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- 11 beta-hydroxysteroid dehydrogenase 2 (1)
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- HSD11B2[CA]n polymorphism (1)
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Background/Aims: Cortisol plays an important role during pregnancy. It controls maternal glucose metabolism and fetal development. Cortisol metabolism is partially controlled by the 11b-HSD2. This enzyme is expressed in the kidney and human placenta. The activity of the enzyme is partially controlled by functional polymorphisms: the HSD11B2[CA]n microsatellite polymorphism. The impact of this functional gene polymorphism on cortisol metabolism and potential effects on the newborn's is unknown so far. Methods: In the current prospective birth cohort study in southern Asia, we analyzed the association of the HSD11B2[CA]n microsatellite polymorphisms in 187 mothers and their newborn's on maternal and newborn's serum cortisol concentrations. Results: Using multivariable regression analyses considering known confounding ( gestational age, newborn's gender, the labor uterine contraction states and the timing during the day of blood taking), we showed that the fetal HSD11B2[CA]n microsatellite polymorphisms in the first intron was related to maternal cortisol concentration ( R2=0.26, B=96.27, p=0.007), whereas as the newborn's cortisol concentrations were independent of fetal and maternal HSD11B2[CA] n microsatellite polymorphism. Conclusions: Our study showed for the first time that the fetal HSD11B2[CA]n microsatellite polymorphism of the HSD11B2 gene in healthy uncomplicated human pregnancy is associated with maternal cortisol concentration. This indicates that fetal genes controlling cortisol metabolism may affect maternal cortisol concentration and hence physiology in healthy pregnant women.
The interplay between soil structure, roots, and microbiota as a determinant of plant-soil feedback
(2016)
Plant-soil feedback (PSF) can influence plant community structure via changes in the soil microbiome. However, how these feedbacks depend on the soil environment remains poorly understood. We hypothesized that disintegrating a naturally aggregated soil may influence the outcome of PSF by affecting microbial communities. Furthermore, we expected plants to differentially interact with soil structure and the microbial communities due to varying root morphology. We carried out a feedback experiment with nine plant species (five forbs and four grasses) where the training phase consisted of aggregated versus disintegrated soil. In the feedback phase, a uniform soil was inoculated in a fully factorial design with soil washings from conspecific- versus heterospecific-trained soil that had been either disintegrated or aggregated. This way, the effects of prior soil structure on plant performance in terms of biomass production and allocation were examined. In the training phase, soil structure did not affect plant biomass. But on disintegrated soil, plants with lower specific root length (SRL) allocated more biomass aboveground. PSF in the feedback phase was negative overall. With training on disintegrated soil, conspecific feedback was positively correlated with SRL and significantly differed between grasses and forbs. Plants with higher SRL were likely able to easily explore the disintegrated soil with smaller pores, while plants with lower SRL invested in belowground biomass for soil exploration and seemed to be more susceptible to fungal pathogens. This suggests that plants with low SRL could be more limited by PSF on disintegrated soils of early successional stages. This study is the first to examine the influence of soil structure on PSF. Our results suggest that soil structure determines the outcome of PSF mediated by SRL. We recommend to further explore the effects of soil structure and propose to include root performance when working with PSF.
This study pushes our understanding of research reliability by reproducing and replicating claims from 110 papers in leading economic and political science journals. The analysis involves computational reproducibility checks and robustness assessments. It reveals several patterns. First, we uncover a high rate of fully computationally reproducible results (over 85%). Second, excluding minor issues like missing packages or broken pathways, we uncover coding errors for about 25% of studies, with some studies containing multiple errors. Third, we test the robustness of the results to 5,511 re-analyses. We find a robustness reproducibility of about 70%. Robustness reproducibility rates are relatively higher for re-analyses that introduce new data and lower for re-analyses that change the sample or the definition of the dependent variable. Fourth, 52% of re-analysis effect size estimates are smaller than the original published estimates and the average statistical significance of a re-analysis is 77% of the original. Lastly, we rely on six teams of researchers working independently to answer eight additional research questions on the determinants of robustness reproducibility. Most teams find a negative relationship between replicators' experience and reproducibility, while finding no relationship between reproducibility and the provision of intermediate or even raw data combined with the necessary cleaning codes.