Not All Organisms Use Amino Acids the Same Ways

Organisms vary greatly in the GC (guanine plus cytosine) content of their DNA, and yet all organisms can still make ribosomal proteins, DNA and RNA polymerases, and the various other essential proteins of life, no matter what their DNA vocabulary limitations might be. A high-GC organism like Streptomyces can make a given enzyme (DNA polymerase, say) using mostly G and C bases in its DNA, but a low-GC organism like Clostridium botulinum can also make the same kind of enzyme, even though it uses mostly A and T in its DNA. How is this possible?

It's possible in part because of the many synonyms for amino acids available in the genetic code. But it's a mistake to think the same amino acids are used in equal numbers by high-GC organisms and low-GC organisms. Organisms at opposite ends of the GC spectrum use different amino acids.

I was curious to see which amino acids correlate most strongly with genomic GC, so I gathered codon usage statistics for 109 organisms of widely varying genomic GC content and used JavaScript to calculate Pearson correlation coefficients for all 20 amino acids with respect to  GC content. The results are shown in the following table.

TABLE 1. Correlation (r) between amino acid usage and genome GC content (N=109 organisms). 

Code	Amino Acid	r
A	Alanine (Ala)	0.9634
R	Arginine (Arg)	0.9495
G	Glycine (Gly)	0.9472
P	Proline (Pro)	0.9436
V	Valine (Val)	0.7725
W	Tryptophan (Trp)	0.7497
H	Histidine (His)	0.4660
L	Leucine (Leu)	0.3364
D	Aspartic Acid (Asp)	0.3347
T	Threonine (Thr)	0.3099
C	Cysteine (Cys)	-0.1280
Q	Glutamine (Gln)	-0.1668
M	Methionine (Met)	-0.2863
E	Glutamic Acid (Glu)	-0.4621
S	Serine (Ser)	-0.6831
F	Phenylalanine (Phe)	-0.8550
Y	Tyrosine (Tyr)	-0.8983
K	Lysine (Lys)	-0.9389
N	Asparagine (Asn)	-0.9391
I	Isoleucine (Ile)	-0.9558

Ten amino acids correlate positively with GC and ten correlate negatively. Alanine and arginine have the strongest positive correlation with GC, while isoleucine and asparagine have the strongest negative correlation with genomic GC content. (But note that these data apply only to the 109 organisms studied. For the complete list of 109 organisms, see this post.)

If you were to extract all the amino acids out of Clostridium botulinum (28% GC), you would get far more lysine than alanine. Conversely, if you were to hydrolyze all the proteins in Streptomyces griseus (GC 72%), you would find far more alanine than lysine.

Interestingly, serine has six synonymous codons (AGT, AGC, CTA, CTG, CTC, CTT) and can just as easily be specified with G and C as with A and T; so overall, you'd expect little correlation with genomic GC. And yet serine use correlates strongly with low GC. In a sense, this is not surprising. Certain low-GC organisms (like Streptococcus) are known to produce serine-rich cell-coat proteins, some of which are important determinants of pathogenicity. But it may simply be that the high utilization of serine in low-GC organisms is related to one-carbon chemistry. Serine, after all, is the source of the methyl group that, by way of methylenetetrahydrofolate, converts dUMP to TMP (thymidine monophosphate, a DNA precursor). Any organism whose DNA is unusually rich in thymine (low in GC) will almost certainly be processing large quantities of serine. Serine is also a carbon source in the biosynthetic pathways for cysteine and methionine, both of which, like serine itself, are negatively correlated with genomic GC content.

Which Direction Does the Gene Point?

A maddening problem in genome annotation is determining the "sense" strand for a gene, especially when the gene is short and/or the genome has a high GC content (and thus contains few or no stop codons in reverse translation). To convince yourself this is a very real and serious problem, all you have to do is browse a few genomes to see the ridiculously high number of "hypothetical proteins" (over 40% in some genomes), bogus overlaps, genes that score BLAST hits in reverse-complement mode but not frame zero, and other artifacts that are a direct result of the aforesaid problem.

I've presented examples of this problem before, but just so there's no confusion, I want to show you a particularly maddening example so you can see what I'm talking about. (Then I'll suggest a solution.) The following graphic shows a region of E. coli UTI89 in which several genes are shown as overlapping (that is to say, existing on opposite strands of DNA in the same coverage area). Small overlaps sometimes happen between genes, but whole genes rarely, if ever, overlap, and never in clusters. The situation shown below is bogus, but you see it all the time in public genomes. In fact, some of the genes shown below also show up as overlaps in Mycobacterium abscessus M93 (see gene OUW_18941), Citrobacter koseri strain ATCC BAA-895 (gene CKO_00072), and quite a few others. Glimmer has choked on this exact situation many times, in many genomes.  

A region with overlapping genes in the genome of E. coli UTI89.

The big gene on the top strand, middle, is UTI89_C4288 (DNA sequence here). It's annotated as (what else?) a "hypothetical protein." The M. abscessus version of the gene (here) is marked as a "cellobiose phosphorylase," and you can find many BLAST hits (the Rothia version gives an E-value of 6.0×10^-101) for similar "cellobiose phosphorylase" genes in other organisms at UniProt.org and elsewhere. Of course, they're all bogus and represent Glimmer choke points, but the question is how one can determine that, and be sure about it.

E. coli's hypothetical protein (UTI89_C4288) has a wobble-base GC percentage of 64.3%, whereas the gene on the opposite strand (just below it, pointing left), namely UTI89_C4287 (marked as "membrane-bound ATP synthase F1 sector alpha-subunit"), has GC3 = 54.5%. In a much higher-GC organism like Mycobacterium or Pseudomonas, you would find out which gene has the higher GC3 percentage and crown it the winner (and most of the time, you'd be right). In this case, it's not so simple. The gene with the higher GC3 value isn't necessarily the winner.

Of course, in this particular example, you can cheat and look at the identities of the genes in the immediate vicinity of the hypothetical protein, on the bottom strand, and if you do, you'll find that all of the bottom-strand genes are ATPase subunits. Mystery solved, right? Sure, in this particular case. But what about situations where overlapping genes are all shown as "hypothetical protein"? (You can find many such cases in the genome for Burkholderia pseudomallei strain 1710b, for example.) When a hypothetical overlaps a hypothetical in a low-GC genome, then what?

One of my favorite cheats (but this isn't the final solution!) is to check the gene's AG1 percentage (adenine plus guanine, codon base one). This percentage averages ~60% in something like 90% of protein-coding genes. The problem is, AG1 is often 60% whether you read the gene forward, or backward (off the antisense strand). The reverse complement of a gene usually has high AG1, because the forward AG3 is usually under 50%.

Almost any trick you can dream up will fail under edge cases. GC3 is helpful, but only in high-GC genomes. AG1 is helpful, but only sometimes. Shine Dalgarno signals are not universally used by all organisms, and even in those that do use them, they're usually reserved for highly conserved genes encoding things like ribosomal proteins. Gene context is helpful in some cases but not others.

