Next month in Tartu there will be a conference on antibiotics and protein synthesis organized by Tanel Tenson.
Registration is FREE and now open!
Confirmed speakers:
James Williamson (Scripps Research Institute),
Alexander Mankin (University of Illinois at Chicago),
Steven Douthwaite (University of South Denmark),
Daniel Wilson (University of Munich),
Karen Shaw (Trius Therapeutics),
Ada Yonath (Weizmann Institute of Science).
Birte Vester (University of Southern Denmark)
Joyce Sutcliffe (Tetraphase Pharmaceuticals)
Mans Ehrenberg (Uppsala University)
Chaitan Khosla (Stanford University)
Markus Zeitlinger (Medical University of Vienna)
It's also probable that Vasili Hauryliuk and I will be speaking too.
Field of Science
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Change of address1 year ago in Variety of Life
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Change of address1 year ago in Catalogue of Organisms
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Earth Day: Pogo and our responsibility1 year ago in Doc Madhattan
the evolutionary rate of protein–protein interactions
This is my first post for a while, since I've been pretty busy - first I was writing a grant proposal for some research money, then I was on holiday in Bavaria and Austria, after which I was busy finishing off a manuscript on evolution of starvation response enzymes. As of yesterday, the manuscript is with my boss, so time to catch up with the world.
I noticed an interesting upcoming PNAS paper: Measuring the evolutionary rate of protein–protein interaction. This tackles a subject close to my heart - functional evolution of proteins. The authors tackle measuring the rate at which functional changes happen, with function in this case measured by gain and loss of PPIs (protein-protein interactions).
They start by comparing yeast S. cerevisiae, which has abundant PPI data with another yeast Kluyveromyces waltii. These two diverged ∼150 MYA, and they are sort of special relatives since a whole genome duplication occurred in the lineage to S. cerevisiae after the divergence of K. waltii. This worried me that this could affect the rate of PPI change, due to the sudden influx of homologues in the S. cerevisiae lineage inflating the PPI count. However, the problem of duplicates was surmounted by only considering one to one orthologs (ie proteins related by vertical descent and not gene duplication (which would be paralogues)). In all, 43 proteins passed the yeast 2 hybrid test for PPIs, and all of these were found to be conserved in both yeasts. From this, they estimated that the 95% confidence interval of the total rate of PPI evolution is between 0 and 4.6 × 10−10 per PPI per year
They then went on to consider animals. Using PPI data from nemtodes, they found two of five confirmed S. cerevisiae PPIs are conserved in C. elegans. These two species diverged ~1,300 MYA, so the 95% confidence interval is 1.6 × 10−10 to 2.0 × 10−9. Using transcription factor PPI data from humans and mice, which diverged 90 MYA, they found that six of six mouse PPIs are conserved in humans. From this, they estimate the 95% confidence interval is 0 to 5.5 × 10−9. Using all the dataset together, they arrive at the final value for the rate of PPI change: (2.6 ± 1.6) × 10−10 per PPI per year.
It's great to have a value for the rate of this sort of rare evolutionary change, and the authors are certainly very rigorous is eliminating the possibility of false positives and false positives. However, I'm left wondering whether after all this filtering, they're left with enough data to be really sure of their estimates. I count 54 PPIs in total, of which only 3 are lost, and that's in one lineage. Is that really enough data to go on? Well, I'm certainly not a statistician, so I can only assume that this was checked out thoroughly by folks much more informed on this kind of thing than I am.
An interesting future route would be to compare protein substitution and PPI rates between lineages. I'm wondering whether organisms with high amino acids substituion rates (like nematodes and other parasites) have a PPI rate that's (relatively) just as high, or whether this is dampened by compensatory mutations in binding interfaces. It'd also be interesting to compare the eukaryotic PPI rate to the bacterial one.
Qian W, He X, Chan E, Xu H, & Zhang J (2011). Measuring the evolutionary rate of protein-protein interaction. Proceedings of the National Academy of Sciences of the United States of America PMID: 21555556
I noticed an interesting upcoming PNAS paper: Measuring the evolutionary rate of protein–protein interaction. This tackles a subject close to my heart - functional evolution of proteins. The authors tackle measuring the rate at which functional changes happen, with function in this case measured by gain and loss of PPIs (protein-protein interactions).
