New applications of evolutionary biology are transforming our understanding of cancer. The articles in this special issue provide many specific examples, such as microorganisms inducing cancers, the significance of within-tumor heterogeneity, and the possibility that lower dose chemotherapy may sometimes promote longer survival. Underlying these specific advances is a large-scale transformation, as cancer research incorporates evolutionary methods into its toolkit, and asks new evolutionary questions about why we are vulnerable to cancer. Evolution explains why cancer exists at all, how neoplasms grow, why cancer is remarkably rare, and why it occurs despite powerful cancer suppression mechanisms. Cancer exists because of somatic selection; mutations in somatic cells result in some dividing faster than others, in some cases generating neoplasms. Neoplasms grow, or do not, in complex cellular ecosystems. Cancer is relatively rare because of natural selection; our genomes were derived disproportionally from individuals with effective mechanisms for suppressing cancer. Cancer occurs nonetheless for the same six evolutionary reasons that explain why we remain vulnerable to other diseases. These four principles—cancers evolve by somatic selection...
Organisms exhibit an incredible diversity of form, a fact that makes the evolution of novelty seemingly self-evident. However, despite the “obvious” case for novelty, defining this concept in evolutionary terms is highly problematic, so much so that some have suggested discarding it altogether. Approaches to this problem tend to take either an adaptation or development-based perspective, but we argue here that an exclusive focus on either of these misses the original intent of the novelty concept and undermines its practical utility. We instead propose that for a feature to be novel it must have evolved both by a transition between adaptive peaks on the fitness landscape and that this transition must have overcome a previous developmental constraint. This definition focuses novelty on the explanation of apparently difficult or low probability evolutionary transitions and highlights how the integration of developmental and functional considerations is necessary to evolutionary explanation. It further reinforces that novelty is a central concern not just of evolutionary developmental biology (i.e., “evo-devo”) but of evolutionary biology more generally. We explore this definition of novelty in light of four examples that range from the obvious to subtle.
The authors present the development and validation of the EvoDevoCI, a concept inventory for evolutionary developmental biology. This CI measures student understanding of six core evolutionary developmental biology (evo-devo) concepts using four scenarios and 11 multiple-choice items, all inspired by authentic scientific examples. Distracters were designed to represent the common conceptual difficulties students have with each evo-devo concept.
There have been considerable attempts in the past to relate phenotypic trait—habitat temperature of organisms—to their genotypes, most importantly compositions of their genomes and proteomes. However, despite accumulation of anecdotal evidence, an exact and conclusive relationship between the former and the latter has been elusive. We present an exhaustive study of the relationship between amino acid composition of proteomes, nucleotide composition of DNA, and optimal growth temperature (OGT) of prokaryotes. Based on 204 complete proteomes of archaea and bacteria spanning the temperature range from -10 °C to 110 °C, we performed an exhaustive enumeration of all possible sets of amino acids and found a set of amino acids whose total fraction in a proteome is correlated, to a remarkable extent, with the OGT. The universal set is Ile, Val, Tyr, Trp, Arg, Glu, Leu (IVYWREL), and the correlation coefficient is as high as 0.93. We also found that the G + C content in 204 complete genomes does not exhibit a significant correlation with OGT (R = -0.10). On the other hand, the fraction of A + G in coding DNA is correlated with temperature, to a considerable extent, due to codon patterns of IVYWREL amino acids. Further, we found strong and independent correlation between OGT and the frequency with which pairs of A and G nucleotides appear as nearest neighbors in genome sequences. This adaptation is achieved via codon bias. These findings present a direct link between principles of proteins structure and stability and evolutionary mechanisms of thermophylic adaptation. On the nucleotide level...
