long memories of RelA

Enzymes have their cycles, catalytic ones: bind a substrate, catalyse some sort of reaction, release the product... then do it again. These cycles have memory effects: long turnover is likely to be followed by another long one, and short one is likely to be followed by another short one. This makes total sense: efficient act of catalysis is possible only when appropriate conformation is achieved and all the residues are aligned as they should be... and that is a recipe for one more efficient round!

Now let us look at RelA. Based on in vivo single molecule tracking investigations we recently proposed a model of RelA catalytic cycle: it sits on the ribosome, gets activated by arrival of deacylated tRNA to the A site, falls off and performs multiple acts of ppGpp synthesis from ATP and GDP. Importantly, RelA must go through the ribosome-bound stage in order to get activated. This seems to be an extreme cause of memory effects - while active off the ribosome, RelA remembers the activation event that happened on the ribosome!

It is quite a scary thought... what else does it remember? Does it remember the moment I started working on it? Well, surely not, even I don't remember that moment any more. Or may be I am just blocking out that memory.

References:

H. Peter Lu, Phys. Chem. Chem. Phys (2011), 13 pp. 6734-6749, PIMD 21409227

Brian P. English et al., PNAS (2011), 108(13) pp. E365-373, PIMD 21730169

Single-molecule investigations of the stringent response machinery in living bacterial cells

Wikipedia: "reductionism, an approach to understanding the nature of complex things by reducing them to the interactions of their parts, or to simpler or more fundamental things". This approach was very successful in unrevealing the basic mechanisms of biological systems. Modern biochemistry is reductionism in its pure form: we purify individual components, mix them together in a test tube and make this in vitro system jump through the hoops and this way we learn how it works. Then we extrapolate what we learned from the in vitro system to the cell, and test our model in vivo: overexpress some components, knock-out the other, introduce mutations etc.

However, sometimes producing in vitro system is not feasible, either because it is to laborious or because we simply do not know what are the components. A good solution would be then to do biochemistry, but... inside the living cell. This approach became technically feasible in the recent decades, and was highly successful in cracking these hard problems for which in vitro investigations are just not cutting it. In vivo biochemistry relies on labeling the protein (proteins) of interest with a fluorescent tag, usually a GFP derivative, and then following its movement inside the living cell on the single molecule level. Movement of the protein can tell us about its functional cycle: binding to a partner will slow its diffusion, for instance.

Now this approach was applied to investigation of the stringent response (I have discussed this fascinating bacterial adaptation system quite at length here). In short, when bacteria are starving for amino acids, they accumulate deacylated tRNAs. These bind to the ribosomal A-site, and this situation is sensed by a protein called RelA, which starts producing alarmone molecule ppGpp. One important thing about RelA functional cycle is that it has two states with distinctly different difusion properties: ribosome bound and free.

This was taken advantage of in the recent paper by English at al. RelA was labelled with a fluorescent GFP variant and its diffusion was followed at ms time resolution. Indeed, inactive RelA turned out to be tightly associated with the ribosomes and diffusing slowly (Fig. 1). However, when stress was induced, either by amino acid limitation or by the heat shock, RelA fell off the ribosome and started moving about much, much faster (Fig. 1).

It is known that under these conditions RelA is enzymatically active and produces ppGpp. Since active RelA seems to spend its time off, rather than on the ribosome, it was suggested that ppGpp production is happening off the ribosome as well. And this is a rather unique mechanism for a ribosome-associated factor. Usually on the ribosome is when the protein is active: RelE binds to the ribosome and cuts the mRNA, EF-G binds, hydrolyses GTP and translocates A and P site tRNAs, ricin binds and cuts the ribosomal RNA.

Fig. 1. MSD (Mean Square Displacement) analysis of the RelA diffusion in vivo. Diffusive behavior of active and inactive RelA is compared to that of ribosomes carrying fluorescent label on L25 protein (green triangles) and freely diffusing protein mEos2. Insert shows the difference in the individual trajectories of active (right trajectory) and inactive (left trajectory) RelA.

Now, of course, this mechanism of RelA has to be tested by other methods. As any approach, single molecule tracking in its current form has its limitations, and the biggest one is the labels used, GFP in this case. RelA fused with GFP is not RelA, it can behave somewhat different.

PS: and now this story was covered in the news! HFSP and UppsalaBio (in Swedish). Also it is covered as a Research highlight in Biopolymers.

PPS: a great review of the single molecule investigations in vivo just came out in Nature: Gene-Wei Li and Sunney Xie (2011). Central dogma at the single-molecule level in living cells. Nature, 475, 308-315 PIMD 21776976. Too bad, we are not mentioned!

PPPS: this blog post is covered in The MolBio Carnival #13!

PPPPS: and now our paper made it to F1000.

