Relationships Among Isoacceptor tRNAs Seems to Support the CoevolutionTheory of the Origin of the Genetic Code
M.B. Chaley,1 E.V. Korotkov, 1 D.A. Phoenix2
1Center ‘‘Bioengineering,’’ Russian Academy of Sciences, 60-letiya Oktyabrya Prospect, 7/1, 117312 Russia, Moscow2University of Central Lancashire, Department of Applied Biology, Preston PR1 2HE, UK
Received: 4 May 1998 / Accepted: 11 July 1998
Abstract. A new method for looking at relationshipsbetween nucleotide sequences has been used to analyzedivergence both within and between the families ofisoaccepting tRNA sets. A dendrogram of the relation-ships between 21 tRNA sets with different amino acidspecificities is presented as the result of the analysis.Methionine initiator tRNAs are included as a separateset. The dendrogram has been interpreted with respect tothe final stage of the evolutionary pathway with the de-velopment of highly specific tRNAs from ambiguousmolecular adaptors. The location of the sets on the den-drogram was therefore analyzed in relation to hypotheseson the origin of the genetic code: the coevolution theory,the physicochemical hypothesis, and the hypothesis ofambiguity reduction of the genetic code. Pairs of 16 setsof isoacceptor tRNAs, whose amino acids are in biosyn-thetic relationships, occupied contiguous positions on thedendrogram, thus supporting the coevolution theory ofthe genetic code.
Key words: Comparative analysis — tRNA — Mo-lecular evolution — Origin of the genetic code
Introduction
Transfer RNAs have been central to the development ofthe genetic code into its current state and have therefore
become the focus of discussions on the main reason forits establishment (Szathmary and Zintzaras 1992; Di Gi-ulio 1994, 1995). The discussions have revolved aroundthe biosynthetic relationship between amino acids(Wong 1975; Taylor and Coates 1989) and their physicaland chemical properties (Sonneborn 1965; Woese et al.1966a, b). Because of the importance of tRNAs as adap-tors for the genetic code, they have been chosen to in-vestigate its origin (Fitch and Upper 1987). In spite ofstructural conservatism, transfer RNAs (tRNAs) containenough sites of information (52 of the total length of 76bases) for comparative analysis (Eigen et al. 1989), andindeed tRNA sequences have been popular objects forsuch analysis. The first phylogenetic trees of isoaccept-ing tRNAs have already shown the divergence of tRNAsequences among different species (LaRue et al. 1979).The construction of a phylogenetic tree for the VAL andGLY superfamilies of tRNAs has shown that tRNA di-vergence followed the divergence of anticodons and laterthe divergence of species. The evolution of transferRNAs has been considered in detail by Cedergren et al.(1981). By comparing known tRNAs and their genes, theconservative cloverleaf structural elements in each isoac-ceptor tRNA family and archetypical features in all fami-lies have been revealed (Nicoghosian et al. 1987). A newmethod (Eigen et al. 1989) has suggested that, at themoment of archaebacteria and eubacteria division, thelevel of divergence between the tRNA sequences wasalready equal to one-third of the current level of tRNAdivergence (Eigen et al. 1989). This method of statisticalgeometry in sequence space has been offered as a moreCorrespondence to:M.B. Chaley;e.mail: [email protected]
reliable means of revealing hidden kinship betweennucleotide sequences (Eigen et al. 1988, 1989), but anynew method which provides insight into earlier relation-ships between tRNA species and the evolutionary diver-gence of these sequences is of importance. This paperdescribes a method for studying relationships betweennucleotide sequences and gives insight into the evolu-tionary divergence of tRNA families and their relation-ship with the genetic code.
