Friday, April 25, 2014

The GGAGG Reflex

A common myth in biology is that genes coding for proteins need to have a Shine Dalgarno sequence upstream of the start codon. Students sometimes spout this as an inarguable fact; a kind of molecular biology catechism. I call it the GGAGG reflex.

In fact, no SD sequence is required. None at all. It's important to be clear on this.

In case you're not a biogeek: In the 1970s,  Australian scientists John Shine and Lynn Dalgarno were the first to notice that the tail end of the 16S bacterial ribosomal RNA contains a short nucleotide sequence whose reverse complement is often found immediately upstream of a protein gene's start codon. The exact sequence varies from organism to organism, but the rRNA trailer sequence is usually pyrimidine-rich. In E. coli, the sequence is CACCTCCTTA. (Here, I am of course talking about the DNA sequence. In RNA it's CUCCUCCUUA.)  If you reverse the sequence, the Watson-Crick complement is TAAGGAGGTG. Some portion of the latter is often found a few nucleotides upstream of a start codon; not 100% of the time, but too often to be by chance.

The key intuition here is that Watson-Crick binding of the tail end of the 16S rRNA to the corresponding antisequence ahead of the start codon helps stabilize the ribosome so that it is more likely to translate the gene. The degree of binding depends, of course, on the fidelity of the SD sequence ahead of the gene. Usually, the purine-rich SD area is not an exact match for the 16S rRNA trailer, and in fact the SD region quite often has no detectable SD signature whatsoever.

How often is "quite often"? In 2002, Ma et al. undertook a survey of 30 organisms representing bacteria from all major taxonomic groups. Somewhat surprisingly, they found that in 17 out of 30 organisms, a Shine Dalgarno sequence was present at fewer than half of all CDS (protein-encoding) genes. Among the bacteria most likely to use SD sequences were Bacillus subtilis and Thermotoga thermophilus, in which 90% of known protein genes have an upstream SD signal. Among those least likely to use SD sequences were low-GC/small-genome organisms (intracellular parasites, Mycoplasmas, and pathogens), with many groups, like the Actinobacteria (47%), falling somewhere in the middle.

Before taking these findings to heart, though, it's worth noting some serious weaknesses in the Ma et al. study. In obtaining the above numbers, Ma et al. used a rather permissive definition of "SD sequence," based on a minimum binding-energy cutoff (∆G) of -4.4 kcal/mol, which means they counted GAGG as a SD sequence (and also GGAG and AGGA). If one were to count only GGAGG (length 5) and longer motifs, the percentages given by Ma et al. would be much lower. (I present some data of my own on this further below.) The reason this is a very serious issue is that the probability of random occurrence of short (length-4) sequences like GAGG is substantial. Ma et al. failed to report the expectation odds for the various "signals" they looked for. Hence, for short motifs, we have no way of knowing, for the various organisms, what the expected rate of occurrence of short signals was. If an organism with genomic GC content of 66% has a putative SD motif of GAGG, AGGA, or GAGG in the 20-bp target region for 20% of its genes, how does that compare with the random occurrence rate for those sequences, given the organism's DNA base composition? We're not told.

Bearing in mind the weaknesses of the study, a number of nonethelesss interesting findings came out of the Ma et al. survey, including:
  • A SD sequence is rarely long or canonical; many times it's just GAGG or GGAG or AGGA (putatively) or a corruption of the expected form (e.g., GGTGG instead of GGAGG)
  • SD sequences occur more often with highly expressed genes (such as genes for ribosomal proteins and core energy metabolism genes) than with low-expression genes
  • In some (not all) organisms, the SD sequence is more likely to occur in conjunction with an ATG start codon and less likely to occur with GTG or TTG
  • Vanishingly few SD signals are located further than 14 bases or closer than 4 bases away from a start codon
Anybody with modest JavaScript skills can write scripts that verify some of these findings against public genomes. I took a quick look at the genome for Rothia mucilaginosa DY-18 (a member of the Actinobacteria family and a common inhabitant of the human mouth). First, I determined the most likely SD sequence for Rothia based on the 16S rRNA trailer of CCTCCTTTCT (implying a SD sequence of AGAAAGGAGG), then I had my script scan the genome in both directions, looking for any of the six possible length-5 motifs within the full-length sequence (so, AGAAA, GAAAG, etc.), in the 20 base pairs upstream of every annotated open reading frame start codon. In total, I found 686 putative SD sequences within 4 to 14 bases of an annotated start codon. Since Rothia mucilaginosa has 1905 CDS genes, this means 36.0% of protein genes carry a putative length-5 Shine Dalgarno signal. When I re-ran the check using all possible length-4 SD signal variants (using the relaxed criteria of Ma et al.), I found 1160 positives. Thus, 60.9% of CDS genes in R. mucilaginosa have a length-4 SD signal per Ma et al.