References:

Xie XS, Choi PJ, Li GW, Lee NK, & Lia G (2008). Single-molecule approach to molecular biology in living bacterial cells. Annual review of biophysics, 37, 417-44 PMID: 18573089

Potrykus K, & Cashel M (2008). (p)ppGpp: still magical? Annual review of microbiology, 62, 35-51 PMID: 18454629

Gallant J, Palmer L, & Pao CC (1977). Anomalous synthesis of ppGpp in growing cells. Cell, 11 (1), 181-5 PMID: 326415

Brian P. English, Vasili Hauryliuk, Arash Sanamrad, Stoyan Tankov, Nynke H. Dekker, and Johan Elf (2011). Single-molecule investigations of the stringent response machinery in living bacterial cells PNAS 108(31), E359-364 PIMD: 21730169 and the PNAS Author Summary

Mendeley group on stringent response

Photoactivatable organic fluorophores for in vivo imaging

Imaging techniques heavily rely on the fluorophores they use. GFP was a great breakthrough because it allowed labeling specific targets, photoconvertable GFP variants were a great breakthrough because they allowed generation of single fluorescent molecules and this lead to development of a whole array of tracking and superresolution techniques.

However, GFP variants were always pretty bad fluorophores as compared to organic dyes due to their low quantum yield, pH and redox-potential sensitivity. Organic dyes, good as they are, had issues with selective labeling and thus were mostly used in vitro.

It seems that it might be about to change.  Lee at al. report a novel strategy for labeling proteins of interest in vivo with organic dyes. They use HaloTag technology by Promega and label a whole bunch of proteins both in mammalian cells and bacteria with photoactivatable azido DCDHF labels.

And it works beautifully. The only problem is that HaloTag is even bigger than GFP - 33 kD. That's a lot.

References:

Lee at al. Superresolution imaging of targeted proteins in fixed and living cells using photoactivatable organic fluorophores. JACS 2010 vol. 132 (43) pp. 15099-15101 PIMD 20936809

Lord at al. Azido push-pull fluorophores photoactivate to produce bright fluorescent labels. J Phys Chem B 2010 vol. 114 (45) pp. 14154-14167 PIMD 19860443

photoconvertable GFP variants for SPT

Many different fluorescent labels are used for super-resolution imaging, and photoconvertable GTP variants are probably the most widely used. And again, there are many photoconvertable GFP variants: PATagRFP, PA-GFP, Dendra, EosFP and many others.

What makes a good photoconvertable GFP for single particle tracking (SPT)? Well, all the things that make a good label (bright, stable, fast to mature, small, inert, this convenient and nicely separated adsorbance and emission spectra - the obvious stuff) and one more thing that is not so obvious.

Photoconvertable label should not convert by itself. It should have a stable dark state and conversion should be strictly upon illumination by the photo-converting light. Otherwise creating single molecules for SPT is hard - they just pop into existence by themselves! In than sense EosFP is much better than Dendra.

Sounds obvious, but it wasn't to me...

Increasing signal-to-noise ratio by use of highly inclined thin illumination beam

Signal-to-noise ratio (SNR) is a crucial parameter in imaging. In order to improve it researchers use different tricks, optimizing the labels and reducing the background.

Here is one interesting imaging trick called Highly Inclined and Laminated Optical sheet (HILO) microscopy. Using highly inclined thin imaging beam it is possible to increase SNR dramatically.

In a sense this approach is similar to TIRF, where illuminating light is approaching the sample at a critical angle, so that only molecules very close to the coverslip surface are illuminated by the evanescent wave. The problem with TIRF is exactly that - only molecules very close to the surface can be imaged, which is fine for working with membrane proteins, but not so for the cytosolic ones.

HILO can do just that: instead of illuminating only the molecules in close proximity to the coverslide (as TIRF does) it illuminates a thin slice cutting through the cell at an angle.

References:

Tokunaga et al. Nature Methods 2008, 5 (2) pp. 159-161 PIMD 18176568

Single Particle Tracking (SPT) in vivo: interpretation of trajectories

Using a single particle tracking approaches it is possible to approach diffusion trajectories with millisecond time resolution. Analyzing these and figuring out what exactly is happening in the cell is a rather complex undertaking. Here are two papers discussing analysis of particle diffusion in vivo:

1. van den Wildenberg et al. Biopolimers 2011 PIMD 21240922

Use of tensors for describing asymmetry of diffusion in the cell (i.e. along the long axis we have less confinement than along the short axis, thus mean square displacement (MSD) plateau is different). Modeling of different confinement geometries (tube, cube, sector) + adding repulsive or attractive potentials describing interactions between the particle and environment.

