Discovering local patterns of co-evolution: computational aspects and biological examples

Widespread formation of intracellular calcium carbonates by the bloom-forming cyanobacterium Microcystis

The formation of intracellular amorphous calcium carbonates (iACC) has been recently observed in a few cultured strains of Microcystis, a potentially toxic bloom-forming cyanobacterium found worldwide in freshwater ecosystems. If iACC-forming Microcystis are abundant within blooms, they may represent a significant amount of particulate Ca. Here, we investigate the significance of iACC biomineralization by Microcystis. First, the presence of iACC-forming Microcystis cells has been detected in several eutrophic lakes, indicating that this phenomenon occurs under environmental conditions. Second, some genotypic (presence/absence of ccyA, a marker gene of iACC biomineralization) and phenotypic (presence/absence of iACC) diversity have been detected within a collection of strains isolated from one single lake. This illustrates that this trait is frequent but also variable within Microcystis even at a single locality. Finally, one-third of publicly available genomes of Microcystis were shown to contain the ccyA gene, revealing a wide geographic and phylogenetic distribution within the genus. Overall, the present work shows that the formation of iACC by Microcystis is common under environmental conditions. While its biological function remains undetermined, this process should be further considered regarding the biology of Microcystis and implications on the Ca geochemical cycle in freshwater environments.

Neurohormetic phytochemicals: An evolutionary-bioenergetic perspective

The impact of dietary factors on brain health and vulnerability to disease is increasingly appreciated. The results of epidemiological studies, and intervention trials in animal models suggest that diets rich in phytochemicals can enhance neuroplasticity and resistance to neurodegeneration. Here we describe how interactions of plants and animals during their co-evolution, and resulting reciprocal adaptations, have shaped the remarkable characteristics of phytochemicals and their effects on the physiology of animal cells in general, and neurons in particular. Survival advantages were conferred upon plants capable of producing noxious bitter-tasting chemicals, and on animals able to tolerate the phytochemicals and consume the plants as an energy source. The remarkably diverse array of phytochemicals present in modern fruits, vegetables spices, tea and coffee may have arisen, in part, from the acquisition of adaptive cellular stress responses and detoxification enzymes in animals that enabled them to consume plants containing potentially toxic chemicals. Interestingly, some of the same adaptive stress response mechanisms that protect neurons against noxious phytochemicals are also activated by dietary energy restriction and vigorous physical exertion, two environmental challenges that shaped brain evolution. In this perspective article, we describe some of the signaling pathways relevant to cellular energy metabolism that are modulated by 'neurohormetic phytochemicals' (potentially toxic chemicals produced by plants that have beneficial effects on animals when consumed in moderate amounts). We highlight the cellular bioenergetics-related sirtuin, adenosine monophosphate activated protein kinase (AMPK), mammalian target of rapamycin (mTOR) and insulin-like growth factor 1 (IGF-1) pathways. The inclusion of dietary neurohormetic phytochemicals in an overall program for brain health that also includes exercise and energy restriction may find applications in the prevention and treatment of a range of neurological disorders.

Practical aspects of protein co-evolution

Co-evolution is a fundamental aspect of Evolutionary Theory. At the molecular level, co-evolutionary linkages between protein families have been used as indicators of protein interactions and functional relationships from long ago. Due to the complexity of the problem and the amount of genomic data required for these approaches to achieve good performances, it took a relatively long time from the appearance of the first ideas and concepts to the quotidian application of these approaches and their incorporation to the standard toolboxes of bioinformaticians and molecular biologists. Today, these methodologies are mature (both in terms of performance and usability/implementation), and the genomic information that feeds them large enough to allow their general application. This review tries to summarize the current landscape of co-evolution-based methodologies, with a strong emphasis on describing interesting cases where their application to important biological systems, alone or in combination with other computational and experimental approaches, allowed getting new insight into these.

The coordinated expression, interaction and evolution of the neuroendocrine genes

The neuroendocrine system is a complex biological system controlled by various neuropeptides and hormones. The evolution and network properties of neuroendocrine genes are analyzed along with their expression profiles. The neuroendocrine genes show very similar expression profiles and local network properties across a wide range of tissues consistent with the physiological roles of their proteins. Moreover, the coordinated evolution of 10 neuroendocrine genes involved in mammalian reproduction and homeostasis is demonstrated using several methods, such as correlated evolution, relative-rate test, relative-ratio test and codon usage bias. The neuroendocrine genes seem to evolve predominantly under similar selective strengths and regimes of purifying selection, which is well reflected in their evolutionary fingerprints. This result demonstrates for the first time a key role of natural selection in creating and maintaining a well-designed neuroendocrine system at the genomic level. It also indicates that component properties of a complex system at a higher physiological scale may determine component properties at a lower genomic scale and/or vice versa.

Quantifying protein modularity and evolvability: a comparison of different techniques

Modularity increases evolvability by reducing constraints on adaptation and by allowing preexisting parts to function in new contexts for novel uses. Protein evolution provides an excellent context to study the causes and consequences of biological modularity. In order to address such questions, however, an index for protein modularity is necessary. This paper proposes a simple index for protein modularity-"module density"-which is the number of evolutionarily independent modules that compose a protein divided by the number of amino acids in the protein. The decomposition of proteins into constituent modules can be accomplished by either of two classes of methods. The first class of methods relies on "suppositional" criteria to assign amino acids to modules, whereas the second class of methods relies on "coevolutionary" criteria for this task. One simple and practical method from the first class consists of approximating the number of modules in a protein as the number of regular secondary structure elements (i.e., helices and sheets). Methods based on coevolutionary criteria require more elaborate data, but they have the advantage of being able to specify modules without prior assumptions about why they exist. Given the increasing availability of datasets sampling protein mutational spectra (e.g., from comparative genomics, experimental evolution, and computational prediction), methods based on coevolutionary criteria will likely become more promising in the near future. The ability to meaningfully quantify protein modularity via simple indices has the potential to aid future efforts to understand protein evolutionary rate determinants, improve molecular evolution models and engineer novel proteins.

Co-evolution is incompatible with the Markov assumption in phylogenetics

Markov models are extensively used in the analysis of molecular evolution. A recent line of research suggests that pairs of proteins with functional and physical interactions co-evolve with each other. Here, by analyzing hundreds of orthologous sets of three fungi and their co-evolutionary relations, we demonstrate that co-evolutionary assumption may violate the Markov assumption. Our results encourage developing alternative probabilistic models for the cases of extreme co-evolution.