They start by comparing yeast S. cerevisiae, which has abundant PPI data with another yeast Kluyveromyces waltii. These two diverged ∼150 MYA, and they are sort of special relatives since a whole genome duplication occurred in the lineage to S. cerevisiae after the divergence of K. waltii. This worried me that this could affect the rate of PPI change, due to the sudden influx of homologues in the S. cerevisiae lineage inflating the PPI count. However, the problem of duplicates was surmounted by only considering one to one orthologs (ie proteins related by vertical descent and not gene duplication (which would be paralogues)). In all, 43 proteins passed the yeast 2 hybrid test for PPIs, and all of these were found to be conserved in both yeasts. From this, they estimated that the 95% confidence interval of the total rate of PPI evolution is between 0 and 4.6 × 10−10 per PPI per year
They then went on to consider animals. Using PPI data from nemtodes, they found two of five confirmed S. cerevisiae PPIs are conserved in C. elegans. These two species diverged ~1,300 MYA, so the 95% confidence interval is 1.6 × 10−10 to 2.0 × 10−9. Using transcription factor PPI data from humans and mice, which diverged 90 MYA, they found that six of six mouse PPIs are conserved in humans. From this, they estimate the 95% confidence interval is 0 to 5.5 × 10−9. Using all the dataset together, they arrive at the final value for the rate of PPI change: (2.6 ± 1.6) × 10−10 per PPI per year.
It's great to have a value for the rate of this sort of rare evolutionary change, and the authors are certainly very rigorous is eliminating the possibility of false positives and false positives. However, I'm left wondering whether after all this filtering, they're left with enough data to be really sure of their estimates. I count 54 PPIs in total, of which only 3 are lost, and that's in one lineage. Is that really enough data to go on? Well, I'm certainly not a statistician, so I can only assume that this was checked out thoroughly by folks much more informed on this kind of thing than I am.
An interesting future route would be to compare protein substitution and PPI rates between lineages. I'm wondering whether organisms with high amino acids substituion rates (like nematodes and other parasites) have a PPI rate that's (relatively) just as high, or whether this is dampened by compensatory mutations in binding interfaces. It'd also be interesting to compare the eukaryotic PPI rate to the bacterial one.
Qian W, He X, Chan E, Xu H, & Zhang J (2011). Measuring the evolutionary rate of protein-protein interaction. Proceedings of the National Academy of Sciences of the United States of America PMID: 21555556
Detecting mutual exclusivity of gene families
Gene networks are popularly used in systems biology to show functional associations among genes within a single genome, taking advantage of available experimental data on intermolecular interactions. Co-evolutionary networks are another way of showing functional associations among genes, in this case using presence/absence patterns of homologous genes across genomes to predict likely interaction partners. A nice example from proteins that I'm interested in are the components of the selenocysteine incorporation machinery for incorporating the amino acid selenocysteine into growing peptides. Not all organisms utilise selenonocysteine, but those who do encode a whole package of genes for its synthesis, charging onto tRNA and delivery to the ribosome. If a gene X was to be found in only that strange collection of (not always closely related) organisms with the selenocysteine machinery, chances are that X either uses selenocysteine or is also involved in its metabolism. As an aside, STRING is a really nice web application for visualising networks of functional associations compiled from various sources of evidence (co-occurence, co-expression, gene neighbourhood, and experiments).
A new paper by Zhang et al. in GBE presents a new and interesting approach for analysing co-evolutionary networks, by detecting Mutually Exclusive Orthologous Modules (MEOMs). In their words: "A MEOM is composed of two sets of gene families, each including gene families that tend to appear in the same organisms, such that the two sets tend to mutually exclude each other (if one set appears in a certain organism the second set does not)."
MEOMs are interesting because they reflect the replacement of one set of genes by another. This could be due to lineage-specific or environment-specific adaptations. The authors analyze a co-evolutionary network based on 383 organisms from across the tree of life and find that MEOMs most often include gene families involved in transport, energy production, metabolism, and translation. They suggest that changes in the metabolic environment of an organism require adaptation to new sources of energy, and this triggers of replacement of genes, complexes and pathways in individual lineages. They also find many outer membrane proteins in their MEOMs, suggesting that as these proteins interact with the extracellular environment, they are frequently replaced during adaptation.