It turns out, the best clue for positively identifying the correct strand and correct reading frame is codon usage frequency patterns. If you know what the codon frequencies are, genome-wide, for a given organism, you can use this information to good advantage, even if the genome (and therefore the codon table) contains inaccuracies. As long as the codon frequencies are approximately correct, you can use them to verify the reading frame of a protein-coding gene.

The algorithm I came up with is very simple, yet effective. For a given gene, read each triplet of bases sequentially, and score each triplet twice: keep two scores going. First, score it according to its frequency in the codon table for the organism. Then score it according to a second table developed for reverse-complement codons.

The following table shows codon frequencies in Caulobacter crescentus NA1000. If you were to encounter a "hypothetical protein" gene in Caulobacter, and you couldn't decide whether the strand assignment was correct or not, first develop a score for the gene by reading its triplets and adding the frequency value of the corresponding codon to the running total. For example, if you encounter the triplet "CTG," add 6.84 to the score (see table). For every occurrence of CTT, add 0.60, for CTC add 1.70, and so on, using the values in the table.

Codon frequencies for Caulobacter crescentus NA1000.

But you also have to create an anticodon frequency table as follows: For every codon in the original table, apply the same score to the corresponding reverse-complement codon in the "antcodon table." E.g., for CTG, the first table would contain 6.84 (as above), but the value 6.84 would apply to CAG (the reverse complement of CTG) in the second table. I call the first table the "forward" table and the second table the "back" table. One represents the frequencies of codons encountered in protein genes in the forward reading direction. The other represents those same frequencies applied to the reverse-complement of the codons (the same codons read in the reverse direction, off the opposite DNA strand).

When scoring an unknown gene, you tally a "forward table" score, and keep a separate score using the "back" table. When you're done, the gene's "forward table" score should be greater than the "back table" score. If it's not, you're reading the gene off the wrong strand.

When I scored all 3,737 C. crescentus CB15 genes using this technique, I found 136 genes that gave a "back" score higher than the "forward" score. Interestingly, when I checked the identities of those 136 putative "backwards-annotated" genes, 132 of them were listed as "hypothetical proteins." Only four genes with assigned functions gave suspect scores, and one of those (CC_0662) turns out to be a 100%-identity match for the reverse of gene CCNA_00700 in Caulobacter crescenstus NA1000.  The other three are less than 200 bases long and could well be non-coding regions.

For a more challenging test, I turned to the genome of Rothia mucilaginosa DY-18, one of the most disastrous annotation nightmares of all time. In the genome for DY-18 you will find 524 protein-coding genes (out of 1,905 total) that are involved in significant overlaps. (Some overlaps are 2-on-1, some are 1-on-1; but the genome is almost certainly overannotated by at least 260 genes.) I trained my program on the codon usage table of R. mucilaginosa M508 (which contains fewer overlaps than DY-18), then tallied codon and anticodon scores on all of DY-18's CDS genes. In the end, 276 genes gave scores indicative of a reversed reading frame. Of those, 265 were, in fact, involved in overlaps.

Codon scoring is such an effective method, I don't know why programs like Glimmer don't use it. It's quite obvious they're not using it, though, because every genome has reverse-annotated genes (by the hundreds, in some cases) that are easily detected using this simple method.

Here, for the record, are the Caulobacter crescentus CB15 genes that appear to be annotated on the wrong strand:

CC_0023
CC_0048
CC_0073
CC_0099
CC_0149
CC_0354
CC_0480
CC_0546
CC_0564
CC_0605
CC_0662
CC_0666
CC_0676
CC_0677
CC_0680
CC_0681
CC_0687
CC_0728
CC_0739
CC_0775
CC_0782
CC_0786
CC_0825
CC_0850
CC_0853
CC_0913
CC_0987
CC_0996
CC_0997
CC_1020
CC_1022
CC_1031
CC_1032
CC_1050
CC_1069
CC_1073
CC_1084
CC_1094
CC_1123
CC_1127
CC_1161
CC_1174
CC_1212
CC_1222
CC_1238
CC_1245
CC_1274
CC_1312
CC_1322
CC_1340
CC_1349
CC_1392
CC_1393
CC_1394
CC_1395
CC_1414
CC_1416
CC_1513
CC_1561
CC_1648
CC_1789
CC_1793
CC_2000
CC_2086
CC_2116
CC_2163
CC_2184
CC_2193
CC_2240
CC_2256
CC_2308
CC_2334
CC_2338
CC_2351
CC_2376
CC_2413
CC_2424
CC_2442
CC_2445
CC_2450
CC_2452
CC_2471
CC_2475
CC_2499
CC_2519
CC_2525
CC_2571
CC_2574
CC_2597
CC_2602
CC_2621
CC_2624
CC_2665
CC_2698
CC_2705
CC_2718
CC_2719
CC_2720
CC_2731
CC_2732
CC_2738
CC_2739
CC_2756
CC_2769
CC_2800
CC_2850
CC_2865
CC_2875
CC_2878
CC_2907
CC_2916
CC_2949
CC_3050
CC_3055
CC_3251
CC_3302
CC_3318
CC_3342
CC_3360
CC_3429
CC_3437
CC_3438
CC_3451
CC_3453
CC_3463
CC_3479
CC_3517
CC_3519
CC_3547
CC_3548
CC_3553
CC_3554
CC_3608
CC_3665
CC_3671
CC_3700

Anaerobic Catalases and Codon Pseudodegeneracy

Today I want to demolish two myths, one being that strict anaerobes (such as members of the Clostridia, a class of bacteria that includes the botulism organism as well as the C. diff bug that sickens half a million people a year in the U.S.) lack a catalase enzyme, and the other being that codons have very little degeneracy in base one. Don't worry, I'll parse all this for you so that even if you're not a bio-geek, you'll get it.

Bacteriology students are taught from Day One that strict anaerobes are killed by oxygen because they lack a catalase enzyme. Catalase is the nearly universal enzyme that breaks down peroxides into oxygen and water. The catechism about anaerobes being catalase-negative is true for some anaerobes, but by no means all. In fact, if you go to UniProt.org and do a search on "catalase clostridium" you'll get a long list of hits representing anaerobes that do, in fact, have a perfectly normal catalase enzyme. Some anaerobes have the classic katG (E. coli) version of catalase while others (like C. difficile) have a special manganese-containing catalase. Pathogenic anaerobes probably use the enzyme to combat the respiratory burst that accompanies engulfment by white blood cells. I've written about anaerobic  catalases before; for an overview of the enzymology and phylogenetic breakdown by enzyme type, start here.

Methanospirillum (a strict anaerobe found
in sewage) produces colonies with
characteristic striations spaced two
cell-lengths apart.

I thought it would be interesting to compare the katG genes of two anaerobes to see how they differ. I decided to focus on C. botulinum B (the nasty food-poisoning organism) and the archeon Methanospirillum hungatei strain JF-1, which is named after a professor I had in grad school, Bob Hungate, a true pioneer in the study of anaerobic bacteria.

