Two genetic codes: Repetitive syntax for active non-coding RNAs; non-repetitive syntax for the DNA archives

Current knowledge of the RNA world indicates 2 different genetic codes being present throughout the living world. In contrast to non-coding RNAs that are built of repetitive nucleotide syntax, the sequences that serve as templates for proteins share—as main characteristics—a non-repetitive syntax. Whereas non-coding RNAs build groups that serve as regulatory tools in nearly all genetic processes, the coding sections represent the evolutionarily successful function of the genetic information storage medium. This indicates that the differences in their syntax structure are coherent with the differences of the functions they represent. Interestingly, these 2 genetic codes resemble the function of all natural languages, i.e., the repetitive non-coding sequences serve as appropriate tool for organization, coordination and regulation of group behavior, and the nonrepetitive coding sequences are for conservation of instrumental constructions, plans, blueprints for complex protein-body architecture. This differentiation may help to better understand RNA group behavioral motifs

Adaptive evolution of transcription factor binding sites

The regulation of a gene depends on the binding of transcription factors to specific sites located in the regulatory region of the gene. The generation of these binding sites and of cooperativity between them are essential building blocks in the evolution of complex regulatory networks. We study a theoretical model for the sequence evolution of binding sites by point mutations. The approach is based on biophysical models for the binding of transcription factors to DNA. Hence we derive empirically grounded fitness landscapes, which enter a population genetics model including mutations, genetic drift, and selection. We show that the selection for factor binding generically leads to specific correlations between nucleotide frequencies at different positions of a binding site. We demonstrate the possibility of rapid adaptive evolution generating a new binding site for a given transcription factor by point mutations. The evolutionary time required is estimated in terms of the neutral (background) mutation rate, the selection coefficient, and the effective population size. The efficiency of binding site formation is seen to depend on two joint conditions: the binding site motif must be short enough and the promoter region must be long enough. These constraints on promoter architecture are indeed seen in eukaryotic systems. Furthermore, we analyse the adaptive evolution of genetic switches and of signal integration through binding cooperativity between different sites. Experimental tests of this picture involving the statistics of polymorphisms and phylogenies of sites are discussed.Comment: published versio

Rethinking quasispecies theory: From fittest type to cooperative consortia

Recent investigations surprisingly indicate that single RNA "stem-loops" operate solely by chemical laws that act without selective forces, and in contrast, self-ligated consortia of RNA stem-loops operate by biological selection. To understand consortial RNA selection, the concept of single quasi-species and its mutant spectra as drivers of RNA variation and evolution is rethought here. Instead, we evaluate the current RNA world scenario in which consortia of cooperating RNA stem-loops are the basic players. We thus redefine quasispecies as RNA quasispecies consortia and argue that it has essential behavioral motifs that are relevant to the inherent variation, evolution and diversity in biology. We propose that qs-c is an especially innovative force. We apply qs-c thinking to RNA stem-loops and evaluate how it yields altered bulges and loops in the stem-loop regions, not as errors, but as a natural capability to generate diversity. This basic competence-not error-opens a variety of combinatorial possibilities which may alter and create new biological interactions, identities and newly emerged self identity functions. Thus RNA stem-loops typically operate as cooperative modules, like members of social groups. From such qs-c of stem-loop groups we can trace a variety of RNA secondary structures such as ribozymes, viroids, viruses, mobile genetic elements as abundant infection derived agents that provide the stem-loop societies of small and long non-coding RNAs