Materials and Methods
The sequences were taken from a compilation of tRNA sequences andtRNA genes from the EMBL database (Steinberg et al. 1993). Thesequences were aligned according to the standard cloverleaf structure.The modified RNA bases were exchanged for the original unmodifiedform. The tRNA sequences whose anticodons imply a binding withknown exceptions to the universal genetic code (Osawa et al. 1992),were excluded from the comparative analysis. According to the mecha-nisms of ‘‘codon reassignment’’ (Osawa and Jukes 1989) and ‘‘codonswapping’’ (Szathmary 1991), these exceptions are due to later eventsin genome evolution rather than the original correspondence which wasestablished between codons (anticodons) and amino acids. tRNAs ofcellular organelles were also excluded from the analysis based on thesymbiotic theory of the organelle origin (Alberts et al. 1995). It isreasonable to assume that the evolution of cellular organelle tRNAsimplies an individual character and should be studied separately. Thisis supported by the fact that mitochondrial tRNAs often have nonstan-dard secondary structures (Cedergren 1982) and show deviations fromthe universal coding (Osawa et al. 1992). The accession codes for thesequences used in the present analysis are given in the Appendix.
Isoacceptor tRNAs and their genes were put into distinct sets ac-cording to their different amino acid specificities. Elongator and ini-tiator methionine tRNAs were also divided into separate sets. Table 1gives the number of the sequences in each set and distribution ofanticodons within the sets. It is noticeable that anticodons span thewhole table of amino acid codons. Table 2 shows the distribution of thesequences among kingdoms of protista (viruses, archaebacteria, andeubacteria are represented separately), plants, and animals. As can beseen, in most cases each set contains representatives from all kingdoms.The method developed here for observing the relationship betweennucleotide sequences can be described as follows.
Generalization of Relationships Between theNucleotide Sequences
(1) The nucleotide sequences of tRNAs and tRNA genes have beencompared in pairs within each set and between the sets of different
Table 1. The number of investigated isoacceptor tRNAs and tRNAgenesa
Ser 62 UGA 18 UCAIGA 15 UCUCGA 14 UCGGGA 5 UCCGCU 10 AGC— — AGU
Thr 34 UGU 14 ACAGGU 9 ACCCGU 6 ACGIGU 5 ACU
Trp 20 CCA 18 UGGUCA 2 —
Tyr 35 GUA 34 UACCUA 1 —— — UAU
Table 1. Continued
Aminoacid
Number ofisoacceptingtRNAs Anticodon
Number of tRNAswith identicalanticodons
Aminoacid codon
Val 42 IAC 14 GUUUAC 12 GUAGAC 8 GUCCAC 8 GUG
a Distribution of anticodons corresponding to the codons of amino ac-ids. Anticodons and codons are represented in a 58 to 38 direction. If theI base is shown at the first position of the anticodon, it means that theA base was present in the original tRNA gene sequence.
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amino acid specificities. The similarity measure obtained by pairwisecomparison was twice the value of the mutual information measure,(2I), as defined in Eq. (1).
2I = 2S(i=1
4
(j=1
4
mij lnmij − (i=1
4
milnmi − (j=1
4
mjlnmj +LlnLD (1)
L = (i=1
4
mi = (j=1
4
mj (2)
mi = (j=1
4
mij , mj = (i=1
4
mij (3)
Heremij are the elements of the base coincidence matrix denotedM.Matrix M is a 4 × 4 matrix which contains numerical values for allcoincidences among A, G, U, and C bases, when two tRNAs werecompared. It has been shown that 2I has ax2 distribution with ninedegrees of freedom (Kullback 1958). This permits the statistical sig-nificance of any similarity to be ascertained.
Then, a matrixRXZ was formed containing measures of the simi-larity between tRNA pairs for each pair of sets (X and Z). This wasfilled according to the results of the comparative analysis, with the 2Ivalues forming the elements of theRXZ matrix.
(2) As shown in Table 1, the sets with different amino acid speci-ficities contain unequal numbers of sequences. Consequently, theRXZ
matrices for pairwise comparison of the sets had different dimensions.The similarities between tRNA sets were analyzed via integration toallow a comparison independent of the dimensions of theRXZ matrixes.The distribution of the 2I value was considered over the following 12intervals:
The distribution of the experimentally determined frequencies of the 2Ivalue over the intervals are denotedfn, n 4 1, . . . , 12. Theoreticalprobabilitiesen that the 2I value belonged to each interval were calcu-lated by integration of the probability density of thex2 distribution with9 df. The measure of an interrelationship between two sets of tRNAs,i.e., the deviation of the comparison matriceRXZ from a matrix showingaccidental alignments, was given by
FXZ = KXZS(n=1
r
fnlnfn − (n=1
r
fnlnenD (5)
Here KXZ is the product of column and row numbers of matrixRXZ
corresponding to the pair of tRNA sets:X and Z. r is the number ofdiscrete intervals of 2I value, withr 4 12 in this case.