On a probability of abundance basis (given Rothia's actual base composition stats), we would expect to see 203 length-5 SD motifs by pure chance in the genome's 1905 20-bp regions. The actual number (686) is obviously quite a bit higher than expected, tending to validate the notion that these are, indeed, SD motifs we're looking at. For length-6 motifs, the trend is even sharper: The expectation is 40 occurrences by chance; the actual number is 340. So at a length of 6, a motif has high odds (~90% chance) of being real.

By contrast, the statistical expectation for length-4 motifs calculates out at 957, which is only slightly less than the number found (1160). Therefore, in dealing with Shine Dalgarno sequences, at least in Rothia, it's meaningful to deal with length-5 and longer motifs, but probably not meaningful to deal with length-4. When you spot a length-4 motif, odds are very high you're looking at a randomly occurring pattern.

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Thursday, April 24, 2014

Are Overapping Genes Real?

Bacteria belonging to the Pseudomonas family are a perennial favorite among bacteriology instructors (and students) because of the curious ability of some of its members to produce pigments that fluoresce under an ultraviolet light. If you're unlucky enough to get an infected cut on the arm while working in the garden, it's possible your cut will fluoresce under a black light. That's enough of a diagnosis to pronounce the infectious agent. 
Fluorescent colonies of Pseudomonas.

Silby and Levy, investigating the adaptation of the bacterium Pseudomonas fluorescens to soil, uncovered the existence of at least ten antisense genes in P. fluorescens. They went on to demonstrate experimentally that one of the genes, cosA, produces not just antisense RNA but an associated protein. Tellingly, Silby and Levy commented:
These findings suggest that current genome annotations provide an incomplete view of the genetic potential of a given organism.
The implication is that additional antitranscriptome genes remain to be found, not only in Pseudomonas but in other organisms.

There's a good reason they haven't been found yet. Overlapping genes are automatically rejected by many of the annotation programs that are commonly used to find, identify, and label genes in genome sequences. (The oft-used freeware Glimmer 2 program allows you to set the overlap-rejection threshold.) Many yet-to-be-discovered antisense genes have been deliberately and systematically obscured in published genomes.

Still, once in a while such genes do surface. For example, in Pseudomonas stutzeri A1501, we find a pair of overlapping genes at an offset of 3035137 on the chromosome (see illustration below).

Overapping genes in Pseudomonas stutzeri.

The top gene is annotated merely as a "hypothetical protein," while the underlying gene on the opposite strand is an aspartyl-tRNA synthetase. One's normal inclination is to dismiss a hypothetical protein as being unimportant, but this may not be wise. Twenty percent or more of bacterial genes are annotated as hypothetical proteins; common sense says they can't all be unimportant. In fact, in "Transcriptome Analysis of Pseudomonas syringae Identifies New Genes, Noncoding RNAs, and Antisense Activity" by Filiatrault et al. (2010), researchers found that 818 out of 1,646 protein genes in P. syringae annotated as "hypothetical proteins" were expressed under iron-limited conditions. Many (probably most) genes annotated as "hypothetical protein" are quite real and should probably be re-annotated as PUF: "protein of unknown function."