2. Hall and Hoshino Biophys Rev 2010 PIMD 21088688

Focuses on protein diffusion in the bacterial membrane, comparing experimental data for TatA diffusion with modeling. Again, confinement effects are discussed - SPT data are 2D projection of the 3D diffusion. They model random walk on the surface of bacterial cell was in the same way as Deich at al. PNAS 2004 PIMD 15522969.

Geometric constraints and 2D-to-3D projection affect MSD and CPD (cumulative probability distributions), but not enough to explain thir experimental data.

In order to account for the experimental deviations unexplainable by geometry effects they assume existence of slow and fast particles and add an estimate of the localization precision, and now they get a nice agreement with the experimental data. Roughly 50 / 50 slow and fast.

Effect of localization precision on SPT was earlier discussed in Martin et al. Biophys J. 2002 PIMD 12324428 story: "Apparent subdiffusion inherent to single particle tracking".

Playing chicken, single molecule

Many things happen to DNA. Proteins bind, slide along, dissociate. Sometimes they bump into each other, and then... what happens then?

This was exactly the question adressed in Finkelstein at al., Nature 2010. They were particularly interested in a bacterial protein called RecBCD, which is a powerful helicase. Using single-molecule microscopy they
had a look at what happens when RecBCD rams into some other protein.

And they had several to look at. RNA polymerase was the first one, and this is a formidable roadblock, and yet - RecBCD pushed it off the DNA with ease. Here are the actual images:

RecBCD sliding RNA polymerase along the DNA (76.5 %):

RecBCD ejecting RNA polymerase (8.5 %):

And RecBCD being stalled by RNA polymerase (rare events!) (15 %):

Then they tried other proteins, EcoRI endonuclease and lac repressor, and again, RecBCD was victorious over and over. Only the mighty nucleosome (never mind that it is a eukaryotic protein and it never met RecBCD before!) managed to put up a fight - 24% of the head-on collisions resulted with RecBCD being stalled, 11% resulted in nuceosome ejection and in remaining 65% RecBCD was sliding the nucleosome along the DNA.

What next? Well, now authors are preparing for experiments on RecBCD head-on collisions with Higgs bosons in the LHC.  I bet on RecBCD.

PS: it is all very convenient that the authors happened to study RecBCD - neat story, their protein is stronger than all the others, yay! But wat if they would have started with some lame one, like PNA polumerase? But hey! I never write my results in the order I get them... do you? Did THEY?

References:

Finkelstein IJ, Visnapuu ML, & Greene EC (2010). Single-molecule imaging reveals mechanisms of protein disruption by a DNA translocase. Nature, 468 (7326), 983-7 PMID: 21107319

GFP-based quantification of proteins in the cell - don't try that at home!

This is something that come out of our tracking experiments in J. Elf lab. I guess we will never publish any quantitive account of it, so it guess it constitutes perfect blogging material.

GFP and its derivatives are widely used to label and track proteins in live cells. This way people follow localization of the protein of interest and accessing changes its concentration.

And here is where the fun begins. GFP has a fluorofor that is bringt only in a) oxidized b) deprotonized state. Therefore redox potential and pH of the cellular environment have profound effect on the brightness of GFP. Cultural media per se can change GFP behavior dramatically, and this is something outside of the cell...

Another problem with GFP is that it can radically affect stability of the fusion protein. We are not even talking about function here, we are talking about the number of molecules per se.

While struggling with our Dendra2 GFP variant and RelA_Dendra2 fusion in vivo we observed all these problems constantly. Unhappy cells were dark, with GFP in the dark state (pH? redox? you simply don't know!) and the numbers of proteins you detect were invariably much lower than what is estimated for the wt, un-tagged RelA.

But all this still does not stop brave souls from using GFP for single molecule quantification of bacterial proteins en masse. Whole library of YFP (yellow fluorescent protein) fusions was created and their copy number as well as diffusion characteristics were analyzed, and then systems biology happened to the dataset. No one knows what exactly does it all mean and how the numbers of detected YFP-fused proteins relate to numbers of the wt proteins, but this is details.

References:

Bogdanov AM, Bogdanova EA, Chudakov DM, Gorodnicheva TV, Lukyanov S, & Lukyanov KA (2009). Cell culture medium affects GFP photostability: a solution. Nature methods, 6 (12), 859-60 PMID: 19935837

Taniguchi Y, Choi PJ, Li GW, Chen H, Babu M, Hearn J, Emili A, & Xie XS (2010). Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science (New York, N.Y.), 329 (5991), 533-8 PMID: 20671182

Brian P. English, Arash Sanamrad, Stoyan Tankov, Vasili Hauryliuk, & Johan Elf (2010). Tracking of individual freely diffusing fluorescent protein molecules in
the bacterial cytoplasm arXiv q-bio arXiv: 1003.2110v1