(3) It has been shown that the 2FXZ value is distributed asx2 with(r − 1) degrees of freedom (Kullback 1958). This allows the introduc-tion of GXZ as a measure of the interrelationship between tRNA sets.This measure is independent of the dimension of the comparison matrixRXZ:
GXZ =2FXZ
KXZK0 (6)
HereK0 is a mean volume for the comparison matrix among all pairsof tRNA sets. We considerK0 4 1296 in further calculations.
(4) To represent relationships between the sets of isoacceptortRNAs more adequately, thex2 distribution with 11 df was transformedto the standard normal distribution by the formula
NXZ 4 (2GXZ)1/2 − (2r − 1)1/2 (7)
(5) Finally, the strength of the relationship between a pair of tRNA setshas been represented as an argument of the standard normal distribu-tion. All such relationships have been placed in matrixN(x,z)—a simi-larity matrix for the 21 sets of isoacceptor tRNAs. TheN(x,z) matrix isgiven in Table 3. Using the well-known algorithm for cluster analysisby ‘‘nearest neighbor’’ (Duda and Hart 1973), a dendrogram based onthe N(x,z) similarity matrix has been constructed. In constructing thedendrogram, the arguments of the normal distribution were expressedto three significant figures. Each set was drawn as a separate branch.Each branch of the dendrogram was continued horizontally to the mark,which was equal to the measure of the similarity between the sequenceswithin one set (see Fig. 1).
Results and Discussion
Using mutual information [2I value, introduced by for-mula (1)] as a basic measure of similarity between se-quence pairs allows phylogenetic reconstruction withoutthe need to assume a constant rate of mutations in allbranches of a phylogenetic tree. This approach does notfocus on the number of different nucleotides between thesequences since relationships between them are mea-sured by summarizing all possible nucleotide coinci-dences. This provides greater insight to the similarity ofnucleotide sequences in comparison to the commonlyused approach of representing similarity in terms of per-centage homology. Further, this method allows a statis-tical measure to be assigned to each pairwise comparisonof groups of sequences for different isoacceptor tRNAsindependent of the groups’ size [formulas (5)–(7)].
A dendrogram, showing the interrelationships amongthe tRNA sets with different amino acid specificities, isshown in Fig. 1. The interrelationship values are given asarguments of a normal distribution ranging from 195 to330 units. The arguments for a normal distribution be-longing to such an interval have corresponding probabili-
Table 2. Numbers of representatives of all kingdoms of living organisms for each set of isoaccepting tRNAs and their genes: The order of thesets from left to right follows the dendrogram in Fig. 1
Gln His Pro Ser Leu Gly Glu Asp Cys Trp Arg Val Ala Metf Tyr Thr Met Ile Phe Asn Lys
ties of accidentally establishing relationships betweenthe set pairs which are less than 10−8. A difference be-tween the interrelationship values of even one unit leadsto a difference in the probabilities of more than one orderof magnitude. It can be seen that the sets of lysine andelongator methionine tRNAs contain the most closelyrelated sequences.
