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The time has come to examine aspects of health and disease on a broader basis than immediate cause and effect. Recognition of the biological processes of adaptation shaped by natural selection has become a requirement for improved understanding of human health in the modern environment, and the time has come for its incorporation into medical teaching and clinical practice.
Spread of biological species from primary into novel habitats leads to within-species adaptive niche differentiation and is commonly driven by acquisition of point mutations in individual genes that increase fitness in the alternative environment. However, finding footprints of adaptive niche differentiation in specific genes remains a challenge. Here we describe a novel method to analyze the footprint of pathogenicity-adaptive, or pathoadaptive, mutations in the Escherichia coli gene encoding FimH-the major, mannose-sensitive adhesin. Analysis of distribution of mutations across the nodes and branches of the FimH phylogenetic network shows (1) zonal separation of evolutionary primary structural variants of FimH and recently derived ones, (2) dramatic differences in the ratio of synonymous and nonsynonymous changes between nodes from different zones, (3) evidence for replacement hot-spots in the FimH protein, (4) differential zonal distribution of FimH variants from commensal and uropathogenic E. coli, and (5) pathoadaptive functional changes in FimH brought by the mutations. The selective footprint in fimH indicates that the pathoadaptive niche differentiation of E. coli is either in its initial stages or undergoing an evolutionary "source/sink" dynamic.
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The concept of the mechanostat was not new in 1983, when Harold Frost coined the term to describe a mechanism by which bone responded to habitual exercise and changes in loading with structurally appropriate alterations in bone architecture. However, the word "mechanostat" has a meaning that is immediately apparent, and its adoption has led to a much wider appreciation of the process of functional adaptation by other scientists than those whose primary research focus is in the biology of adaptation. One problem exists though: it is widely thought that in a single individual, there is a setting for the mechanostat, just as a single thermostat might set the temperature for a whole house, and this is reflected in the idea that bones throughout the skeleton require a specific strain magnitude for maintenance. Increases in loading above that threshold are expected to induce bone formation and a stiffer structure that then experiences again the habitual strain magnitude. Reductions in strain magnitude supposedly induce resorption to reduce tissue mass and architectural properties so that the lower loading restores habitual strain magnitude. That widely held belief of a single unifying number of strain is fundamentally flawed. The purpose of this article is to explain the real basis of the mechanostat; that the skeleton responds to a complex strain stimulus, made up of numerous different parameters, of which peak magnitude is only one, and that the strain stimulus is different in different parts of the skeleton, so there is no universal number to describe a tissue strain magnitude that underlies the mechanostat's setting. Furthermore, males and females have different responses to loading, and those responses change in response to many factors including genetic constitution, age, concomitant disease, nutrient availability, and exposure to drugs or biochemicals. In summary then, there is not a single mechanostat controlling the skeleton of each of us. At a fundamental tissue level, small functional units of bone each have their own multifactorial threshold target strain stimuli for a given set of dynamic modifying influences. Understanding the biology behind the way that each of these mechanostats functions independently is likely to have pervasive consequences on our ability to control bone mass by manipulation of loading, either directly through different exercise regimens, or in a targeted manner using tailored site and individual specific pharmaceuticals.
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Stem cell research has stimulated considerable recent interest, but the concepts are old. Nevertheless, our understanding of the basic biology of different stem cell systems is poor. Many questions remain to be answered: How can we recognize stem cells? Are the underlying control mechanisms common to different types of stem cell, the so-called stemness concept, or is the control of self-renewal and commitment distinct in different stem cell types? What is the significance of differences in stem cells from different species? Do stem cells from somatic tissues really show plasticity with an ability to generate cells from distinct lineages, or are the observed examples consequences of experimental artifact, or rare events of no physiological significance? Do genetic mutations in the genes controlling stem cell self-renewal and differentiation lie at the heart of carcinogenesis? Answers to these and related questions now offer exciting future possibilities for both basic biology and medicine.