In this case, the "hypothetical protein" shown in yellow (above) turns up medium-strength protein-BLAST hits with other "hypothetical proteins" from other organisms, including a hit with an E-value of 3.0×10-49 in Parasutterella excrementihominis YIT 11859 and a comparable hit on a predicted phosphatase/phosphohexomutase in Rothia mucilaginosa DY-18.

In this particular case, the hypothetical-protein gene lacks a strong upstream Shine Dalgarno sequence (a sequence preceding many genes that helps bind a ribosome to the mRNA). But so too does the gene on the opposite strand. (This is not unusual. The SD sequence is not required for translation and in fact, in about half of bacterial species, a Shine Dalgarno sequence is associated with fewer than 50% of genes.) Hence, the jury's out on whether the antigene is expressed. It could be that no protein is made from the top strand but the gene provides RNA-mediated control of the gene on the bottom strand. We won't know for sure until someone investigates.

In Pseudomonas aeruginosa strain PADK2_CF510, we find another instance of a bidirectional overlapping gene pair (see graphic below). In this case, the gene on the top strand (CF510_06030) encodes the large subunit of an isopropylmalate isomerase. The gene on the bottom strand (CF510_06025, shown in yellow) is annotated as "Flp pilus assembly protein TadG." It could very well be a misannotated non-gene. However, five genes away is FimV (CF510_06060), another pilus-assembly (motility) protein. Moreover, the gene marked TadG has a strong upstream SD sequence containing the canonical GGAGG motif. The gene above it has a weaker GGAAA motif.

P. aeruginosa has an overlap of an isopropylmalate isomerase gene and a gene for a motility protein. The latter is shown in yellow.

In previous posts, I've mentioned (and shown data for) the fact that in the overwhelming majority of protein-encoding genes (across every kind of genome), the first base of a codon tends to be purine-rich. One check of whether a bidi-overlap gene is "real" or not ought to be that the first codon base should be purine rich in both reading directions. This is, in fact, the case for the examples shown above. The aspartyl-tRNA synthetase gene for P. stutzeri has AG1 (1st base, purine) content averaging 59.8%, whereas its bidirectional partner gene ("hypothetical protein") has AG1 = 58.5%. The isopropylmalate isomerase of P. aeruginosa has AG1 = 65.9%, while its antisymmetric partner (TadG) has AG1 = 56.2%.

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Tuesday, April 22, 2014

How Antisense Genes Are Discovered

In the past ten years or so, a great deal of research has focused on antisense transcription of genes. Normally, RNA gets transcribed from one strand of DNA only. But it turns out, in many cases RNA also gets transcribed off the opposite strand of DNA (an antisense copy), either at the original gene (so-called cis transcription) or at a copy of the gene some distance away (trans transcription). The latter can be a pseudogene, or a normal copy of the gene.

Antisense transcripts occur very widely not only in human DNA but in bacteria, yeast, and (in fact) every place where scientists have looked, and places where they haven't looked. Some of the most interesting discoveries have happened when researchers weren't specifically looking for antisense transcripts but found them by accident. How does that happen? It happens in experiments involving IVET (in vivo expression technology), an important experimental technique for uncovering new genes.

IVET is a powerful gene manipulation strategy for discovering which genes in an organism (a pathogen, usually) are up-regulated or turned on during host infection. Let's say you're studying a new pathogen and you want to get an idea of which genes, in the pathogen, are turned on during the infection process. First, you need a strain of the organism that's disabled by virtue of lacking a working copy of a particular metabolic enzyme, say an enzyme needed for purine metabolism, e.g. purA. Secondly, you need a vector for inserting a promoterless copy of the working gene into the bacterium. What this usually means is, you need a plasmid (a small extra chromosome; many bacteria have them, and they can often be manipulated in the lab) on which to place a functional purA gene. The gene won't be expressed, however, if it lacks a suitable promoter region on the DNA upstream of the gene. That's good; that's what you want. You want to put a promoterless copy of the good gene on the plasmid, along with (this is crucial) a random chunk of DNA from the pathogen, inserted ahead of purA on the plasmid. In practice, it's easy to create a bunch of plasmids with this arrangement: a working copy of purA, and ahead of it, a random chunk of pathogen DNA. The idea is that you now attempt to infect a lab animal with the bacterium containing the plasmid. If the bacterium establishes infection in the animal, presumably it's because a random chunk of DNA happened to contain a promoter region (and associated downstream genes) that gets turned on during infection. If you now isolate the bacterium from the sick animal, you can look to see what kind(s) of genes got transduced into the bacterium.