Two processes have accompanied the development ofcurrent isoacceptor tRNA sets. The first is the evolution-ary differentiation of molecular adaptors into sets ofhigh-specificity molecules which concentrate amino ac-ids. The second process is that of species evolution,which would have influenced the levels of interrelation-ships both within and across the sets. Usually, by con-structing a common ancestral sequence, the influence ofspecies evolution is reduced to a minimum in the se-quence phylogeny (Fitch and Upper 1987; Di Giulio1995). Then trees of ancestral sequences can be con-structed in accordance with the hypothesis under inves-tigation, but statistical testing of phylogenetic hypothesesis complicated due to the vast number of all possibletrees, which consist of the same number of taxons. Thenumber of unrooted trees ofn taxons has a value ofP(2i − 1), i 4 1, . . . ,(n − 2) (Fitch and Margoliash1968). The approach used in the present work avoids theneed for the reconstruction of an ancestral sequence foreach set of isoacceptor tRNAs. Nevertheless, the analysisgiven below provides evidence that the generalization ofinterrelationships across the nucleotide sequencessmoothes the influence of species evolution. First, con-sider Table 2. The 21 tRNA sets are shown, along withthe number of tRNA representatives from each kingdom:
viruses, archaebacteria, eubacteria, protista (excludingthe last three groups), plants, and animals. The tRNA setsin Table 2 go from left to right as they are arranged inFig. 1. There is no obvious relationship between thenumber of representatives from each kingdom and thearrangement of the tRNA sets in the dendrogram. Thismay imply that the order of the tRNA sets in the den-drogram reflects mainly the evolutionary process, whichformed tRNAs of precise amino acid specificities. Sec-ond, in Fig. 1, it can be observed that the lower thestrength of the internal relationship between tRNAs in aset, the less the isoacceptor tRNA family is related toother tRNA sets. Naturally, the more ancient the se-quences of the isoacceptor tRNAs, the more divergencethey show between themselves. Furthermore, their rela-tionships with other isoacceptor tRNAs, which wereformed later, are weaker. It may therefore be supposedthat the dendrogram in Fig. 1 correctly reflects the orderof tRNA evolution.
Being the molecular adaptors between the geneticcode and the amino acid sequences of proteins, transferRNAs, surely, must bear some traces of evolutionarystructuring of the genetic code. So the dendrogram ofinterrelations between the sets of isoacceptor tRNAs issupplemented with a scheme-table of amino acid codonsin Fig. 2. Moving from top to bottom, specific codons inFig. 2 repeat the arrangement of tRNA sets in Fig. 1 fromleft to right. The codons of amino acids, in which the firsttwo bases differ only by transitions, are distinguished byseparate boxes. The first column in the scheme-table inFig. 2 includes the first two bases of the codons; thesecond column contains the third codon base. Amino
Table 3. A matrix of generalized interrelationships among 21 isoacceptor tRNA sets (methionine tRNAs are divided into initiator and elongatorsets)a
acid names are in the third column. In the case where acodon might have previously been assigned to anotheramino acid, the name of that acid is shown in parenthe-ses. Capital letters C, P, and R in the fourth column markthe clustering of amino acid codons (which is the same asfor the sets of isoaccepting tRNAs in Fig. 1) in accor-dance with the three main hypotheses on the origin of thegenetic code. ‘‘C’’ corresponds to the coevolution hy-pothesis (Wong 1975); ‘‘P,’’ the physicochemical hy-pothesis (Woese et al. 1966a); and ‘‘R,’’ the hypothesis
of ambiguity reduction of the genetic code (Fitch andUpper 1987). Leucine codons are listed to the right of thescheme-table in Fig. 2 because transfer RNAs of leucineand serine, according to the dendrogram in Fig. 1 (seealso Table 3), have the same value for relationships be-tween themselves and with other isoacceptor tRNA sets.Though clustering of the tRNALEU and tRNASER sets isnot in agreement with any of the three hypotheses men-tioned above, it is quite natural from the point of view ofa united box of PyPyN codons. The stop- codon UGA is
Fig. 1. A dendrogram constructed on theN(x,z) matrix (Table 3) ofgeneralized interrelationships between the isoacceptor tRNA sets. Themethod of clustering is nearest neighbor (single-link). Values of inter-nal and external relationships are the arguments of standard normal
distribution. The horizontal growth of each branch is terminated on thelevel of the generalized interrelationship value within the isoacceptortRNA set. The values of internal relationships are present on the sameline together with the set’s name. See text for further explanation.
a The interrelationship values are given as the arguments for a standard normal distribution. The matrix is symmetrical relative to its two maindiagonals.
