Field of Science

Microbes and Climate Change

Since the very first little blobs of entropy-defying life first appeared around four billion years ago, micro-organisms have played a major role in shaping the temperature of the planet by adjusting the balance of gases in the air. It could even be argued that global warming was one of the first effects of life, when the first methanogens (methane producing bacteria) started pumping greenhouse gasses into the new atmosphere. The evolution of photosynthesis lead to the great oxidation event, and over time the balance of gasses in the atmosphere stabilised into its current composition: lots of nitrogen (controlled by nitrogen fixing bacteria), medium amounts of oxygen (controlled by photosynthesis) and much smaller amounts of carbon dioxide (also controlled by photosynthesis, of both plants and bacteria).

Until humans, the general gaseous air composition was controlled almost exclusively by bacteria, with plants (mostly algae) having a lesser effect on carbon and oxygen levels. Animals didn't really get much of a look in until humans started releasing all the locked up carbon in fossil fuels.

Bacteria that are currently contributing to global warming are the methanogens, most notably those in the gut of ruminant mammals (i.e cows, sheep and other edible things). Cows and sheep can't break down cellulose in the plant material that they eat, so they have bacteria that do it for them. Unfortunately this process releases huge amounts of methane, and methane is around 20 more planet-warming than carbon dioxide.

When I went to Copenhagen last year (I didn't go for the conference, in fact I didn't realise it was on until I started wondering why it was so hard to find a hostel room!) someone handed me a leaflet saying that climate change could be prevented if everyone in the world became a vegetarian. It was an ... interesting point of view, but you could see where the idea came from. Cows are little methane factories.

Just in case anyone forgot what a cow was.

However bacteria are also heavily involved in keeping climate change under control with photosynthesis, which uses up carbon and releases oxygen into the environment. Despite being very leafy and green, forests (even rainforests) tend not to be huge carbon sinks, they take up carbon during the day certainly, but at night they respire and use most of it up again, and anything they've stored tends to be released once they die and decompose. Marine cyanobacteria, however, take in carbon like its going out of fashion, and when they die they sink down to the bottom of the ocean and lock it all away in calcified rocks. One of the most prolific carbon-eating bacteria is Prochlorococcus. Around 100 million Prochlorococcus can be found in every litre of seawater and, along with fellow bacteria Synechococcus it removes about 10 billion tons of carbon from the air every year.

In terms of helping to moderate climate change, there are plenty of ideas floating around as too how bacteria could be useful, but one of the more helpful ones is trying to make a bacterial-based carbon neutral biofuel. The idea is that if you find bacteria that take up as much carbon for their growth as they release while being used as fuel they are technically 'carbon-neutral'. You can grow pretty much anything in bacteria, up to and including oils that can drive cars, it's just currently not very efficient.

Whether or not anything can be done to stop climate change (or, more importantly, whether or not people can agree to do anything) may be an unresolved issue, but its becoming clear that the issue of how the worlds climates are changing is a subject for microbiologists and plants-scientists as much as for meterologists. Whatever happens to the climate in the future, bacteria will still have a large part to play.


Exams and course-work are all over, so from now on I am hoping to keep this blog purely for the prokaryotes:

Craig Venter's Synthetic Genome

ResearchBlogging.orgI'm taking a miniscule break away from revision to quickly write my thoughts about the news thatt Craig Venter has finally made a 'synthetic cell' or, as Psi Wavefunction more correctly pointed out, a synthetic genome inside a normal cell. It's quite a landmark for synthetic biology; not only has an entire genome been constructed from scratch, but it's also able to replicate and make new bacteria with the same genome.

What the researchers did was to synthesise an entire genome, that is all the DNA present in the bacterial species Mycoplasma mycoides (1.08 Mbp - mega-base-pair for anyone interested), by making lots of 6 kpb (kilo-base-pair) pieces and splicing them together in yeast. They then had to carefully get the completed genome out of the yeast, and put it into an empty (i.e containing no other DNA) M. mycoides cell. The resulting bacterial cell contained only synthetically made DNA, and was capable of surviving and replicating quite happily.

Above is a scanning electron microscope picture of the dividing cells

Probably the first thing to notice about this is despite it being pretty damn impressive, it's not exactly the creation of new life. It fact, I'm not sure I'd say it's even the creation of life, just the creation of a working genome. And despite what Richard Dawkins might think you need a lot more than just a working genome to be defined as life, especially life as complicated as a bacteria. What's been achieved here is sort of the bacterial-genome equivalent of in vitro fertilisation; the DNA has been synthetically made, but it's been put into a working bacteria, containing all the proteins, lipids and other molecules that are essential for life.