The construction of transcription factor networks through natural selection

Transcription regulation plays a key role in determining cellular function, response to external stimuli and development. Regulatory proteins orchestrate gene expression through thousands of interactions resulting in large, complex networks. Understanding the principles on which these networks are constructed can provide insight into the way the expression patterns of different genes co-evolve. One method by which this question can be addressed is to focus on the evolution of the structure of transcription factor networks (TFNs). In order to do this, a model for their evolution through cis mutation, trans mutation, gene duplication and gene deletion is constructed. This model is used to determine the circumstances under which the asymmetrical in and out degree distributions observed in real networks are reproduced. In this way it is possible to draw conclusions about the contributions of these different evolutionary processes to the evolution of TFNs. Conclusions are also drawn on the way rates of evolution vary with the position of gene in the network. Following this, the contributions of cis mutations, which occur in the promoters of regulated genes, and trans mutations, which occur in the coding reign of transcription factors, to the evolution of TFNs are investigated. A space of neutral genotypes is constructed, and the evolution of TFNs through cis and trans mutations in this space is characterised. The results are then used to account for large scale rewiring observed in the yeast sex determination network. Finally the principles governing the evolution of autoregulatory motifs are investigated. It is shown that negative autoregulation, which functions as a noise reduction mechanism in haploid TFNs, is not evolvable in diploid TFNs. This is attributed to the effects of dominance in diploid TFNs. The fate of duplicates of autoregulating genes in haploid networks is also investigated. It is shown that such duplicates are especially prone to loss of function mutations. This is used to account for the lack of observed autoregulatory duplicates participating in network motifs. From this work, it is concluded that the relative rates of different evolutionary processes are responsible for shaping the global statistical properties of TFN structure. However, the more detailed TFN structure, such as network motif distribution, is strongly influenced by the population genetic details of the system being considered. In addition, extensive neutral evolution is shown to be possible in TFNs. However, the effects of neutral evolution on network structure are shown to depend strongly on the structure of the space on neutral genotypes in which the TFN is evolving

Kinetic modelling of competition and depletion of shared miRNAs by competing endogenous RNAs

Non-conding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.Comment: review article, 29 pages, 7 figure

Under-dominance constrains the evolution of negative autoregulation in diploids

Regulatory networks have evolved to allow gene expression to rapidly track changes in the environment as well as to buffer perturbations and maintain cellular homeostasis in the absence of change. Theoretical work and empirical investigation in Escherichia coli have shown that negative autoregulation confers both rapid response times and reduced intrinsic noise, which is reflected in the fact that almost half of Escherichia coli transcription factors are negatively autoregulated. However, negative autoregulation is rare amongst the transcription factors of Saccharomyces cerevisiae. This difference is surprising because E. coli and S. cerevisiae otherwise have similar profiles of network motifs. In this study we investigate regulatory interactions amongst the transcription factors of Drosophila melanogaster and humans, and show that they have a similar dearth of negative autoregulation to that seen in S. cerevisiae. We then present a model demonstrating that this stiking difference in the noise reduction strategies used amongst species can be explained by constraints on the evolution of negative autoregulation in diploids. We show that regulatory interactions between pairs of homologous genes within the same cell can lead to under-dominance - mutations which result in stronger autoregulation, and decrease noise in homozygotes, paradoxically can cause increased noise in heterozygotes. This severely limits a diploid's ability to evolve negative autoregulation as a noise reduction mechanism. Our work offers a simple and general explanation for a previously unexplained difference between the regulatory architectures of E. coli and yeast, Drosophila and humans. It also demonstrates that the effects of diploidy in gene networks can have counter-intuitive consequences that may profoundly influence the course of evolution

Evidence for the Concerted Evolution between Short Linear Protein Motifs and Their Flanking Regions

BACKGROUND: Linear motifs are short modules of protein sequences that play a crucial role in mediating and regulating many protein-protein interactions. The function of linear motifs strongly depends on the context, e.g. functional instances mainly occur inside flexible regions that are accessible for interaction. Sometimes linear motifs appear as isolated islands of conservation in multiple sequence alignments. However, they also occur in larger blocks of sequence conservation, suggesting an active role for the neighbouring amino acids. RESULTS: The evolution of regions flanking 116 functional linear motif instances was studied. The conservation of the amino acid sequence and order/disorder tendency of those regions was related to presence/absence of the instance. For the majority of the analysed instances, the pairs of sequences conserving the linear motif were also observed to maintain a similar local structural tendency and/or to have higher local sequence conservation when compared to pairs of sequences where one is missing the linear motif. Furthermore, those instances have a higher chance to co-evolve with the neighbouring residues in comparison to the distant ones. Those findings are supported by examples where the regulation of the linear motif-mediated interaction has been shown to depend on the modifications (e.g. phosphorylation) at neighbouring positions or is thought to benefit from the binding versatility of disordered regions. CONCLUSION: The results suggest that flanking regions are relevant for linear motif-mediated interactions, both at the structural and sequence level. More interestingly, they indicate that the prediction of linear motif instances can be enriched with contextual information by performing a sequence analysis similar to the one presented here. This can facilitate the understanding of the role of these predicted instances in determining the protein function inside the broader context of the cellular network where they arise