172
Fig. 2. A scheme-table, where amino acid codons range from top tobottom in the same order as the sets of isoacceptor tRNAs in Fig. 1 gofrom left to right. Codons and names of amino acids occupy the firstthree columns in the table. If a codon might have previously beenassigned to another amino acid, then the name of that amino acid isshown inparentheses. Boxesdivided by arrows include amino acidcodons, which differ by transitions. Separate columns ofcapital
lettersdenote the codons clustering in accordance with the coevolutionhypothesis (C), the physicochemical hypothesis (P), and the hypothesisof ambiguity reduction of the genetic code (R). Each column representsone cluster. Leucine and UGA stop codons are shown to theright tosimplify the clustering pattern of the other codons. However, placingthe UAN codon box at the right emphasizes the interruption of theAPyN codon box assimilation. See text for discussion.
173
also listed at the right of the scheme-table to allow moreconvenient analysis of amino acid codon clustering. Thebox containing codons for tyrosine and two stop- codonsis similarly displaced to emphasize its role in interruptionof the AYN codon cluster.
The codon clustering in Fig. 2 is now considered withrespect to the hypotheses on the origin of the geneticcode. The hypothesis of coevolution (Wong 1975, 1981;Taylor and Coates 1989) implies that, through a media-tion of ancestral tRNA molecules, accepted amino acidswhich are biosynthetically linked via a precursor–product relationship could allow the precursor to passsome of its codons to the product. In this case transferRNAs, whose amino acids are related via biosyntheticconversions, would show a higher correlation betweenthemselves than with other tRNAs. Groups of amino acidcodons, which are clustered in Fig. 2 in accordance withthe coevolution hypothesis, are indicated by C and in-clude (GLN,HIS,PRO), (SER,GLY), (ASP,GLU), (TRP,CYS), (ALA,VAL), (THR,ILE,MET), (ASN,LYS). Dataon biosynthetic relationships were taken from Wong(1975) and Taylor and Coates (1989). The codons of 16amino acids of 20 possible are clustered in agreementwith the coevolution theory.
The physicochemical hypothesis (Sonneborn 1965;Woese et al. 1966a, Jungck 1978; Weber and Lacey1978; Lacey et al. 1992) put forward physicochemicalproperties of amino acids as the basis for the structuringof the genetic code. In this case, it is expected that tRNAsof similar amino acids would show a higher correlationthan tRNAs of amino acids with different physicochem-ical properties. There are many directions to the polardistances between amino acids if basic factors were in-volved in the arrangement of amino acids over the ge-netic code (Woese et al. 1966a, b; Di Giulio 1989; Haigand Hurst 1991; Goldman 1993). So the absolute mea-sure of the difference between amino acid polarity valueswas chosen as the distance measure between two aminoacids. The values of amino acid polarities were takenfrom Woese et al. (1966a). Pairs of tRNA sets seem tocluster according to the minimum differences in polarityvalues of amino acids, denoted P, and include (GLN,HIS), (SER,GLY), (ASP,GLU), (CYS,GLU), and (ASN,LYS). In Fig. 2 codon clusters of 9 amino acids of 20possible fit the physicochemical hypothesis on the originof the genetic code.
The hypothesis of ambiguity reduction of the geneticcode (Fitch and Upper 1987) supposes that ancestral mo-lecular adaptors were initially unable to distinguish be-tween amino acid codons and only later developed theability to differentiate between purines and pyrimidinesin either the first or the second codon base position.Finally, tRNAs developed a sensitivity to both kinds ofpurine and pyrimidine. Following Di Giulio (1995) wedistinguished the second-position codon as a marker be-cause of the correlation between amino acid physico-
chemical properties and base position (Nelsestuen 1978;Wolfenden et al. 1979; Sjostrom and Wold 1985). Theclustering pattern of isoacceptor tRNA sets in Fig. 1,which supports the ambiguity reduction of the geneticcode, is best seen in Fig. 2, where the codons of differentamino acids with the same second base are nearest neigh-bors. In this case only UCN were considered as serinecodons, so codons AGU and AGC could be captured byserine much later. Codons of phenylalanine UUU andUUC were not considered in this case because UUNcodons might be the subject of controversy between phe-nylalanine and leucine amino acids. Groups of codons ofamino acids supporting this hypothesis are noted R andinclude (GLN,HIS), (PRO,SER), (ASP,GLU), (CYS,TRP,ARG), (ILE,MET), and (ASN,LYS). Thirteen oftwenty possible amino acids show clustering which is inagreement with the hypothesis.
Figure 2 shows that of 20 possible amino acids, 16 areclustered in accordance with the coevolution hypothesis,13 in accordance with the hypothesis of ambiguity re-duction of the genetic code, and 9 according to the phys-icochemical hypothesis of the origin of the genetic code,i.e., 80, 65, and 45%, respectively. The results thereforeimply that the greatest contribution to the structuring ofthe genetic code was from relationships between precur-sor and product amino acids and support earlier conclu-sions in favor of the coevolution theory on the origin ofthe genetic code (Di Giulio 1994, 1995). It is worthnoting, though, that the clustering pattern according tothe coevolution hypothesis overlaps with the patterns ofthe other two hypotheses by about 50%.
The separate boxes in Fig. 2 link amino acid codons inwhich the first two bases are either identical or connectedby transitions, so, moving from the top to bottom of thetable, PyPuN→PyPyN→PuPuN→PyPuN→PuPyN→PyPuN→PuPyN→PyPyN→PuPuN. Exchange betweenPu and Py in the second codon position led us to supposean ordered assimilation of codons by ancestral tRNAsduring the evolution of tRNAs. If highly specific adap-tors have arisen for certain codons, the triplets of nucleo-tides, which were complementary to these codons, havebeen given a priority to form their own specific transferRNAs. Though mechanisms by which this could occurare not obvious, some speculation is possible. The hy-percycle theory (Eigen and Schuster 1979) postulatesthat the ancestors of modern transfer RNAs haveemerged as short symmetrical double-helixes about 73bases or more long, whose complementary strandsserved as pre-tRNAs with complementary anticodons(Rodin et al. 1993). It is true that further improvement ofthe adaptors to complementary codons should have beenproceeding in parallel and under close competition. Un-fortunately, such a model of ancestral adaptors is not inagreement with the pre-tRNA model which follows fromthe genomic tag model (Maizels and Weiner 1993,1994). This model defines the upper half of the modern
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tRNA (the acceptor stem and TCC arm) as the moreancient structural domain, with the lower half (the dihi-drouracil arm and anticodon arm) arising later. This viewof tRNA evolution is currently dominant (Schimmel andde Populana 1995). Nevertheless, we believe that it ispossible to combine the pre-tRNA model of Rodin et al.(1993) with the contemporary idea of tRNA consisting oftwo structural domains.
For many codons Fig. 2 seems to allow for sequentialassimilation of codons with specific adaptors. For ex-ample, leucine codons (UUG,CUG) are complementaryto glutamine codons (CAA,CAG) and the high-speci-ficity tRNAsLEU were established soon after tRNAsGLN.This could also be true of complementary codon pairsGAG–Gln and CUC–Leu, GGG-Gly and CCC–Pro, andmany others. If such a development occurred, then thebreak in the assimilation of PuPyN codons and transferto tyrosine codons must be explained (Fig. 2). The for-mation of specific adaptors for codons PuPyN startedfrom the methionine codon AUG and development couldhave been prolonged due to the additional demands ofbinding with the initiating factor and small ribosomalsubunit from initiator tRNAMET. At this point the tyro-sine codon UAC was already distinguished as comple-mentary to the valine codon GUA and consequently hadpriority to form its own specific adaptors. Owing to theprolonged pause in assimilation of PuPyN codons, theformation of high-specificity adaptors for tyrosinecodons was complete before methionine initiator tRNAs.
In conclusion, the results obtained here for transferRNAs support the theory of coevolution, with biosyn-thetic links between amino acids as the dominant factorin shaping the structure of the genetic code. For 80% ofamino acids their isoacceptor tRNAs were foundgrouped according to the biosynthetic pathways of theaccepted amino acids. Our results also demonstrate thesignificance of physicochemical properties (namely, po-larity) in establishing biosynthetic relationships sinceclusters of isoacceptor tRNAs which support both thephysicochemical and the coevolution hypotheses coin-cided in 50% of cases. Some support was also obtainedfor the ambiguity reduction hypothesis.
Acknowledgment. We thank Dr. M. Di Giulio for his critical andhelpful remarks.
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Appendix
Fig. A1. The accession codes of the tRNA sequences and tRNA genes from Steinberg et al. (1993).
