A thermodynamic theory of codon bias in viral genes

DNA thermodynamic pressure: a potential contributor to genome evolution

Codon usage bias is a feature of living organisms. The origin of this bias might be explained not only by external factors but also by the nature of the structure of deoxyribonucleic acid (DNA) itself. We have developed a point mutation simulation program of coding sequences, in which nucleotide replacement follows thermodynamic criteria. For this purpose we calculated the hydrogen bond-like and electrostatic energies of non-canonical base pairs in a 5 bp neighbourhood. Although the rate of non-canonical base pair formation is extremely low, such pairs occur with a preference towards a guanine (G) or cytosine (C) rather than an adenine (A) or thymine (T) replacement due to thermodynamic considerations. This feature, according to the simulation program, should result in an increase in the GC content of the genome over evolutionary time. In addition, codon bias towards a higher GC usage is also predicted. DNA sequence analysis of genes of the Trypanosomatidae lineage supported the hypothesis that DNA thermodynamic pressure is a driving force that impels increases in GC content and GC codon bias.

The Ising model in physics and statistical genetics

Interdisciplinary communication is becoming a crucial component of the present scientific environment. Theoretical models developed in diverse disciplines often may be successfully employed in solving seemingly unrelated problems that can be reduced to similar mathematical formulation. The Ising model has been proposed in statistical physics as a simplified model for analysis of magnetic interactions and structures of ferromagnetic substances. Here, we present an application of the one-dimensional, linear Ising model to affected-sib-pair (ASP) analysis in genetics. By analyzing simulated genetics data, we show that the simplified Ising model with only nearest-neighbor interactions between genetic markers has statistical properties comparable to much more complex algorithms from genetics analysis, such as those implemented in the Allegro and Mapmaker-Sibs programs. We also adapt the model to include epistatic interactions and to demonstrate its usefulness in detecting modifier loci with weak individual genetic contributions. A reanalysis of data on type 1 diabetes detects several susceptibility loci not previously found by other methods of analysis.

Diagrammatization of codon usage in 339 human immunodeficiency virus proteins and its biological implication

The occurrence frequencies of bases A (adenine), C (cytosine, G (guanine), and T (thymine) occurring in the 1st, 2nd, and 3rd codon positions in the codon usage table of viral genes for the 339 human immunodeficiency virus (HIV) proteins compiled recently have been calculated and diagrammatized. For comparison, the corresponding diagrammatic representations for the 2681 human proteins from the codon usage table for primate genes are also presented. The analyzed results based on these characteristic diagrams indicate that considerably similar features have been found between HIV and human proteins for the 1st and 2nd codon positions; i.e., they are all occupied predominantly by purine, especially base A. However, a significant difference in the 3rd codon position between HIV and human proteins has been observed; i.e., human proteins are of high C + G content and low A + G content in the 3rd codon position, whereas the case is just the opposite for HIV proteins. The biological implication of such a duality on the codon bias of HIV against human proteins is discussed. It is suggested that the 1st and 2nd codon positions can be termed as the structure-determining position, and the 3rd codon position termed as the species-determining position. The diagrammatic representation and analysis method described here possess a great potential for the study of molecular evolution from the viewpoint of the genetic code for which data have been accumulated rapidly and will continue to grow at a much faster pace.

Frequencies of codons in histones, tubulins and fibrinogen: bias due to interference between transcription signals and protein function

The distribution of codons was studied in 65 proteins: 48 histones, 14 tubulins, and three fibrinogens, With the methodology used, (1) we confirmed that the preterminator state of a codon has no detectable effect on codon bias. (2) The well-known effect of CG suppression was visible. We also found that (3) some codons which are very rare, are equal to parts of known transcription signals. Thus, we advanced that to avoid signal interference, the use of these codons is suppressed when a synonymous codon is available. In addition we found that in the whole series of codons, transcription signals are less frequent than in a random sequence of equal composition. Finally we observed (4) that tryptophan is absent in histones. This absence was related not to the TGG codon itself, but to characteristics of the amino acid. We conclude that the functional constraints of a protein can influence, at least for synonymous codon usage, the evolution of its own coding sequence.

A generalized information function applied to the genetic code

The problem of the partitioning of the degeneracy of the codons in the genetic code is considered in the framework of a generalized information function IG = c sigma kpk(ln pk + G(Ek] where k represents the number of codons in a specific degeneracy class and G(Ek) is an arbitrary real valued function. For G(Ek) = 0 the Shannon information function is recovered. For a particular choice of G(Ek) that takes the dominance of even degeneracies into account, it is found by direct numerical calculations that the correct degeneracy partitioning appears as optimal values of the Ig function. This results is also supported by optimization calculations in which the generalized information function is regarded as a continuous function in the degeneracy variables.

On the information content of the genetic code

In living organisms 20 amino acids along with the terminator value(s) are encoded by 64 codons giving a degeneracy of the codons as described by the genetic code. A basic theoretical problem of genetic codes is to explain the particular distribution of degeneracies of partitions involved in the codes. In this work the degeneracy problem is considered in the framework of information theory. It is shown by direct numerical evaluation of a certain degeneracy information function associated with the genetic code that the degeneracy of the codes is observed to be related to the optimization of this function.

A three-dimensional representation for base composition of protein-coding DNA sequences

Multi-dimensional scaling is applied to our codon space data on the protein coding sequences of DNA from a wide variety of organisms in an attempt to find the smallest number of parameters which will accurately represent these sequences. I find that a three-dimensional representation is satisfactory. One of the three resulting co-ordinates separates eukaryotes and their associated viruses from prokaryotes and their associated phages, while an orthogonal co-ordinate separates those organisms capable of synthesizing proteins (eukaryotes and prokaryotes) from those not so capable (viruses and phages). Mitochondria show no relation in our plots to any of these groups.

A tetrahedral representation of poly-codon sequences and a possible origin of codon degeneracy

A novel and interesting representation of codon space is introduced in which the 64 codons are grouped and re-arranged as vertices in a tetrahedral lattice. Amino acids are then assigned to the lattice in a suggestive way which, when combined with earlier work, leads to an interesting speculation on the origin and development of the genetic code.

Cluster analysis of genes in codon space

We construct a "codon space" in which a given DNA sequence can be plotted as a function of its base composition in each of the three codon positions. We demonstrate that the base composition is very highly nonrandom, with sequences from more primitive organisms having the least random compositions. By using cluster analysis on the points plotted in codon space we show that there is a strong correlation between base composition and type of organism, with the most primitive organisms having the highest A or T content in the second and third codon positions. A smooth transition toward lower A + T and higher G + C content is observed in the second and third codon positions as the evolutionary complexity of the organism increases. Besides this general trend, more detailed structure can be observed in the clustering that will become clearer as the data base is increased.

On the informational content of viral DNA

This paper is concerned primarily with how information is stored in viral DNA. The general problem of defining information content is discussed and a procedure for analysis extended from that of Gatlin (1972) is developed. Long range correlations in base sequences are analyzed for several viral genomes. The relationship of these correlations to the existence of strong codon biases is examined and the consequences discussed.