It's all very interesting, and I hope the authors will consider making a searchable web interface to their database of MEOMs. Their supplementary data is a bit awkward to navigate, and this kind of data is just crying out for visualisation. I would love to be able to scan proteins in my data sets for potential MEOM membership.
Xiuwei Zhang, Martin Kupiec, Uri Gophna, & Tamir Tuller (2011). Analysis of Co-evolving Gene Families Using Mutually Exclusive Orthologous Modules Genome Biology and Evolution : 10.1093/gbe/evr030
A new paper by Zhang et al. in GBE presents a new and interesting approach for analysing co-evolutionary networks, by detecting Mutually Exclusive Orthologous Modules (MEOMs). In their words: "A MEOM is composed of two sets of gene families, each including gene families that tend to appear in the same organisms, such that the two sets tend to mutually exclude each other (if one set appears in a certain organism the second set does not)."
MEOMs are interesting because they reflect the replacement of one set of genes by another. This could be due to lineage-specific or environment-specific adaptations. The authors analyze a co-evolutionary network based on 383 organisms from across the tree of life and find that MEOMs most often include gene families involved in transport, energy production, metabolism, and translation. They suggest that changes in the metabolic environment of an organism require adaptation to new sources of energy, and this triggers of replacement of genes, complexes and pathways in individual lineages. They also find many outer membrane proteins in their MEOMs, suggesting that as these proteins interact with the extracellular environment, they are frequently replaced during adaptation.
It's all very interesting, and I hope the authors will consider making a searchable web interface to their database of MEOMs. Their supplementary data is a bit awkward to navigate, and this kind of data is just crying out for visualisation. I would love to be able to scan proteins in my data sets for potential MEOM membership.
Xiuwei Zhang, Martin Kupiec, Uri Gophna, & Tamir Tuller (2011). Analysis of Co-evolving Gene Families Using Mutually Exclusive Orthologous Modules Genome Biology and Evolution : 10.1093/gbe/evr030
The ancestral ribosome: my reservations
I work on deep evolution of ribosome-associated proteins, so of course I'm very much excited by research on deep evolution of ribosomal RNA. However, I have some concerns about some of the work in this field relating to the composition and structure of the ancestral ribosome, or as sometimes called, proto-ribosome. Actually, it’s less that I have concerns about the work, more that I have some small, but (at least to me) important concerns about the interpretations and subsequent speculations. Anyway, the other day, as I listed to Ada Yonath's and Loren Williamson's talks at the Suddath Symposium on the Ribosome, I was reminded about these concerns, and decided it's probably a good idea to blog about them.
Before I start moaning, I want to stress that it's really exciting that people are trying to answer such deep evolutionary questions, and I genuinely think they have made some interesting and important discoveries about the relative ages of parts of the ribosome, and about small catalytic RNAs that can behave like ribosomes, I just don't think those catalytic RNAs are ancestral ribosomes. I think they are perhaps some shared component of ancestral and modern ribosomes
What it comes down to is that in general the people working on proto-ribosomes are assuming that evolution proceeds from small and simple to large and complex. In fact, this is not necessarily the case, as I have blogged about previously. Small and perfectly formed is hard to evolve, while big and clumsy with time for optimisation is less hard.
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| Figure 1. My crude drawing to demonstrate evolutionary progression. Green is the modern ribosome, with the predicted ancient parts in red. Black is sequence nonhomologous to the modern ribosome. A) shows the idea that is often presented: a small protoribosome gets bigger over time. B) shows my hypothesis: continual loss and gain of sequence with some retention of a conserved core. |
Ada Yonath’s talk at the symposium on the ancestral peptidyl transferase centre (PTC, the region where peptide bonds are formed between amino acids) really captured my imagination. The PTC is buried right in the middle of the ribosome and consists of two fragments of rRNA with rotational structural symmetry between the P (peptidyl) site tRNA binding rRNA and the A (acceptor) tRNA binding rRNA. This symmetrical region is highly conserved in sequence (98% identity among organisms), but not between each symmetrical unit. Ada proposes that this symmetrical region is the oldest part of the ribosome, and that this minimal region is a functional machine on its own. In support of this, the CCA-end of tRNA fits in perfectly, and their structural studies indicate it could provide a rotary motion of tRNAs that is required get peptidyl transfer. This is a really nice story, and so far I’m totally in support. What I have problems accepting is that this minimal rRNA dimer IS all that was present of the protoribosome (as in fig 1A). Why could there not have been extra RNA around it that was replaced during evolution (as in fig 1B)? Via the online participation (which was fantastic by the way) for the symposium I asked Ada about this:
Gem: The small symmetrical region might be the only region modern ribosomes have in common with the ancestral proto-ribosome. But it almost seems TOO streamlined. Could the protoribosome actually have been bigger than that core region, and there could have been loss as well as gain of sequence?
