The RelA/SpoT Homolog (RSH) Superfamily: Distribution and Functional Evolution of ppGpp Synthetases and Hydrolases across the Tree of Life

Stringent response is run by the RSH (RelA / SpoT Homologue) proteins, but there are more RSHs then just these two. Usually researchers were finding them using an ad hoc approach: take your favorite bug you worked with for 10 years, blast its genome with RelA gene, find anything that looks like RelA, test  it.

Finally there is a proper analysis of RSHs across the tree of life: The RelA/SpoT Homolog superfamily: distribution and functional evolution of ppGpp synthetases and hydrolases across the tree of life by Atkinson GC, Tenson T, Hauryliuk V, PLoS ONE, 6(8): e23479. 

Here is the take home message:

• there are loads of different RSHs out there: we identified 30 subgroups! 
• all the RSHs out there are now are classified (for now, that is. New genomes are coming out every day, damn the progress!).
• Archaea, Bacteria, Eucaryotes: they all have RSHs. I repeat: Archaea too.
• there are the long RSHs (i.e. Rel, RelA and SpoT) and there are the short ones. 
• The short ones have either ppGpp synthesis or ppGpp hydrolysis domain. The long ones have both, but not always both are functional.
• by comparing the long ones vs the short ones we identified residues potentially involved in the inter-domain cross-talk in the long ones (the short ones have only one domain thus there is no cross talk there!).

The bottom line: if you work on a strange RSH protein from a strange bug, do check out our paper. 

Fig. 1.  Maximum likelihood phylogeny of the ppGpp hydrolase domain.  Subgroups are labeled and shading behind the branches shows the most common domain structure observed for those groups, as per the legend in the inset box. Symbols on branches indicate bootstrap support, as per the inset box.

Hurray to Gem! 

Observing protein synthesis inside the living mammalian cell

Biologists really love seeing things for themselves. Take for instance the central dogma: DNA - RNA - protein. It all well and good when represented as childish-looking blobs fooling around and passing amino acids one to each other, but how about it actually happening in the real 3D cell stuffed with other goodies? Well, obviously, people tried looking into the question.

The first option is you can to label fluorescently some components of the machinery - ribosomes, mRNAs, factors - and plonk the bug under the microscope. The problem here is that you can't really see what these components are doing - are they idle? are they active? are they so fucked up by adding the fluoresecnt label that now they are being rapidly degraded? One possible solution is to interfere with the cell inhibiting some crucial step the component of interest is involved in and see what happens to the distribution of the labeled component. For instance, one can add an antibiotic. The problem with this approach is that you a) interfere with the system b) interfere with the system c) antibiotics are often fluorescent, so you interfere with the system even more.

One solution that is often used in vitro for converting fluorescence signal into the distance signal is by using FRET, Förster resonance energy transfer (for an excellent review of FRET-based single molecule investigations of translation in vitro see Blanchard). The idea behind FRET is that one uses two fluorofores at the same time. Fluorofore 1 is excited at wavelength λ1, and emits at λ2. Fluorofore 2 is adsorbs at λ2 and emits at λ3. In order for all this cascade to work, the two fluorofores have to be very, very close because efficiency of FRET decreases with distance with an inverse 6th power law.

Now, back to observing translation in vivo. Barhoom at al. exploited the FRET strategy in their piece that just came out in NAR. They used a FRET pair consisting of two labeled tRNAs. These two are getting very close on the ribosome, generating a FRET signal (a strategy recently used in vitro by Uemura at al.). And they are getting close on the ribosome only if they are actively engaged in translation (Fig. 1). Therefore by looking at the tRNA FRET Barhoom and colleagues can observe spots of active translation inside the cell under different conditions, such as viral infection etc.

The paper is open access, so do take advantage of that.

Fig. 1. Two labeled tRNAs are constituting a FRET pair. When they are in close proximity on the ribosome they produce a FRET signal. When floating about in the cytoplasm, they are too far to generate FRET.


PS: this post is part of the MolBio carnival Nr 14!

References:

Blanchard SC (2009). Single-molecule observations of ribosome function. Current opinion in structural biology, 19 (1), 103-9 PMID: 19223173

Barhoom S, Kaur J, Cooperman BS, Smorodinsky NI, Smilansky Z, Ehrlich M, & Elroy-Stein O (2011). Quantitative single cell monitoring of protein synthesis at subcellular resolution using fluorescently labeled tRNA. Nucleic acids research PMID: 21795382

Uemura S, Aitken CE, Korlach J, Flusberg BA, Turner SW, & Puglisi JD (2010). Real-time tRNA transit on single translating ribosomes at codon resolution. Nature, 464 (7291), 1012-7 PMID: 20393556

Adjacent gene pairing and regulation of protein expression

Just had a great talk by Mike McAlear who was visiting us on his way to Poland. He gave a talk about co-regulation of adjustment genes, specifically ribosomal proteins and ribosomal assembly enzymes. I know what you are thinking about now, transcriptional read-through and the like. But it seems to be much more fun than that!

