Evolution of adaptation mechanisms: Adaptation energy, stress, and oscillating death

Modeling the evolution of recombination plasticity: A prospective review

Meiotic recombination is one of the main sources of genetic variation, a fundamental factor in the evolutionary adaptation of sexual eukaryotes. Yet, the role of variation in recombination rate and other recombination features remains underexplored. In this review, we focus on the sensitivity of recombination rates to different extrinsic and intrinsic factors. We briefly present the empirical evidence for recombination plasticity in response to environmental perturbations and/or poor genetic background and discuss theoretical models developed to explain how such plasticity could have evolved and how it can affect important population characteristics. We highlight a gap between the evidence, which comes mostly from experiments with diploids, and theory, which typically assumes haploid selection. Finally, we formulate open questions whose solving would help to outline conditions favoring recombination plasticity. This will contribute to answering the long-standing question of why sexual recombination exists despite its costs, since plastic recombination may be evolutionary advantageous even in selection regimes rejecting any non-zero constant recombination.

Magnesium Potentiates the Vortioxetine’s Effects on Physical Performances and Biological Changes in Exercise-Induced Stress in Rats

Background and objectives: Vortioxetine (VRT) is a relatively new selective serotonin reuptake inhibitor (SSRI) antidepressant and serotonin receptor modulator, approved for the treatment of major depression and generalized anxiety disorder. Depression has been linked with psychomotor disengagement, oxidative stress burden and decreased blood levels of brain-derived neurotrophic factor (BDNF). In our study we performed the experimental investigation of VRT, magnesium and of their association on the rats’ endurance capacity, motor behavior and blood biological disturbances in rats subjected to forced exercise in treadmill test. Materials and Methods: The substances were administered orally for 14 consecutive days, as follows: group 1 (control): distilled water 0.3 mL/100 g body; group 2 (Mg): magnesium chloride 200 mg/kg body; group 3 (VRT): VRT 20 mg/kg body; group 4 (VRT+Mg): VRT 20 mg/kg body + magnesium chloride 200 mg/kg body. Magnesium was used as positive control substance with known effects in treadmill test. The consequences of VRT treatment on glucose, cortisol, BDNF and oxidative stress biomarkers (superoxide-dismutase, malondialdehyde, glutathione-peroxidase, lactate dehydrogenase) were also assessed. Results and conclusions: The use of VRT resulted in an improvement in motor capacity and an increase of the rats’ endurance to physical effort. The administration of VRT increased the serum BDNF levels and reduced the oxidative stress in rats subjected to physical effort. The association of magnesium potentiated the effects of VRT on physical performances, the antioxidant activity and the decreasing in serum stress markers in treadmill test in rats.

Can the Concepts of Energy and Psychological Energy Enrich Our Understanding of Psychosocial Adaptation to Traumatic Experiences, Chronic Illnesses and Disabilities?

The aim of this paper is to familiarize the reader with the concept of psychological energy (PE), and the role it plays in deepening our understanding of psychosocial adaptation to traumatic life events and, more pointedly, the onset of chronic illness and disability (CID). In order to implement this aim, the following steps were undertaken: First, a brief historical review of the nature of energy, force and action, as traditionally conceived in the field of physics, is provided. Second, an overview of PE is presented, with a shared emphasis on both its historical underpinnings and its present conceptualizations in the fields of social, health and rehabilitation psychology. Particular emphasis is placed upon applications of PE in the domains of adaptation to stress, trauma and CID onset. Third, reviewed are measuring instruments that have been traditionally applied to the assessment of the nature, content and magnitude of PE and its dynamics. Finally, new perspectives are offered on the dimensional structure, processes and dynamics, assumed to undergird PE, its underlying conceptual similarities to physical energy, and its potential and deeper link to the process of psychosocial adaptation in the aftermath of experiencing trauma and CID.

Dynamic and thermodynamic models of adaptation

The concept of biological adaptation was closely connected to some mathematical, engineering and physical ideas from the very beginning. Cannon in his "The wisdom of the body" (1932) systematically used the engineering vision of regulation. In 1938, Selye enriched this approach by the notion of adaptation energy. This term causes much debate when one takes it literally, as a physical quantity, i.e. a sort of energy. Selye did not use the language of mathematics systematically, but the formalization of his phenomenological theory in the spirit of thermodynamics was simple and led to verifiable predictions. In 1980s, the dynamics of correlation and variance in systems under adaptation to a load of environmental factors were studied and the universal effect in ensembles of systems under a load of similar factors was discovered: in a crisis, as a rule, even before the onset of obvious symptoms of stress, the correlation increases together with variance (and volatility). During 30 years, this effect has been supported by many observations of groups of humans, mice, trees, grassy plants, and on financial time series. In the last ten years, these results were supplemented by many new experiments, from gene networks in cardiology and oncology to dynamics of depression and clinical psychotherapy. Several systems of models were developed: the thermodynamic-like theory of adaptation of ensembles and several families of models of individual adaptation. Historically, the first group of models was based on Selye's concept of adaptation energy and used fitness estimates. Two other groups of models are based on the idea of hidden attractor bifurcation and on the advection-diffusion model for distribution of population in the space of physiological attributes. We explore this world of models and experiments, starting with classic works, with particular attention to the results of the last ten years and open questions.

