Two new preprints

Microbial Evolutionary Medicine – from theory to clinical practice.  S Breum Andersen, BJ Shapiro, C Vandenbroucke-Grauls, MGJ de Vos. 2018, PeerJ Preprint.

Abstract. Bacteria and other microbes play a crucial role in human health and disease. Medicine and clinical microbiology have traditionally attempted to identify the etiological agents that causes disease, and how to eliminate them. Yet this traditional paradigm is becoming inadequate for dealing with a changing disease landscape. Major challenges to human health are noncommunicable chronic diseases, often driven by altered immunity and inflammation, and persistent communicable infections whose agents harbor antibiotic resistance. It is increasingly recognized that microbe-microbe interactions, as well as human-microbe interactions are important. Here, we review the “Evolutionary Medicine” framework to study how microbial communities influence human health. This approach aims to predict and manipulate microbial influences on human health by integrating ecology, evolutionary biology, microbiology, bioinformatics and clinical expertise. We focus on the potential promise of evolutionary medicine to address three key challenges: 1) detecting microbial transmission; 2) predicting antimicrobial resistance; 3) understanding microbe-microbe and human-microbe interactions in health and disease, in the context of the microbiome.

Ecology dictates evolution? About the importance of genetic and ecological constraints in adaptation. MGJ de Vos, SE Schoustra, JAGM de Visser. 2018, OSF Preprint.

Abstract. The topography of the adaptive landscape is a major determinant of the course of evolution. In this review we use the adaptive landscape metaphor to highlight the effect of ecology on evolution. We describe how ecological interactions modulate the shape of the adaptive landscape, and how this affects adaptive constraints. We focus on microbial communities as model systems.

Published in Europhysics Letters (EPL), Volume 122, Number 5 – Focus Issue Evolutionary Modeling and Experimental Evolution

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