Metabolomics 1 Flashcards
What are metabolites?
- small molecular weight organic and inorganic biochemicals
-> molecular weight <1500 Da
-> typically not proteins or peptides
-> some exceptions: glutathione is a trial-peptide but has a metabolic function
-> S-adenosyl-methionine is adenosine linked to methionine - building blocks for larger biochemicals
-> proteins, RNA, DNA - many possible classifications:
-> polar (hydrophilic): amino acids, carbohydrates, organic acids
-> non-polar (lipophilic): fatty acids, glycerophospholipids, bile acids, steroids - lipoproteins in blood: HDL, LDL, VLDL (and sub-classes) - strictly not metabolites
Definitions
- Metabolism is the integration of physical and chemical processes employing small biochemicals (metabolites) involved in the maintenance and reproduction of life
- Metabolism involves the conversion of one metabolite (a substrate or precursor) to another metabolite (a product) via an enzymatic reaction (and in many cases in the presence of a co- factor)
- Catabolism - reactions involving the breaking down of organic substrates, typically by oxidation, to provide chemically available energy (e.g. ATP) and/or to generate metabolic intermediates used in subsequent anabolic reactions
- Anabolism - processes of metabolism that result in the synthesis of cellular components from precursors of low molecular weight
- Amphibolic metabolism - a metabolic pathway which involves both catabolic and anabolic processes (e.g. TCA cycle)
- Metabolism operates through defined metabolic pathways, e.g.
- Glycolysis
- TCA cycle
Definitions
-> Metabolomics vs Metabonomics
Metabolomics “Metabolomics is the comprehensive study of all metabolites present in a biological system.” (Dunn et al., 2011; Fiehn, 2002)
“Metabolomics is a field of omics science that uses cutting edge analytical chemistry techniques and advanced computational methods to characterize complex biochemical mixtures.“ (Wishart, 2016)
Metabonomics measure the global, dynamic metabolic response of living systems to biological stimuli or genetic manipulation (Nicholson 2008).
Practical Approach (Wishart)
Genomics: A field of life science research that uses high-throughput (HT) Technologies to identify and/or characterise all genes in a given cell, tissue or organism (i.e. the genome).
Metabolomics: A field of life science that uses high-throughput (HT) technologies to identify and/or characterise all the small molecules or metabolites in a given cell, tissue or organism (i.e. the metabolome)
A brief history of metabolomics
Linus Pauling hypothesized on the predictive capacity of chromatographic profiling of body fluids for detection and diagnosis of human disease.
Chromatographic separation techniques were developed in the late 1960s
Robinson and Pauling published “Quantitative Analysis of Urine Vapor and Breath by Gas-Liquid Partition Chromatography” in 1971.
The Metabolome and Metabolomics concept were proposed in the 1990s. (Holistic approach - in contrast to reductionist).
In January 2007, the Human Metabolome Project, completed the first draft of the human metabolize, consisting of 2,500 metabolites, 1.200 drugs and 3.500 food components. The database now contains 115.434 compounds.
Urine Flavor Wheels
It describes possible colors, smells and taste of urine, and uses them for diagnostic purposes.
Sir Henry Wellcome’s 1911 overview of the history of uroscopy,
The Evolution and Development of Urine Analysis, assembles
a variety of urine flavour and fragrance notes from throughout history.
From “antient Sanskrit works of medicine,” he culls a list of morbid urine varieties that include:
* Iksumeha, cane-sugar juice urine.
The urine is very sweet, cold, sticky, opaque, like the juice of cane sugar.
* Ksuermeha, potash urine.
The urine has the taste, smell, touch and colour of potash.
* Sonitameha, urine containing blood.
The urine is of bad odor, hot, and tastes of salt, like blood.
* Hastimeha, elephant urine.
The patient continuously passes turbid urine like a mad elephant.
* Madhumeha, honey urine.
The urine is astringent, sweet, white and sharp.
The last is known today as the urine of diabetes mellitus. English physician Thomas Willis noted the same relationship in 1674, reporting that diabetic urine tastes “wonderfully sweet as if it were imbued with honey or sugar.”
-> Urine spectra can identify individuals
History of NMR-based metabolomics
1974: Wilson and Burlingame
1977-1984: Bob Shulman: 13C- and 31P-NMR in cell systems 1984: Jeremy Nicholson: First NMR of urine
1986: “Fossel” NMR test for cancer
1991: Jim Otvos NMR-based HDL/LDL/VLDL test
1993: LCmodel by Provencher
1999: Metabonomics Nicholson
2000: Metabolomics Drysdale
2004: Brüschweiler covariance NMR for mixture deconvolution Since 2006: Wishart, Markley – Large Metabolome databases
Otvos: HDL/LDL
Early Metabolomics via NMR 1991
NMR can detect lipoprotein associated molecules
Lipoprotein transport system
- Initial transport of dietary fats
- Secondary transport of processed cholesterol particles for steroid hormone and membran synthesis
- Processing of free fatty acids
Metabolomics approaches
- fingerprinting
- footprinting
- profiling
- target analysis
- flux analysis
Typical Metabolomics Workflow
- Samples
- Record chemical data
- Process dataset
- Analyse/Model data/Identify
- Interpret the results
Typical routes to metabolites: UNTARGETED ANALYSIS
- no Prior knowledge of metabolites of interest
- untargeted analysis
- Fingerprinting (binned spectra) or Profiling (concetrations of all quantifiable metabolites)
- statistical approaches (multivariate analysis or univariate analysis)
-> chemometric methods -> classification based on metabolic fingerprint
Typical routes to metabolites: TARGETED ANALYSIS
- Prior knowledge of metabolites of interest
- targeted analysis
- statistical approaches (multivariate analysis or univariate analysis)
-> quantitative approach -> concentration of quantifiable of metabolites
Multivariate analysis
- Explore data without any class membership -> unsupervised methods
- Discrimination among the groups of interest -> supervised methods
Why Metabolomics is difficult?
-> way more chemical diversity
Genomics (4 bases-> coverage right now: 22.000 genes)
Proteomics (20 amino acids-> coverage right now: 8000 proteins)
Metabolomics (8x10^5 chemicals -> coverage right now: 200 chemicals)
