Spontaneous emergence of modularity in a model of evolving individuals and in real networks

Evolutionary feedback from the environment shapes mechanisms that generate genome variation

Darwin recognized that 'a grand and almost untrodden field of inquiry will be opened, on the causes and laws of variation.' However, because the Modern Synthesis assumes that the intrinsic probability of any individual mutation is unrelated to that mutation's potential adaptive value, attention has been focused on selection rather than on the intrinsic generation of variation. Yet many examples illustrate that the term 'random' mutation, as widely understood, is inaccurate. The probabilities of distinct classes of variation are neither evenly distributed across a genome nor invariant over time, nor unrelated to their potential adaptive value. Because selection acts upon variation, multiple biochemical mechanisms can and have evolved that increase the relative probability of adaptive mutations. In effect, the generation of heritable variation is in a feedback loop with selection, such that those mechanisms that tend to generate variants that survive recurring challenges in the environment would be captured by this survival and thus inherited and accumulated within lineages of genomes. Moreover, because genome variation is affected by a wide range of biochemical processes, genome variation can be regulated. Biochemical mechanisms that sense stress, from lack of nutrients to DNA damage, can increase the probability of specific classes of variation. A deeper understanding of evolution involves attention to the evolution of, and environmental influences upon, the intrinsic variation generated in gametes, in other words upon the biochemical mechanisms that generate variation across generations. These concepts have profound implications for the types of questions that can and should be asked, as omics databases become more comprehensive, detection methods more sensitive, and computation and experimental analyses even more high throughput and thus capable of revealing the intrinsic generation of variation in individual gametes. These concepts also have profound implications for evolutionary theory, which, upon reflection it will be argued, predicts that selection would increase the probability of generating adaptive mutations, in other words, predicts that the ability to evolve itself evolves.

Penicillin Trunk Injection Affects Bacterial Community Structure in Citrus Trees

Huanglongbing (HLB), caused by Candidatus Liberibacter asiaticus (CLas), an uncultured α-proteobacterium, is the most destructive disease of citrus trees worldwide. In previous studies, trunk injections of penicillin reduced CLas titers and HLB symptoms in citrus. However, antibiotic effects on the whole plant microbial community, which include effects on taxa that interact with CLas, have not yet been addressed. In this study, we investigated the effects of penicillin injection (0, 1000, and 6000 mg L^-1) on rhizospheric and endophytic bacterial communities of grapefruit trees in field and greenhouse experiments through culture-independent high-throughput sequencing. DNA extractions from petioles and roots were subjected to 16S rRNA high-throughput sequencing, and reads were clustered by sequence similarity into operational taxonomic units (OTUs). Principal coordinates analysis based on weighted-UniFrac distances did not reveal differences in bacterial communities among treatments in any of the sample sources. However, pairwise linear discriminant analysis indicated significant differences in relative abundance of some taxa (including CLas) among treatments. Network analysis showed that penicillin produced major changes in root bacterial community structure by affecting interspecific microbial associations. This study provides new knowledge of the effect of antimicrobial treatments on interspecific relationships in citrus microbial communities.

The thermodynamics of protein aggregation reactions may underpin the enhanced metabolic efficiency associated with heterosis, some balancing selection, and the evolution of ploidy levels

Identifying the physical basis of heterosis (or "hybrid vigor") has remained elusive despite over a hundred years of research on the subject. The three main theories of heterosis are dominance theory, overdominance theory, and epistasis theory. Kacser and Burns (1981) identified the molecular basis of dominance, which has greatly enhanced our understanding of its importance to heterosis. This paper aims to explain how overdominance, and some features of epistasis, can similarly emerge from the molecular dynamics of proteins. Possessing multiple alleles at a gene locus results in the synthesis of different allozymes at reduced concentrations. This in turn reduces the rate at which each allozyme forms soluble oligomers, which are toxic and must be degraded, because allozymes co-aggregate at low efficiencies. The model developed in this paper can explain how heterozygosity impacts the metabolic efficiency of an organism. It can also explain why the viabilities of some inbred lines seem to decline rapidly at high inbreeding coefficients (F > 0.5), which may provide a physical basis for truncation selection for heterozygosity. Finally, the model has implications for the ploidy level of organisms. It can explain why polyploids are frequently found in environments where severe physical stresses promote the formation of soluble oligomers. The model can also explain why complex organisms, which need to synthesize aggregation-prone proteins that contain intrinsically unstructured regions (IURs) and multiple domains because they facilitate complex protein interaction networks (PINs), tend to be diploid while haploidy tends to be restricted to relatively simple organisms.

Ancient hot and cold genes and chemotherapy resistance emergence

We use a microfabricated ecology with a doxorubicin gradient and population fragmentation to produce a strong Darwinian selective pressure that drives forward the rapid emergence of doxorubicin resistance in multiple myeloma (MM) cancer cells. RNA sequencing of the resistant cells was used to examine (i) emergence of genes with high de novo substitution densities (i.e., hot genes) and (ii) genes never substituted (i.e., cold genes). The set of cold genes, which were 21% of the genes sequenced, were further winnowed down by examining excess expression levels. Both the most highly substituted genes and the most highly expressed never-substituted genes were biased in age toward the most ancient of genes. This would support the model that cancer represents a revision back to ancient forms of life adapted to high fitness under extreme stress, and suggests that these ancient genes may be targets for cancer therapy.