C. botulinum and M. hungatei are worlds apart, taxonomically. The genome of the former has a G+C (guanine, cytosine) content of just 27%, while the latter has G+C of 45%. Comparing their catalases, I found 717 nucleotide differences between the two genes (which were 2193 bases long in one case and 2157 in the other case). On the surface, the genes seem (from a DNA sequence point of view) quite far apart phylogenetically. But after performing an alignment in Mega6, I was able to study the codons base by base and found that of 717 differences, 500 changes were synonoymous (resulting in no amino acid substitution) while only 217 changes were non-synonymous. Not unexpectedly, most of the synonymous changes were due to differences in the third codon base (where the genetic code is highly degenerate; see below).

Codon Usage in Clostridium botulinum B
Codon Usage: The Standard Code (transl_table=1)

CCA(P) 1.45% CCG(P) 0.10% CCT(P) 1.00% CCC(P) 0.06%		CGA(R) 0.08% CGG(R) 0.01% CGT(R) 0.18% CGC(R) 0.02%		CAA(Q) 2.01% CAG(Q) 0.25% CAT(H) 1.10% CAC(H) 0.16%		CTA(L) 0.77% CTG(L) 0.10% CTT(L) 1.67% CTC(L) 0.06%
GCA(A) 2.66% GCG(A) 0.20% GCT(A) 2.18% GCC(A) 0.19%		GGA(G) 3.30% GGG(G) 0.47% GGT(G) 2.08% GGC(G) 0.40%		GAA(E) 6.40% GAG(E) 1.11% GAT(D) 5.12% GAC(D) 0.62%		GTA(V) 2.79% GTG(V) 0.45% GTT(V) 2.81% GTC(V) 0.14%
ACA(T) 2.36% ACG(T) 0.16% ACT(T) 2.22% ACC(T) 0.19%		AGA(R) 2.43% AGG(R) 0.31% AGT(S) 1.89% AGC(S) 0.48%		AAA(K) 7.10% AAG(K) 2.17% AAT(N) 6.06% AAC(N) 0.89%		ATA(I) 5.68% ATG(M) 2.50% ATT(I) 4.01% ATC(I) 0.42%
TCA(S) 2.10% TCG(S) 0.12% TCT(S) 1.68% TCC(S) 0.12%		TGA(*) 0.02% TGG(W) 0.69% TGT(C) 1.02% TGC(C) 0.25%		TAA(*) 0.24% TAG(*) 0.07% TAT(Y) 3.61% TAC(Y) 0.55%		TTA(L) 5.68% TTG(L) 0.74% TTT(F) 3.74% TTC(F) 0.57%

Pink-highlighted codons are examples of where the genetic code is degenerate for base one. 
A change of CGA to AGA (for example) results in no amino acid change: the 
coded-for amino acid is arginine in either case.

"Degeneracy" means a change in a particular base results in no change of amino acid. An example is the codon AAA (see above), which is the code for lysine (K). Changing the last base to G (resulting in a codon of AAG) still produces lysine in the resulting protein. Codons that begin with 'AA' are two-fold degenerate, because two versions (AAA and AAG) produce the same amino acid. A codon that begins with 'CC' is said to be four-fold degenerate, because any of four different endings (CCA, CCT, CCG, CCC) produce the same amino acid (P, proline).

What's less obvious is that a change in the first (rather than the third) base of a codon can produce synonyms as well. For example, changing CGA to AGA results in the same amino acid: arginine (R). Likewise, CTA changing to TTA still results in leucine (L).

But there's more degeneracy to  the first codon base than just the classic cases outlined in pink in the above table. A change of CTA to ATA produces a switch from leucine to isoleucine, which is tantamount to no change at all, because leucine and isoleucine are isomers (both have the same chemical formula; they differ slightly in arrangement).

Likewise, a change from CTx to GTx produces a change from leucine to valine, which are workalikes. One could even argue that a change from CTT or CTC to TTT or TTC (leucine to phenylalanine) is functionally a degenerate change, because leucine and phenylalanine are nonpolar, hydrophobic amino acids with similar chemical properties.

So in fact, many changes to the first base of a codon can be considered pseudodegenerate, if you will, by virtue of producing non-synonymous changes that are tantamount to very little actual chemical change. We would expect to see a significant number of such changes in mutations affecting base one of codons. And we do.

In the two catalase genes, I looked at changes involving just the first base of a codon and found the following changes:

Synonymous:
AGA<-->CGA (R:R)
AGA<-->CGA (R:R)
AGA<-->CGA (R:R)
TTA<-->CTA (L:L)
TTG<-->CTG (L:L)
TTG<-->CTG (L:L)
TTG<-->CTG (L:L)
TTG<-->CTG (L:L)

Non-Synonymous:
AAA<-->CAA (K:Q)
AAA<-->GAA (K:E)
AAA<-->GAA (K:E)
AAG<-->CAG (K:Q)
AAG<-->CAG (K:Q)
AAT<-->CAT (N:H)
AAT<-->GAT (N:D)
AAT<-->GAT (N:D)
ATT<-->CTT (I:L) *
ATT<-->GTT (I:V) *
CAT<-->GAT (H:D)
CTG<-->ATG (L:M)
CTT<-->ATT (L:I) *
GCA<-->TCA (A:S)
TAT<-->CAT (Y:H)
TCA<-->GCA (S:A)
TCT<-->CCT (S:P)
TTT<-->CTT (F:L) *

Notice that 4 out of 18 non-synonymous changes (see asterisks) fall in the category of pseudodegeneracies. That's slightly higher than the 17% we would expect by chance. So instead of 8 out of 26 first-base changes being degenerate (in the catalase genes), more like 12 out of 26 are either synonymous or near-synonymous. Bottom line, roughly half of first-base mutations are effectively synonymous, at least in this example.

Genes, Codons, and Purines

A universal feature of protein-coding genes is that they tend to use a lot of codons that begin with a purine (A or G). In fact, it's typical for a given gene's codons to use a purine in position one 60% or more of the time. But it's fair to ask: How universal is this trend, exactly? Does the rule apply for organisms with extremely high (or low) genomic G+C content? Does it apply for endosymbionts with greatly reduced genomes? Is it just a "sometimes" rule? Are there important exceptions?

I decided to collect codon statistics for 109 different bacterial species, representing members of all major taxonomic groups, with a wide range of genome sizes and GC percentages. For each organism, I determined the average percent A+G content in codon base one (AG1) across all CDS genes. Then I plotted AG1 against the genomic A+T content for each organism. (A+T is of course just one minus the G+C content.) Here's the graph of AG1 content for all the organisms:

Codon base-one purine content (average for all CDS genes) versus genomic A+T content for N=109 bacterial species. Dot size corresponds to genome size.

