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Tag: RNAi

Degradation Situation

Look at me, just third to last month on this paper-of-the-month thing, and I’m over 2 weeks late on this.

This month’s paper goes into something that is very near and dear to my heart – the application of math/models to biological systems (more commonly referred to as “Systems Biology”). The most interesting thing about Systems Biology to me is the contrast between the very physics-like approach it takes to biology – it immediately tries to approximate “pieces” of biology as very basic equations – and the relative lack of good quantitative data in biology to validate those models.

The paper I picked for this month bridges this gap and looks at a system with what I would consider to be probably the most basic systems biology equation/relationship possible: first-order degradation.

The biological system in question is very hot in biology: RNA-induced silencing. For those of you not in the know, RNA-induced silencing refers to the fact that short RNAs could act as more than just the “messenger” between the information encoded in DNA and the rest of the cell, but also as a regulator of other “messenger” RNAs, resulting in their destruction. This process not only let Craig Mello and Andrew Fire win a Nobel prize, but it became a powerful tool for scientists to study living cells (by selectively shutting down certain RNA “messages” with short RNA molecules) and has even been touted as a potential future medicine.

But, one thing scientists have noticed about RNA-induced silencing is that it how well it works depends on the RNA that it is trying to silence. For some genes, RNA-induced silencing works super-effectively. For others, RNA-induced silencing does a miserable job. Why?

While there are a number of factors at play, the Systems Biologist/physicist would probably go to the chalkboard and start with a simple equation. After all, logic would suggest that the amount of a given RNA in a cell is related to a) how quickly the RNA is being destroyed and b) how quickly the RNA is being created. If you write out the equation and make a few simplifying assumptions (that the rate the particular RNA is being created was relatively constant and that the rate at which a particular RNA was destroyed was proportional to the amount of RNA that is there), then you get a first-order degradation equation which has a few easy-to-understand properties:

    • The higher the speed of RNA creation, the higher the amount of RNA you would expect when the cell was “at balance”
    • The faster the rate at which RNA is destroyed, the lower the “balance” amount of RNA
    • The amount of “at balance” RNA is actually the ratio of the speed of RNA creation to the speed of RNA destruction
    • There are many possible values of RNA creation/destruction rates which could result in a particular “at balance” RNA level

And of course, you can’t forget the kicker:

    • When the rate of RNA creation/destruction is higher, the “at balance” amount of RNA is more stable

Intuitively, this makes sense. If I keep pouring water into a leaky bathtub, the amount of water in the bathtub is likely to be more stable if the rate of water coming in and the rate of water leaking out are both extremely high, because then small changes in leak rate or the water flow won’t have such a big impact. But, intuition and a super-simple equation don’t prove anything. We need data to bore this out.

And, data we have. The next two charts come from Figure 3 and highlight the controlled experiment the researchers set up. The researchers took luciferase, which is a firefly gene which glows in the dark, and tacked on 0, 3, 5, or 7 repeats of a short gene sequence which increases the speed at which the corresponding messenger RNA is destroyed to set up the experiment. You can see below that the brightness for the luciferase gene with 7 of these repeats is able to only produce 40% of the light of the “natural” luciferase, suggesting that the procedure worked – that we have created artificial genes which work the same but which degrade faster!

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So, we have our test messenger RNA’s. Moment of truth: let’s take a look at what happens to the luciferase activity after we subject them to RNA-induced silencing. From Figure 3C:

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The chart above shows that the same RNA-induced silencing is much more effective at shutting down “natural” luciferase than luciferase which has been modified to be destroyed faster.

But what about genes other than luciferase? Do those still work too? The researchers applied microarray technology (which allows you to measure at different points in time the amount of almost any specific RNA you may be interested in) to study both the “natural” degradation rate of RNA and the impact of RNA-induced silencing. This chart on the left from Figure 4C shows a weak, albeit distinct positive relationship between rate of RNA destruction (the “specific decay rate”) and resistance to RNA-induced silencing (the “best-achieved mRNA ratio”).

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The chart on the right from figure 5A shows the results of another set of experiments with HeLa cells (a common lab cell line). In this case, genes that had RNAs with a long half-life (a slow degradation rate) were the most likely to be extremely susceptible to RNA-induced silencing [green bar], whereas genes with short half-life RNAs (fast degradation rate) were the least likely to be extremely susceptible [red bar].

This was a very interesting study which really made me nostalgic and, I think, provided some interesting evidence for the simple first-order degradation model. However, the results were not as strong as one would have hoped. Take the chart from Figure 5A – although there is clearly a difference between the green, yellow, and red bars, the point has to be made using somewhat arbitrary/odd categorizations: instead of just showing me how decay rate corresponds to the relative impact on RNA levels from RNA-induced silence, they concocted some bizarre measures of “long”, “medium”, and “short” half-lives and “fraction [of genes which, in response to RNA-induced silencing become] strongly repressed”. It suggests to me that the data was actually very noisy and didn’t paint the clear picture that the researchers had hoped.

That noise was probably a product of the fact that RNA levels are regulated by many different things, which is not the researcher’s fault. But, what the researchers could have done better, however, was quantify and/or rule out the impact of those other factors on the results we noticed using a combination of quantitative analysis and controlled experiments.

Those criticisms aside, I think the paper was a very cool experimental approach at verifying a systems biology-oriented hypothesis built around quite possibly the first equation that a modern systems biology class would cover!

(Images from Figure 3B, 3C, 4C, and 5A)

Paper: Larsson et al, “mRNA turnover limits siRNA and microRNA efficacy.” Molecular Systems Biology 6:433 (Nov 2010) – doi:10.1038/msb.2010.89

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Of Ticks and Bacteria

Another month, another paper to blog.

imageOne of the most fascinating things about studying biology is finding out the numerous techniques living things use to survive through adversity. This month’s paper digs into an alliance between a tick species Ixodes scapularis and a bacterium Anaplasma phagocytophilum to help the pair survive through long winter months.

In places where winters can get extremely cold, people will oftentimes use antifreeze to help protect their car engines. Many cold-blooded animals (ectotherms) survive harsh winters in the same way. They produce antifreeze proteins (AFPs) and antifreeze glycoproteins (AFGPs) which are believed to bind to ice crystals and limit their growth and ability to damage the organism.

As Ixodes ticks are fairly active during winter months and are a known carrier of Anaplasma phagocytophilum which is a cause of human granulocytic anaplasmosis, a team of researchers from Yale Medical decided to investigate whether or not Anaplasma had any impact on the ability of Ixodes to survive cold weather.

Panel A of Figure 1 (below) is a survival curve. It shows what % of ticks which have Anaplasma (in dark black circles) and which don’t (in white circles) survived being placed in –20 degrees (Celsius, of course, not Farenheit: this is science after all!) for a given amount of time. While all the ticks died after 45 minutes, at any given timepoint more ticks with Anaplasma survived than the ticks without. While only ~50% of ticks without Anaplasma survived after ~25 minutes in the cold, over 80% of ticks with the bacterium survived!

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What could explain this difference? The researchers suspected some sort of antifreeze protein, and, after combing through the tick’s genome, they were able to locate a protein which they called IAFGP which bore a striking resemblance to other antifreeze glycoproteins.  But, was IAFGP the actual antifreeze mechanism which kept Ixodes alive? And did Anaplasma somehow increase its effectiveness?

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Panel C of Figure 4 (above) shows the key findings of the experiments designed to answer those two questions. Along the vertical axis, the researchers measured the amount of IAFGP gene expression (relative to the gene expression of a control, actin [a structural protein which shouldn’t vary]). Along the horizontal, the researchers tested four different temperature states (23, 10, 4, and 0 Celsius) with Ixodes ticks that were carrying Anaplasma (dark circles) and those that were not (white circles). Each individual circle is an individual tick and the line is the average value of all the ticks in the experimental group (the reason its not in the middle is because the vertical axis is a log scale). This sort of chart is one of my favorites, as it packs in a lot of information in one small area but without generating too much noise:

    • The lines get higher the further to the right we get: Translation: when temperatures go down, IAFGP levels go up – as you would expect if IAFGP was an antifreeze coping mechanism for Ixodes. (And if you could see Panel B of Figure 4, you’ll notice that IAFGP levels at 4 Celsius and 0 Celsius are statistically significantly higher than at 23 and 10)
    • The black dots on average are higher than the white dots: Translation: just carrying Anaplasma seems to push Ixodes’s “natural” levels of antifreeze protein up. And, judging from the P value comparisons, the differences we are seeing are statistically significant.

So, it would seem that IAFGP is somehow related to the affect of Anaplasma on Ixodes, but, is that the only link? To test that, the researchers used an experimental technique called RNA interference (RNAi) which allows a researcher to shut down the expression of a particular protein. In this case, the researchers shut down IAFGP to see what would happen.

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These results are interesting. Although, sadly, the charts (Panels B and F of Figure 5) are not on the same scale and are for different experiments, the numbers are striking. In Panel B, the researchers tested for the survival of ticks which were given a control RNAi (simulates the RNAi process except without what it takes to actually silence IAFGP, white circles) versus those which had IAFGP shut down via RNAi (white triangles). As you can see from the chart, after 25 min at –20 degrees Celsius, the control group hit 50% survival whereas the RNAi group’s survival rate plummeted to only 20%.

The researchers then repeated the experiment with Ixodes ticks which were given control RNAi (black circles) vs. the real thing (black triangles) and then allowed to feed on Anaplasma-infected mice for 48-hours. These ticks were then tested for survival after 50 min at –20 degrees Celsius. As you can see in Panel F, a 75% survival level amongst Anaplasma carrying ticks became less than 50% when IAFGP was shut down with RNAi.

All in all, a very simple positive-control, negative-control experiment showing a pretty clear linkage between Anaplasma, IAFGP gene expression levels, and the ability of Ixodes ticks to survive the cold. However, a few things still bug me about the study and stand out as clear next steps:

    • Panels B and F of Figure 5 are fundamentally different experiments, but presented as comparable. At face value, its hard to tell if IAFGP is the primary mechanism for how Anaplasma alters tick response to the cold. The survival levels of Anaplasma-carrying ticks when IAFGP is shut down is still higher than the survival levels of Anaplasma-free ticks which also undergo the RNAi – but this could be a result of the different experimental conditions (feeding conditions and time). The paper text also reveals that the –20 degrees for 50 min condition was selected because it was supposed to be the point at which there was 50% survival for that particular experimental condition – but clearly, the control group was experiencing 75% survival (and the group with IAFGP shut off was at 50%). Something is off here… but I’m not sure what.
    • Most of this research was conducted on a very abstract level – showing the impact of IAFGP expression levels on cold survival. While the RNAi experiments are very compelling, the lack of clear functional studies is problematic in my mind as I cannot tell from this data if IAFGP is directly responsible for cold survival or linked to other, potentially more important responses to cold.
    • No mechanism was proposed for how Anaplasma increases IAFPG levels in Ixodes. Understanding that would be very powerful and could unveil a whole world of cross-species gene regulation which we were previously unaware of (and could reveal new potential targets for medical treatments of diseases borne by insects).

Regardless of my criticisms, though, this was an interesting study with a very cool result. However, its probably of no comfort to people who have to deal with ticks which can survive cold winter months…

(Image credit – tick) (Figures 1, 4, and 5 from paper)

Paper: Neelakanta, Grisih et al. “Anaplasma phagocytophilum induces Ixodes scapularis ticks to express an antifreeze glycoprotein gene that enhances their survival in the cold.” Journal of Clinical Investigations 120:9, 3179-3190 (Sep 2010) — doi:10.1172/JCI42868

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