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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Exploring protein interactions: yeast two hybrid systems

>Proteins are one of the key molecules inside cells; involved in signalling, intracellular transport, metabolism and gene control. They rarely work alone, most of the proteins in the cell are part of large complex networks consisting of many interacting proteins. Various techniques exist in order to find these interactions, and one of the most common is the use of yeast two hybrid systems.

Yeast is apparently quite a nice organism to work with (I've never worked with it myself, I must say, apart from a few practicals in second year, where I almost set my lab-partners hand on fire). The genome is fully annotated, the organism is well characterised, and yeast grows and responds quite fast, so experiments shouldn't take too long.

The yeast-two hybrid system is based around molecules called transcription factors, which are normally used by the cell to active gene expression. In yeast, there are several transcription factors which consist of two separate molecules, which need to be in close proximity in order for a gene to be expressed. If you attach an experimental protein to one half of the transcription factor, and another experimental protein (that you think interacts with the first) to the other half you can test for interactions. If the two proteins do interact then the transcription factors will be brought close together and the gene downstream of them (which acts as a reporter gene) will be expressed:The diagram above (taken from the reference below) shows this process. The blue jigsaw-shaped 'X' and 'Y' proteins are the experimental proteins, being tested for interactions and the yellow shapes are the two parts of the transcription factor. The big white cloud is the polymerase, which begins the process of turning the DNA into protein. The reporter gene can be set to code for a vital compound such as histidine; stick the whole system into a histidine deficient mutant and you have a marker system to see if the proteins interact. If they do, the histidine is produced and the cells can grow, if they don't, the cells die.

One of the most useful things about this technique is that it can be automated, and used to scan whole libraries of proteins to see if they interact. By using a matrix, each protein X (the 'bait' protein) can be given a defined position and then systematically exposed to a number of different protein Y (the 'prey' protein). If you're taking Y from a clonal library, and have a sufficiently intelligent robot, the whole procedure can be carried out with minimal human input.

The yeast two-hybrid system has been invaluable for determining many important protein-protein interactions however there are some problems with it. Firstly, this is a yeast two component system, and most protein complexes consist of many interacting proteins, certainly more than two! Secondly, this whole system relies on two soluble proteins interacting in the nucleus (where the DNA is) and so doesn't work for membrane bound protein interactions.

In view of this, several modifications have been made to the original methodology to make it more useful for trapping a wider range of protein interactions. It's been expanded into the three component system, which identifies proteins that interact with (or inhibit) both the the bait and the prey. Using another natural yeast system (the G protein system) has allowed transmembrane proteins to be identified as well:
In this system protein Y contains a binding site for a subunit of the G protein, while X is a membrane-spanning protein. G proteins are membrane bound proteins that activate transcription factors inside the nucleus. If the two proteins interact then the G protein subunit bound to the Y is sequestered away from the rest of the complex, and the G protein signal cannot be transmitted. There is therefore no signal to the transcription factors, meaning reporter genes (in this case genes for pigmentation rather than for death) will be turned off.

Even with these modifications there still are problems with the yeast two hybrid system. There is very little quantitative analysis involved to see how strongly the protein is binding, and the discovery of new interactions will always be limited by the choice of proteins to screen. However one of the largest advantages of yeast two hybrid systems is that it carries out protein binding analysis inside the cell, in proper cellular conditions, unlike affinity binding which, while it can identify much larger protein complexes, involves taking proteins out of the cell and handling them in vitro.

Yeast two hybrid systems are therefore still one of the main practical methods used for determining protein interactions. The field of systems biology is a fascinating one, and while attempting to catalogue the whole spectrum of cellular interactions may seem like a daunting task ('interactomics' for the funding people) working towards it will only reveal more and more useful information about the complex and fascinating networks of proteins within the cell.

[btw: The reference below is a great source of information about the many different modifications made to the basic yeast two hybrid system, thoroughly recommended for anyone interested in protein interactions or systems biology]

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Brückner A, Polge C, Lentze N, Auerbach D, & Schlattner U (2009). Yeast two-hybrid, a powerful tool for systems biology. International journal of molecular sciences, 10 (6), 2763-88 PMID: 19582228

Presenting Histones

Bravery is an interesting word. It's one of those words that has many different subtle shades of definition; ranging from altruism to stupidity. The big question is, of course, was it brave or stupid of me to volunteer to do a presentation at the first supervision of term?

