The Pathosome: A Dynamic Three‐Dimensional View of Disease–Environment Interaction

Learning mitigates genetic drift

Genetic drift is a basic evolutionary principle describing random changes in allelic frequencies, with far-reaching consequences in various topics ranging from species conservation efforts to speciation. The conventional approach assumes that genetic drift has the same effect on all populations undergoing the same changes in size, regardless of different non-reproductive behaviors and history of the populations. However, here we reason that processes leading to a systematic increase of individuals` chances of survival, such as learning or immunological memory, can mitigate loss of genetic diversity caused by genetic drift even if the overall mortality rate in the population does not change. We further test this notion in an agent-based model with overlapping generations, monitoring allele numbers in a population of prey, either able or not able to learn from successfully escaping predators' attacks. Importantly, both these populations start with the same effective size and have the same and constant overall mortality rates. Our results demonstrate that even under these conditions, learning can mitigate loss of genetic diversity caused by drift, by creating a pool of harder-to-die individuals that protect alleles they carry from extinction. Furthermore, this effect holds regardless if the population is haploid or diploid or whether it reproduces sexually or asexually. These findings may be of importance not only for basic evolutionary theory but also for other fields using the concept of genetic drift.

Merging the exposome into an integrated framework for “omics” sciences

The exposome concept encourages holistic consideration of the non-genetic factors (environmental exposures including lifestyle) that influence an individual’s health over their life course. However, disconnect between the concept and practical application has promoted divergent interpretations of the exposome across disciplines and reinforced separation of the environmental (emphasizing exposures) and biological (emphasizing responses) research communities. In particular, while knowledge of biological responses can help to distinguish actual (i.e. experienced) from potential exposures, the inclusion of endogenous processes has generated confusion about the position of the exposome in a multi-omics systems biology context. We propose a reattribution of “exposome” to exclusively represent the totality of contact with external factors that a biological entity experiences, and introduce the term “functional exposomics” to denote the systematic study of exposure-phenotype interaction. This reoriented definition of the exposome allows a more readily integrable dataset for multi-omics and systems biology research.

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Reattribution of exposome concept to exclusively represent environmental exposures

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Generalized the exposome concept for all levels of biological organization

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Functional exposome presented as the totality of exposure-phenotype interaction

Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach

Currently, the research of multi-omics, such as genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, and radiomics, are hot spots. The relationship between multi-omics data, drugs, and diseases has received extensive attention from researchers. At the same time, multi-omics can effectively predict the diagnosis, prognosis, and treatment of diseases. In essence, these research entities, such as genes, RNAs, proteins, microbes, metabolites, pathways as well as pathological and medical imaging data, can all be represented by the network at different levels. And some computer and biology scholars have tried to use computational methods to explore the potential relationships between biological entities. We summary a comprehensive research strategy, that is to build a multi-omics heterogeneous network, covering multimodal data, and use the current popular computational methods to make predictions. In this study, we first introduce the calculation method of the similarity of biological entities at the data level, second discuss multimodal data fusion and methods of feature extraction. Finally, the challenges and opportunities at this stage are summarized. Some scholars have used such a framework to calculate and predict. We also summarize them and discuss the challenges. We hope that our review could help scholars who are interested in the field of bioinformatics, biomedical image, and computer research.

Can aging research generate a theory of health?

While aging research and policy aim to promote 'health' at all ages, there remains no convincing explanation of what this 'health' is. In this paper, I investigate whether we can find, implicit within the sciences of aging, a way to know what health is and how to measure it, i.e. a theory of health. To answer this, I start from scientific descriptions of aging and its modulators and then try to develop some generalizations about 'health' implicit within this research. After discussing some of the core aspects of aging and the ways in which certain models describe spatial and temporal features specific to both aging and healthy phenotypes, I then extract, explicate, and evaluate one potential construct of health in these models. This suggests a theory of health based on the landscape of optimized phenotypic trajectories. I conclude by considering why it matters for more candidate theories to be proposed and evaluated by philosophers and scientists alike.

Eustress and Distress: Neither Good Nor Bad, but Rather the Same?

The terms "eustress" and "distress" are widely used throughout the scientific literature. As of February 2020, 203 items in the Web of Science show up in a search for "eustress," however, there are almost 16 400 items found in a search for the term "distress." Based on the reasoning in this article, however, it is believed there is no such thing as eustress or distress. The adaptation reaction of an organism under stress is not intrinsically good or bad, and its effect on health or performance depends on a plethora of other interactions of the body with the environment as well as on the history of such interactions. The vagueness of the terms "eustress/distress" has historically led to vast differences in the perception and application of the terms across disciplines. While psychology or sociology perceive eustress as something inextricably linked to positive perception and enhanced cognition, biomedicine perceives eustress as generally associated with better survival, health, or increased longevity, no matter how the event is perceived. In this paper, the authors review the current understanding of the term "eustress" in different fields, discuss possible implications of its misleading use, and suggest that the term may be replaced by "stress" only.