The Importance of Changes Observed in the Alternative Genetic Codes

Paweł Bła

zej, Małgorzata Wnetrzak and Paweł Mackiewicz

Department of Genomics, Faculty of Biotechnology, University of Wrocław, F. Joliot-Curie 14a, 50-383 Wrocław, Poland

Keywords:

Alternative Genetic Code, Error Minimization, Genetic Code, Mutation.

Abstract:

The standard genetic code is a way of transmitting genetic information from DNA into protein world. The code

is universal for almost all living organisms on Earth but small deviations have been observed for many cellular

organelles and some speciﬁc groups of microorganisms with highly reduced genomes. Such modiﬁcations are

called alternative genetic codes. There is no consensus about the factors that caused or allowed these changes.

A popular concept assumes that the codes evolved under neutral evolution without adaptive constraints. In

this paper we present ﬁndings that argue with such view. We examined the level of error minimization in

amino acid replacements generated by the standard genetic code and its alternatives. We found that only

3 out of 23 tested alternative codes have worse quality than the standard genetic code. In agreement with

that, many single codon reassignments observed in the variants of the standard genetic code are generally

responsible for improving the quality of the codes under the studied criteria. These results indicate that the

codon reassignments observed in the existing alternative genetic codes could play an adaptive role in their

evolution to minimize translational and mutational errors. The study can help in designing alternative genetic

codes for artiﬁcially modiﬁed organisms in the framework of synthetic biology.

1 INTRODUCTION

The assumption about the universality of the standard

genetic code (SGC) was challenged by the discover-

ies of the genetic code variants (Osawa et al., 1992;

Jukes, 1996) especially because the SGC was initially

considered a ’frozen accident’ (Crick, 1968). These

deviations are mainly observed in the codes operat-

ing in organelles, especially in mitochondria (Osawa

et al., 1989; Crozier and Crozier, 1993; Boore and

Brown, 1994). The alternative codes are also involved

in translation of proteins coded in nuclear genomes of

various unicellular eukryotes (Schneider et al., 1989;

Sanchez-Silva et al., 2003) and some bacteria, espe-

cially in parasites and symbionts with highly reduced

genomes (Lim and Sears, 1992; McCutcheon et al.,

2009; Campbell et al., 2013). In recent years, the

number of newly discovered alternative genetic codes

has signiﬁcantly increased (Heaphy et al., 2016; Za-

honova et al., 2016). This quite large set of alternative

codes is a good starting point to analyze the proper-

ties and the potential evolutionary tendencies of these

codes and the SGC.

There are three main types of deviations from the

standard genetic code found in its alternatives: (i) re-

assignments of codons coding for typical 20 amino

acids and stop translation signal, (ii) loss of codon

assignments resulting in the occurrence of unused

codons, (iii) incorporation of new amino acids, e.g.

selenocysteine and pyrrolysine. These three types of

changes were discussed and modeled by (Sengupta

et al., 2007). They result usually from mutations and

the editing of tRNA genes or the posttranscriptional

modiﬁcations of bases in tRNA molecules (Santos

et al., 2004). However, it is not clear how and why

these changes occurred and were ﬁxed. It is gener-

ally believed that they evolved neutrally without any

adaptive pressure through genetic drift and mutational

pressure that drove small populations and their tiny

genomes toward the high AT-content (Freeland et al.,

2000; Koonin, 2017). Such changes can inﬂuence

codon usage and, in extreme cases, can lead to dis-

appearance of GC-containing codons (Santos et al.,

2004). Alternatively, the variants of the SGC could

have evolved to reduce protein synthesis costs (Swire

et al., 2005) or to minimize effects of point muta-

tions; such properties were observed in some of these

codes (Kurnaz et al., 2010; Morgens and Cavalcanti,

2013). Here we focused on the latter aspects of the

genetic code evolution and analyzed the optimality of

many genetic code variants to assess their robustness

in terms of amino acid replacements.

154

Bła

zej, P., Wnetrzak, M. and Mackiewicz, P.

The Importance of Changes Observed in the Alternative Genetic Codes.

DOI: 10.5220/0006642001540159

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 3: BIOINFORMATICS, pages 154-159

ISBN: 978-989-758-280-6

2 METHODS

The examined alternative genetic codes were

downloaded from the NCBI taxonomy web page:

www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi.

From the whole set of 30 available genetic code

variants we chose those which differed in codon as-

signments from the standard genetic code, including

also the codes with ambiguous codon assignments.

In total, we studied 23 alternative codes (Table 1).

Table 1: The genetic code variants studied in this work

with the number of codon reassignments. In the case of the

genetic codes with ambiguous reassignments, we included

two different versions of their structures: with all possible

codon assignments (all) and with only unambiguous assign-

ments (unamb).

Genetic code name Num. of reassig.

