In silico evo-devo: reconstructing stages in the evolution of animal segmentation

The evolution of animal segmentation is a major research focus within the field of evolutionary–developmental biology. Most studied segmented animals generate their segments in a repetitive, anterior-to-posterior fashion coordinated with the extension of the body axis from a posterior growth zone. In the current study we ask which selection pressures and ordering of evolutionary events may have contributed to the evolution of this specific segmentation mode. To answer this question we extend a previous in silico simulation model of the evolution of segmentation by allowing the tissue growth pattern to freely evolve. We then determine the likelihood of evolving oscillatory sequential segmentation combined with posterior growth under various conditions, such as the presence or absence of a posterior morphogen gradient or selection for determinate growth. We find that posterior growth with sequential segmentation is the predominant outcome of our simulations only if a posterior morphogen gradient is assumed to have already evolved and selection for determinate growth occurs secondarily. Otherwise, an alternative segmentation mechanism dominates, in which divisions occur in large bursts through the entire tissue and all segments are created simultaneously. Our study suggests that the ancestry of a posterior signalling centre has played an important role in the evolution of sequential segmentation. In addition, it suggests that determinate growth evolved secondarily, after the evolution of posterior growth. More generally, we demonstrate the potential of evo-devo simulation models that allow us to vary conditions as well as the onset of selection pressures to infer a likely order of evolutionary innovations

In silico evo-devo: reconstructing stages in the evolution of animal segmentation

The evolution of animal segmentation is a major research focus within the field of evolutionary–developmental biology. Most studied segmented animals generate their segments in a repetitive, anterior-to-posterior fashion coordinated with the extension of the body axis from a posterior growth zone. In the current study we ask which selection pressures and ordering of evolutionary events may have contributed to the evolution of this specific segmentation mode. To answer this question we extend a previous in silico simulation model of the evolution of segmentation by allowing the tissue growth pattern to freely evolve. We then determine the likelihood of evolving oscillatory sequential segmentation combined with posterior growth under various conditions, such as the presence or absence of a posterior morphogen gradient or selection for determinate growth. We find that posterior growth with sequential segmentation is the predominant outcome of our simulations only if a posterior morphogen gradient is assumed to have already evolved and selection for determinate growth occurs secondarily. Otherwise, an alternative segmentation mechanism dominates, in which divisions occur in large bursts through the entire tissue and all segments are created simultaneously. Our study suggests that the ancestry of a posterior signalling centre has played an important role in the evolution of sequential segmentation. In addition, it suggests that determinate growth evolved secondarily, after the evolution of posterior growth. More generally, we demonstrate the potential of evo-devo simulation models that allow us to vary conditions as well as the onset of selection pressures to infer a likely order of evolutionary innovations

Prenatal exposure to TCDD and atopic conditions in the Seveso second generation: a prospective cohort study

Abstract Background 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a toxic environmental contaminant that can bioaccumulate in humans, cross the placenta, and cause immunological effects in children, including altering their risk of developing allergies. On July 10, 1976, a chemical explosion in Seveso, Italy, exposed nearby residents to a high amount of TCDD. In 1996, the Seveso Women’s Health Study (SWHS) was established to study the effects of TCDD on women’s health. Using data from the Seveso Second Generation Health Study, we aim to examine the effect of prenatal exposure to TCDD on the risk of atopic conditions in SWHS children born after the explosion. Methods Individual-level TCDD was measured in maternal serum collected soon after the accident. In 2014, we initiated the Seveso Second Generation Health Study to follow-up the children of the SWHS cohort who were born after the explosion or who were exposed in utero to TCDD. We enrolled 677 children, and cases of atopic conditions, including eczema, asthma, and hay fever, were identified by self-report during personal interviews with the mothers and children. Log-binomial and Poisson regressions were used to determine the association between prenatal TCDD and atopic conditions. Results A 10-fold increase in 1976 maternal serum TCDD (log10TCDD) was not significantly associated with asthma (adjusted relative risk (RR) = 0.93; 95% CI: 0.61, 1.40) or hay fever (adjusted RR = 0.99; 95% CI: 0.76, 1.27), but was significantly inversely associated with eczema (adjusted RR = 0.63; 95% CI: 0.40, 0.99). Maternal TCDD estimated at pregnancy was not significantly associated with eczema, asthma, or hay fever. There was no strong evidence of effect modification by child sex. Conclusions Our results suggest that maternal serum TCDD near the time of explosion is associated with lower risk of eczema, which supports other evidence pointing to the dysregulated immune effects of TCDD

Computational models in cardiology

The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions