellweger syndrome • Lysosomal storage disorders – Gaucher's disease
  • Sample preparation study
  • Methods • Bruker 500MHz spectrometer with sample changer • Urine from a healthy volunteer – 1st urine of the day – Collected midstream • 1M Phosphate buffer • TSP, D2O • Na Azide • Storage conditions variable
  • Study design Untreated Centrifuged First morning urine Room Temp. on ice / -20°C 1% Sodium Azide Ultra-Filtrated (MWCO 10 kDa) The samples were stored either at -20o C or at RT and were measured on day 0, 2 and 9. Sterile filtration 0.2µm
  • Day 0 1% Sodium Azide Ultra-Filtrated Centrifuged Untreated Blue Spectra – Room Temperature Red Spectra – In Ice • Spectra are identical, irrespective of treatments • Ultra-filtrated samples had addition glycerol signals Glycerol
  • Day 2 -20°C Untreated RT Untreated -20°C 1% Sodium Azide RT 1% Sodium Azide -20°C Centrifuged RT Centrifuged Formate Formate Formate Acetate Ethanol Acetate Acetate Ethanol Ethanol
  • Comparison between treatments days 0-9 Day 0 Day 2 Day 9 Day 9 Day 2 Day 0 Centrifuged Formate Formate Formate Formate Ethanol Ethanol Acetate Acetate Acetate Acetate Untreated
  • Sample analysis SDS-PAGE SF1 ULF1 ULF2 untreated2 ULF3 ULF4 SF1 SF2 untreated1 SF2 Urine sample SDS-PAGE Microbiological testing: 1. Microscopy 2. Growth on basal media (agar plates) Stain with Coomasie Scoring for growth on plates and presence/absence of yeast or bacteria by microscopy
  • Summary • Storing samples at RT caused them to have ageing effects formed from microbial contamination in the samples • Sterile filtration samples kept at -20 °C were the only treatment that showed consistently no presence of those metabolites • Optimal method: 1. Sterile filter samples 2. add 1% sodium azide 3. Measure at Day 0 4. Store at -20°C • Ethanol, acetate and formate signals should be excluded in all statistical study as it was proven that elevated concentrations of these metabolites were due to external ageing reactions.
  • Metabolomics standards initiative (MSI, http://msi-workgroups.sourceforge.net/ ) • Sample information – Collection details (date, place, method, frequency) – Extracts from tissues or solid state analysis - processing details – Patient information (diet, drugs, infections, chronic diseases, etc) – Volume collected, pH, osmolarity, – Sample storage (temperature, additives) – Sample processing details • NMR/MS data acquisition – QC procedures, used of internal standards, – Instrument performance and maintenance logs has to be documented – Acquisition protocol details - SOP establishment – Instrument specifications • Data analysis – All data manipulations should be specified, MSI has a format for reporting analysis • Data format – Exchangeable file format – Raw data access for future re-analysis or reference
  • FDA and metabonomic studies • Metabolomics data is currently being evaluated by the voluntary genomics data submission (VGDS) group. • Such data is likely to be included in FDA submissions to support: – A mechanism of a drug – Metabolite/s are used biomarkers in evaluations • Metabolic markers are seen as most relevant for understanding the mechanism of action, defining the safety of a compound, and for monitoring clinical efficacy. • A metabolomic report should include a number of information mentioned before. The two important issues are the selection of controls and the analysis methods used to identify the biomarker/s
  • Acknowledgments IMB Dr. Horst J. Schirra Cameron Anderson Linda Kerr Sheryl Maher Jenny Rowland Prof. Mike J. Waters Prof. David J. Craik Mater Children’s Hospital Prof. Francis Bowling Teresa Munce Dr. Gary Leong Tony Huynh School of Dentistry/IMB Shaffinaz Abd Rahman University of Ohio Prof. John Kopchik Queensland Smart State Fellowship