To put this more in context, I haven't actually done a scientific presentation since, well, at all. We did some mini ones in our supervision group last year, but they did not go so well. I haven't had to speak in front of a large crowd of people for about three years, not since the upper sixth performance of 'Dracula' where I stumbled on stage for a few minutes to play Translyvanian Peasant With Godawful Accent.

Here is the title and the link (for those who can get it) for what I have to present:
"Rb targets histone H3 methylation and HP1 to promoters"

I'm going to go through the paper now and try to provide a quick summary of what it is about. I have no idea how I'm meant to present it (hopefully there will be a brief meeting at some point to discuss this) but I can't help but feel things will go slightly better if I actually know what the paper is talking about.

okay... a look at the abstract and one brief scribbled diagram later this is what I've got:

Pretty pictures if you follow the links!

There is lots of DNA in the cell, so in order for it to fit into the nucleus it has to be coiled. One method for coiling involves wrapping the DNA around histone proteins (beads on a string) to keep them coiled. As well as keeping the DNA wound up, histones can also signal to transcription factors (proteins that start the complex process of turning DNA into protein) which bits of the DNA they need to read by displaying chemical signals.

One such signal is the methyl group, -CH3. Sticking a methyl group onto the end of a histone signals to the cells that this DNA is in Do Not Disturb mode, and should not be turned into protein. The study the paper was doing focused on a protein that goes around putting up all the nuclear Do Not Disturb signs; SUV39H1 (which shall henceforth be known as SUVy). This methylated the histones at a certain point (lysine 9 of histone H3 for anyone interested) and keeps the DNA associated with them from being expressed. It does this by recruting HP1 which binds to the DNA and, as far as I can work out from this, just sits there and stops it being expressed.

There are two forms that DNA in the nucleus can take: heterochromatin, which is all coiled up and not doing anything, and enchromatin, which is being actively expressed. This paper was getting fairly excited because while it was known that SUVy and HP1 were good at keeping heterochromatin quiet, they found them interacting with euchromatin! What's more they were consorting with Rb, a very well known protein that is involved in all sorts of processes that supress the expression of DNA, particularly in different parts of the cell cycle.

By doing various assays involving pulling out the Rb bound to DNA and then finding what bit of DNA it was bound to, they discovered that it methylated the same H3 on the lycine that HP1 did. Furthermore, Rb can interact with SUVy, due to a 'pocket domain' which SUVy fits into quite well. The end conclusion of all this is that Rb and SUVy interact together, methylate a part of the DNA which people hadn't really known SUVy was methylating, and then HP1 comes and sits on it.

The exciting thing here (alright not that exciting, but fairly interesting at the least) is that they put forward at the end that there may be other euchromatin repressor proteins out there that bind to SUVy and mobilise the DNA repression in euchromatin. Also, as Rb is involved in cell cycle control, it helps to build a bigger picture of just what is going on in the cell cycle (which cancer reseachers tend to like).

And woohoo I get to do a presentation on it. :)

Proteomics

So, term has started. Lab Rat is out of the lab and back in the lecture theatre, which means probably less blog posts as I try to get through my rather scary reading list (thankfully it is made up mostly of papers but there's still rather a lot of it).

The first topic for this term is Proteomics; the study of the structure and function of proteins within a cell and living proof that adding the term -omics onto something gets you exciting amounts of funding. Proteomics is turning out to be fairly interesting, the paper I've just read, for example (here if you can get to it) talks about how large scale proteomics was used to compare the proteins in the malaria parasite P. falciparum in its different states of growth. The idea is to find a protein in one stage that isn't in the body naturally and then target it to kill off the parasite. Also it's really interesting finding out which proteins are expressed when, and which ones the parasite turns off at different stages for various reasons.

*sigh*

Yeah, I miss phages :(