The fun thing about this graph is that each data point is sized according to the genome size of the organism in question (in other words, the area of the dot is proportional to genome size). As you can see, bacteria at the high end of the A+T scale (low G+C) tend to have smaller genomes. But the more important thing to notice is that AG1 is 58% or more for all 109 genomes. This means that the phenomenon of high average purine content in codon base one appears to be universal, at least for the sample group. (Organism names are listed in a table below.)

Of course, within a given genome, genes vary somewhat in terms of the per-gene average AG1, but it's still quite rare to find a protein gene that has average AG1 under 50%. For example, below is a histogram plot of AG1 content for all protein-coding genes of Sorangium cellulosum, a bacterium with genomic GC content of 72% (A+T = 28%).

Per-gene AG1 usage (codon base-one purine content) for all CDS genes of Sorangium cellulosum.

As you can see, very few genes lie to the left of x = 0.5. (Of Sorangium's 10,400 protein genes, only 321 have an average AG1 under 50%. Those could easily be mis-annotated genes or gene fragments.) Most organisms show much the same distribution of average AG1 values across CDS genes.

Gene annotation programs could probably benefit from using a check of AG1 to verify that a putative gene is in the correct reading frame. GC3 content is often used in this way, but AG1 is actually a much more discriminating test, especially with low-GC genomes (where the "wobble base" GC percentage is not particularly helpful).

Listed below are the 109 organisms (and their taxonomic categorizations) used in this investigation.

Organism	Taxon
Acidaminococcus fermentans strain DSM 20731	Firmicutes:Clostridia
Acidovorax avenae subsp. citrulli strain AAC00-1	Proteobacteria:Betaproteobacteria
Aerococcus urinae strain ACS-120-V-Col10a	Firmicutes:Lactobacillales
Aeromonas hydrophila strain ML09-119	Proteobacteria:Gammaproteobacteria
Aggregatibacter actinomycetemcomitans D11S-1	Proteobacteria:Gammaproteobacteria
Agrobacterium radiobacter strain K84	Proteobacteria:Alphaproteobacteria
Anaerobaculum mobile strain DSM 13181	Synergistetes:Synergistia
Anaerocellum thermophilum strain DSM 6725	Firmicutes:Clostridia
Anaerolinea thermophila strain UNI-1	Chloroflexi:Anaerolineae
Anaplasma marginale strain Florida	Proteobacteria:Alphaproteobacteria
Arcobacter butzleri ED-1	Proteobacteria:Epsilonproteobacteria
Atopobium vaginae strain DSM 15829	Actinobacteria:Coriobacteridae
Azospirillum brasilense strain Sp245	Proteobacteria:Alphaproteobacteria
Bacillus amyloliquefaciens strain Y2	Firmicutes:Bacilli
Bacillus anthracis strain CDC 684	Firmicutes:Bacillales
Bacillus subtilis BEST7613 strain PCC 6803	Firmicutes:Bacilli
Bacteroides dorei strain 5_1_36/D4	Bacteroidetes:Bacteroidia
Bartonella quintana strain RM-11	Proteobacteria:Alphaproteobacteria
Blastococcus saxobsidens strain DD2	Actinobacteria:Actinobacteridae
Borrelia miyamotoi strain LB-2001	Spirochaetes:Spirochaetales
Brachybacterium faecium strain DSM 4810	Actinobacteria:Actinobacteridae
Brucella ovis strain ATCC 25840	Proteobacteria:Alphaproteobacteria
Buchnera aphidicola (Acyrthosiphon pisum) strain 5A	Proteobacteria:Gammaproteobacteria
Burkholderia pseudomallei strain 1710b	Proteobacteria:Betaproteobacteria
Caldicellulosiruptor lactoaceticus strain 6A	Firmicutes:Clostridia
Calditerrivibrio nitroreducens strain DSM 19672	Deferribacteres:Deferribacterales
Campylobacter concisus strain 13826	Proteobacteria:Epsilonproteobacteria
Candidatus Cloacamonas acidaminovorans	candidate division WWE1:Candidatus Cloacamonas
Candidatus Methylomirabilis oxyfera	candidate division NC10:Candidatus Methylomirabilis
Candidatus Pelagibacter ubique strain HTCC1062	Proteobacteria:Alphaproteobacteria
Carboxydothermus hydrogenoformans strain Z-2901	Firmicutes:Clostridia
Chlamyda trachomatis (i) strain L2/434/Bu; i	Chlamydiae:Chlamydiales
Clostridium botulinum A strain Hall	Firmicutes:Clostridia
Coprobacillus sp. strain 8_2_54BFAA	Firmicutes:Erysipelotrichia
Coprococcus catus strain GD/7	Firmicutes:Clostridia
Cycloclasticus zancles strain 7-ME	Proteobacteria:Gammaproteobacteria
Deinococcus radiodurans strain R1	Deinococcus-Thermus:Deinococci
Desulfococcus oleovorans strain Hxd3	Proteobacteria:Deltaproteobacteria
Ehrlichia canis strain Jake	Proteobacteria:Alphaproteobacteria
Enterobacter cloacae strain SCF1	Proteobacteria:Gammaproteobacteria
Erwinia amylovora strain ATCC 49946	Proteobacteria:Gammaproteobacteria
Escherichia coli B strain REL606	Proteobacteria:Gammaproteobacteria
Geobacillus kaustophilus strain HTA426	Firmicutes:Bacillales
Geobacillus thermoleovorans strain CCB_US3_UF5	Firmicutes:Bacillales
Geobacter metallireducens strain GS-15	Proteobacteria:Deltaproteobacteria
Geobacter sulfurreducens strain KN400	Proteobacteria:Deltaproteobacteria
Geobacter sulfurreducens strain PCA	Proteobacteria:Deltaproteobacteria
Geobacter uraniireducens strain Rf4	Proteobacteria:Deltaproteobacteria
Geodermatophilus obscurus strain DSM 43160	Actinobacteria:Actinobacteridae
Gordonia bronchialis strain DSM 43247	Actinobacteria:Actinobacteridae
Haemophilus ducreyi strain 35000HP	Proteobacteria:Gammaproteobacteria
Halogeometricum borinquense DSM 11551	Euryarchaeota:Halobacteria
Helicobacter pylori (Helicobacter pylori SAfr7) strain SouthAfrica7	Proteobacteria:Epsilonproteobacteria
Klebsiella oxytoca strain 10-5243	Proteobacteria:Gammaproteobacteria
Kribbella flavida strain DSM 17836	Actinobacteria:Actinobacteridae
Ktedonobacter racemifer DSM 44963	Chloroflexi:Ktedonobacteria
Lactobacillus acidophilus strain 30SC	Firmicutes:Lactobacillales
Lactobacillus reuteri strain MM4-1A	Firmicutes:Lactobacillales
Lactococcus lactis subsp. cremoris strain A76	Firmicutes:Bacilli
Leptolyngbya sp. PCC 7376	Cyanobacteria:Oscillatoriophycideae
Leptonema illini strain DSM 21528	Spirochaetes:Spirochaetales
Leptospira biflexa serovar Patoc strain Ames; Patoc 1	Spirochaetes:Spirochaetales
Leuconostoc gasicomitatum LMG 18811 strain type LMG 18811	Firmicutes:Lactobacillales
Mesorhizobium australicum strain WSM2073	Proteobacteria:Alphaproteobacteria
Mesorhizobium ciceri biovar biserrulae strain WSM1271	Proteobacteria:Alphaproteobacteria
Methylobacillus flagellatus strain KT	Proteobacteria:Betaproteobacteria
Methylophaga sp. strain JAM7	Proteobacteria:Gammaproteobacteria
Mycobacterium tuberculosis = ATCC 35801 strain ATCC35801; Erdman	Actinobacteria:Actinobacteridae
Mycoplasma gallisepticum strain F	Tenericutes:Mollicutes
Neisseria gonorrhoeae strain NCCP11945	Proteobacteria:Betaproteobacteria
Nocardia brasiliensis ATCC 700358 strain HUJEG-1	Actinobacteria:Actinobacteridae
Nocardia cyriacigeorgica strain GUH-2	Actinobacteria:Actinobacteridae
Nostoc sp. PCC 7120 (Anabaena sp. PCC 7120) strain PCC7120	Cyanobacteria:Nostocales
Novosphingobium aromaticivorans strain DSM 12444	Proteobacteria:Alphaproteobacteria
Oceanobacillus kimchii strain X50	Firmicutes:Bacilli
Orientia tsutsugamushi strain Ikeda	Proteobacteria:Alphaproteobacteria
Paenibacillus polymyxa strain M1	Firmicutes:Bacilli
Polynucleobacter necessarius strain STIR1	Proteobacteria:Betaproteobacteria
Propionibacterium acnes TypeIA2 strain P.acn33	Actinobacteria:Actinobacteridae
Proteus mirabilis strain HI4320	Proteobacteria:Gammaproteobacteria
Pseudomonas fluorescens strain Pf0-1	Proteobacteria:Gammaproteobacteria
Pseudonocardia dioxanivorans strain CB1190	Actinobacteria:Actinobacteridae
Ralstonia eutropha strain H16	Proteobacteria:Betaproteobacteria
Rhizobium tropici strain CIAT 899	Proteobacteria:Alphaproteobacteria
Rhodobacter sphaeroides ATCC 17029	Proteobacteria:Alphaproteobacteria
Shigella boydii strain Sb227	Proteobacteria:Gammaproteobacteria
Slackia heliotrinireducens strain DSM 20476	Actinobacteria:Coriobacteridae
Sorangium cellulosum strain So0157-2	Proteobacteria:Deltaproteobacteria
Staphylococcus aureus strain 04-02981	Firmicutes:Bacillales
Streptococcus agalactiae strain 2603V/R	Firmicutes:Lactobacillales
Streptomyces cf. griseus strain XylebKG-1	Actinobacteria:Actinobacteridae
Streptosporangium roseum strain DSM 43021	Actinobacteria:Actinobacteridae
Sulfurimonas denitrificans DSM 1251 strain ATCC 33889	Proteobacteria:Epsilonproteobacteria
Thioalkalivibrio nitratireducens strain DSM 14787	Proteobacteria:Gammaproteobacteria
Thiobacillus denitrificans strain ATCC 25259	Proteobacteria:Betaproteobacteria
Treponema azotonutricium strain ZAS-9	Spirochaetes:Spirochaetales
Treponema pedis strain T A4	Spirochaetes:Spirochaetales
Turneriella parva strain DSM 21527	Spirochaetes:Spirochaetales
Vibrio cholerae strain BX 330286	Proteobacteria:Gammaproteobacteria
Wolbachia endosymbiont strain TRS of Brugia malayi	Proteobacteria:Alphaproteobacteria
Yersinia pestis D106004	Proteobacteria:Gammaproteobacteria
Bacillus thuringiensis serovar andalousiensis strain BGSC 4AW1	Firmicutes:Bacillales
Ureaplasma urealyticum serovar 5 strain ATCC 27817	Tenericutes:Mollicutes
Bordetella pertussis strain 18323	Proteobacteria:Betaproteobacteria
Comamonas testosteroni strain KF-1	Proteobacteria:Betaproteobacteria
Eikenella corrodens strain ATCC 23834	Proteobacteria:Betaproteobacteria
Janthinobacterium sp. strain Marseille	Proteobacteria:Betaproteobacteria
Rhodopirellula baltica SH strain 1	Planctomycetes:Planctomycetacia
Blastopirellula marina strain DSM 3645	Planctomycetes:Planctomycetacia