Genes can be on the opposite strands, they can face in opposite directions, but still, there is co-regulation which can not be contributed simply to a shared regulatory system. Something with chromatin structure, probably. Being positioned next to each other was shown to be an important factor for noise in protein expression. Becsei at al. showed in yeast that noise in protein expression is sensitive to gene position in the chromosome, and, consequently, genes positioned next to each other show somewhat correlated behavior. I think it is all different sides of one story...

References:

Adjacent gene pairing plays a role in the coordinated expression of ribosome biogenesis genes MPP10 and YJR003C in Saccharomyces cerevisiae. Arnone JT, McAlear MA. Eukaryot Cell. 2011 Jan;10(1):43-53. PIMD: 21115740

Prime movers of noisy gene expression. Paulsson J. Nat Genet. 2005 Sep;37(9):925-6. PIMD: 16132049

Contributions of low molecule number and chromosomal positioning to stochastic gene expression. Becskei A, Kaufmann BB, van Oudenaarden A. Nat Genet. 2005 Sep;37(9):937-44. PIMD: 16086016

Co-expression of adjacent genes in yeast cannot be simply attributed to shared regulatory system. Tsai HK, Su CP, Lu MY, Shih CH, Wang D. BMC Genomics. 2007 Oct 3;8:352. PIMD: 17910772

The peculiar story of PTC124

Several years ago a very, very cool drug was discovered - PTC124. This one was inhibiting NMD (nonsense mediated mRNA decay), and since NMD is implicated in several deseases, PTC124 was of great interest. I have discussed this story here.

Well, it seems that there is a twist in this story. PTC124 was discovered using firefly luciferase (FLuc) as a reporter. And now is seems that it rather than acting on the NMD level, PTC124 interacts with the FLuc itself and modulates its activity!

What's especially interesting, is that work on PTC124 continues, and many more papers are getting published, not necessarily using FLuc... So what was it? Just a fluke?

...this story is somehow similar to that one.

References:

Auld, D. S., Lovell, S., Thorne, N., Lea, W. A., Maloney, D. J., Shen, M., Rai, G., et al. (2010). Molecular basis for the high-affinity binding and stabilization of firefly luciferase by PTC124 Proceedings of the National Academy of Sciences of the United States of America, 107(11), 4878–4883. PIMD 20194791

Auld, D. S., Thorne, N., Maguire, W. F., & Inglese, J. (2009). Mechanism of PTC124 activity in cell-based luciferase assays of nonsense codon suppression Proceedings of the National Academy of Sciences of the United States of America, 106(9), 3585–3590. PIMD 19208811

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

Systems biology approach to stringent response

Bacterial cells constantly need to monitor their environment and act accordingly.

The trouble is, bacteria are very small and when you are so very small, all the effects of being quantized in terms of molecule numbers are becoming very strong: number of mRNA molecules for a certain gene is an integer value, and not a very high at that, events of receptor getting activated or RNA polymerase binding to the promoter are stochastic in nature, and since not too many of the individual events occur at a time, they are not averaged out due to the law of large numbers.

All this results in bacteria gambling all the time: some react to stimulus, some don't, some produce more proteins in response to it, some less. This leads to so called phenotypic heterogeneity, when otherwise (genetically) identical bacteria become very different in terms of their responses.

This could be a good thing and also could be a bad thing. Having a collection of different bugs instead of a clone army will provide certain versatility: some are ready for one conditions, and some are ready for others. For instance, some are ready to grow and divide right away and some are slower and more cautious. Both types of cells can be beneficial in different conditions: the active ones will drive the population growth, but will be sensitive to the antibiotic treatment, and the passive ones will wait until the treatment is over and then they will come to life. Sounds like a good strategy (and it has a name, this strategy - "bed hedging") and I guess it is exactly the reason why clone armies never caught on.

On the other hand, this noise makes it really hard for bacteria to make an educated guess and respond to a stimulus in the best possible way - there is simply too much background to filter through!

One of the widely used cellular response systems used by bacteria is so called stringent response. This one is mediated by a family of proteins called RSH (RelA SpoH Homologue, with RelA and SpoT being the first members to be discovered). These proteins sense different cues (aminoacid starvation, fatty acid starvation, heat shock and so on) and translate these input signals into modulation of the intracellular concentration of ppGpp - a modified G nucleotide which acts as a second messenger modulating numerous cellular processes, such as transcription, translation, replication and lots more.