IVET is a promoter trap technology for selecting bacterial genes that are specifically induced when bacteria infect a host organism. A plasmid vetor contains a random fragment of the chromosome of the pathogen (red) and a promoterless gene (selective marker, burgundy) that encodes an enzyme required for survival. Pooled plasmid-containing clones are inoculated into the mouse (B). Only those bacteria that contain the selective marker fused to a random gene that is transcriptionally active in the host are able to survive. After a suitable infection period, bacteria that express the marker are isolated from the spleen or other organs. The inclusion of a lacZY mutant gene (blue) allows post-selection screening for promoters that are active only in vivo. What you want are bacteria that are lac-positive only in the host environment, not "constitutive" (always-on).
Exactly this sort of technique was used by Silby, Rainey, and Levy to determine which genes were activated in Pseudomonas during colonization of soil. (The IVET technique can be adapted to any scenario in which an organism differentially expresses genes in its adaptation to a "host" environment, even if the environment is, in fact, a plant, or soil in this case, rather than a mouse.) They were looking to see which genes in Pseudomonas play an essential role in that organism's ability to thrive in soil, and they successfully identified more than 50 promoters (and associated fusions) that come alive during soil colonization. When they looked at 22 "soil genes" that got turned on, they found ten previously undescribed genes that were transcribed in the antisense direction from regions overlapping known genes. They called these ten genes "cryptic fusions" because of their un-annotated existence on the supposedly silent, antisense side of known genes.

Cryptic fusions discovered by Silby et al. are shown in grey, in their antisense orientation to known genes (darker grey).

It's not unusual to find that antisense transcripts are playing a regulatory role. When a gene gets transcribed in both directions, the resulting sense and antisense RNAs can combine (by Watson-Crick pairing) to form a double-stranded RNA product, preventing translation of the RNA into protein. But incredibly, sometimes an antisense RNA transcript encodes a legitimate protein (a protein that gets made off the antisense copy). Silby and Levy documented this for the previously unknown cosA gene in Pseudomonas. It seems likely additional antisense proteins await discovery. (Most studies stop at the level of identifying RNA products.)

The finding of antisense transcripts in IVET experiments is common. One of the authors of the Pseudomonas study (Rainey) had previously published a study of rhizosphere-induced genes in Pseudomonas but had not published the fact that 20% of genes found this way were in an antisense orientation to normal genes. Likewise, a 1996 study of Pseudomonas aeruginosa infection in the mouse (Pseudomonas is an opportunistic pathogen) found antisense activity. In fact, the first-ever paper on IVET (by Mahan et al., 1993) described finding antiscript products.

IVET has uncovered a previously unknown "antitranscriptome" world hidden inside living cells. Until we explore this world fully, we won't know how much undiscovered biology we've left on the table.

Sunday, April 20, 2014

Bidirectionally Overlapping Genes

The occurrence of bidirectionally overlapping genes in bacteria is rare, and most such examples are dismissed as chimeric or representative of simple genome mis-annotation. After all, how can a gene make sense in one direction, but also make sense on the reverse-reading complementary strand of DNA? Such a situation is more than a mere palindrome. It's akin to the phrase:
Warsaw won, eh?
He now was raw.
The phrase has a sensical message in each direction, yet is not a mere bidi-symmetry of the "A man, a plan, a canal, Panama" kind. It defies credulity to believe a stretch of DNA spanning several hundred bases (several hundred "letters") could evolve to give a useful message in both directions. And yet, what is life itself, if not credulity-defying? Somehow, life began from primordial chemistry and evolved toward DNA genes coding for proteins. Is it so hard to believe that early replicant molecules (probably RNA) were transcribed and translated in both directions, and that some of the happy accidents survived? Is it so hard to believe that some proteins began life as reverse transcripts ("nonsense" proteins) that then evolved toward specialized functionality?