A high level of robustness against gene deletion is observed in many organisms. However, it is still not clear which biochemical features underline this robustness and how these are acquired during evolution. One hypothesis, specific to metabolic networks, is that robustness emerges as a byproduct of selection for biomass production in different environments. To test this hypothesis we performed evolutionary simulations of metabolic networks under stable and fluctuating environments. We find that networks evolved under the latter scenario can better tolerate single gene deletion in specific environments. Such robustness is underlined by an increased number of independent fluxes and multifunctional enzymes in the evolved networks. Observed robustness in networks evolved under fluctuating environments was “apparent,” in the sense that it decreased significantly as we tested effects of gene deletions under all environments experienced during evolution. Furthermore, when we continued evolution of these networks under a stable environment, we found that any robustness they had acquired was completely lost. These findings provide evidence that evolution under fluctuating environments can account for the observed robustness in metabolic networks. Further...
The mxaF gene, coding for the large ((alpha)) subunit of methanol dehydrogenase, is highly conserved among distantly related methylotrophic species in the Alpha-, Beta- and Gammaproteobacteria. It is ubiquitous in methanotrophs, in contrast to other methanotroph-specific genes such as the pmoA and mmoX genes, which are absent in some methanotrophic proteobacterial genera. This study examined the potential for using the mxaF gene as a functional and phylogenetic marker for methanotrophs. mxaF and 16S rRNA gene phylogenies were constructed based on over 100 database sequences of known proteobacterial methanotrophs and other methylotrophs to assess their evolutionary histories. Topology tests revealed that mxaF and 16S rDNA genes of methanotrophs do not show congruent evolutionary histories, with incongruencies in methanotrophic taxa in the Methylococcaceae, Methylocystaceae, and Beijerinckiacea. However, known methanotrophs generally formed coherent clades based on mxaF gene sequences, allowing for phylogenetic discrimination of major taxa. This feature highlights the mxaF gene’s usefulness as a biomarker in studying the molecular diversity of proteobacterial methanotrophs in nature. To verify this, PCR-directed assays targeting this gene were used to detect novel methanotrophs from diverse environments including soil...
The bacterium Streptococcus pneumoniae (pneumococcus) is one of the most important human bacterial pathogens, and a leading cause of morbidity and mortality worldwide. The pneumococcus is also known for undergoing extensive homologous recombination via transformation with exogenous DNA. It has been shown that recombination has a major impact on the evolution of the pathogen, including acquisition of antibiotic resistance and serotype-switching. Nevertheless, the mechanism and the rates of recombination in an epidemiological context remain poorly understood. Here, we proposed several mathematical models to describe the rate and size of recombination in the evolutionary history of two very distinct pneumococcal lineages, PMEN1 and CC180. We found that, in both lineages, the process of homologous recombination was best described by a heterogeneous model of recombination with single, short, frequent replacements, which we call micro-recombinations, and rarer, multi-fragment, saltational replacements, which we call macro-recombinations. Macro-recombination was associated with major phenotypic changes, including serotype-switching events, and thus was a major driver of the diversification of the pathogen. We critically evaluate biological and epidemiological processes that could give rise to the micro-recombination and macro-recombination processes.
Thesis (PhD) - Indiana University, Ecology and Evolutionary Biology, 2007; Natural selection favors traits that fit not only the external environment, but also the internal environment of the organism. As a consequence, traits often show a pattern of correlation, or phenotypic integration. In this dissertation, I examined both the evolutionary processes and the physiological mechanisms that generate phenotypic integration. I studied a natural population of a songbird, the dark-eyed junco (Junco hyemalis), focusing on the male "mating phenotype," the suite of morphology, physiology, and behavior used to attract and compete for mates. In Chapter 1, I review literature suggesting that correlational selection, which occurs when traits interact in their effects on fitness, may have effects on the physiological mechanisms that underlie integrated suites of traits. In Chapter 2, I found that correlational sexual selection favored an association between body size and a white patch on the tail feathers ("tail white"), an ornament used both in courtship and male-male competition. I also found that body size and tail white were genetically correlated. These results suggest that correlational selection may maintain the integration of the two traits. In Chapters 3-5...