Paradoxes of cancer: Survival at the brink

The fundamental understanding of how Cancer initiates, persists and then progresses is evolving. High-resolution technologies, including single-cell mutation and gene expression measurements, are now attainable, providing an ever-increasing insight into the molecular details. However, this higher resolution has shown that somatic mutation theory itself cannot explain the extraordinary resistance of cancer to extinction. There is a need for a more Systems-based framework of understanding cancer complexity, which in particular explains the regulation of gene expression during cell-fate decisions. Cancer displays a series of paradoxes. Here we attempt to approach them from the view-point of adaptive exploration of gene regulatory networks at the edge of order and chaos, where cell-fate is changed by oscillations between alternative regulators of cellular senescence and reprogramming operating through self-organisation. On this background, the role of polyploidy in accessing the phylogenetically pre-programmed "oncofetal attractor" state, related to unicellularity, and the de-selection of unsuitable variants at the brink of cell survival is highlighted. The concepts of the embryological and atavistic theory of cancer, cancer cell "life-cycle", and cancer aneuploidy paradox are dissected under this lense. Finally, we challenge researchers to consider that cancer "defects" are mostly the adaptation tools of survival programs that have arisen during evolution and are intrinsic of cancer. Recognition of these features should help in the development of more successful anti-cancer treatments.

High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons

Codifying memories is one of the fundamental problems of modern Neuroscience. The functional mechanisms behind this phenomenon remain largely unknown. Experimental evidence suggests that some of the memory functions are performed by stratified brain structures such as the hippocampus. In this particular case, single neurons in the CA1 region receive a highly multidimensional input from the CA3 area, which is a hub for information processing. We thus assess the implication of the abundance of neuronal signalling routes converging onto single cells on the information processing. We show that single neurons can selectively detect and learn arbitrary information items, given that they operate in high dimensions. The argument is based on stochastic separation theorems and the concentration of measure phenomena. We demonstrate that a simple enough functional neuronal model is capable of explaining: (i) the extreme selectivity of single neurons to the information content, (ii) simultaneous separation of several uncorrelated stimuli or informational items from a large set, and (iii) dynamic learning of new items by associating them with already "known" ones. These results constitute a basis for organization of complex memories in ensembles of single neurons. Moreover, they show that no a priori assumptions on the structural organization of neuronal ensembles are necessary for explaining basic concepts of static and dynamic memories.

Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets

Independent component analysis (ICA) is a matrix factorization approach where the signals captured by each individual matrix factors are optimized to become as mutually independent as possible. Initially suggested for solving source blind separation problems in various fields, ICA was shown to be successful in analyzing functional magnetic resonance imaging (fMRI) and other types of biomedical data. In the last twenty years, ICA became a part of the standard machine learning toolbox, together with other matrix factorization methods such as principal component analysis (PCA) and non-negative matrix factorization (NMF). Here, we review a number of recent works where ICA was shown to be a useful tool for unraveling the complexity of cancer biology from the analysis of different types of omics data, mainly collected for tumoral samples. Such works highlight the use of ICA in dimensionality reduction, deconvolution, data pre-processing, meta-analysis, and others applied to different data types (transcriptome, methylome, proteome, single-cell data). We particularly focus on the technical aspects of ICA application in omics studies such as using different protocols, determining the optimal number of components, assessing and improving reproducibility of the ICA results, and comparison with other popular matrix factorization techniques. We discuss the emerging ICA applications to the integrative analysis of multi-level omics datasets and introduce a conceptual view on ICA as a tool for defining functional subsystems of a complex biological system and their interactions under various conditions. Our review is accompanied by a Jupyter notebook which illustrates the discussed concepts and provides a practical tool for applying ICA to the analysis of cancer omics datasets.

Simple model of complex dynamics of activity patterns in developing networks of neuronal cultures

Living neuronal networks in dissociated neuronal cultures are widely known for their ability to generate highly robust spatiotemporal activity patterns in various experimental conditions. Such patterns are often treated as neuronal avalanches that satisfy the power scaling law and thereby exemplify self-organized criticality in living systems. A crucial question is how these patterns can be explained and modeled in a way that is biologically meaningful, mathematically tractable and yet broad enough to account for neuronal heterogeneity and complexity. Here we derive and analyse a simple network model that may constitute a response to this question. Our derivations are based on few basic phenomenological observations concerning the input-output behavior of an isolated neuron. A distinctive feature of the model is that at the simplest level of description it comprises of only two variables, the network activity variable and an exogenous variable corresponding to energy needed to sustain the activity, and few parameters such as network connectivity and efficacy of signal transmission. The efficacy of signal transmission is modulated by the phenomenological energy variable. Strikingly, this simple model is already capable of explaining emergence of network spikes and bursts in developing neuronal cultures. The model behavior and predictions are consistent with published experimental evidence on cultured neurons. At the larger, cellular automata scale, introduction of the energy-dependent regulatory mechanism results in the overall model behavior that can be characterized as balancing on the edge of the network percolation transition. Network activity in this state shows population bursts satisfying the scaling avalanche conditions. This network state is self-sustainable and represents energetic balance between global network-wide processes and spontaneous activity of individual elements.

Major Depression as a Complex Dynamic System

In this paper, we characterize major depression (MD) as a complex dynamic system in which symptoms (e.g., insomnia and fatigue) are directly connected to one another in a network structure. We hypothesize that individuals can be characterized by their own network with unique architecture and resulting dynamics. With respect to architecture, we show that individuals vulnerable to developing MD are those with strong connections between symptoms: e.g., only one night of poor sleep suffices to make a particular person feel tired. Such vulnerable networks, when pushed by forces external to the system such as stress, are more likely to end up in a depressed state; whereas networks with weaker connections tend to remain in or return to a non-depressed state. We show this with a simulation in which we model the probability of a symptom becoming 'active' as a logistic function of the activity of its neighboring symptoms. Additionally, we show that this model potentially explains some well-known empirical phenomena such as spontaneous recovery as well as accommodates existing theories about the various subtypes of MD. To our knowledge, we offer the first intra-individual, symptom-based, process model with the potential to explain the pathogenesis and maintenance of major depression.