Thermodynamic description of Beta amyloid formation using physicochemical scales and fractal bioinformatic scales

Protein function depends on both protein structure and amino acid (aa) sequence. Here we show that modular features of both structure and function can be quantified economically from the aa sequences alone for the small (40,42 aa) plaque-forming (aggregative) amyloid beta fragments. Some edge and center features of the fragments are predicted. Bioinformatic scales based on β strand formation propensities and the thermodynamically second order fractal hydropathicity scale based on evolutionary optimization (self-organized criticality) are contrasted with the standard first order physicochemical scale based on complete protein (water-air) unfolding. The results are consistent with previous studies of these physicochemical factors that show that aggregative properties, even of beta fragments, are driven primarily by near-equilibrium hydropathic forces.

Hierarchy in gene expression is predictive of risk, progression, and outcome in adult acute myeloid leukemia

Cancer progresses with a change in the structure of the gene network in normal cells. We define a measure of organizational hierarchy in gene networks of affected cells in adult acute myeloid leukemia (AML) patients. With a retrospective cohort analysis based on the gene expression profiles of 116 AML patients, we find that the likelihood of future cancer relapse and the level of clinical risk are directly correlated with the level of organization in the cancer related gene network. We also explore the variation of the level of organization in the gene network with cancer progression. We find that this variation is non-monotonic, which implies the fitness landscape in the evolution of AML cancer cells is non-trivial. We further find that the hierarchy in gene expression at the time of diagnosis may be a useful biomarker in AML prognosis.

Quasispecies theory for evolution of modularity

Biological systems are modular, and this modularity evolves over time and in different environments. A number of observations have been made of increased modularity in biological systems under increased environmental pressure. We here develop a quasispecies theory for the dynamics of modularity in populations of these systems. We show how the steady-state fitness in a randomly changing environment can be computed. We derive a fluctuation dissipation relation for the rate of change of modularity and use it to derive a relationship between rate of environmental changes and rate of growth of modularity. We also find a principle of least action for the evolved modularity at steady state. Finally, we compare our predictions to simulations of protein evolution and find them to be consistent.

Mutation rules and the evolution of sparseness and modularity in biological systems

Biological systems exhibit two structural features on many levels of organization: sparseness, in which only a small fraction of possible interactions between components actually occur; and modularity – the near decomposability of the system into modules with distinct functionality. Recent work suggests that modularity can evolve in a variety of circumstances, including goals that vary in time such that they share the same subgoals (modularly varying goals), or when connections are costly. Here, we studied the origin of modularity and sparseness focusing on the nature of the mutation process, rather than on connection cost or variations in the goal. We use simulations of evolution with different mutation rules. We found that commonly used sum-rule mutations, in which interactions are mutated by adding random numbers, do not lead to modularity or sparseness except for in special situations. In contrast, product-rule mutations in which interactions are mutated by multiplying by random numbers – a better model for the effects of biological mutations – led to sparseness naturally. When the goals of evolution are modular, in the sense that specific groups of inputs affect specific groups of outputs, product-rule mutations also lead to modular structure; sum-rule mutations do not. Product-rule mutations generate sparseness and modularity because they tend to reduce interactions, and to keep small interaction terms small.

Stress-induced hypermutation as a physical property of life, a force of natural selection and its role in four thought experiments

The independence of genetic mutation rate from selection is central to neo-Darwinian evolutionary theory. However, it has been continuously challenged for more than 30 years by experimental evidence of genetic mutation rate transiently increasing in response to stress (stress-induced hypermutation, SIH). The prominent concept of evolved evolvability (EE) explains that natural selection for strategies more competitive at evolutionary adaptation itself gives rise to mechanisms dynamically adjusting mutation rates to environmental stress. Here, we theoretically investigate the alternative (not mutually exclusive) hypothesis that SIH is an inherent physical property of all genetically reproducing life. We define stress as any condition lowering the capability of utilizing metabolic resources for genome storage and replication. This thermodynamical analysis indicates stress-induced increases in the genetic mutation rate in genome storage and in genome replication as inherent physical properties of genetically reproducing life. Further integrating SIH into an overall organismic thermodynamic budget identifies SIH as a force of natural selection, alongside death rate, replication rate and constitutive mutation rate differences. We execute four thought experiments with a non-recombinant lesion mutant strain to predict experimental observations due to SIH in response to different stresses and stress combinations. We find (1) acceleration of adaptation over models without SIH, (2) possibility of adaptation at high stresses which are not explicable by mutation in genome replication alone and (3) different adaptive potential under high growth-inhibiting versus high lethal stresses. The predictions are directly comparable to culture experiments (colony size time courses, antibacterial resistance assay and occurrence of lesion-reversion mutant colonies) and genome sequence analysis. Considering suggestions of drug-mediated disruption of SIH and attempts to target mutation-associated sites with chemotherapeutic agents to prevent resistance, our findings seem to be relevant knowledge for resistance-averse drug development and administration.