A bonafide example of a bidirectionally transcribed and translated gene was verified experimentally in 2008 by Silby and Levy, who were investigating the soil bacterium Pseudomonas fluorescens PF0-1. They found that the hitherto unknown cosA gene, which overlaps (on the opposite DNA strand) a gene for a fusaric acid resistance protein, is not only expressed as a protein but is required for soil colonization.

A section of P. fluorescens PF0-1 genome showing the existence of overlapping genes (note the yellow-colored segment, representing the cosA gene; the larger green gene above it, on the opposite strand, encodes a fusaric acid resistance protein). The overlapping genes have been shown experimentally to be expressed as protein.
Ironically, a month after Silby and Levy published their results, BMC Genetics published a study by Pallejà et al. looking at large gene overlaps in bacterial genomes. The Pallejà study concluded:
Among the 968 overlaps larger than 60 bps which we analysed, we did not find a single real one among the co-directional and divergent orientations and concluded that there had been an excessive number of misannotations. Only convergent orientation seems to permit some long overlaps, although convergent overlaps are also hampered by misannotations. We propose a simple rule to flag these erroneous gene length predictions to facilitate automatic annotation.
Silby and Levy argue that, to the contrary, current genome annotations are obscuring potentially important discoveries:
[Our] findings suggest that current genome annotations provide an incomplete view of the genetic potential of a given organism . . . In eukaryotes, the concept that genomes include numerous sense/antisense gene pairs is becoming increasingly obvious with genome-wide transcriptional studies in yeast [8] and Arabidopsis [10]. Antisense transcripts have been implicated in eye development [20] and control of entry into meiosis in yeast [21]. However, discussion of antisense transcription is limited to possible regulatory roles for antisense RNA [e.g. 8], without consideration of the possibility that they may specify proteins. Genome annotations do not routinely predict the existence of two protein-coding genes on opposite DNA strands, and in fact normally deliberately eliminate predicted overlaps. Moreover, small protein-coding genes can be missed by predictive algorithms. For example, the blr gene in E. coli specifies a 41 residue protein, and was discovered in a sequence believed to be intergenic [22]. The fact that antisense genes have been implicated in important biological functions indicates that more attention should be given to this emerging class of genes.
I happen to agree with Silby and Levy. It would be a shame if bidirectional overlaps in genomes are not investigated. The notion (furthered by Pallejà) that annotation software should suppress such findings automatically is repulsive. It's the kind of intolerant, rigid, dogmatic thinking science, quite frankly, doesn't need more of.

Saturday, April 19, 2014

Codons and Reverse Complement Codons

A very unusual and surprising property of protein-coding genes is that if a codon A appears with a certain frequency in genes, the reverse-complement codon of A will also have a similar frequency of occurrence. For example: If CTT (leucine) appears at a frequency of 1%, the reverse complement codon AAG (lysine) will also appear at roughly 1%. If CGT (arginine) appears at 0.2%, ACG (threonine) will appear at around 0.2%. (These are whole-genome frequencies.)

This correlation is strongest (r=0.75) for organisms with a high genomic G+C content, such as Streptomyces griseus, and lowest (r=0.28) in low-GC organisms like Clostridium botulinum.

This is a very peculiar property, when you think about it. We don't usually imagine an organism being constrained in its choice of codons for a particular protein. If a particular protein calls for a huge amount of leucines (CTTCTTCTT) we don't imagine that there's a requirement for an equivalent quantity of AAG to be used somewhere else. And yet, the correlation between frequency-of-occurrence of a codon and its antisymmetric twin is, as I say, surprisingly high in many organisms.

This sort of thing is very hard to explain without invoking a theory of proteogenesis that involves antisense proteins. Imagine a poly-lysine gene of AAA repeated 100 times. The gene gets duplicated on the opposite strand. Now the original strand has 100 AAAs and a run of 100 TTTs. If a reading frame opens up on the TTT stretch (and the protein is beneficial to the organism; it survives), there is now codon/anticodon parity of the kind I'm describing, between codons in the poly-lysine gene and the poly-phenylalanine (TTT) gene.