Ada: we haven’t thought of that… but you can speculate anything.
That’s exactly my concern, that you can speculate anything in this field. There are very few clues to go on, and they don’t give anything conclusive. Where evidence dries up, all you can do are thought experiments, based on examples we know of. And we know from extant ribosomes that there have been lineage-specific loss and gain of sequence. Good examples are mitochondrial ribosomes which have lost a good deal of rRNA and replaced it with protein.
A similar model of ribosome evolution to Ada's, proposing progressive addition of rRNA onto a minimal but functional PTC frame is presented by Bokov and Steinberg, Nature (2009). In this paper, the authors examine the inter-domain interactions and structural dependencies in the large subunit to infer relative age. Again, this is great, fascinating work, and it is also consistent with a model of replacement and optimisation, rather that the “aggrandizement” that they presume in their model.
After Ada Yonath’s talk in the symposium came Loren Williams, who also works on figuring out the ancestral ribosome. Williams and colleagues compared the sequences and structures of archaeon H. marismortui and bacterium T. thermophilus ribosomes and found that sequence and conformational similarity of the rRNAs are greatest near the PTC, and diverge smoothly with distance from it. They show a beautiful figure of the ribosome as an onion, which makes their point perfectly.
Again, these particular results are very clear and interesting, it’s just some of the assumptions about the evolutionary process that I have issues with. I noticed in the talk that Loren consistently equated “conserved” with “old.” In fact, “conserved” usually means “important”. Jamie Williamson who was in the audience also made this point during the talk, and Loren replied that he could not argue with that. In the case of the ribosome, the central parts are not only involved in catalysis, they are also important for maintaining the three dimensional structure. So they are very important. It’s the same reason why proteins show strong conservation of buried amino acids.
Some other evolutionary statements and suppositions by Loren also were a bit iffy, such as: "mitochondrial ribosomes are running evolution backwards." Yikes. Drastically cutting down rRNA and replacing with protein independently in multiple lineages is definitely not running evolution backwards… in fact evolution is never, ever backwards. I also have a problem with supposing things that it isn’t necessary to suppose: Loren hypothesises that the ribosome binding tails of ribosomal proteins are older than the globular domains, and were originally non-coded, they then became fused to globular domains. There really is no evidence for this as far as I can see. The tails and insertions that protrude into the ribosome are very biased in amino acid content, and if they’re anything like ribosome-binding extensions of translation factors such as IF3, they readily appear and vary in length and primary sequence during evolution. These sort of structures seem easy to add.
Maybe my complaints can be considered to be petty in a field that is necessarily rife with speculations, but I just think it’s important not to push the speculations too far, in order to keep our scientific integrity and not become like the cranks that publish their “evolutionary biology” in the Journal of Cosmology. For example, I loved the first half of Ada’s talk, but she finished it with a discussion of the ability of her two symmetrical fragments to dimerise, and suggested that in a population of these fragments, their non-uniform tendency to dimerise was a kind of "pre darwinian Darwinian” ribosome evolution that took place in the prebiotic world. She also suggested that these fragments may also be proto-tRNAs. For me, this is too far removed from the evidence, and these are speculations too far.