The rapid proliferation of antibiotic-resistant pathogens has spurred the use of drug combinations to maintain clinical efficacy and combat the evolution of resistance. Drug pairs can interact synergistically or antagonistically, yielding inhibitory effects larger or smaller than expected from the drugs' individual potencies. Clinical strategies often favor synergistic interactions because they maximize the rate at which the infection is cleared from an individual, but it is unclear how such interactions affect the evolution of multi-drug resistance. We used a mathematical model of in vivo infection dynamics to determine the optimal treatment strategy for preventing the evolution of multi-drug resistance. We found that synergy has two conflicting effects: it clears the infection faster and thereby decreases the time during which resistant mutants can arise, but increases the selective advantage of these mutants over wild-type cells. When competition for resources is weak, the former effect is dominant and greater synergy more effectively prevents multi-drug resistance. However, under conditions of strong resource competition, a tradeoff emerges in which greater synergy increases the rate of infection clearance, but also increases the risk of multi-drug resistance. This tradeoff breaks down at a critical level of drug interaction...
Since genetic algorithm was proposed by John Holland (Holland J. H., 1975) in
the early 1970s, the study of evolutionary algorithm has emerged as a popular
research field (Civicioglu & Besdok, 2013). Researchers from various scientific
and engineering disciplines have been digging into this field, exploring the
unique power of evolutionary algorithms (Hadka & Reed, 2013). Many applications
have been successfully proposed in the past twenty years. For example,
mechanical design (Lampinen & Zelinka, 1999), electromagnetic optimization
(Rahmat-Samii & Michielssen, 1999), environmental protection (Bertini, Felice,
Moretti, & Pizzuti, 2010), finance (Larkin & Ryan, 2010), musical orchestration
(Esling, Carpentier, & Agon, 2010), pipe routing (Furuholmen, Glette, Hovin, &
Torresen, 2010), and nuclear reactor core design (Sacco, Henderson,
Rios-Coelho, Ali, & Pereira, 2009). In particular, its function optimization
capability was highlighted (Goldberg & Richardson, 1987) because of its high
adaptability to different function landscapes, to which we cannot apply
traditional optimization techniques (Wong, Leung, & Wong, 2009). Here we review
the applications of evolutionary algorithms in bioinformatics.
Evolutionary models measure the probability of amino acid substitutions
occurring over different evolutionary distances. We examine various
evolutionary models based on empirically derived amino acid substitution
matrices. The models are constructed using the PAM and BLOSUM amino acid
substitution matrices. We rescale these matrices by raising them to powers to
model substitution patterns that account for different evolutionary distances.
We also examine models that account for the dissimilarity of substitution rates
along a protein sequence. We compare the models by computing the likelihood of
each model across different alignments. We also present a specific example to
illustrate the subtle differences in the estimation of evolutionary distance
computed using the different models.; Comment: Paper presented at the Biological Language Conference November 20-21,
2003 University of Pittsburgh
Evolutionary game theory has become one of the most diverse and far reaching
theories in biology. Applications of this theory range from cell dynamics to
social evolution. However, many applications make it clear that inherent
non-linearities of natural systems need to be taken into account. One way of
introducing such non-linearities into evolutionary games is by the inclusion of
multiple players. An example is of social dilemmas, where group benefits could
e.g.\ increase less than linear with the number of cooperators. Such
multiplayer games can be introduced in all the fields where evolutionary game
theory is already well established. However, the inclusion of non-linearities
can help to advance the analysis of systems which are known to be complex, e.g.
in the case of non-Mendelian inheritance. We review the diachronic theory and
applications of multiplayer evolutionary games and present the current state of
the field. Our aim is a summary of the theoretical results from well-mixed
populations in infinite as well as finite populations. We also discuss examples
from three fields where the theory has been successfully applied, ecology,
social sciences and population genetics. In closing, we probe certain future
directions which can be explored using the complexity of multiplayer games
while preserving the promise of simplicity of evolutionary games.; Comment: 14 pages...
