Gene as a dynamical notion: An extensive and integrative vision. Redefining the gene concept, from traditional to genic-interaction, as a new dynamical version

The current concept of gene has been very useful during the 20th and 21st centuries. However, recent advances in molecular biology and bioinformatics, which have further diversified the functional and adaptive profile of genetic information and its integration with cell physiology and environmental response, have contributed to focusing on additional new gene properties besides the traditional definition. Considering the inherent complexity of gene expression, whose adaptive objective must be referred to the Tortoise-Hare model, in which two tendencies converge, one focused on rapid adaptation to achieve survival, and the other that prevents an over-adaptation effect. In this context, a revision of the gene concept must be made, which must include these new mechanisms and approaches. In this paper, we propose a new conception of the idea of a gene that moves from a static and defined version of hereditary information to a dynamic idea that preponderates gene interaction (circumscribed to that established between protein-protein, protein-nucleic acid, and nucleic acid-nucleic acid) and the selection it exerts, as the irreducible element that works in a coordinated way in a genomic regulatory network (GRN).

Paralogue Interference Affects the Dynamics after Gene Duplication

Proteins tend to form homomeric complexes of identical subunits, which act as functional units. By definition, the subunits are encoded from a single genetic locus. When such a gene is duplicated, the gene products are suggested initially to cross-interact when coexpressed, thus resulting in the phenomenon of paralogue interference. In this opinion article, we explore how paralogue interference can shape the fate of a duplicated gene. One important outcome is a prolonged time window in which both copies remain under selection increasing the chance to accumulate mutations and to develop new properties. Thereby, paralogue interference can mediate the coevolution of duplicates and here we illustrate the potential of this phenomenon in light of recent new studies.

Modeling catalytic promiscuity in the alkaline phosphatase superfamily

In recent years, it has become increasingly clear that promiscuity plays a key role in the evolution of new enzyme function. This finding has helped to elucidate fundamental aspects of molecular evolution. While there has been extensive experimental work on enzyme promiscuity, computational modeling of the chemical details of such promiscuity has traditionally fallen behind the advances in experimental studies, not least due to the nearly prohibitive computational cost involved in examining multiple substrates with multiple potential mechanisms and binding modes in atomic detail with a reasonable degree of accuracy. However, recent advances in both computational methodologies and power have allowed us to reach a stage in the field where we can start to overcome this problem, and molecular simulations can now provide accurate and efficient descriptions of complex biological systems with substantially less computational cost. This has led to significant advances in our understanding of enzyme function and evolution in a broader sense. Here, we will discuss currently available computational approaches that can allow us to probe the underlying molecular basis for enzyme specificity and selectivity, discussing the inherent strengths and weaknesses of each approach. As a case study, we will discuss recent computational work on different members of the alkaline phosphatase superfamily (AP) using a range of different approaches, showing the complementary insights they have provided. We have selected this particular superfamily, as it poses a number of significant challenges for theory, ranging from the complexity of the actual reaction mechanisms involved to the reliable modeling of the catalytic metal centers, as well as the very large system sizes. We will demonstrate that, through current advances in methodologies, computational tools can provide significant insight into the molecular basis for catalytic promiscuity, and, therefore, in turn, the mechanisms of protein functional evolution.

Redundancy and divergence in the amyloid precursor protein family

Gene duplication provides genetic material required for functional diversification. An interesting example is the amyloid precursor protein (APP) protein family. The APP gene family has experienced both expansion and contraction during evolution. The three mammalian members have been studied quite extensively in combined knock out models. The underlying assumption is that APP, amyloid precursor like protein 1 and 2 (APLP1, APLP2) are functionally redundant. This assumption is primarily supported by the similarities in biochemical processing of APP and APLPs and on the fact that the different APP genes appear to genetically interact at the level of the phenotype in combined knockout mice. However, unique features in each member of the APP family possibly contribute to specification of their function. In the current review, we discuss the evolution and the biology of the APP protein family with special attention to the distinct properties of each homologue. We propose that the functions of APP, APLP1 and APLP2 have diverged after duplication to contribute distinctly to different neuronal events. Our analysis reveals that APLP2 is significantly diverged from APP and APLP1.

Influence of homology and node age on the growth of protein-protein interaction networks

Proteins participating in a protein-protein interaction network can be grouped into homology classes following their common ancestry. Proteins added to the network correspond to genes added to the classes, so the dynamics of the two objects are intrinsically linked. Here we first introduce a statistical model describing the joint growth of the network and the partitioning of nodes into classes, which is studied through a combined mean-field and simulation approach. We then employ this unified framework to address the specific issue of the age dependence of protein interactions through the definition of three different node wiring or divergence schemes. A comparison with empirical data indicates that an age-dependent divergence move is necessary in order to reproduce the basic topological observables together with the age correlation between interacting nodes visible in empirical data. We also discuss the possibility of nontrivial joint partition and topology observables.

The role of structural disorder in the rewiring of protein interactions through evolution

Structurally disordered regions play a key role in protein-protein interaction networks and the evolution of highly connected proteins, enabling the molecular mechanisms for multiple binding. However, the role of protein disorder in the evolution of interaction networks has only been investigated through the analysis of individual proteins, making it impossible to distinguish its specific impact in the (re)shaping of their interaction environments. Now, the availability of large interactomes for several model organisms permits exploration of the role of disorder in protein interaction networks not only at the level of the interacting proteins but of the interactions themselves. By comparing the interactomes of human, fly, and yeast, we discovered that, despite being much more abundant, disordered interactions are significantly less conserved than their ordered counterparts. Furthermore, our analyses provide evidence that this happens not only because disordered proteins are less conserved but also because they display a higher capacity to rewire their interaction neighborhood through evolution. Overall, our results support the hypothesis that conservation of disorder gives a clear evolutionary advantage, facilitating the change of interaction partners during evolution. Moreover, this mechanism is not exclusive of a few anecdotal cases but a global feature present in the interactome networks of entire organisms.

The fine details of evolution

Charles Darwin's theory of evolution was based on studies of biology at the species level. In the time since his death, studies at the molecular level have confirmed his ideas about the kinship of all life on Earth and have provided a wealth of detail about the evolutionary relationships between different species and a deeper understanding of the finer workings of natural selection. We now have a wealth of data, including the genome sequences of a wide range of organisms, an even larger number of protein sequences, a significant knowledge of the three-dimensional structures of proteins, DNA and other biological molecules, and a huge body of information about the operation of these molecules as systems in the molecular machinery of all living things. This issue of Biochemical Society Transactions contains papers from oral presentations given at a Biochemical Society Focused Meeting to commemorate the 200th Anniversary of Charles Darwin's birth, held on 26-27 January 2009 at the Wellcome Trust Conference Centre, Cambridge. The talks reported on some of the insights into evolution which have been obtained from the study of protein sequences, structures and systems.