Why do so many codons begin with a purine?

With the advent of sites like genomevolution.org (where you can download genomes, create synteny graphs, run BLAST searches, and do all sorts of desktop bioinformatics), it's ridiculously easy for someone interested in comparative genomics to . . . well, compare genomes, for one thing. And if you look at enough gene sequences, a couple of things pop out.

One thing that pops out is that most codons, in most genes, begin with a purine (namely A or G: adenine or guanine). Also, codons typically show the greatest GC swing in base number three. These trends can be seen in the chart below, where I show average base composition (by codon position) for three well-studied organisms. For clarity, base-one purines are shown in bold and base-three G and C are shown highlighted in yellow.

Organism	Codon base	A	G	C	T
S. griseus	1	0.166	0.434	0.287	0.112
	2	0.224	0.219	0.295	0.261
	3	0.037	0.394	0.530	0.038
E. coli	1	0.256	0.343	0.238	0.161
	2	0.291	0.181	0.222	0.304
	3	0.186	0.285	0.261	0.265
C. botulinum	1	0.395	0.299	0.094	0.210
	2	0.374	0.136	0.161	0.328
	3	0.442	0.108	0.064	0.383

Streptomyces griseus is a common soil bacterium that happens to have very high genomic G+C content (72.1% overall, although you can see that in base three of codons the G+C content is more like 92%).

E. coli represents a middle-of-the-road organism in terms of G+C content (50.8% overall), while our ugly friend Clostridium botulinum (the soil organism that can ruin your whole day if it finds its way into a can of tomatoes) has very low genomic G+C content (around 28%).

Even though these organisms differ greatly in G+C content, they all illustrate the (universal) trend toward usage of purines (A or G) in the first position of a codon. Something like 59% to 69% of the time (depending on the organism), codons look like Pnn, where P is a purine base and 'n' represents any base. This is true for viruses as well as cellular genomes.

This pattern is so universal, one wonders why it exists. I think a credible, parsimonious explanation is that when protein-coding genes look like PnnPnnPnn... (etc.) it makes for a crisp reading frame. It's easy to see that a +1 frameshift results in a repeating nPn pattern and a +2 frameshift results in repeats of nnP. These are easily distinguished from Pnn.