Why does this relationship hold for high-GC organisms but not as much for low-GC organisms? Probably because antisense genes in high-AT organisms contain a lot of stop codons (TAA, TGA, TAG, which by the way occur at about the same frequencies as TTA, TCA, and CTA, respectively). The presence of few stop codons in high-GC antisense genes gives those genes a chance to be expressed and evolve further. Of course, if you buy this theory, it tends to argue for a "GC World"  scenario in which the early proteosome evolved from GC-rich double-stranded genomes.

To illustrate the unusual correlation I'm talking about, I took the codon frequencies of Pseudomonas fluorescens PF01 (genome-wide) and made a graph that plots the frequency of occurrence of each codon on the x-axis, versus the frequency of occurrence of the corresponding reverse-complement codon on the y-axis. (So if CTA occurs at 0.3% and TAG occurs at 0.2%, I plot a point at [0.3,  0.2].) The SVG graph (below) is interactive: You should be able to hover over a point and see a tooltip that shows the identity of the corresponding codon, and its reverse twin, and their respective frequencies.

NOTE: If your browser does not support SVG, a PNG copy of the graph is here.

The symmetry pattern is expected: For every codon/anticodon there's a corresponding anticodon/codon pair with frequencies swapped. What's more important than the symmetry pattern is the fact that frequency values in Y increase monotonically in X and vice versa, with a correlation coefficient in this case of r=0.63 (F-statistic 41, p < .001). This means that codons tend to occur at about the same frequencies as their reverse complement codons. There are outliers, to be sure, but the overall trend is statistically solid.

Leave a comment if you have any thoughts on what's going on here.

Thursday, April 17, 2014

The Pathogen's Playbook

When comparing pathogenic bacteria with non-pathogenic species of the same genus or family, we often find a common pattern. In the pathogen:
  • The genome is often reduced in size (particularly in endosymbionts, but also in others).
  • The genome is often shifted in the direction of higher A+T content (lower G+C content).
  • Many pseudogenes are present.
  • Often, the pathogen is a slow-grower in pure culture (if it can be cultured at all).
  • The pathogen has special nutritional needs.
An extreme case that illustrates all of these points is Mycobacterium leprae, the leprosy bacterium. It has fewer genes than its cousin, M. tuberculosis (which in turn has fewer genes than non-pathogenic Mycobacteria); its genomic G+C content is 8% lower than most other Mycobacteria; it contains over 1100 pseudogenes; it has a doubling time of two weeks; and it cannot be grown in pure culture (presumably because of fastidious nutritional requirements).

M. tuberculosis can be grown in the laboratory, but it, and its M. avium-group cousins, are very slow growers, taking anywhere from four days to two weeks to develop colonies on solid media.

It seems likely that some pathogens (certainly members of the Mycobacteria, but also the tiny Tenericutes, e.g. Mycoplasma, among many others) have evolved slow growth as a survival strategy. Certainly, organisms that have evolved an intracellular parasitic lifestyle need to be careful not to out-grow the host, if the relationship is to be a long one.

All of the factors listed above suggest a certain scenario, a "pathogen's playbook," if you will, which can be summarized as follows:
  1. The organism invades a warm-booded host.
  2. Phagocytes (white blood cells) ingest the organism.
  3. The phagocytes undergo a respiratory burst, flooding the microbe(s) with peroxides, hypochlorites, nitrous oxide, and other noxious oxidants.
  4. The flood of reactive oxygenated species triggers an SOS response in the microbe.
  5. The microbe's DNA undergoes massive damage. 
  6. Any surviving microbial cells are now pathogenic.
The SOS response is known to trigger mutagenicity. In Mycobacterium, for example, peroxides (as well as UV light) can induce up-regulation of dnaE, an error-prone polymerase. Since Mycobacteria are known to lack a MutS mismatch repair system, SOS-induced errors in DNA replication will almost certainly include uncorrected frameshift errors leading to the creation of pseudogenes. But that's a good thing, if you're a Mycobacterium interested in forming a longterm relationship with a host cell. The loss of certain genes (as long as they're not essential!) will likely slow your metabolism and make you dependent on host nutrients. Truly non-essential pseudogenes will simply be jettisoned over time, reducing the footprint of the remaining genome. Any pseudogenes that survive will likely have done so because they're now playing an essential gene-silencing role.

