Untargeted metabolomics reveals a mild impact of remote ischemic conditioning on the plasma metabolome and α-hydroxybutyrate as a possible cardioprotective factor and biomarker of tissue ischemia

Introduction

Remote ischemic conditioning (RIC) is a maneuver by which short non-lethal ischemic events are applied on distant organs or limbs to reduce ischemia and reperfusion injuries caused by e.g. myocardial infarct. Although intensively investigated, the specific mechanism of this protective phenomenon remains incompletely understood and in particular, knowledge on the role of small metabolites is scarce.

Objectives

In this study, we aimed to study perturbations in the plasma metabolome following RIC and gain insight into metabolic changes by the intervention as well as to identify potential novel cardio-protective metabolites.

Methods

Blood plasma samples from ten healthy males were collected prior to and after RIC and tested for bioactivity in a HL-1 based cellular model of ischemia–reperfusion damage. Following this, the plasma was analyzed using untargeted LC-qTOF-MS and regulated metabolites were identified using univariate and multivariate statistical analysis. Results were finally verified in a second plasma study from the same group of volunteers and by testing a metabolite ester in the HL-1 cell model.

Results

The analysis revealed a moderate impact on the plasma metabolome following RIC. One metabolite, α-hydroxybutyrate (AHB) however, stood out as highly significantly upregulated after RIC. AHB might be a novel and more sensitive plasma-biomarker of transient tissue ischemia than lactate. Importantly, it was also found that a cell permeable AHB precursor protects cardiomyocytes from ischemia–reperfusion damage.

Conclusion

Untargeted metabolomics analysis of plasma following RIC has led to insight into metabolism during RIC and revealed a possible novel metabolite of relevance to ischemic-reperfusion damage.

     

Genomic prediction of starch content and chipping quality in tetraploid potato using genotyping-by-sequencing

Key message

Genomic prediction models for starch content and chipping quality show promising results, suggesting that genomic selection is a feasible breeding strategy in tetraploid potato.

Abstract

Genomic selection uses genome-wide molecular markers to predict performance of individuals and allows selections in the absence of direct phenotyping. It is regarded as a useful tool to accelerate genetic gain in breeding programs, and is becoming increasingly viable for crops as genotyping costs continue to fall. In this study, we have generated genomic prediction models for starch content and chipping quality in tetraploid potato to facilitate varietal development. Chipping quality was evaluated as the colour of a potato chip after frying following cold induced sweetening. We used genotyping-by-sequencing to genotype 762 offspring, derived from a population generated from biparental crosses of 18 tetraploid parents. Additionally, 74 breeding clones were genotyped, representing a test panel for model validation. We generated genomic prediction models from 171,859 single-nucleotide polymorphisms to calculate genomic estimated breeding values. Cross-validated prediction correlations of 0.56 and 0.73 were obtained within the training population for starch content and chipping quality, respectively, while correlations were lower when predicting performance in the test panel, at 0.30–0.31 and 0.42–0.43, respectively. Predictions in the test panel were slightly improved when including representatives from the test panel in the training population but worsened when preceded by marker selection. Our results suggest that genomic prediction is feasible, however, the extremely high allelic diversity of tetraploid potato necessitates large training populations to efficiently capture the genetic diversity of elite potato germplasm and enable accurate prediction across the entire spectrum of elite potatoes. Nonetheless, our results demonstrate that GS is a promising breeding strategy for tetraploid potato.

     

Surviving winter: Food,but not habitat structure,prevents crashes in cyclic vole populations

Vole population cycles are a major force driving boreal ecosystem dynamics in northwestern Eurasia. However, our understanding of the impact of winter on these cycles is increasingly uncertain, especially because climate change is affecting snow predictability, quality, and abundance. We examined the role of winter weather and snow conditions, the lack of suitable habitat structure during freeze‐thaw periods, and the lack of sufficient food as potential causes for winter population crashes. We live‐trapped bank voles Myodes glareolus on 26 plots (0.36 ha each) at two different elevations (representing different winter conditions) in southeast Norway in the winters 2013/2014 and 2014/2015. We carried out two manipulations: supplementing six plots with food to eliminate food limitation and six plots with straw to improve habitat structure and limit the effect of icing in the subnivean space. In the first winter, all bank voles survived well on all plots, whereas in the second winter voles on almost all plots went extinct except for those receiving supplemental food. Survival was highest on the feeding treatment in both winters, whereas improving habitat structure had no effect. We conclude that food limitation was a key factor in causing winter population crashes.     

Computational visual ecology in the pelagic realm

Visual performance and visual interactions in pelagic animals are notoriously hard to investigate because of our restricted access to the habitat. The pelagic visual world is also dramatically different from benthic or terrestrial habitats, and our intuition is less helpful in understanding vision in unfamiliar environments. Here, we develop a computational approach to investigate visual ecology in the pelagic realm. Using information on eye size, key retinal properties, optical properties of the water and radiance, we develop expressions for calculating the visual range for detection of important types of pelagic targets. We also briefly apply the computations to a number of central questions in pelagic visual ecology, such as the relationship between eye size and visual performance, the maximum depth at which daylight is useful for vision, visual range relations between prey and predators, counter-illumination and the importance of various aspects of retinal physiology. We also argue that our present addition to computational visual ecology can be developed further, and that a computational approach offers plenty of unused potential for investigations of visual ecology in both aquatic and terrestrial habitats.     

Upstream migration of adult Atlantic salmon, Salmo salar L., in the River Vefsna, northern Norway

Daily ascents of adult Atlantic salmon ( Salmo salar ) in the Laksforsen waterfall in the River Vefsna were recorded over 3 years. Ascent was correlated to 12 physical and meteorological parameters by multiple regression analysis. Change in water temperature was included in the regression model in all 3 years, and change in river flow was also included in 2 of the 3 years. No other variable was included in the regression model at the 0.05 significance level.