Testing fit of data to model is fundamentally important to any science, but
publications in the field of phylogenetics rarely do this. Such analyses
discard fundamental aspects of science as prescribed by Karl Popper. Indeed,
not without cause, Popper (1978) once argued that evolutionary biology was
unscientific as its hypotheses were untestable. Here we trace developments in
assessing fit from Penny et al. (1982) to the present. We compare the general
log-likelihood ratio (the G or G2 statistic) statistic between the evolutionary
tree model and the multinomial model with that of marginalized tests applied to
an alignment (using placental mammal coding sequence data). It is seen that the
most general test does not reject the fit of data to model (p~0.5), but the
marginalized tests do. Tests on pair-wise frequency (F) matrices, strongly (p <
0.001) reject the most general phylogenetic (GTR) models commonly in use. It is
also clear (p < 0.01) that the sequences are not stationary in their nucleotide
composition. Deviations from stationarity and homogeneity seem to be unevenly
distributed amongst taxa; not necessarily those expected from examining other
regions of the genome. By marginalizing the 4t patterns of the i.i.d. model to
observed and expected parsimony counts...
A fundamental question for evolutionary biology is why rates of evolution
vary dramatically between proteins. Perhaps surprisingly, it is controversial
how much a protein's functional importance affects its rate of evolution. In
most studies, functional importance has been measured on the coarse scale of
protein knock-outs, while evolutionary rate has been measured on the fine scale
of amino acid substitutions. Here we introduce dynamical influence, a finer
measure of protein functional importance. To measure dynamical influence, we
first use detailed biochemical models of particular reaction networks to
measure the influence of each reaction rate constant on network dynamics. We
then define the dynamical influence of a protein to be the average influence of
the rate constants for all reactions it is involved in.
Using models of a dozen biochemical systems and sequence data from
vertebrates, we show that dynamical influence and evolutionary rate are
negatively correlated; proteins with greater dynamical influence evolve more
slowly. We also show that proteins with greater dynamical influence are not
more likely to be essential. This suggests that there are many cellular
reactions whose presence is essential for life, but whose quantitative rate is
relatively unimportant to fitness. We also provide evidence that the effect of
dynamical influence on evolutionary rate is independent of protein expression
One way to understand the role history plays on evolutionary trajectories is
by giving ancient life a second opportunity to evolve. Our ability to
empirically perform such an experiment, however, is limited by current
experimental designs. Combining ancestral sequence reconstruction with
synthetic biology allows us to resurrect the past within a modern context and
has expanded our understanding of protein functionality within a historical
context. Experimental evolution, on the other hand, provides us with the
ability to study evolution in action, under controlled conditions in the
laboratory. Here we describe a novel experimental setup that integrates two
disparate fields - ancestral sequence reconstruction and experimental
evolution. This allows us to rewind and replay the evolutionary history of
ancient biomolecules in the laboratory. We anticipate that our combination will
provide a deeper understanding of the underlying roles that contingency and
determinism play in shaping evolutionary processes.; Comment: 8 pages, 4 figures
We propose and study a class-expansion/innovation/loss model of genome
evolution taking into account biological roles of genes and their constituent
domains. In our model numbers of genes in different functional categories are
coupled to each other. For example, an increase in the number of metabolic
enzymes in a genome is usually accompanied by addition of new transcription
factors regulating these enzymes. Such coupling can be thought of as a
proportional "recipe" for genome composition of the type "a spoonful of sugar
for each egg yolk". The model jointly reproduces two known empirical laws: the
distribution of family sizes and the nonlinear scaling of the number of genes
in certain functional categories (e.g. transcription factors) with genome size.