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.

Scaffold translation: barriers between concept and clinic

Translation of scaffold-based bone tissue engineering (BTE) therapies to clinical use remains, bluntly, a failure. This dearth of translated tissue engineering therapies (including scaffolds) remains despite 25 years of research, research funding totaling hundreds of millions of dollars, over 12,000 papers on BTE and over 2000 papers on BTE scaffolds alone in the past 10 years (PubMed search). Enabling scaffold translation requires first an understanding of the challenges, and second, addressing the complete range of these challenges. There are the obvious technical challenges of designing, manufacturing, and functionalizing scaffolds to fill the Form, Fixation, Function, and Formation needs of bone defect repair. However, these technical solutions should be targeted to specific clinical indications (e.g., mandibular defects, spine fusion, long bone defects, etc.). Further, technical solutions should also address business challenges, including the need to obtain regulatory approval, meet specific market needs, and obtain private investment to develop products, again for specific clinical indications. Finally, these business and technical challenges present a much different model than the typical research paradigm, presenting the field with philosophical challenges in terms of publishing and funding priorities that should be addressed as well. In this article, we review in detail the technical, business, and philosophical barriers of translating scaffolds from Concept to Clinic. We argue that envisioning and engineering scaffolds as modular systems with a sliding scale of complexity offers the best path to addressing these translational challenges.

The emergence of modularity in biological systems

In this review, we discuss modularity and hierarchy in biological systems. We review examples from protein structure, genetics, and biological networks of modular partitioning of the geometry of biological space. We review theories to explain modular organization of biology, with a focus on explaining how biology may spontaneously organize to a structured form. That is, we seek to explain how biology nucleated from among the many possibilities in chemistry. The emergence of modular organization of biological structure will be described as a symmetry-breaking phase transition, with modularity as the order parameter. Experimental support for this description will be reviewed. Examples will be presented from pathogen structure, metabolic networks, gene networks, and protein-protein interaction networks. Additional examples will be presented from ecological food networks, developmental pathways, physiology, and social networks.

Evo Devo and cognitive science

Evo Devo (evolutionary developmental) biology forges a synthesis of evolutionary and developmental processes. Evo Devo is the result of collaborative work of evolutionary and developmental biologists after the discovery of regulatory genes that human beings share with many other animals, including fruit flies, frogs, and rats. Compared to traditional evolutionary biologists, Evo Devo biologists focus on processes underlying the generation of evolutionary novelties, rather than on how natural selection changes gene frequencies in populations and how organisms are adapted to their environment. Evo Devo biologists try to answer questions such as: How do novel structures arise? Which mechanisms facilitate or constrain evolutionary change? In this article we argue that insights from Evo Devo research can contribute to the understanding of the evolution and development of cognition, and of the origin of neurocognitive disorders. We discuss three major Evo Devo topics: modularity, evolvability, and developmental constraints. We argue that each of these topics are relevant for research in cognitive science, and we argue that interdisciplinary research is necessary in order to unravel the evolutionary and developmental mechanisms of cognitive traits and disorders. WIREs Cogni Sci 2011 2 429-440 DOI: 10.1002/wcs.137 For further resources related to this article, please visit the WIREs website.

Noise and the evolution of neural network modularity

We study the selective advantage of modularity in artificially evolved networks. Modularity abounds in complex systems in the real world. However, experimental evidence for the selective advantage of network modularity has been elusive unless it has been supported or mandated by the genetic representation. The evolutionary origin of modularity is thus still debated: whether networks are modular because of the process that created them, or the process has evolved to produce modular networks. It is commonly argued that network modularity is beneficial under noisy conditions, but experimental support for this is still very limited. In this article, we evolve nonlinear artificial neural network classifiers for a binary classification task with a modular structure. When noise is added to the edge weights of the networks, modular network topologies evolve, even without representational support.

Structure and response in the world trade network

We examine how the structure of the world trade network has been shaped by globalization and recessions over the last 40 years. We show that by treating the world trade network as an evolving system, theory predicts the trade network is more sensitive to recessionary shocks and recovers more slowly from them now than it did 40 years ago, due to structural changes in the world trade network induced by globalization. We also show that recession-induced change to the world trade network leads to an increased hierarchical structure of the global trade network for a few years after the recession.

Theoretical aspects of immunity

The immune system recognizes a myriad of invading pathogens and their toxic products. It does so with a finite repertoire of antibodies and T cell receptors. We here describe theories that quantify the dynamics of the immune system. We describe how the immune system recognizes antigens by searching the large space of receptor molecules. We consider in some detail the theories that quantify the immune response to influenza and dengue fever. We review theoretical descriptions of the complementary evolution of pathogens that occurs in response to immune system pressure. Methods including bioinformatics, molecular simulation, random energy models, and quantum field theory contribute to a theoretical understanding of aspects of immunity.