Categories
Uncategorized

United States of Amoeba

Most people know that viruses are notoriously tricky disease-causing pathogens to tackle. Unlike bacteria which are completely separate organisms, viruses are parasites which use a host cell’s own DNA-and-RNA-and-protein producing mechanisms to reproduce. As a result, most viruses are extremely small, as they need to find a way into a cell to hijack the cell’s  machinery, and, in fact, are oftentimes too small for light microscopes to see as beams of light have wavelengths that are too large to resolve them.

However, just because most viruses are small, doesn’t mean all viruses are. In fact, giant MimivirusesMamaviruses, and Marseillesviruses have been found which are larger than many bacteria. The Mimivirus (pictured below), for instance, was so large it was actually identified incorrectly as a bacteria at first glance!

Source: Wikipedia

Little concrete detail is known about these giant viruses, and there has been some debate about whether or not these viruses constitute a new “kingdom” of life (the way that bacteria and archaebacteria are), but one thing these megaviruses have in common is that they are all found within amoeba!

This month’s paper (HT: Anthony) looks into the genome of the Marseillesvirus to try to get a better understanding of the genetic origins of these giant viruses. The left-hand-side panel of picture below is an electron micrograph of an amoeba phagocytosing Marseillesvirus (amoeba, in the search for food, will engulf almost anything smaller than they are) and the right-hand-side panel shows the virus creating viral factories (“VF”, the very dark dots) within the amoeba’s cytoplasm. If you were to zoom in even further, you’d be able to see viral particles in different stages of viral assembly!

Source: Figure 1, Boyer et al.

Ok, so we can see them. But just what makes them so big? What the heck is inside? Well, because you asked so nicely:

  • ~368000-base pairs of DNA
    • This constitutes an estimated 457 genes
    • This is much larger than the ~5000 base pair genome of SV40, a popular lab virus, the ~10000 base pairs in HIV, the ~49000 in lambda phage (another scientifically famous lab virus), but is comparable to the genome sizes of some of the smaller bacterium
    • This is smaller than the ~1 million-base pair genome of the Mimivirus, the ~4.6 million of E. coli and the ~3.2 billion in humans
  • 49 proteins were identified in the viral particles, including:
    • Structural proteins
    • Transcription factors (helps regulate gene activity)
    • Protein kinases (primarily found in eukaryotic cells because they play a major role in cellular signaling networks)
    • Glutaredoxins and thioredoxins (usually only found in plant and bacterial cells to help fight off chemical stressors)
    • Ubiquitin system proteins (primarily in eukaryotic cells as they control which proteins are sent to a cell’s “garbage collector”)
    • Histone-like proteins (primarily in eukaryotic cells to pack a cell’s DNA into the nucelus)

As you can see, there are a whole lot of proteins which you would only expect to see in a “full-fledged” cell, not a virus. This begs the question, why do these giant viruses have so many extra genes and proteins that you wouldn’t have expected?

To answer this, the researchers ran a genetic analysis on the Marseillesvirus’s DNA, trying to identify not only which proteins were encoded in the DNA but also where those protein-encoding genes seem to come from (by identifying which species has the most similar gene structure). A high-level overview of the results of the analysis is shown in the circular map below:

The outermost orange bands in the circle correspond to the proteins that were identified in the virus itself using mass spectrometry. The second row of red and blue bands represents protein-coding genes that are predicted to exist (but have yet to be detected in the virus; its possible they don’t make up the virus’s “body” and are only made while inside the amoeba, or even that they are not expressed at all). The gray ring with colored bands represents the researchers’ best guess as to what a predicted protein-coding gene codes for (based on seeing if the gene sequence is similar to other known proteins; the legend is below-right) whereas the colored bands just outside of the central pie chart represents a computer’s best determination of what species the gene seems to have come from (based on seeing if the gene sequence is similar to/the same as another species).

Of the 188 genes that a computational database identified as matching a previously characterized gene (~40% of all the predicted protein-coding genes), at least 108 come from sources outside of the giant viruses “evolutionary family”. The sources of these “misplaced” genes include bacteria, bacteria-infecting viruses called bacteriophages, amoeba, and even other eukaryotes! In other words, these giant viruses were genetic chimeras, mixed with DNA from all sorts of creatures in a way that you’d normally only expect in a genetically modified organism.

As many viruses are known to be able to “borrow” DNA from their hosts and from other viruses (a process called horizontal gene transfer), the researchers concluded that, like the immigrant’s conception of the United States of America, amoebas are giant genetic melting pots where genetic “immigrants” like bacteria and viruses comingle and share DNA (pictured below). In the case of the ancestors to the giant viruses, this resulted in viruses which kept gaining more and more genetic material from their amoeboid hosts and the abundance of bacterial and virus parasites living within.

Source: Figure 5, Boyer et al.

his finding is very interesting, as it suggests that amoeba may have played a crucial role in the early evolution of life. In the same way that a cultural “melting pot” like the US allows the combination of ideas from different cultures and walks of life, early amoeba “melting pots” may have helped kickstart evolutionary jumps by letting eukaryotes, bacteria, and viruses to co-exist and share DNA far more rapidly than “regular” natural selection could allow.

Of course, the flip side of this is that amoeba could also very well be allowing super-viruses and super-bacteria to breed…

Paper: Boyer, Mickael et al. “Giant Marseillevirus highlights the role of amoebae as a melting pot in emergence of chimeric microorganisms.” PNAS 106, 21848-21853 (22 Dec 2009) – doi:10.1073/pnas.0911354106

Check out my other academic paper walkthroughs/summaries

Categories
Uncategorized

Slime Takes a Stroll

The paper I read for this month brought up an interesting question I’ve always had but never really dug into: how do individual cells find things they can’t “see”? After all, there are lots of microbes out there who can’t always see where their next meal is coming from. How do they go about looking?

A group of scientists at Princeton University took a stab at the problem by studying the motion of individual slime mold amoeba (from the genus Dictyostelium) and published their findings in the (open access) journal PLoS ONE.

As one would imagine, if you have no idea where something is, your path to finding it will be somewhat random. What this paper sought to discover is what kind of random motion do amoeboid-like cells use? To those of you without the pleasure of training in biophysics or stochastic processes, that may sound like utter nonsense, but suffice to say physicists and mathematicians have created mathematically precise definitions for different kinds of “random motion”.

Now, if the idea of different kinds of randomness makes zero sense to you, then the following figure (from Figure 1 in the paper) might be able to help:

Source: Figure 1, Li et al.

anel A describes a “traditional” random walk, where each “step” that a random walker takes is completely random (unpredictable and independent of the motion before it). As you can see, the path doesn’t really cover a lot of ground. After all, if you were randomly moving in different directions, you’re just as likely to move to the left as you are to move to the right. The result of this chaos is that you’re likely not to move very far at all (but likely to search a small area very thoroughly). As a result, this sort of randomness is probably not very useful for an amoeba hunting for food, unless for some reason it is counting on food to magically rain down on its lazy butt.

Panel B and C describe two other kinds of randomness which are better suited to covering more ground. Although the motion described in panel B (the “Levy walk”) looks very different from the “random walk” in Panel A, it is actually very similar on a mathematical/physical level. In fact, the only difference between the “Levy walk” and the “random walk” is that, in a “normal” random walk, the size of each step is constant, whereas the size of each “step” in a “Levy walk” can be different and, sometimes, extremely long. This lets the path taken cover a whole lot more ground.

A different way of using randomness to cover a lot of ground is shown in Panel C where, instead of taking big steps, the random path actually takes on two different types of motion. In one mode, the steps are exactly like the random walk in Panel A, where the path doesn’t go very far, but “searches” a local area very thoroughly. In another mode, the path bolts in a straight line for a significant distance before settling back into a random walk. This alternation between the different modes defines the “two-state motion” and is another way for randomness to cover more ground than a random walk.

And what do amoeba use? Panel D gives a glimpse of it. Unlike the nice theoretical paths from Panels A-C rooted around random walks and different size steps or different modes of motion, the researchers found that slime mold amoeba like to zig-zag around a general direction which seems to change randomly over the course of ~10 min. Panel A of Figure 2 (shown below) gives a look at three such random paths taken over 10 hours.

Source: Figure 2, Li et al.

The reason for this zig-zagging, or at least the best hypothesis at the time of publication, is that, unlike theoretical particles, amoeba can’t just move in completely random directions with random “step” sizes. They move by “oozing” out pseudopods (picture below), and this physical reality of amoeba motion basically makes the type of motion the researchers discussed more likely and efficient for a cell trying to make its way through uncharted territory.

The majority of the paper actually covers a lot of the mathematical detail involved in understanding the precise nature of the randomness of amoeboid motion, and is, frankly, an overly-intimidating way to explain what I just described above. In all fairness, that extra detail is more useful and precise in terms of understanding how amoeba move and give a better sense of the underlying biochemistry and biophysics of why they move that way. But what I found most impressive was that the paper took a very basic and straightforward experiment (tracking the motion of single cells) and applied a rigorous mathematical and physical analysis of what they saw to understand the underlying properties.

The paper was from May 2008 and, according to the PLoS One website, there have been five papers which have cited it (which I have yet to read). But, I’d like to think that the next steps for the researchers involved would be to:

  1. See how much of this type of zig-zag motion applies to other cell types (i.e., white blood cells from our immune system), and why these differences might have emerged (different cell motion mechanisms? the need to have different types of random search strategies?)
  2. Better understand what controls how quickly these cells change direction (and understand if there are drugs that can be used to modulate how our white blood cells find/identify pathogens or how pathogens find food)

Paper: Li, Liang et al. “Persistent Cell Motion in the Absence of External Signals: a Search Strategy for Eukaryotic Cells.” PLoS ONE3 (5): e2093 (May 2008) – doi:10.1371/journal.pone.0002093

Check out my other academic paper walkthroughs/summaries