| Literature DB >> 26631109 |
Yoav Soen1, Maor Knafo2, Michael Elgart3.
Abstract
BACKGROUND: During the lifetime of an organism, every individual encounters many combinations of diverse changes in the somatic genome, epigenome and microbiome. This gives rise to many novel combinations of internal failures which are unique to each individual. How any individual can tolerate this high load of new, individual-specific scenarios of failure is not clear. While stress-induced plasticity and hidden variation have been proposed as potential mechanisms of tolerance, the main conceptual problem remains unaddressed, namely: how largely non-beneficial random variation can be rapidly and safely organized into net benefits to every individual. PRESENTATION OF THE HYPOTHESIS: We propose an organizational principle which explains how every individual can alleviate a high load of novel stressful scenarios using many random variations in flexible and inherently less harmful traits. Random changes which happen to reduce stress, benefit the organism and decrease the drive for additional changes. This adaptation (termed 'Adaptive Improvisation') can be further enhanced, propagated, stabilized and memorized when beneficial changes reinforce themselves by auto-regulatory mechanisms. This principle implicates stress not only in driving diverse variations in cells tissues and organs, but also in organizing these variations into adaptive outcomes. Specific (but not exclusive) examples include stress reduction by rapid exchange of mobile genetic elements (or exosomes) in unicellular, and rapid changes in the symbiotic microorganisms of animals. In all cases, adaptive changes can be transmitted across generations, allowing rapid improvement and assimilation in a few generations. TESTING THE HYPOTHESIS: We provide testable predictions derived from the hypothesis. IMPLICATIONS OF THE HYPOTHESIS: The hypothesis raises a critical, but thus far overlooked adaptation problem and explains how random variation can self-organize to confer a wide range of individual-specific adaptations beyond the existing outcomes of natural selection. It portrays gene regulation as an inseparable synergy between natural selection and adaptation by improvisation. The latter provides a basis for Lamarckian adaptation that is not limited to a specific mechanism and readily accounts for the remarkable resistance of tumors to treatment.Entities:
Mesh:
Year: 2015 PMID: 26631109 PMCID: PMC4668624 DOI: 10.1186/s13062-015-0097-y
Source DB: PubMed Journal: Biol Direct ISSN: 1745-6150 Impact factor: 4.540
Fig. 1Putative distribution of (trait) flexibility and deleterious potential (risk level). The probability density curve corresponds to the density of traits (y-axis) at a given level of stability (x-axis). The flexibility of a trait (1/Trait Stability) is represented by the standard deviation, divided by the mean (Std./Mean), both computed over time in a single individual. The expected density of traits is an increasing function of trait flexibility and a decreasing function of the trait’s deleterious potential (risk level). We also expect a wide range of flexibility values, illustrated in this example by a (scale-free) power-law increase of the density as a function of trait flexibility. The inverse correlation between the flexibility of a trait and its deleterious potential is represented by a color code, with red (‘high risk’) and yellow (‘low risk’) associated, respectively, with high and low probabilities for a detrimental outcome of a change in the respective trait
Fig. 2A visual metaphor illustrating adaptive improvisation by random drive reduction. Improvisation (or exploration) is defined as a change in state (or part of the change) which is not specified by pre-evolved mechanisms. Improvisation which reduces the stress is termed 'adaptive' (or ‘beneficial’). Illustrations with and without re-enforcement are shown in (a) and (b), respectively. a Heuristic depiction of the state space available to the organism (colored area). At any given moment, the state is represented by a high dimensional vector X, which specifies a point in the available state space. The overall amount of stress, S, at a given state, is displayed in red color code (for simplicity of the illustration, we only consider here a single measure of stress at the whole organism level). The available state space is divided into two subspaces, X and X, defined as follows: Changes in state upward along X increase the overall stress while changes along X have no effect on the stress (‘no effect’ means that the change in stress is below a small threshold). Thus, the stress S in this representation, is an increasing function ƒ of the state along X, i.e. S = ƒ(X). Additionally, the characteristic magnitude of exploratory changes (|ΔX| char) is assumed to be an increasing function G of the stress, i.e. |ΔX| char = G(S). Consequently, the lower the stress, the smaller the ‘perimeter’ of subsequent exploration, and the state of the organism is more likely to remain (over a specified time interval) within a given neighborhood of the starting state (circle). This biases the outcome towards less stressful states, even without directed movements in the state of possible changes. The tendency toward lower stress is counteracted by having a smaller number of stress-reducing states compared with states associated with increased stress. The balance between the tendencies to decrease and increase the stress depends on the characteristics of G(S) and the relative abundance in the number of stress-reducing versus stress increasing states. We assume that in the regime of very high stress, the over-abundance of stress-increasing states becomes small and the overall balance would favor decrease in stress due to drive reduction. However, in the inverse regime of very low stress, states which increase the stress are much more abundant than stress-reducing states and the exploratory changes will tend to increase the stress. The combined effects of upward and downward tendencies create an intermediate domain, in which the organism is most likely to be found (around Xǁ typ). A qualitative profile of a probability density function (Pdf) for a particular state along X, is shown to the left. b Amplification and stabilization of beneficial changes by auto-regulatory processes. Random occurrence of beneficial processes that are also capable of re-enforcing themselves (or each other), enhance the process of stress reduction thus increasing the benefit. Since the resources of every system are limited, activation of beneficial auto-regulatory processes tend to repress other processes, thereby stabilizing the beneficial processes. Subsequent changes, in this case, become more likely to decrease stress (indicated by substantially asymmetric arrows), thus expediting the adaptation, stabilizing its outcomes and reducing the probability of more harmful changes
Fig. 3Potential realization of adaptive improvisation by host-microbiome interactions in animals (illustrated here using flies as an example). A novel stress induces rapid changes in the composition of bacterial species, as well in the intrinsic properties of individual bacteria, their spatial distributions and their interactions. The characteristic rate and magnitude of changes (represented by the diameter of the color-coded halos) tend to increase with the strength of the stress (displayed in red color code). Many of these changes occur during the lifetime ‘T’ of an individual (i.e. t2 - t0 < ‘T’). The modified microbiome influences the state and properties of the host, potentially increasing or alleviating the stress in the host and the holobiont. Changes which alleviate the stress also reduce the drive for further changes, thereby decreasing the characteristic magnitude of subsequent changes (smaller halo). Bacterial species are represented by specific colors. Variation within a particular species (e.g. physiological, epigenetic and certain genetic changes) is indicated by shape modifications without a change in color