BUT! Having said all that, wild speculation is bloody well fun, so I will offer my own hypothesis (see fig. 1 B). I think the first ribosome could have been big, flabby and clumsy, an amalgamation of RNAs that were perhaps already involved in some other catalysis such as nucleic acid polymerisation, that through chance flopping around, managed to catalyse (probably in a very inefficient way) peptide bond formation. The efficiency of bond formation between particular amino acids may have been influenced by the certain nucleic acids being polymerised in the active site, as in some primitive ‘code’. This protein synthesising proto-machine maybe had nothing recognisably in common with modern ribosomes, but it was subsequently fine-tuned through loss of gain of sequence until it became something resembling the ribosome that we know and love.
Refs and further reading
Bokov K, & Steinberg SV (2009). A hierarchical model for evolution of 23S ribosomal RNA. Nature, 457 (7232), 977-80 PMID: 19225518
Hsiao C, Mohan S, Kalahar BK, & Williams LD (2009). Peeling the onion: ribosomes are ancient molecular fossils. Molecular biology and evolution, 26 (11), 2415-25 PMID: 19628620
Promiscuous proteins
Gone are the days when the one protein, one function presumption prevailed. Many proteins are multifunctional and multispecific, that is they have multiple binding partners for carrying out various roles in the cell. Here's a new review by Erijiman et al. in Biochemistry about multispecifity, covering various examples of promiscuous proteins and the different ways in which they achieve their multispecificity.
Proteins can interact with multiple binding partners by having distinct binding interfaces or domains. By this route, it's possible for the protein to optimise each binding site for its specific partner as the interfaces are independent (although there may be some cross-talk). An example of this from the proteins that I'm interested in is the Rel protein of bacteria. This protein has a synthesis domain for producing the alarmone ppGpp, and a hydrolysis domain for degrading it. The interfaces are on different sides of the protein, so are in some sense independent, although binding of a molecule in one site may influence the function of the other site by switching the conformation of the protein.
Proteins can interact with multiple binding partners by having distinct binding interfaces or domains. By this route, it's possible for the protein to optimise each binding site for its specific partner as the interfaces are independent (although there may be some cross-talk). An example of this from the proteins that I'm interested in is the Rel protein of bacteria. This protein has a synthesis domain for producing the alarmone ppGpp, and a hydrolysis domain for degrading it. The interfaces are on different sides of the protein, so are in some sense independent, although binding of a molecule in one site may influence the function of the other site by switching the conformation of the protein.
As an alternative solution, a protein may bind through one interface that is able to interact with multiple partners. An example of this is the archaeal elongation factor EF1A, which delivers aminoacylated tRNA, release factor aRF1 and mRNA decay protein aDom34 to the ribosome, binding all three by overlapping binding sites.
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| My rather simplistic representation of how a protein's binding interfaces can be distributed. A: independent binding sites eg Rel. B: overlapping binding sites eg aEF1A. |
Multispecificity is great for the cell (especially cells with reduced, streamlined genomes) in that from just one gene, you get a lot of functional value. However, it also introduces some compromises for the protein, in terms of optimising its specificity for binding partners (especially true for proteins with overlapping binding sites), and brings about challenges in terms of regulating the different functions. A way to escape these problems is by gene duplication and subfunctionalisation for the different binding functions of the protein. Indeed this has occurred in some organisms for both of my examples above. In proteobacteria, Rel has been duplicated, resulting in RelA and SpoT, specialised for ppGpp synthesis and hydrolysis respectively. Similarly, in eukaryotes, two duplications of EF1A-like proteins have led to eEF1A, eRF3 and Hbs1, specialised for binding aa-tRNA, eRF1 and eDom34 respectively. However, it would be wrong to say that eEF1A now only has one function, as in fact it has many many more functions... but that's another story!
For more info on these proteins, check out my other blog posts:
Refs
Erijman A, Aizner Y, & Shifman JM (2011). Multispecific recognition: mechanism, evolution, and design. Biochemistry, 50 (5), 602-11 PMID: 21229991Hogg T, Mechold U, Malke H, Cashel M, & Hilgenfeld R (2004). Conformational antagonism between opposing active sites in a bifunctional RelA/SpoT homolog modulates (p)ppGpp metabolism during the stringent response [corrected]. Cell, 117 (1), 57-68 PMID: 15066282
Saito K, Kobayashi K, Wada M, Kikuno I, Takusagawa A, Mochizuki M, Uchiumi T, Ishitani R, Nureki O, & Ito K (2010). Omnipotent role of archaeal elongation factor 1 alpha (EF1α in translational elongation and termination, and quality control of protein synthesis. Proceedings of the National Academy of Sciences of the United States of America, 107 (45), 19242-7 PMID: 20974926
Tsunami and earthquake crisis - Non-Believers Giving Aid
Because praying for Japan doesn't help anyone, but donations save lives.