Let's expand on that last part. Take the dnaE gene, for example. M leprae has two copies of this gene, only one of which is functional. Suppose both copies were functional at the time of the massive pseudogenization event that converted so many of M. leprae's genes to pseudogenes 9 to 20 million years ago. After the pseudogenization event (probably a phagocytic respiratory burst), one copy of dnaE became a pseudogene. But continued transcription of the pseudogene in the forward direction means the pseudo-mRNA competes with the "normal" dnaE transcript for ribosomal attention. Transcription of the antisense strand of the disabled gene would, of course, create a messenger RNA product that could silence the normal transcript by doublestranded interaction. Either way, once the pseudogenization event is over, dnaE expression is attenuated—as it should be, once pathogenicity has been established.

Is it realistic to think M. leprae transcribes antisense strands of its pseudogenes? Given that E. coli has been found to contain ~1000 antisense transcripts, and given that we know M. leprae transcribes many of its pseudogenes, I think the answer has to be yes.

So the pattern is: infection, respiratory burst, massive mutation, silencing of many genes, and (oh by the way) creation of many brand-new gene products, some of them no doubt quite toxic to the host, as the result of gene truncation and pseudogene expression.

Tuesday, April 15, 2014

Coming to Grips with Pseudogenes

The term pseudogene was coined in 1977, when Jacq et al. discovered a version of the gene coding for 5S rRNA in the African clawed frog (Xenopus laevis) that was truncated yet retained homology with the active gene. Subsequent work has shown that in higher life forms, pseudogenes (genes that have been inactivated through one event or another) are almost as numerous as coding genes, with (for example) the human genome containing 10,000 or more pseudogenes. (A more recent estimate puts the number at 20,000.) Many of these pseudogenes are highly conserved. Looking at pseudogenes in the mouse and human, Svensson et al. found that of a group of 74 such genes that occur in both species, 30 appear to have been conserved since before the evolutionary divergence of mice and humans.

In higher organisms, pseudogenes are sometimes transcribed into RNA, with the RNA filling a regulatory function. For example, Korneev et al. found that simultaneous transcription of neural nitric oxide synthase (nNOS) and the antisense strand of a homologous pseudogene in the same neurons of Lymnaea stagnalis (a snail) leads to the formation of a duplex between the two strands and a reduction in nNOS translation. Further examples can be found in Pink et al. (2007), "Pseudogenes: Pseudo-functional or key regulators in health and disease?"

In bacteria, pseudogenes are somewhat rarer than in eukaryotes, but exist in significant numbers in many pathogens (including many species of Mycobacterium, Shigella, Brucella, Bordetella, and others). A study by Kuo and Ochman (2010) found that pseudogenes are swiftly eliminated from Salmonella. They describe "evidence of a strong deletional bias in Salmonella, such that genes that are not maintained by selection are rapidly inactivated and eliminated by mutational events." In fact, Kuo and Ochman found that pseudogenes are eliminated more rapidly than could be explained by the so-called neutral theory of evolution, indicating that the continued presence of pseudogenes exacts a high cost to the cell.

And yet, many bacteria with slow-evolving genomes (such as Mycobacterium species) retain their pseudogenes with high fidelity across evolutionary timespans. The most celebrated "pseudogene hoarder" of all time, M. leprae (the leprosy bacterium) appears to have acquired its 1000+ pseudogenes 9 to 20 million years ago. Meanwhile, the half-life of pseudogenes in Buchnera aphidicola was measured at 23.9 million years—a staggering number.

So on the one hand, we have work by Kuo and Ochman showing that pseudogenes in bacteria are rapidly eliminated, and on the other hand we have some bacterial lineages in which it seems pseudogenes are not only conserved but actively repaired over periods of tens of millions of years!