Alternative Flatworm Mitochondrial Code 5

Trematode Mitochondrial Code 5

Invertebrate Mitochondrial Code 4

Flatworm Mitochondrial Code 4

Ascidian Mitochondrial Code 4

Vetebrate Mitochondrial Code 4

Candylostoma Nuclear Code (all) 3

Karyorelict Nuclear Code (all) 3

Blastocrithidia Nuclear Code (all) 3

Pterobranchia Mitochondrial Code 3

Hexamita Nuclear Code 2

Karyorelict Nuclear Code (unamb) 2

Mesodium Nuclear Code 2

Peritrich Nuclear Code 2

Scenedesmus Mitochondrial Code 2

Gracilibacteria Code 1

Euploid Nuclear Code 1

Blastocrithidia Nuclear Code (unamb) 1

Protozoan Mitochondrial Code 1

Chlorophycean Mitochondrial Code 1

Tannophilus Nuclear Code 1

Alternative Yeast Nuclear Code 1

Thraustochytrium Mitochondrial Code 1

To test the optimality of a given code, we had to use

a speciﬁc measure describing costs of amino acid re-

placements. In this work, for a given genetic code

(code) we used the following cost measure:

F(code) =

∑

<i, j>∈D

[ f (i) − f ( j)]

, (1)

where D is the set of pairs of codons that differ in

one nucleotide substitution, whereas f (i) and f ( j) are

the polarity values of the amino acids (Woese, 1973)

coded by the codons i and j, respectively. There-

fore, the measure F(code) represents the total sum of

the squared differences between polarity properties of

amino acids for the codon pairs differing in one sub-

stitution. The main reason for using the polarity prop-

erty to evaluate the cost of a genetic code follows from

the fact that this characteristic is independent of the

speciﬁcity of the SGC structure and was commonly

applied in testing the optimality of the standard ge-

netic code (Di Giulio, 1989; Haig and Hurst, 1991;

Freeland and Hurst, 1998; Santos and Monteagudo,

2010; Bła

zej et al., 2016).

All the single nucleotide substitutions that lead

to nonsense mutations, i.e. to the replacement of an

amino acid by a stop translation codon, were included

in the calculation as the maximum of squared differ-

ences computed for any possible pair of amino acids.

One could argue with this assumption. However, it

is known that the nonsense mutations are very dele-

terious because they result in incomplete and usually

nonfunctional proteins. Therefore, it seems reason-

able to assume such large costs for this type of substi-

tution.

Furthermore, we calculated three characteristics

in the case of the genetic codes with ambiguous

codon assignments. F(code all) included all possi-

ble codon assignments and F(code unamb) included

only unambiguous codon assignments. Additionally,

we calculated the arithmetic mean F(code mean)

from F(code all) and F(code unamb). As a re-

sult, F(code mean) assumes that ambiguous codon

assignments occur with equal probability.

To validate the properties of the genetic codes we

compare them with all possible 1240 theoretical codes

that differed from the SGC by one codon assignment.

3 RESULTS

3.1 Optimality of the Standard and

Alternative Genetic Codes

The comparison of the cost function F calculated for

the standard genetic code and its alternatives showed

that the SGC is not the best optimized code, in

terms of the polarity property, similarly to the re-

sults obtained by (Morgens and Cavalcanti, 2013).

Only 3 out of 23 considered alternative genetic codes

have greater (i.e. worse) F value than the SGC, i.e.

F(SGC) = 5641.46 (Figure 1):

1. Vetebrate Mitochondrial Code: F(code) =

6716.48;

2. Alternative Yeast Nuclear Code: F(code) =

5651.86;

3. Thraustochytrium Mitochondrial Code:

F(code) = 6283.02.

The best found code according to the polarity

costs is Karyorelict Nuclear Code including all am-

The Importance of Changes Observed in the Alternative Genetic Codes

155

●

●●

●

●●

●

●●

●

1 2 3 4 5

3000 4000 5000 6000

number of reassignments

cost value

Figure 1: The cost values (black circles) calculated for alter-

native genetic codes. They are compared with the number

of codon reassignments (x-axis) and also with the cost of

the standard genetic code (the bold horizontal line). It is

visible that many alternative codes lie below the horizontal

line and are better optimized to minimize the costs of amino

acid replacements than the SGC.

biguous reassignments of codons. It reaches the min-

imum cost value F(code) = 2945.12 which is nearly

two times lower than F(SGC). Similarly, there are

theoretical genetic codes with one codon reassign-

ment that have smaller cost values than the SGC.

The best one of them has the cost value F(code) =

4766.28. These results suggest that the standard ge-

netic code can be signiﬁcantly improved even by a

small number of codon reassignments.

On the other hand, there is a quite large probabil-

ity to deteriorate the SGC structure in terms of the F

value just by one random reassignment because more

than 330 theoretical codes out of all 1240 considered,

i.e. 27% have lower cost values than the SGC (Figure

2).

3.2 The Properties of Codon

Assignments in Alternative Genetic

Codes

It is interesting to examine the features of the alter-

native genetic codes which are better optimized than

the SGC. First we calculated the number of occur-

rences of all individual codon reassignments observed

in the alternative genetic codes under study (Table 3).

It is evident that these changes can be classiﬁed into

three groups (Figure 3). The ﬁrst one (A) contains

all codon reassignments from stop translation signal

Table 2: The cost values F calculated for all genetic code

variants studied in this work in comparison to the the stan-

dard genetic code (SGC) and the best theoretical genetic

code with single codon reassignment. We included three

different characteristics of the genetic code variants: with

all possible codon assignments (all) and with only unam-

biguous assignment (unamb) and the average value (mean)

of the cost value.

genetic code name cost

Candylostoma Nuclear Code (all) 2945.12

Karyorelict Nuclear Code (all) 2945.12

Karyorelict Nuclear Code (mean) 3484

Blastocrithidia Nuclear Code (all) 3697.04

Alternative Flatworm Mitochondrial Code 3942.66

Hexamita Nuclear Code 4022.88

Karyorelict Nuclear Code (unamb) 4022.88

Mesodium Nuclear Code 4116.26

Blastocrithidia Nuclear Code (mean) 4250.7

Candylostoma Nuclear Code (mean) 4293.29

Peritrich Nuclear Code 4692.9

Trematode Mitochondrial Code 4692.92

Invertebrate Mitochondrial Code 4696.84

Flatworm Mitochondrial Code 4706.84

Ascidian Mitochondrial Code 4748.84

the best theoret. code with one reassign. 4766.28

Gracilibacteria Code 4783.56

Euploid Nuclear Code 4795.08

Blastocrithidia Nuclear Code (unamb) 4804.36

Protozoan Mitochondrial Code 4804.36

Pterobranchia Mitochondrial Code 4839.88

Chlorophycean Mitochondrial Code 4936.3

Scenedesmus Mitochondrial Code 5575.14

Tannophilus Nuclear Code 5630.96

the standard genetic code 5641.46

Alternative Yeast Nuclear Code 5651.86

Thraustochytrium Mitochondrial Code 6283.02

Vetebrate Mitochondrial Code 6716.48

to one of the 20 standard amino acids. There are 31

such changes which make over 55% of all possible

56 codon reassignments found in the studied alterna-

tive genetic codes. The second group (B) includes in

total 21 codon reassignments which are involved in

changing encoded amino acids. Finally, we have only

4 cases in which codons originally encoding amino

acids change their meaning to the stop translation sig-

nal (the group C).

It should be noted that the codon reassignments

belonging to the ﬁrst and, at the same time, the largest

group (Table 3) are the most desired in terms of mini-

mizing the F value because each of these changes de-

creases the cost value in comparison with the F(SGC)

(Figure 3). For example, the single assignment of stop

codon TGA to tryptophane, which is the most fre-

quently observed change, i.e. 12 times, diminishes the

cost of the F(SGC) = 5641.46 to F(T GA → Trp) =

4804.36. This reassignment is one of the best ones

because the resulting cost for the code differs by less

BIOINFORMATICS 2018 - 9th International Conference on Bioinformatics Models, Methods and Algorithms

156

5000 5500 6000 6500

0.000 0.001 0.002 0.003 0.004 0.005

cost value

Density

Figure 2: The density plot of the frequency of cost values F

calculated for the theoretical genetic codes that differ from

the standard genetic code in one codon assignment. The

cost value calculated for the standard genetic code F(SGC)

is marked by the vertical bold line. It is evident that F(SGC)

value is situated closer to smaller values. Thereby, it is less

probable to generate at random a code better than the SGC

just by one codon reassignment.

than 1% from the best possible cost obtained by the

single change F(T GA → Ala) = 4766.28.

The reassignments in the second group have a

rather small inﬂuence on the cost values in compar-

ison with the change of codon’s meaning from a stop

translation signal to an amino acid (Figure 3). How-

ever, many of these missense reassignments can also

improve, although slightly, the F value in comparison

with F(SGC), i.e. F(AGG → Ser) = 5601.14. The

reassignments that decrease the F value are more fre-

quent in the studied alternative genetic codes than the

reassignments increasing the costs of amino acid re-

placement, e.g. F(AGG → Lys) = 5713.46. The for-

mer were found in 21 cases, whereas the latter in only

four cases.

The third group of reassignments contain codons

that formerly coded for an amino acid but then

changed their meaning into the stop translation sig-

nal. Such changes have the most dramatic impact on

the cost value and encoded proteins. Generally, they

are responsible for the signiﬁcant increase (over 10%)

of the F value in comparison with F(SGC) (Figure 3).