I'm certainly not putting this down, it's an amazing piece of work which makes my excitement over getting a 6kb gene synthesised over the summer seem very childish. But heralding it (or indeed condemning it) as 'Scientists create life' is a little over the top. DNA is, if anything, one of the easiest things to make in the cell, given it consists of different rearrangements of four base-pairs, all in a long string. Small bits of DNA have been synthesised for a while, but as yet, no one really has much of a clue how to synthesise bacterial cell membranes, let alone how to get them to synthesise and replicate themselves.

This is the genome that Venter built. The text in the middle shows the process in full. The little letters around the edge (BssH II etc) show sites for restriction enzymes which are used to cut the genome into little pieces for analysis.

One of the questions that always comes up whenever synthetic biology is mentioned is "how safe is it?" after all, this is a man-made genome going into a bacterial cell. Surely you could make another, more dangerous genome, and put that inside a bacteria and then use it to cause destruction, or a B-movie sci-fi plot? I suppose the risk is always there but in all reality, there are much better, cheaper and faster ways to ensure destruction happens. It took Venter's team six years to get this whole thing completed and working and while it's true that the process is only going to get faster I don't see it getting any quicker than rummaging around under the sink and coming up with enough ingredients to explode. Last summer it took around one and a half months to get my 6kb gene sequenced, and two months of work completely failing to make two very small mutations in another 2kb gene. There are people who fiddle around in their garages doing synthetic gene cloning, but there appears to be a pretty non-existent overlap with terrorist activity.

So this is a big step for genomes, but a tiny step towards a fully synthetic cell. Getting the full genome was a matter of time, patience, a large supply of base-pairs, plenty of money, and doing something clever with the base-pair methylation. Trying to make a synthetic membrane requires understanding how the things work first. Every new organism starts its life in a little cocoon of useful proteins, internal-membrane structures and little filaments which help to organise DNA expression even as the DNA controls their production and regulation. Putting new DNA into this pre-existing system is something that organisms do every time they replicate. Making the whole system from scratch is something that's never actually been done before, given that each generation has at best just tweaked the design a little from whatever the original cellular background was - probably just a quick scattering of proteins surrounded by a couple of glycolipid layers. Over the billions of years it's had to evolve, this has created a mysterious and highly complex system which would be incredibly difficult for a research to attempt to replicate.

I bet Venter's labs are trying though. They had pretty-much succeeding at creating synthetic ribosomes last time I looked.

Gibson DG, Glass JI, Lartigue C, Noskov VN, Chuang RY, Algire MA, Benders GA, Montague MG, Ma L, Moodie MM, Merryman C, Vashee S, Krishnakumar R, Assad-Garcia N, Andrews-Pfannkoch C, Denisova EA, Young L, Qi ZQ, Segall-Shapiro TH, Calvey CH, Parmar PP, Hutchison CA 3rd, Smith HO, & Venter JC (2010). Creation of a Bacterial Cell Controlled by a Chemically Synthesized Genome. Science (New York, N.Y.) PMID: 20488990

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Exam Term

Aka ... will you look at that, it's another hiatus!

Exams are coming up in less than three weeks now (scary thought ...) and as these are my finals I really do have to try and make a good go of them. So there will be a spooky silence on the twitter and blogging front, although I may occasionally jump into the Other Blog for some literature-related stress relief.

After exams though I will be completely free and still very much into science. I have a summer project all lined up, so expect more bacteria-related posts to return in full flood once I get these pesky tests out the way.

And if anyone is in Cambridgeshire, and owns a lab, and is happy to pay a Lab Rat to work in it from October, please do get in touch. :)

Student Symposiums

I'm currently in the middle of a two day symposium for the students. We all make a presentation of our work, and then show it to fellow students and any staff that happen to be floating around. I know this has been done before, but it honestly feels like speaking (and listening!) in code, especially having talked to all my friends before hand and heard the true stories behind the neat little slides. So here's the student version of what you say in a presentation (on the left) vs. what you actually mean (on the right):

In vivo

It works, but I don’t know why

In vitro

It works if I fiddle the salt concentration

In silico

The computer says it works!

