Mutations are the ultimate source of heritable variation for evolution. Understanding how mutation rates themselves evolve is thus essential for quantitatively understanding many evolutionary processes. According to theory, mutation rates should be minimized for well-adapted populations living in stable environments, whereas hypermutators may evolve if conditions change. However, the long-term fate of hypermutators is unknown. Using a phylogenomic approach, we found that an adapting Escherichia coli population that first evolved a mutT hypermutator phenotype was later invaded by two independent lineages with mutY mutations that reduced genome-wide mutation rates. Applying neutral theory to synonymous substitutions, we dated the emergence of these mutations and inferred that the mutT mutation increased the point-mutation rate by ∼150-fold, whereas the mutY mutations reduced the rate by ∼40-60%, with a corresponding decrease in the genetic load. Thus, the long-term fate of the hypermutators was governed by the selective advantage arising from a reduced mutation rate as the potential for further adaptation declined. experimental evolution | genomics | mutators | phylogenomics M utations are the ultimate source of heritable variation for evolution. Therefore, understanding how selection can change mutation rates is crucial for quantitatively describing evolutionary processes (1). More mutations are deleterious than beneficial (2), and organisms from bacteria to eukaryotes encode proofreading and repair enzymes that reduce mutation rates (3). If selection for beneficial mutations is weak relative to selection against deleterious mutations, then the rate of adaptation in asexual populations is maximized at some intermediate mutation rate (4). However, when populations encounter new environments, selection for beneficial mutations can be strong (5), and much higher mutation rates may evolve. Indeed, surveys of laboratory populations of microbes (6-10), clinical isolates of bacterial pathogens (11,12), and some types of eukaryotic tumors (13) have revealed a surprisingly high proportion of lineages that have evolved genetic defects in repair pathways. These hypermutators often have 10-to 100-fold increased mutation rates, and such elevated mutation rates can accelerate the progression of chronic diseases and the evolution of resistance to therapeutic agents.Hypermutable mutants can become established in asexual populations while they adapt to changed environments owing to their higher per capita probability of discovering rare beneficial mutations compared with nonmutators (14-18). Although hypermutable genotypes should produce beneficial mutations at a higher rate than their less mutable counterparts, they do not necessarily increase the rate of adaptation to a corresponding, or even measurable, degree. In large asexual populations, the waiting time for new beneficial mutations to occur may be short relative to the time required for a mutant to increase from one individual to fixation in the population, assuming the be...

Background: Pasture-associated severe equine asthma is a warm season, environmentally-induced respiratory disease characterized by reversible airway obstruction, persistent and non-specific airway hyper-responsiveness, and chronic neutrophilic airway inflammation. During seasonal exacerbation, signs vary from mild to life-threatening episodes of wheezing, coughing, and chronic debilitating labored breathing. Purpose: In human asthma, neutrophilic airway inflammation is associated with more severe and steroid-refractory asthma phenotypes, highlighting a need to decipher the mechanistic basis of this disease characteristic. We hypothesize that the collective biological activities of proteins in bronchoalveolar lavage fluid (BALF) of horses with pasture-associated severe asthma predict changes in neutrophil functions that contribute to airway neutrophilic inflammation. Methods: Using shotgun proteomics, we identified 1,003 unique proteins in cell-free BALF from six horses experiencing asthma exacerbation and six control herdmates. Contributions of each protein to ten neutrophil functions were modeled using manual biocuration to determine each protein’s net effect on the respective neutrophil functions. Results: A total of 417 proteins were unique to asthmatic horses, 472 proteins were unique to control horses (p<0.05), and 114 proteins were common in both groups. Proteins whose biological activities are responsible for increasing neutrophil migration, chemotaxis, cell spreading, transmigration, and infiltration, which would collectively bring neutrophils to airways, were over-represented in the BALF of asthmatic relative to control horses. By contrast, proteins whose biological activities support neutrophil activation, adhesion, phagocytosis, respiratory burst, and apoptosis, which would collectively shorten neutrophil lifespan, were under-represented in BALF of asthmatic relative to control horses. Interaction networks generated using Ingenuity ® Pathways Analysis further support the results of our biocuration. Conclusion: Congruent with our hypothesis, the collective biological functions represented in differentially expressed proteins of BALF from horses with pasture-associated severe asthma support neutrophilic airway inflammation. This illustrates the utility of systems modeling to organize functional genomics data in a manner that characterizes complex molecular events associated with clinically relevant disease.

The wealth of publicly available gene expression and genomic data provides unique opportunities for computational inference to discover groups of genes that function to control specific cellular processes. Such genes are likely to have co-evolved and be expressed in the same tissues and cells. Unfortunately, the expertise and computational resources required to compare tens of genomes and gene expression data sets make this type of analysis difficult for the average end-user. Here, we describe the implementation of a web server that predicts genes involved in affecting specific cellular processes together with a gene of interest. We termed the server ‘EvoCor’, to denote that it detects functional relationships among genes through evolutionary analysis and gene expression correlation. This web server integrates profiles of sequence divergence derived by a Hidden Markov Model (HMM) and tissue-wide gene expression patterns to determine putative functional linkages between pairs of genes. This server is easy to use and freely available at http://pilot-hmm.vbi.vt.edu/.

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