http://givingaid.richarddawkins.net/
"Non-Believers Giving Aid and the Richard Dawkins Foundation for Reason and Science are once more partnering with the International Committee of the Red Cross to bring much needed help to people whose lives have been torn apart by natural disaster. Every cent and penny of money donated via Non-Believers Giving Aid will be forwarded to the International Red Cross – and if you are in the UK and you complete the Gift Aid Declaration along with your donation, we will pass that on in its entirety too."
http://givingaid.richarddawkins.net/
"Non-Believers Giving Aid and the Richard Dawkins Foundation for Reason and Science are once more partnering with the International Committee of the Red Cross to bring much needed help to people whose lives have been torn apart by natural disaster. Every cent and penny of money donated via Non-Believers Giving Aid will be forwarded to the International Red Cross – and if you are in the UK and you complete the Gift Aid Declaration along with your donation, we will pass that on in its entirety too."
Well, this is unexpected! Drosophila mitochondrial translation elongation Factor G1 contains a nuclear localization signal.
Most eukaryote genomes encode two mitochondrial translation elongation factor Gs. I recently had a paper in MBE about the origin and evolution of these factors, and I've blogged about it previously. I spotted a very surprising article in PloS One today: "The Drosophila Mitochondrial Translation Elongation Factor G1 Contains a Nuclear Localization Signal and Inhibits Growth and DPP Signaling." For some reason, mtEFG1 is dual targeted to the nucleus as well as the mitochondrion. The localisation signal is proposed to be found at the C terminus, unlike the mitochondrial transit peptide, which is found at the N terminus. The authors suggest a model in which "if mitochondrial ATP synthesis is low or EF-G1 is overexpressed and import of EF-G1 proteins into mitochondria is a limiting step, some EF-G1 proteins can accumulate outside of mitochondria and translocate into the nucleus, where they inhibit cellular growth and proliferation."
The authors carry out mutagenesis and subcellular localization analysis of mtEFG1, and find that although the Drosophila mtEFG1 gene is essential, it's not required in every tissue. This leads them to suggest that in some tissues, mtEFG2 and not mtEFG1 is the primary translocation factor. This would be very unexpected as neither spirochete or human spd/mtEFG2 can not promote translocation, and instead spd/mtEFG2 is proposed to be specialised for EF-G's role in ribosome recycling. Additionally, the alignment in my paper shows mtEFG2s don't have the conserved amino acids involved in translocation functions, such as interaction with peptidyl-tRNA. However intramolecular and ribosome interaction sites are well conserved in mtEFG2, suggesting it maintains EF-G-like structural integrity and ribosome binding abilities. Maybe this is sufficient to promote translocation in some conditions? In fact, even the more distantly related EF-G2 of Thermus, which belongs to a whole other ancient subfamily is capable of translocation, hinting that although classical EF-G is very well conserved at the primary sequence level, at least in some conditions the ribosome can accommodate and translocate with more divergent homologs that maintain an EF-G-like structure.
The model of mtEFG1 subcellular location being related to mitochondrial ATP synthesis proposed by Trivigno and Haerry presents a paradox, which they acknowledge: "If EF-G2 functioned as an elongation factors in tissues like the heart, mitochondrial translation and ATP synthesis would occur at normal levels, and EF-G1 would be imported into mitochondria and not accumulate in the nucleus. On the other hand, in tissues like the liver, where EF-G2 cannot function as an elongation factor, mitochondrial translation would decrease, ATP levels would drop, EF-G1 import into mitochondria would decrease and accumulation in the nucleus increase, which would further exacerbate the problem."
So, in conclusion, it's all rather surprising and the model just doesn't seem quite right... it's all very well for a bioinformatician to say this I know, but more experiments needed!