However, they were observed only in four alternative

genetic codes (Table 3).

2 4 6 8 10 12

5000 5500 6000 6500

number of occurences

cost value

●

groups

Figure 3: The relationship between the cost values and the

number of occurrences of individual codon reassignments

in the studied alternative genetic codes. The reassignments

were classiﬁed into three groups where the codon’s mean-

ing is changed: from a stop translation signal to an amino

acid (A), from one amino acid to another (B) and from an

amino acid to a stop translation signal (C). The results are

compared with the cost value calculated for the standard ge-

netic code (the horizontal bold line).

4 DISCUSSION

Our study on the optimality of the alternative genetic

codes in comparison to the standard genetic code

showed that many of these variants contain codon re-

assignments that decrease the costs of amino acid re-

placements described by the polarity values. It im-

plies that the alternatives did not necessarily originate

as a result of the neutral evolution but they could have

evolved under adaptational factors and at least some

of their codon reassignments were favored by the se-

lection (Kurnaz et al., 2010). Such reorganizations of

the code could have occured in small populations with

tiny genomes, in which the changes did not inﬂuence

the large number of encoded proteins.

The SGC is, however, less optimal in compari-

son to most of its alternatives. This ﬁnding does not

fully support the adaptive hypothesis postulating that

the code structure evolved to minimize the effects

of amino acid replacements and errors during trans-

lation of proteins (Epstein, 1966; Haig and Hurst,

1991; Freeland et al., 2003; Goodarzi et al., 2005).

This concept is attractive but the deleterious effects of

mutations on protein properties can be minimized by

other mechanisms, i.e. the direct optimization of the

The Importance of Changes Observed in the Alternative Genetic Codes

157

Table 3: The number of occurrences of single codon reas-

signments observed in the studied alternative genetic codes.

The table includes also the costs calculated for genetic

codes with exactly one of such substitutions and the type

of reassignment used in Figure 3.

Codon In SGC In altern. Cost Occur. Type

TGA Stp Trp 4804.36 12 A

AGA Arg Ser 5617.78 5 B

TAA Stp Gln 5083.72 4 A

TAG Stp Gln 4843.06 4 A

ATA Ile Met 5624.82 4 B

AGG Arg Ser 5601.14 4 B

AAA Lys Asn 5638.02 3 B

TAA Stp Glu 5377.78 2 A

TAG Stp Glu 5219.02 2 A

TAG Stp Leu 4936.3 2 A

CTG Leu Ala 5630.96 1 B

TGA Stp Cys 4795.08 1 A

AGA Arg Gly 5616.02 1 B

AGG Arg Gly 5602.58 1 B

TGA Stp Gly 4783.56 1 A

AGG Arg Lys 5713.46 1 B

CTG Leu Ser 5651.86 1 B

AGA Arg Stp 6737.06 1 C

AGG Arg Stp 6497.1 1 C

TTA Leu Stp 6283.02 1 C

TCA Ser Stp 6280.3 1 C

TAA Stp Trp 5167.54 1 A

TAA Stp Tyr 5141.14 1 A

TAG Stp Tyr 4879.02 1 A

mutational rate and pattern on the ﬁxed genetic code

(Bła

zej et al., 2015; Bła

zej et al., 2017). There-

fore, the main role of the assignments of amino acids

to codons in the SGC could have been played by

the expansion of biosynthetic pathways and the step-

wise addition of newly synthesized amino acids into

the code, according to the co-evolution hypothesis

(Wong, 1975; Di Giulio, 1999; Wong et al., 2016;

Di Giulio, 2017). Under this scenario, the present

structure of SGC evolved from an ancestral version

including a smaller number of simple amino acids

that were at the beginning of the biosynthetic path-

ways. Next, other amino acids were incorporated

into the code when more complex metabolic networks

evolved. The newly synthesized amino acids took

over the codons of their precursors.

The idea of studying the properties of the genetic

code variants seems very promising in the light of de-

signing alternative versions of the code for artiﬁcially

modiﬁed organisms (Xie and Schultz, 2006; Chin,

2014). Such modiﬁcations can lead to production of

peptides or proteins including unnatural amino acids

and showing enhanced or novel properties. The intro-

duced codon reassignments can also help to test the

protein structure and function in global scale. More-

over, the knowledge about the optimality of the ge-

netic codes may enable us to construct new artiﬁ-

cial organisms in the framework of synthetic biology.

Such organisms could be characterized by for exam-

ple a higher ﬁdelity of the protein synthesis and a

higher resistance to the mutations causing amino acid

replacements.

ACKNOWLEDGMENTS

This work was supported by the National Science

Centre Poland (Narodowe Centrum Nauki, Polska)

under Grant Miniatura no. 2017/01/X/NZ2/00608.

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