There are benefits for a PnnPnnPnn... reading frame. In a previous post, I showed that when most of a gene's codons have a pyrimidine in base two, the resulting protein gets shipped to the cell membrane. (This is a simple consequence of the fact that codons with a pyrimidine in position two tend to code for hydrophobic, lipid-soluble amino acids.) Because a +1 reading-frame shift produces repeats of nPn, the Pnn "default" pattern means that +1 frameshifted gene products, if they occur, won't get shipped to the cell membrane. This is an extremely important outcome, because membrane proteins are, in general, highly transcribed and under strong selective pressure. In addition to specifying antigenic properties and determining phage resistance, membrane proteins make up proton pumps, secretion systems, symporters, kinases, flagellar components, and many other kinds of proteins. They determine the cell's "interface" to the world. They also maintain cell osmolarity and membrane redox potential. Messing with membrane proteins is bound to be risky. Much better to keep frameshifted nonsense proteins away from the membrane.

Fairly strong support for this notion (that Pnn codons provide a crisp reading frame) comes from studies of naturally occurring frameshift signals in DNA. We now know that in many organisms, certain "slippery" DNA signals (usually heptamers, like CCCTGAC) instruct the ribosome to change reading frames. (See, for example, "A Gripping Tale of Ribosomal Frameshifting: Extragenic Suppressors of Frameshift Mutations Spotlight P-Site Realignment," Atkins and Björk, Microbiol. Mol. Biol. Rev. 2009. Also, for fun, be sure to check out some of the papers on quadruplet decoding, which leaves room for alien life forms with 200 amino acids instead of 20.) The "slippery heptamer" frameshift signals that have thus far been identified tend to contain runs of pyrimidines.

Also tending to support the "Pnn = crisp reading frame" notion is the fact that stop codons (TGA, TAA, TAG) look like pPP (where 'p' is a pyrimidine and 'P' is a purine). Again, a crisp distinction.

As for why purines were chosen (and not pyrimidines) to begin the Xxx pattern, again I think a fairly parsimonious answer is available: ATP and GTP are the most abundant nucleoside-triphosphates in vivo. These are the energy sources for nucleic-acid and protein synthesis, respectively.

A prediction: If we run into an alien life form (in the oceans of Europa, say) and it turns out to be the case that UTP (instead of ATP) is the "universal energy molecule" in that life form's cells, then that life form's codons will probably begin with U and form Unn triplets (or Unnn quadruplets, perhaps) a higher-than-average percentage of the time.

A binary signal in the second codon base

In looking at base composition statistics of codons, an amazing fact jumps out.

If you look carefully at the following graph, you can see that the cloud of gold-colored data points (representing the compositional stats for the second base in codons of Clostridium botulinum) has a second, "breakaway" cloud underneath it. (See arrow.)

Codon base composition statistics for Clostridium botulinum. Notice the breakaway cloud of gold points under the main cloud (arrow). These points represent genes in which most codons have a pyrimidine in the middle base of each codon.

To review: I made this plot by going through each DNA sequence for each coding ("CDS") gene of C. botulinum, and for each gene, I went through all codons and calculated the average purine content (as well as the average G+C content) of the bases at a positions one, two, and three in the codons. Thus, every dot represents the stats for one gene's worth of data.

After looking at graphs of this sort, three key facts about codon bases leap out:

• Most codons, for most genes, have a purine as the first base (notice how the red cloud of points is higher than the others, centering on y=0.7).
• The third base (often called the "wobble" base; shown blue here) has the most extreme G+C value. (This is well known.)
• The middle base falls in one of two positions (high or low) on the purine (y) axis. There's a primary cloud of data points and a secondary cloud in a distinct region below the main cloud. The secondary cloud of gold points is centered at about 0.3 on the y-axis, meaning these are genes in which the second codon base tends to be a pyrimidine. 

The question is: What does it mean when you look at a gene with 200 or 300 or 400 codons, and the majority of codons have a pyrimidine in the second base?

If you examine the standard codon translation table (below), you can see that codons with a pyrimidine in the second position (represented by the first two columns of the table) code primarily for nonpolar amino acids. When a pyrimidine is in the second base, the possible amino acids are phenylalanine, serine, leucine, proline, isoleucine, methionine, threonine, valine, and alanine. Of these, all but serine and threonine are nonpolar. Therefore, a pyrimidine in position two of a codon means there's at least a 75% chance that the amino acid will have a nonpolar, hydrophobic side group.

Virtually all proteins contain some nonpolar amino acids, but when a protein contains mostly nonpolar amino acids, that protein is destined to wind up in the cell membrane (which is largely made up of lipids). Thus, we can expect to find that genes in the "breakaway" cloud of gold points in the graph further above represent membrane-associated proteins.

To check this hypothesis, I wrote a script that harvested the gene names, and protein-product names, of all the "breakaway cloud" data points. After purging genes annotated (unhelpfully) as "hypothetical protein," I was left with 37 "known" genes. They're shown in the following table.

Gene	Product
CLC_0571	arsenical pump family protein
CLC_1058	L-lactate permease
CLC_1550	carbohydrate ABC transporter permease
CLC_3115	xanthine/uracil permease family protein
CLC_0813	arsenical-resistance protein
CLC_1018	putative anion ABC transporter, permease protein
CLC_1687	xanthine/uracil permease family protein
CLC_3633	sporulation integral membrane protein YtvI
CLC_2382	phosphate ABC transporter, permease protein PstA
CLC_0189	ZIP transporter family protein
CLC_3351	sodium:dicarboxylate symporter family protein
CLC_0971	cobalt transport protein CbiM
CLC_1534	methionine ABC transporter permease
CLC_2798	xanthine/uracil permease family protein
CLC_1397	manganese/zinc/iron chelate ABC transporter permease
CLC_1836	stage III sporulation protein AD
CLC_0528	high-affinity branched-chain amino acid ABC transporter, permease protein
CLC_0430	electron transport complex, RnfABCDGE type, A subunit
CLC_2523	flagellar biosynthesis protein FliP
CLC_0401	amino acid permease family protein
CLC_0383	lrgB-like family protein
CLC_0457	chromate transporter protein
CLC_0291	sodium:dicarboxylate symporter family protein
CLC_0427	electron transport complex, RnfABCDGE type, D subunit
CLC_1281	putative transcriptional regulator
CLC_2008	ABC transporter, permease protein
CLC_0868	branched-chain amino acid transport system II carrier protein
CLC_1237	monovalent cation:proton antiporter-2 (CPA2) family protein
CLC_1137	methionine ABC transporter permease
CLC_0764	putative drug resistance ABC-2 type transporter, permease protein
CLC_1953	xanthine/uracil permease family protein
CLC_2444	auxin efflux carrier family protein
CLC_0897	putative ABC transporter, permease protein
CLC_1555	C4-dicarboxylate transporter/malic acid transport protein
CLC_0374	xanthine/uracil permease family protein
CLC_0470	undecaprenyl pyrophosphate phosphatase
CLC_2648	monovalent cation:proton antiporter-2 (CPA2) family protein

Notice that with the exception of CLC_1281, a "putative transcriptional regulator," every gene product represents a membrane-associated protein: transporters, carrier proteins, permeases, etc.

