Functional Metagenomics: Sequence Everything and Let DNA Sort The Functions Out

ResearchBlogging.org

One of the cool things you can do with the high throughput DNA analysis of pyrosequencing, is to collect a sample from the environment, isolate the DNA from everything in it and sequence it. Then you can match the DNA up with known sequences and see what sort of microbes you had. Dinsdale and a bunch of coauthors collected the data from a bunch of such studies. They managed to find 45 bacterial samples and 42 viral samples from 9 broad environmental classifications. You can see all the different samples the authors pooled together (circles microbial and squares viral).

Locations of metagenomic samples from Dinsdale et al.

The interesting thing about this study was that instead of looking at the taxonomy of the critters as usual, they looked at the function of the genes. By simply looking at what the genes do, the researchers hoped to get a feel for what activities were going on in that environment without necessarily having to identify the species of the bacteria and viruses. To do this, they fed their 14.5 million sequences (pyrosequencing sure can generate data) into the SEED database, a big collection of genes which have been assigned to functions (for example membrane transport or sulphur metabolism) by experts. They were able to match 1 million of the bacterial and 500,000 of the viral sequences to previously identified gene functions.

It might seem odd that they would look at viral DNA since viruses are rather simple and have only a few basic genes. But the researchers were actually looking at bacterial genetic sequences being carried inside viruses. This of course brings up the question of what bacterial DNA is doing inside viruses. It turns out there are a lot of bacteriophage viruses that like to infect bacteria and sometimes these viruses capture some of the DNA of their bacterial hosts and carry it to their next host. Looking at the bacterial DNA present in a viral population gives an interesting look at what types of genes are being passed around between individual bacteria (and even between bacterial species).

So here are the high level classifications of the function of the genes they found for each environment.

Percentages of gene function of bacterial and viral gene function from Dinsdale et al.

It’s pretty cool that the viruses were carrying around so much of a variety of bacterial DNA. The authors suggest that motility genes coding for things like flagella and cilia (which could help the bacterial host spread the virus further) were enriched in the viral samples but it seems a bit hard to say that for certain without a bit more analysis.

A useful way to look at huge masses of data, like their 1.5 million matches, is to try and reduce all the different counts in the functional categories into a couple of condensed variables. This can be seen in the next couple plots. They could use a little explaining. Bacterial sequences are on top and viral sequences on the bottom. Lines show how the various functional categories have been condensed into the x and y variables. For example, samples that contained lots of genes for making cell walls will tend to be at the top of the plot in the bacterial samples and tend not to have many genes for respiration.

Canonical discriminant function analysis of bacterial and viral gene function from Dinsdale et al.

It’s pretty cool to see how the various environments clustered with other samples from the same environment. For example, all the yellow diamond fish farm samples ended up on the right side of the bacteria graphs even though they were sampled independently. It appears that functions seem to correlate with environmental conditions. For example, the fish food at the fish farms contained a lot of sulfur supplements and the bacteria from those samples were rich in sulfur metabolism genes and the bacteria from corals contained many different respiration genes to deal with the highly variable oxygen concentrations found there. Dinsdale and her coauthors go so far as to suggest that gene function may provide a better indicator of environment than the taxonomy of the bacteria present.

The paper did have a little trouble in the math in one part but the authors already have a correction in for it so it’s really not worth worrying about. Overall, it was a pretty interesting story and a good example of stuff to do with a sequencing machine (also it must have taken a good bit of work to collect all that data together from all those authors).

References

Elizabeth A. Dinsdale, Robert A. Edwards, Dana Hall, Florent Angly, Mya Breitbart, Jennifer M. Brulc, Mike Furlan, Christelle Desnues, Matthew Haynes, Linlin Li, Lauren McDaniel, Mary Ann Moran, Karen E. Nelson, Christina Nilsson, Robert Olson, John Paul, Beltran Rodriguez Brito, Yijun Ruan, Brandon K. Swan, Rick Stevens, David L. Valentine, Rebecca Vega Thurber, Linda Wegley, Bryan A. White, Forest Rohwer (2008). Functional metagenomic profiling of nine biomes Nature, 452 (7187), 629-632 DOI: 10.1038/nature06810

Biologist
Statistician

Comments (1)

Permalink

Fiber Made Out of Viruses?

ResearchBlogging.org M13 virus from Chiang et al. 2007

With advancing nanotechnology, people often need to make custom fibers with special properties such as conducting electricity or sticking to certain substances. These fibers can be created using fancy synthetic materials and complicated chemistry. Or as Chiang and coathors suggest you could try to make fibers out of viruses after modifying the viral DNA to perform the desired task. I’m not sure this is actually all that much easier but it sure seems cooler and perhaps (hopefully) as things develop it actually will be more cost effective.

Procedure for creating virus fiber from Chiang et al. 2007 Viral fibers from Chiang et al. 2007

To put their money where their hypothesis was, Chiang et al. set out to make custom fibers from a virus called M13. The M13 virus is a bacteriophage (a virus that infects bacteria) that looks like a string (instead of the little lunar lander shape in all the textbook virus illustrations). Interestingly, it’s made up of only a handful of proteins; a couple on one end, a couple on the other end, and then a bunch of repeats of a single protien to make a long tube covering it’s DNA. To make virus fiber, the research take a concentrated solution of viruses and squirt it from a syringe into a bath of glutaraldehyde. The glutaraldehyde forms links between neighboring viruses to form a continuous fiber. The researchers found they could adjust the glutaraldehyde concentration, the rate of syringe ejection and how much pull was applied to the fiber to make virus fibers with differing characteristics. They even took the fibers they made and tested them in fiber strength tests. It turns out virus fibers are about as strong as nylon.

To really highlight the benefits of viruses, they also used genetically modified forms of M13 whose DNA coded for proteins that bond well with certain substances. By modifying the highly repeated protein forming the viral tube, they can make viruses (called E4) that really stick to quantum dots. They then used this virus to make fiber containing high concentrations of quantum dots (good for optical sensors [or whatever uses people come up with for quantum dots]). To really show off, they also found a modified M13 virus (p8#9) that showed high affinity to gold (much like my fiancee) and used it to make gold coated virus fibers (think microscopic wires).

When I saw that picture of viral fibers, I was pretty amazed. I’ve always thought of viruses as invisible and problematic (not helped by the fact that I’m fighting off a cold right now) but here these researchers are making real world useful things out of them. And they can manipulate the genetics of the virus to add custom special properties. It’s really cool to see how biotechnology is progressing.

Reference

Chiang, C., Mello, C., Gu, J., Silva, E., Van Vliet, K., Belcher, A. (2007). Weaving Genetically Engineered Functionality into Mechanically Robust Virus Fibers. Advanced Materials, 19(6), 826-832. DOI: 10.1002/adma.200602262

Biologist

Comments (2)

Permalink