In Chapter 5 of Brucella: Molecular Microbiology and Genomics (2012, Caister Academic Press), Garcia-Lobo et al. describe their work with RNA sequence data from the bacterium Brucella abortus:
Twenty-four of the genes selected from the RNAseq data were annotated as pseudogenes in the B. abortus 2308 genome, which was considered a rather unexpected finding. By comparison with other Brucella genomes we can reduce the list of highly expressed pseudogenes to 16 (often, truncated parts of a gene are annotated as different pseudogenes especially in B. abortus 2308). This seems contradictory since high transcription of these genes, which should be not able to translate into functional proteins, will be contrary to biological economy. The high levels of transcription observed for these genes strongly suggest that they could be active genes and their products may perform functions unreported in metabolic reconstructions. High pseudogene expression may also indicate that these are very recently produced pseudogenes that did not turned down transcription yet by accumulation of mutations in their promoter or control regions. It is also possible that these pseudogenes may contain sequencing errors and they are indeed active genes.
It's almost comically obvious from this passage that the authors are troubled by their own finding that some pseudogenes in Brucella are highly transcribed. They try explaining it away by saying it could all be "sequencing errors."

A more parsimonious view is that pseudogenes that haven't been eliminated from a genome are, in fact permanent, legitimate fixtures of the landscape, in microbes just as in higher life forms. And as in higher life forms, pseudogenes in microbes are probably serving perfectly understandable regulatory functions (when they're not actually translated into protein products).

Kuo and Ochman have convincingly shown that useless pseudogenes are quickly eliminated. It follows that any pseudogenes that aren't swiftly eliminated are, in fact, serving a biological purpose, or else they wouldn't be there. This line of reasoning is already well accepted by researchers who study eukaryotic life forms. Those who study bacteria need to take a hint from their up-the-food-chain colleagues.

What could the hundreds of pseudogenes in Bordetella pertussis (or the 1000+ pseudogenes in M. leprae) be doing? First we need to get used to the idea that in bacteria, virtually all genes are transcribed, in both directions. It's been four years since Dornernberg et al. reported finding ~1000 antisense transcripts in E. coli, but no one seems to have gotten the memo.

A section of Rothia mucilaginosa genome (top) and a corresponding portion of Mycobacterium leprae (bottom); click to enlarge. The yellow gene, in each case, is DnaE (error-prone polymerase). Pink bands indicate areas of 65% or more homology between the two organisms. The small-diameter silver genes in the lower panel are M. leprae pseudogenes. "Normal genes" are shown in green. Notice that R. mucilaginosa has open reading frames on both strands of DNA, with many bidirectionally overlapping genes.

A look at the genome of the bacterium Rothia mucilaginosa DY18 shows that a very large proportion of "normal genes" have open reading frames on the opposite strand (see illustration). Bidirectional overlapping genes run throughout the Rothia genome. A massive annotation error? Maybe. Or maybe both strands are transcribed.

If massive wholesale transcription of antisense strands occurs in E. coli, as we know it does, certainly it's no stretch to imagine it occurring in Rothia mucilaginosa. And if it is occurring in Rothia, which is (incidentally) an opportunistic pathogen, how much harder can it be to imagine it occurring in another well-known pathogenic member of the Actinomycetales family, Mycobacterium leprae? We know already that upwards of 40% of M. leprae pseudogenes are transcribed. Antisense transcripts could well be playing a role in silencing certain gene essential genes when attempts are made to grow the organism in defined media. Forward transcripts could be producing nonsense or partial-nonsense/truncated proteins that are excreted as toxins or find their way to the cell wall as surface antigens. Any number of scenarios might be possible.

Some very low-hanging fruit is available to micobiologists who are willing to accept the obvious. Instead of wishing away pseudogenes or imagining them to be useless baggage, we should be looking at them as potential determinants of pathogenicity. We should consider their possible roles in modulating protein expression patterns. We should attempt to learn why they're conserved; what role(s) they're playing in cell physiology. The last thing in the world we should be doing is calling them "junk DNA."