I ran the same experiment on genes from Streptomyces griseus (strain XylbKG-1) and came up with 222 genes having high pyrimidine content in base two. All 222 genes specify membrane-associated proteins. (The full list is in a table below.)

The bottom line: Base two of codons acts as a binary switch. If the base is a pyrimidine, the associated amino acid will most likely (75% chance) be nonpolar. If the base is a purine, the codon will either be a stop codon (3 out of 32 codons) or the amino acid will be polar (26 out of 29 codons).

Here's the list of 222 genes from S. griseus in which the middle codon base is predominantly a pyrimidine:

SACT1_0608	ABC-type transporter, integral membrane subunit
SACT1_3730	major facilitator superfamily MFS_1
SACT1_4066	cation efflux protein
SACT1_5911	ABC-2 type transporter
SACT1_6577	SNARE associated protein
SACT1_6966	ABC-type transporter, integral membrane subunit
SACT1_7160	major facilitator superfamily MFS_1
SACT1_3151	NADH-ubiquinone/plastoquinone oxidoreductase chain 6
SACT1_3682	drug resistance transporter, EmrB/QacA subfamily
SACT1_5431	Citrate transporter
SACT1_3199	proton-translocating NADH-quinone oxidoreductase, chain M
SACT1_7301	putative integral membrane protein
SACT1_3198	NAD(P)H-quinone oxidoreductase subunit 2
SACT1_2008	arsenical-resistance protein
SACT1_3149	proton-translocating NADH-quinone oxidoreductase, chain L
SACT1_3967	MATE efflux family protein
SACT1_3148	proton-translocating NADH-quinone oxidoreductase, chain M
SACT1_5571	major facilitator superfamily MFS_1
SACT1_0651	major facilitator superfamily MFS_1
SACT1_2669	ABC-type transporter, integral membrane subunit
SACT1_1805	NADH dehydrogenase (quinone)
SACT1_3147	NAD(P)H-quinone oxidoreductase subunit 2
SACT1_6961	major facilitator superfamily MFS_1
SACT1_0992	ABC-2 type transporter
SACT1_2619	major facilitator superfamily MFS_1
SACT1_0507	major facilitator superfamily MFS_1
SACT1_0649	ABC-type transporter, integral membrane subunit
SACT1_0800	glycosyl transferase family 4
SACT1_1659	ABC-type transporter, integral membrane subunit
SACT1_1803	multiple resistance and pH regulation protein F
SACT1_4190	putative ABC transporter permease protein
SACT1_5522	drug resistance transporter, EmrB/QacA subfamily
SACT1_5568	drug resistance transporter, EmrB/QacA subfamily
SACT1_7248	Lysine exporter protein (LYSE/YGGA)
SACT1_0266	ABC-2 type transporter
SACT1_0847	Na+/solute symporter
SACT1_4378	ABC-type transporter, integral membrane subunit
SACT1_6766	ABC-type transporter, integral membrane subunit
SACT1_2522	putative integral membrane protein
SACT1_4762	amino acid permease-associated region
SACT1_4901	major facilitator superfamily MFS_1
SACT1_2616	multiple antibiotic resistance (MarC)-related protein
SACT1_3961	major facilitator superfamily MFS_1
SACT1_6236	MIP family channel protein
SACT1_1319	protein of unknown function UPF0016
SACT1_2332	copper resistance D domain protein
SACT1_5327	ABC-type transporter, integral membrane subunit
SACT1_5759	ABC-2 type transporter
SACT1_1133	ABC-type transporter, integral membrane subunit
SACT1_1562	ABC-type transporter, integral membrane subunit
SACT1_5518	major facilitator superfamily MFS_1
SACT1_3430	ABC-type transporter, integral membrane subunit
SACT1_4517	major facilitator superfamily MFS_1
SACT1_4565	drug resistance transporter, EmrB/QacA subfamily
SACT1_4994	ABC-type transporter, integral membrane subunit
SACT1_7197	major facilitator superfamily MFS_1
SACT1_5949	major facilitator superfamily MFS_1
SACT1_6233	protein of unknown function DUF6 transmembrane
SACT1_0936	ABC-type transporter, integral membrane subunit
SACT1_4993	2-aminoethylphosphonate ABC transporter, permease protein
SACT1_6954	ABC-type transporter, integral membrane subunit
SACT1_1846	Lysine exporter protein (LYSE/YGGA)
SACT1_3429	ABC-type transporter, integral membrane subunit
SACT1_3957	membrane protein of unknown function
SACT1_4612	ABC-2 type transporter
SACT1_1998	polar amino acid ABC transporter, inner membrane subunit
SACT1_2093	ABC-type transporter, integral membrane subunit
SACT1_4420	ABC-2 type transporter
SACT1_6613	putative ABC transporter permease protein
SACT1_2564	small multidrug resistance protein
SACT1_3669	major facilitator superfamily MFS_1
SACT1_4186	major facilitator superfamily MFS_1
SACT1_4850	ABC-type transporter, integral membrane subunit
SACT1_0206	major facilitator superfamily MFS_1
SACT1_5418	Lysine exporter protein (LYSE/YGGA)
SACT1_0548	ABC-type transporter, integral membrane subunit
SACT1_3332	protein of unknown function DUF6 transmembrane
SACT1_3764	sodium/hydrogen exchanger
SACT1_6278	protein of unknown function DUF6 transmembrane
SACT1_4143	ABC-2 type transporter
SACT1_3232	ABC-type transporter, integral membrane subunit
SACT1_0256	ABC-type transporter, integral membrane subunit
SACT1_2898	major facilitator superfamily MFS_1
SACT1_6510	protein of unknown function DUF6 transmembrane
SACT1_0980	CrcB-like protein
SACT1_1650	ABC-type transporter, integral membrane subunit
SACT1_4658	acyltransferase 3
SACT1_0306	protein of unknown function DUF6 transmembrane
SACT1_2372	major facilitator superfamily MFS_1
SACT1_7238	ABC-type transporter, integral membrane subunit
SACT1_0202	major facilitator superfamily MFS_1
SACT1_0591	ABC-type transporter, integral membrane subunit
SACT1_5369	protein of unknown function DUF81
SACT1_6227	C4-dicarboxylate transporter/malic acid transport protein
SACT1_6755	ABC-type transporter, integral membrane subunit
SACT1_0978	Urea transporter
SACT1_2418	ATP synthase subunit a
SACT1_5604	major facilitator superfamily MFS_1
SACT1_4891	ABC-type transporter, integral membrane subunit
SACT1_6507	protein of unknown function DUF6 transmembrane
SACT1_6754	ABC-type transporter, integral membrane subunit
SACT1_0787	ABC-2 type transporter
SACT1_2848	sodium/hydrogen exchanger
SACT1_0636	ABC-type transporter, integral membrane subunit
SACT1_1891	putative integral membrane protein
SACT1_1552	xanthine permease