The RSH-mediated system in M. tuberculosis was shown to be  vital for prolonged life in the host, linking it to the phenotypic heterogeneity. This bug has one RSH protein, Rel, which is capable of both producing and degrading ppGpp. The logical question is then - how is Rel activity and abundance regulated, and what is its distribution in the bacterial population?

In the recent two papers (1 and 2) exactly that was done using M. smegmatis as a non-pathogenic model for M. tuberculosis. Rel promotor was fused with a GFP ORF so that its activity can be monitored on the single cell level using flow cytometry. And indeed, they saw that cells are very strongly heterogeneous in respect to the GFP level, which reflects activity of the rel promoter. This heterogeneity turned out to be brought about by the positive feedback loop feeding Rel expression back to its promoter via activity of the MprAB and SigE proteins.

Positive feed back loops are a common way of creating this sort of bistability in the biological systems and is a very common motif in the gene networks. What would be interesting is to see how common is this positive feedback among different bacteria, especially the ones that have different RSH system, such as the most commonly studied E. coli, which has two RSH proteins instead of one in M. tuberculosis: RelA, which is mostly producing ppGpp, and SpoT, which is mostly degrading it.

Effects of noise in protein expression are also different in E. coli: here synthetic and hydrolytic components are split into two independently produced proteins, thus resulting in a system with more degrees of freedom. Sure enough, it is well documented that intrinsic noise in protein expression (that would be roughly variation in the protein expression between the two independent genes in one bacterial cell) is much less than extrinsic one (roughly - variation between different cells), but still - two genes is more than one!

And what about bugs having more RSHs? Streptococcus mutans has three! From classic microbiological studies it seems that similar regulation could be present there as well, linking stress tolerance and genetic competence.

However fruitful, systems biology investigations of the stringent response are suffering from one big limitation: using current approaches, only a few readouts can be followed in the same cell at the same time. Stringent response, however, involves many different systems being rewired, and microarrays in the combination with metabolomics can follow many, many things happening in the cell (though lacking in the single cell resolution!).

References:

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

Seaton K, Ahn SJ, Sagstetter AM, & Burne RA (2011). A transcriptional regulator and ABC transporters link stress tolerance, (p)ppGpp, and genetic competence in Streptococcus mutans. Journal of bacteriology, 193 (4), 862-74 PMID: 21148727

Mittenhuber G (2001). Comparative genomics and evolution of genes encoding bacterial (p)ppGpp synthetases/hydrolases (the Rel, RelA and SpoT proteins). Journal of molecular microbiology and biotechnology, 3 (4), 585-600 PMID: 11545276

Lemos JA, Lin VK, Nascimento MM, Abranches J, & Burne RA (2007). Three gene products govern (p)ppGpp production by Streptococcus mutans. Molecular microbiology, 65 (6), 1568-81 PMID: 17714452

Dahl JL, Kraus CN, Boshoff HI, Doan B, Foley K, Avarbock D, Kaplan G, Mizrahi V, Rubin H, & Barry CE 3rd (2003). The role of RelMtb-mediated adaptation to stationary phase in long-term persistence of Mycobacterium tuberculosis in mice. Proceedings of the National Academy of Sciences of the United States of America, 100 (17), 10026-31 PMID: 12897239

Sureka K, Ghosh B, Dasgupta A, Basu J, Kundu M, & Bose I (2008). Positive feedback and noise activate the stringent response regulator rel in mycobacteria. PloS one, 3 (3) PMID: 18335046

Ghosh S, Sureka K, Ghosh B, Bose I, Basu J, & Kundu M (2011). Phenotypic heterogeneity in mycobacterial stringent response. BMC systems biology, 5 PMID: 21272295

Elowitz MB, Levine AJ, Siggia ED, & Swain PS (2002). Stochastic gene expression in a single cell. Science (New York, N.Y.), 297 (5584), 1183-6 PMID: 12183631

Larson DR, Singer RH, & Zenklusen D (2009). A single molecule view of gene expression. Trends in cell biology, 19 (11), 630-7 PMID: 19819144

Alon U (2007). Network motifs: theory and experimental approaches. Nature reviews. Genetics, 8 (6), 450-61 PMID: 17510665

Ingolia NT, & Murray AW (2007). Positive-feedback loops as a flexible biological module. Current biology : CB, 17 (8), 668-77 PMID: 17398098

Raj A, & van Oudenaarden A (2009). Single-molecule approaches to stochastic gene expression. Annual review of biophysics, 38, 255-70 PMID: 19416069

Lidstrom ME, & Konopka MC (2010). The role of physiological heterogeneity in microbial population behavior. Nature chemical biology, 6 (10), 705-12 PMID: 20852608

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

Balaban NQ, Merrin J, Chait R, Kowalik L, & Leibler S (2004). Bacterial persistence as a phenotypic switch. Science (New York, N.Y.), 305 (5690), 1622-5 PMID: 15308767

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