Metabolomics

'Omics' words tend to get a large amount of bad press in biology. Starting fairly sensibly with genomics, the group expanded to include such things as proteomics, metabolomics, transcriptomics and then when a bit crazy, with seemingly every branch of biology (and possibly even a couple of physicists) wanting to find an 'omics' to work on. The 'Tree of Life' blog even started giving out awards to some of the more hilarious ones.

Omics words are like the cool silent kids in black leather jackets with a hint of drug-taking about them. One of them is fine, a couple of them can be fun but if you get too many it just starts getting messy and looks a bit gratuitous. And eventually people realise there's nothing particularly amazing about them and they become Public Enemy Number One. Which is a pity as many of them are actually quite sensible and can be very useful.

As far as I've been lead to believe 'ome' means 'complete set of '. The genome is the set of all the genes in a cell, proteome the set of all proteins, transcriptome the set of all mRNA transcripts made from the DNA and the metabolome is the set of all metabolic reactions taking place in the cell. 'Omics' is the study of 'omes'.

On the face of it, metabolomics looks to be a mindnumbingly insane task. To study and document every metabolic reaction happening in a cell, to create a model of it, and then use that model to predict how levels of substances change in response to changing conditions seems almost impossible. Just to give some idea of the task, here's a quick diagram of a couple of metabolic pathways involving manipulation of carbon chains:

Diagram taken from the SYSFYS project carried out by the University of Helinski Computer Department. This picture is one of two reasons I am currently studying biochemistry.

Each little dot on the diagram above represents a metabolite, and that diagram doesn't include enzymes, or the things that affect enzymes, or any method of regulation. It doesn't include quantitative analysis of the flux through every pathway, and how different concentrations of metabolites or regulators affect that flux. All of that information is tied up in metabolomics.

One of the first things that's noticeable about that diagram (other than that it looks like the London Tube Map as designed by Tim Burton) is that all the branches appear to be interconnected, everything is joined together. They become a lot more interconnected when you start considering regulation of each step, as many of the metabolic enzymes are regulated by similar compounds (ATP, for example). And what that means is that a change in the levels of one metabolite can have an unprecedented effect on the levels of another. More importantly, anything that accidentally gets missed out of the diagram could cause the model to work incorrectly. To put it in (slightly) more mathematical terms: there are a lot of parameters floating around.

How do you even start studying something like that?

Analysis can be split into two broad categories, open and closed. Open analysis involves taking a sample, and looking for metabolites. It's primarily used to find novel entities, and is rather open ended, in that you start without much of an idea of what you're going to find. 'Looking for metabolites' is done by pretty much any method used to detect proteins; mostly NMR spectroscopy, Liquid and gas chromatography, various Mass Specs and chromatography methods which would take up a whole blog post on their own (which I can write, if anyone's interested, it will be good revision).

Closed analysis focuses on a specific molecule (or molecules) and tries to find out as much as possible about them; what they interact with, what interaction rates are, how it's reactions are controlled, etc. This can be a lot more sensitive than open analysis, and you start with a clear idea of what you're searching for. Apparently it's better for producing papers as well.

By looking for different proteins, and then examining them in detail, a picture can be gradually built up of the metabolic pathways and their interactions. While I'm sure this has many uses in humans (for medical purposes) one of the applications I've been most exposed to is (surprise, surprise) in bacteria, where an understanding of existing metabolic pathways can be used to enhance synthetic ones. By playing around with the enzymes and fluxes of pathways involved in (say) a certain antibiotic precursor, you can encourage bacteria to be far more productive in antibiotic synthesis.

Like all things, metabolomics is at it's best when combined with other methods to give a fuller picture. The information gained from both metabolomics and transcriptomics was used in the reference below to find a key transcriptional compound, Stearyl-CoA desaturase, involved in fatty liver production. Fatty liver is formed from lipid accumulation in the liver, caused by orotic acid supplementation in rats, and excessive drinking in humans. The metabolic diagram below shows the effects of the orotic acid addition (green denotes an increase in a substance, and red a decrease) which for anyone who is not instantly able to pick out glycerol substrates (like me) simply shows just how much work is involved in metabolomics, and how complicated it can get.Despite my fascination with metabolomics and the pretty diagrams they produce, I don't think it's an area I would really go into. Nevertheless it's produced some very fascinating results, with some very worthwhile applications for many different scientific disciplines.

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Griffin, J. (2004). An integrated reverse functional genomic and metabolic approach to understanding orotic acid-induced fatty liver Physiological Genomics, 17 (2), 140-149 DOI: 10.1152/physiolgenomics.00158.2003

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