SACT1_2894	putative secreted protein
SACT1_4508	sodium:dicarboxylate symporter
SACT1_7091	drug resistance transporter, EmrB/QacA subfamily
SACT1_2652	major facilitator superfamily MFS_1
SACT1_1741	amino acid permease-associated region
SACT1_1838	ABC-type transporter, integral membrane subunit
SACT1_2796	gluconate transporter
SACT1_5220	sodium/hydrogen exchanger
SACT1_6991	ABC-type transporter, integral membrane subunit
SACT1_3273	protein of unknown function DUF894 DitE
SACT1_7089	ABC-type transporter, integral membrane subunit
SACT1_7280	major facilitator superfamily MFS_1
SACT1_3467	major facilitator superfamily MFS_1
SACT1_1304	ABC-type transporter, integral membrane subunit
SACT1_6032	protein of unknown function DUF81
SACT1_4312	major facilitator superfamily MFS_1
SACT1_0876	ABC-type transporter, integral membrane subunit
SACT1_6123	citrate/H+ symporter, CitMHS family
SACT1_4359	Cl- channel voltage-gated family protein
SACT1_7325	branched-chain amino acid transport
SACT1_1160	protein of unknown function DUF140
SACT1_2265	Arsenical pump membrane protein
SACT1_3512	ABC-2 type transporter
SACT1_1018	major facilitator superfamily MFS_1
SACT1_3415	Xanthine/uracil/vitamin C permease
SACT1_5214	BioY protein
SACT1_3656	small multidrug resistance protein
SACT1_3895	SpdD2 protein
SACT1_4929	ABC-type transporter, integral membrane subunit
SACT1_3029	major facilitator superfamily MFS_1
SACT1_6312	ABC-type transporter, integral membrane subunit
SACT1_0919	L-lactate transport
SACT1_4356	ABC-2 type transporter
SACT1_0532	ABC-type transporter, integral membrane subunit
SACT1_6693	secretion protein snm4
SACT1_0967	ABC-type transporter, integral membrane subunit
SACT1_6496	major facilitator superfamily MFS_1
SACT1_6983	major facilitator superfamily permease
SACT1_0917	NADH-ubiquinone/plastoquinone oxidoreductase chain 3
SACT1_6887	major facilitator superfamily MFS_1
SACT1_2835	major facilitator superfamily MFS_1
SACT1_5544	drug resistance transporter, Bcr/CflA subfamily
SACT1_5591	ABC-type transporter, integral membrane subunit
SACT1_1201	ABC-type transporter, integral membrane subunit
SACT1_2404	ABC-2 type transporter
SACT1_0870	protein of unknown function DUF803
SACT1_6933	ABC-type transporter, integral membrane subunit
SACT1_1776	ABC-type transporter, integral membrane subunit
SACT1_3213	major facilitator superfamily MFS_1
SACT1_4210	phosphate ABC transporter, inner membrane subunit PstC
SACT1_5398	protein of unknown function DUF81
SACT1_0914	NADH-ubiquinone/plastoquinone oxidoreductase chain 6
SACT1_3261	major facilitator superfamily MFS_1
SACT1_6932	ABC-type transporter, integral membrane subunit
SACT1_0669	major facilitator superfamily MFS_1
SACT1_4255	ABC-2 type transporter
SACT1_4541	major facilitator superfamily MFS_1
SACT1_4638	major facilitator superfamily MFS_1
SACT1_0913	NADH-ubiquinone oxidoreductase chain 4L
SACT1_4443	protein of unknown function DUF81
SACT1_5396	Xanthine/uracil/vitamin C permease
SACT1_0912	NADH dehydrogenase (quinone)
SACT1_2924	Lysine exporter protein (LYSE/YGGA)
SACT1_5922	putative integral membrane protein
SACT1_1243	Bile acid:sodium symporter
SACT1_6967	ABC-type transporter, integral membrane subunit
SACT1_0911	proton-translocating NADH-quinone oxidoreductase, chain M
SACT1_3931	putative ABC transporter permease protein
SACT1_2820	ABC-2 type transporter
SACT1_3298	putative integral membrane transport protein
SACT1_4871	major facilitator superfamily MFS_1
SACT1_5873	major facilitator superfamily MFS_1
SACT1_6636	putative integral membrane protein
SACT1_0905	AbgT transporter
SACT1_5532	2-dehydro-3-deoxyphosphogluconate aldolase/4-hydroxy-2-oxoglutarate aldolase
SACT1_0910	proton-translocating NADH-quinone oxidoreductase, chain N
SACT1_2115	ABC-type transporter, integral membrane subunit
SACT1_4635	protein of unknown function DUF6 transmembrane
SACT1_5777	major facilitator superfamily MFS_1
SACT1_3979	major facilitator superfamily MFS_1
SACT1_5536	major facilitator superfamily MFS_1
SACT1_6782	major facilitator superfamily MFS_1
SACT1_0616	virulence factor MVIN family protein
SACT1_4869	ABC-type transporter, integral membrane subunit
SACT1_1581	putative integral membrane protein
SACT1_4585	major facilitator superfamily MFS_1
SACT1_6536	small multidrug resistance protein
SACT1_4024	cell cycle protein
SACT1_5296	major facilitator superfamily MFS_1
SACT1_1865	major facilitator superfamily MFS_1
SACT1_4868	ABC-type transporter, integral membrane subunit
SACT1_0955	protein of unknown function DUF803
SACT1_4296	major facilitator superfamily MFS_1
SACT1_5104	major facilitator superfamily MFS_1
SACT1_0519	Bile acid:sodium symporter
SACT1_2394	putative ABC transporter permease protein
SACT1_0661	major facilitator superfamily MFS_1
SACT1_2062	major facilitator superfamily MFS_1
SACT1_4295	ABC-type transporter, integral membrane subunit
SACT1_6828	peptidase M48 Ste24p
SACT1_3446	major facilitator superfamily MFS_1
SACT1_6631	Lysine exporter protein (LYSE/YGGA)
SACT1_1048	ABC-type transporter, integral membrane subunit
SACT1_1528	protein of unknown function DUF6 transmembrane
SACT1_2016	branched-chain amino acid transport
SACT1_6154	gluconate transporter
SACT1_5051	major facilitator superfamily MFS_1
SACT1_5531	protein of unknown function DUF81
SACT1_6480	protein of unknown function DUF1290
SACT1_0373	ABC-type transporter, integral membrane subunit
SACT1_2392	putative ABC transporter permease protein
SACT1_2724	protein of unknown function DUF107
SACT1_7257	protein of unknown function UPF0118
SACT1_2772	putative integral membrane protein
SACT1_3201	NAD(P)H-quinone oxidoreductase subunit 4L
SACT1_7066	ABC-type transporter, integral membrane subunit

Some of these genes are labeled "protein of unknown function," but I think we can predict with high confidence, based on what we know about these proteins (namely, that they're hydrophobic) that the gene products in question involve membrane-associated functions.

Bioinformatics geeks, leave a comment below.