Refs
Atkinson GC, & Baldauf SL (2011). Evolution of elongation factor g and the origins of mitochondrial and chloroplast forms. Molecular biology and evolution, 28 (3), 1281-92 PMID: 21097998
Trivigno C, & Haerry TE (2011). The Drosophila Mitochondrial Translation Elongation Factor G1 Contains a Nuclear Localization Signal and Inhibits Growth and DPP Signaling. PloS one, 6 (2) PMID: 21364917
Tsuboi, M., Morita, H., Nozaki, Y., Akama, K., Ueda, T., Ito, K., Nierhaus, K., & Takeuchi, N. (2009). EF-G2mt Is an Exclusive Recycling Factor in Mammalian Mitochondrial Protein Synthesis Molecular Cell, 35 (4), 502-510 DOI: 10.1016/j.molcel.2009.06.028
Connell, S., Takemoto, C., Wilson, D., Wang, H., Murayama, K., Terada, T., Shirouzu, M., Rost, M., Schüler, M., & Giesebrecht, J. (2007). Structural Basis for Interaction of the Ribosome with the Switch Regions of GTP-Bound Elongation Factors Molecular Cell, 25 (5), 751-764 DOI: 10.1016/j.molcel.2007.01.027
The authors carry out mutagenesis and subcellular localization analysis of mtEFG1, and find that although the Drosophila mtEFG1 gene is essential, it's not required in every tissue. This leads them to suggest that in some tissues, mtEFG2 and not mtEFG1 is the primary translocation factor. This would be very unexpected as neither spirochete or human spd/mtEFG2 can not promote translocation, and instead spd/mtEFG2 is proposed to be specialised for EF-G's role in ribosome recycling. Additionally, the alignment in my paper shows mtEFG2s don't have the conserved amino acids involved in translocation functions, such as interaction with peptidyl-tRNA. However intramolecular and ribosome interaction sites are well conserved in mtEFG2, suggesting it maintains EF-G-like structural integrity and ribosome binding abilities. Maybe this is sufficient to promote translocation in some conditions? In fact, even the more distantly related EF-G2 of Thermus, which belongs to a whole other ancient subfamily is capable of translocation, hinting that although classical EF-G is very well conserved at the primary sequence level, at least in some conditions the ribosome can accommodate and translocate with more divergent homologs that maintain an EF-G-like structure.
The model of mtEFG1 subcellular location being related to mitochondrial ATP synthesis proposed by Trivigno and Haerry presents a paradox, which they acknowledge: "If EF-G2 functioned as an elongation factors in tissues like the heart, mitochondrial translation and ATP synthesis would occur at normal levels, and EF-G1 would be imported into mitochondria and not accumulate in the nucleus. On the other hand, in tissues like the liver, where EF-G2 cannot function as an elongation factor, mitochondrial translation would decrease, ATP levels would drop, EF-G1 import into mitochondria would decrease and accumulation in the nucleus increase, which would further exacerbate the problem."
So, in conclusion, it's all rather surprising and the model just doesn't seem quite right... it's all very well for a bioinformatician to say this I know, but more experiments needed!
Refs
Atkinson GC, & Baldauf SL (2011). Evolution of elongation factor g and the origins of mitochondrial and chloroplast forms. Molecular biology and evolution, 28 (3), 1281-92 PMID: 21097998
Trivigno C, & Haerry TE (2011). The Drosophila Mitochondrial Translation Elongation Factor G1 Contains a Nuclear Localization Signal and Inhibits Growth and DPP Signaling. PloS one, 6 (2) PMID: 21364917
Tsuboi, M., Morita, H., Nozaki, Y., Akama, K., Ueda, T., Ito, K., Nierhaus, K., & Takeuchi, N. (2009). EF-G2mt Is an Exclusive Recycling Factor in Mammalian Mitochondrial Protein Synthesis Molecular Cell, 35 (4), 502-510 DOI: 10.1016/j.molcel.2009.06.028
Connell, S., Takemoto, C., Wilson, D., Wang, H., Murayama, K., Terada, T., Shirouzu, M., Rost, M., Schüler, M., & Giesebrecht, J. (2007). Structural Basis for Interaction of the Ribosome with the Switch Regions of GTP-Bound Elongation Factors Molecular Cell, 25 (5), 751-764 DOI: 10.1016/j.molcel.2007.01.027
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