In addition, it allows us to derive a novel relation between the exponents
characterising these two scaling laws, establishing a direct quantitative
connection between evolutionary and functional categories. It predicts that
functional categories that grow faster-than-linearly with genome size to be
characterised by flatter-than-average family size distributions. This relation
is confirmed by our bioinformatics analysis of prokaryotic genomes. This proves
that the joint quantitative trends of functional and evolutionary classes can
be understood in terms of evolutionary growth with proportional recipes.; Comment: 39 pages...
Understanding the mechanisms that underlie the formation of, and innovation in biochemical pathways is an important goal in evolutionary biology. The following work addresses the problem of biochemical pathway evolution in two ways. In the first chapter, I combine genetic manipulations and population genetic analyses to investigate the whether flux control in the aliphatic glucosinolate pathway of Arabidopsis thaliana drives evolutionary rate heterogeneity. My results indicate that the first enzyme in the pathway, CYP79F1, has majority flux control and is the only one to show convincing evidence for positive selection. The second chapter builds on the first by asking whether flux control is stable under a variety of environmental conditions. I find that flux control remains with CYP79F1, in all my environmental treatments. In the final chapter, I address the evolution of one enzyme in this pathway from Boechera stricta that is responsible for a gain-in-function polymorphism that results in increased fitness in nature. With molecular phylogenetic analysis, site-directed mutagenesis, structural biology and enzymatic assays, I determine what residues are under selection and test their functional effects. I find that just two mutations in this enzyme are responsible for the change in function...
Although evolution results from differential reproduction and survival at the level of the individual, most research in evolutionary genetics is concerned with comparisons made at the level of divergent populations or species. This is particularly true in work focused on the evolutionary genetics of natural populations. While this level of inquiry is extremely valuable, in order to develop a complete understanding of the evolutionary process we also need to understand how traits evolve within populations, on the level of differences between individuals, and in the context of natural ecological and environmental variation. A major difficulty confronting such work stems from the difficulty of assessing interindividual phenotypic variation and its sources within natural populations. This level of inquiry is, however, the main focus for many long-term field studies. Here, I take advantage of one such field study, centered on the wild baboon population of the Amboseli basin, Kenya, to investigate the possibilities for integrating functional, population, and evolutionary genetic approaches with behavioral, ecological, and environmental data. First, I describe patterns of hybridization and admixture in the Amboseli population, a potentially important component of population structure. Second...
This dissertation presents some of the first work written and published on the Zero Force Evolutionary Law (McShea and Brandon 2010). It is a collection of four philosophy of biology papers, which together, illustrate the importance of the Zero Force Evolutionary Law (ZFEL) spanning evolutionary studies. In particular, this dissertation includes issues in the history of philosophy of science (chapter 1), group formation and network theory (chapter 2), biological hierarchy and the major transitions in evolution (chapter 3), and the Price equation and quantifying evolutionary change (chapter 4). While these four chapters may differ in focus, they make the same general claim: evolutionary methods and explanations are improved when the underlying tendency of biological systems is characterized correctly as exhibiting increasing variance.
Viruses are the most abundant life forms and the repertoire of viral genes is greater than that of cellular genes. It is also evident that viruses have played a major role in driving cellular evolution, and yet, viruses are not part of mainstream biology, nor are they included in the Tree of Life. A reason for this major paradox in biology is the misleading dogma of viruses as viral particles and their enigmatic evolutionary origin. This article presents an alternative view about the nature of viruses based on their properties during the intracellular stage of their life cycle, when viruses express features comparable to those of many parasitic cellular species. Supporting this view about the nature of viruses is a novel hypothetical evolutionary model for their origin from parasitic cellular species that fused with their host cells. By losing their membrane and cellular structure within the host cell, these new types of parasitic species gained full access to precursors for the synthesis of their specific molecules and to the host’s information processing machineries, such as translation, which created unique parasitic and evolutionary opportunities. To identify viruses during their intracellular stage of their life cycle, in which their specific molecules are free or dispersed within the host cell...