It is known that

I’m sure I read a paper on this

It is thought that

My supervisor thinks that

It is generally thought that

The PostDoc agrees with the supervisor

It is believed that

I think that

Unpublished work by Dr. X shows that

My supervisors friends think that

Results were not conclusive…

It didn’t work

… despite multiple repetitions…

Didn’t work the second time either

…including work done by Dr. Y…

Still didn’t work when my supervisor did it

…and collaboration with Dr. X…

Or my supervisors friends

…and attempts at methods suggested by the literature…


The results show

My correlations are good

The results indicate

My correlations are present

The results suggest

My correlations only work if you ignore the error bars

The results seem to suggest

I have no correlations

The results, although inconclusive, may be helpful…

I have no results

Modelling Virotherapy for Cancer

ResearchBlogging.orgMost cancer-related research, particularly medical cancer-related research tends for rather obvious reasons to involve animal research, and while I'm more than willing to agree that it's a necessary sacrifice it does always make me feel a bit squeamish on a personal level. Which was why the first thing that struck me when a certain jazz-playing poetry-writing philosopher-doctor sent this paper my way was that it was involved in developing a mathematical model for treatments. Not to replace animal testing, obviously, but to cut the tests down to those that were more likely to work, reducing the need for animal use.

The paper is exploring glioma virotherapy, which uses synthetic viral capsules to target cancer cells and kill them, while not harming the surrounding normal cells. These viruses are known as ontolytic viruses and while they may originate from harmful strains they've had almost all of their own DNA knocked out, turning them into little balls of protein designed to target cancer cells only. This approach has several problems associated with it, but one of the main ones is that the human body generally doesn't like having virus's inside it. Any injected cancer-targeting virus capsids are in danger or being effectively destroyed by the bodies immune system.

One solution to this is to use immunosuppressants, which naturally comes with problems of its own, including the question of dosage. How much immunosuppressant do you give the patient, depending on the corresponding number of viruses used, and the size of the cancer. In order to explore this without having to kill huge numbers of rats, Friedman and colleagues developed a model to explore the effects of differing virus and immunosuppressant concentrations.

First, they needed to specify the parameters they were using:

Number of tumour cells infected with virus = y
Number of tumour cells uninfected = x
Necrotic (dying) cells = n
Immune cells (destroying the viruses) = z
Free virus particles = v

Then some rates to take into account:

Proliferation of non-infected cells = λ
Infection rate of tumour cells = B
Diffusion coefficient of virus particles = D

Adding this all together with some mathematical magic (and a function to include immunosuppressant levels) leads to what is to me a totally incomprehensible series of mathematical squiggles (anyone whose desperate to read them can find them in the appendix of the reference paper below). But somewhere along the line the magic works, as seen when they compare it to experimental data:
The table above (from the reference) isn't remarkably clear, suffice to say that the blue bars are the actual experiment results (carried out in rats) while the red bars are the results of the model simulation. Graph A shows infected tumour cells (after 6 and 72 hours), graph B shows immune cells (after 6 hours 72 hours and just before the rat dies) while graph C shows the immune cells after addition of the immunosuppressant (after 6 hours, 72 hours, and just before the rat dies). The x-axis shows the percentage of cells.

Having got a model that shows a reasonable degree of accuracy, the experimenters can then play around with the parameters without any more rats having too be involved. For example they can explore the effects of adding more viruses. More viruses in the system increase the immune cell response, even in suppressed patients (although obviously the response is lessened). However as the numbers of viruses decrease, the immune cells start to leave the area, allowing any viruses that have survived to quickly recover the population and the immune cells rush back in again. This leads to a feedback loop which, with my knowledge of the effects of the immune system, can't be all that good for the patient.

The results below from the model show the effect of adding varying amounts of virus on the number of infected tumour cells.

The cyclical pattern can be clearly seen, especially for the larger numbers of viruses (the blue, red and green lines represent increasing numbers of virus's injected into the system - as the colours are rather faint the blue line is the mostly straight one, the red line is the bumpy one and the green 'line' is a series of peaks). The model was also used to explore different concentrations of immunosuppressant and different dosage schedules (one a week, twice a week etc.) for treatment.

There are limitations to the model, it only considers injections into the centre of spherical tumours, for example, and does nothing to model any potential problems caused by metastasis (bits of the tumour breaking off and moving away). However is does provide the framework of a system to explore different options for bench experimentation, to ensure that any work that is done on animals will be the useful and relevant for the development of this system into a working treatment for human cancers.


Friedman, A. (2006). Glioma Virotherapy: Effects of Innate Immune Suppression and Increased Viral Replication Capacity Cancer Research, 66 (4), 2314-2319 DOI: 10.1158/0008-5472.CAN-05-2661