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Adaptation allows biological sensory systems to adjust to variations in the environment and thus to deal better with them. In this article, we propose a general framework of sensory adaptation. The underlying principle of this framework is the setting of internal parameters of the system such that certain prespecified tasks can be performed optimally. Because sensorial inputs vary probabilistically with time and biological mechanisms have noise, the tasks could be performed incorrectly. We postulate that the goal of adaptation is to minimize the number of task errors. This minimization requires prior knowledge of the environment and of the limitations of the mechanisms processing the information. Because these processes are probabilistic, we formulate the minimization with a Bayesian approach. Application of this Bayesian framework to the retina is successful in accounting for a host of experimental findings.
A new, non-adaptationist theory of evolution of genomic complexity was recently proposed by Lynch and Conery. This concept holds that increase in complexity seen in eukaryotic genomes is a 'syndrome' caused by increase in genome entropy, which is inevitably triggered by reduction of population size. Here, I discuss the definitions of genomic entropy and complexity and the evidence supporting the entropic theory of genome complexity evolution, including new observations on concordant gain and loss of genes and introns in eukaryotic genomes. I further consider the far-reaching biological and philosophical implications of this theory.
BACKGROUND: Developing an understanding of the molecular basis for the divergence of species lies at the heart of biology. The Adaptive Evolution Database (TAED) serves as a starting point to link events that occur at the same time in the evolutionary history (tree of life) of species, based upon coding sequence evolution analyzed with the Master Catalog. The Master Catalog is a collection of evolutionary models, including multiple sequence alignments, phylogenetic trees, and reconstructed ancestral sequences, for all independently evolving protein sequence modules encoded by genes in GenBank [1]. RESULTS: We have estimated from these models the ratio of nonsynonymous to synonymous nucleotide substitution (Ka/Ks), for each branch in their respective evolutionary trees of every subtree containing only chordata or only embryophyta proteins. Branches with high Ka/Ks values represent candidate episodes in the history of the family where the protein may have undergone positive selection, a phenomenon in molecular evolution where the mutant form of a gene must have conferred more fitness than the ancestral form. Such episodes are frequently associated with change in function. We have found that an unexpectedly large number of families (between 10 and 20% of those families examined) have at least one branch with a notably high Ka/Ks value (putative adaptive evolution). As a resource for biologists wishing to understand the interaction between protein sequences and the Darwinian processes that shape these sequences, we have collected these into The Adaptive Evolution Database (TAED). CONCLUSIONS: Placed in a phylogenetic perspective, candidate genes that are undergoing evolution at the same time in the same lineage can be viewed together. This framework based upon coding sequence evolution can be readily expanded to include other types of evolution. In its present form, TAED provides a resource for bioinformaticists interested in data mining and for experimental evolutionists seeking candidate examples of adaptive evolution for further experimental study.
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For years evolutionary biologists have been interested in searching for the genetic bases underlying humanness. Recent efforts at a large or a complete genomic scale have been conducted to search for positively selected genes in human and in chimp. However, recently developed methods allowing for a more sensitive and controlled approach in the detection of positive selection can be employed. Here, using 13,198 genes, we have deduced the sets of genes involved in rate acceleration, positive selection, and relaxation of selective constraints in human, in chimp, and in their ancestral lineage since the divergence from murids. Significant deviations from the strict molecular clock were observed in 469 human and in 651 chimp genes. The more stringent branch-site test of positive selection detected 108 human and 577 chimp positively selected genes. An important proportion of the positively selected genes did not show a significant acceleration in rates, and similarly, many of the accelerated genes did not show significant signals of positive selection. Functional differentiation of genes under rate acceleration, positive selection, and relaxation was not statistically significant between human and chimp with the exception of terms related to G-protein coupled receptors and sensory perception. Both of these were over-represented under relaxation in human in relation to chimp. Comparing differences between derived and ancestral lineages, a more conspicuous change in trends seems to have favored positive selection in the human lineage. Since most of the positively selected genes are different under the same functional categories between these species, we suggest that the individual roles of the alternative positively selected genes may be an important factor underlying biological differences between these species.