Quasispecies theory for evolution of modularity

Characterization of EIAV env Quasispecies during Long-Term Passage In Vitro: Gradual Loss of Pathogenicity

As the only widely used live lentiviral vaccine, the equine infectious anima virus (EIAV) attenuated vaccine was developed by in vitro passaging of a virulent strain for 121 generations. In our previous study, we observed that the attenuated vaccine was gradually selected under increased environmental pressure at the population level (termed a quasispecies). To further elucidate the potential correlation between viral quasispecies evolution and pathogenesis, a systematic study was performed by sequencing env using several methods. Some key mutations were identified within Env, and we observed that increased percentages of these mutations were accompanied by an increased passage number and attenuated virulence. Phylogenetic analysis revealed that env mutations related to the loss of virulence might have occurred evolutionarily. Among these mutations, deletion of amino acid 236 in the V4 region of Env resulted in the loss of one N-glycosylation site that was crucial for virulence. Notably, the 236-deleted sequence represented a "vaccine-specific" mutation that was also found in wild EIAV_LN40 strains based on single genome amplification (SGA) analysis. Therefore, our results suggest that the EIAV attenuated vaccine may originate from a branch of quasispecies of EIAV_LN40. Generally, the presented results may increase our understanding of the attenuation mechanism of the EIAV vaccine and provide more information about the evolution of other lentiviruses.

Sex: The power of randomization

In evolutionary biology, randomness has been perceived as a force that, in and of itself, is capable of inventing: mutation creates new genetic information at random across the genome which leads to phenotypic change, which is then subject to selection. However, in science in general and in computer science in particular, the widespread use of randomness takes a different form. Here, randomization allows for the breaking of pattern, as seen for example in its removal of biases (patterns) by random sampling or random assignment to conditions. Combined with various forms of evaluation, this breaking of pattern becomes an extraordinarily powerful tool, as also seen in many randomized algorithms in computer science. Here we show that this power of randomness is harnessed in nature by sex and recombination. In a finite population, and under the assumption of interactions between genetic variants, sex and recombination allow selection to test how well an allele will perform in a sample of combinations of interacting genetic partners drawn at random from all possible such combinations; consequently, even a small number of tests of genotypes such as takes place in a finite population favors alleles that will most likely perform well in a vast number of yet unrealized genetic combinations. This power of randomization is not manifest in asexual populations.

Modularity enhances the rate of evolution in a rugged fitness landscape

Biological systems are modular, and this modularity affects the evolution of biological systems over time and in different environments. We here develop a theory for the dynamics of evolution in a rugged, modular fitness landscape. We show analytically how horizontal gene transfer couples to the modularity in the system and leads to more rapid rates of evolution at short times. The model, in general, analytically demonstrates a selective pressure for the prevalence of modularity in biology. We use this model to show how the evolution of the influenza virus is affected by the modularity of the proteins that are recognized by the human immune system. Approximately 25% of the observed rate of fitness increase of the virus could be ascribed to a modular viral landscape.

Development of modularity in the neural activity of children's brains

We study how modularity of the human brain changes as children develop into adults. Theory suggests that modularity can enhance the response function of a networked system subject to changing external stimuli. Thus, greater cognitive performance might be achieved for more modular neural activity, and modularity might likely increase as children develop. The value of modularity calculated from functional magnetic resonance imaging (fMRI) data is observed to increase during childhood development and peak in young adulthood. Head motion is deconvolved from the fMRI data, and it is shown that the dependence of modularity on age is independent of the magnitude of head motion. A model is presented to illustrate how modularity can provide greater cognitive performance at short times, i.e. task switching. A fitness function is extracted from the model. Quasispecies theory is used to predict how the average modularity evolves with age, illustrating the increase of modularity during development from children to adults that arises from selection for rapid cognitive function in young adults. Experiments exploring the effect of modularity on cognitive performance are suggested. Modularity may be a potential biomarker for injury, rehabilitation, or disease.