Increasing the statistical power of animal experiments with historical control data

Low statistical power reduces the reliability of animal research; yet, increasing sample sizes to increase statistical power is problematic for both ethical and practical reasons. We present an alternative solution using Bayesian priors based on historical control data, which capitalizes on the observation that control groups in general are expected to be similar to each other. In a simulation study, we show that including data from control groups of previous studies could halve the minimum sample size required to reach the canonical 80% power or increase power when using the same number of animals. We validated the approach on a dataset based on seven independent rodent studies on the cognitive effects of early-life adversity. We present an open-source tool, RePAIR, that can be widely used to apply this approach and increase statistical power, thereby improving the reliability of animal experiments

Deconvolution of Serum Cortisol Levels by Using Compressed Sensing

The pulsatile release of cortisol from the adrenal glands is controlled by a hierarchical system that involves corticotropin releasing hormone (CRH) from the hypothalamus, adrenocorticotropin hormone (ACTH) from the pituitary, and cortisol from the adrenal glands. Determining the number, timing, and amplitude of the cortisol secretory events and recovering the infusion and clearance rates from serial measurements of serum cortisol levels is a challenging problem. Despite many years of work on this problem, a complete satisfactory solution has been elusive. We formulate this question as a non-convex optimization problem, and solve it using a coordinate descent algorithm that has a principled combination of (i) compressed sensing for recovering the amplitude and timing of the secretory events, and (ii) generalized cross validation for choosing the regularization parameter. Using only the observed serum cortisol levels, we model cortisol secretion from the adrenal glands using a second-order linear differential equation with pulsatile inputs that represent cortisol pulses released in response to pulses of ACTH. Using our algorithm and the assumption that the number of pulses is between 15 to 22 pulses over 24 hours, we successfully deconvolve both simulated datasets and actual 24-hr serum cortisol datasets sampled every 10 minutes from 10 healthy women. Assuming a one-minute resolution for the secretory events, we obtain physiologically plausible timings and amplitudes of each cortisol secretory event with R[superscript 2] above 0.92. Identification of the amplitude and timing of pulsatile hormone release allows (i) quantifying of normal and abnormal secretion patterns towards the goal of understanding pathological neuroendocrine states, and (ii) potentially designing optimal approaches for treating hormonal disorders.National Science Foundation (U.S.). Graduate Research Fellowship ProgramNational Institutes of Health (U.S.) (NIH DP1 OD003646)National Science Foundation (U.S.) (0836720)National Science Foundation (U.S.). Office of Emerging Frontiers in Research and Innovation (EFRI-0735956

The Origin of Glucocorticoid Hormone Oscillations

Characterization of a peripheral hormonal system identifies the origin and mechanisms of regulation of glucocorticoid hormone oscillations in rats

Impact of glucocorticoid receptor density on ligand-independent dimerization, cooperative ligand-binding and basal priming of transactivation: a cell culture model

Glucocorticoid receptor (GR) levels vary between tissues and individuals and are altered by physiological and pharmacological effectors. However, the effects and implications of differences in GR concentration have not been fully elucidated. Using three statistically different GR concentrations in transiently transfected COS-1 cells, we demonstrate, using co-immunoprecipitation (CoIP) and fluorescent resonance energy transfer (FRET), that high levels of wild type GR (wtGR), but not of dimerization deficient GR (GRdim), display ligand-independent dimerization. Whole-cell saturation ligand-binding experiments furthermore establish that positive cooperative ligand-binding, with a concomitant increased ligand-binding affinity, is facilitated by ligand-independent dimerization at high concentrations of wtGR, but not GRdim. The down-stream consequences of ligand-independent dimerization at high concentrations of wtGR, but not GRdim, are shown to include basal priming of the system as witnessed by ligand-independent transactivation of both a GRE-containing promoter-reporter and the endogenous glucocorticoid (GC)-responsive gene, GILZ, as well as ligand-independent loading of GR onto the GILZ promoter. Pursuant to the basal priming of the system, addition of ligand results in a significantly greater modulation of transactivation potency than would be expected solely from the increase in ligand-binding affinity. Thus ligand-independent dimerization of the GR at high concentrations primes the system, through ligand-independent DNA loading and transactivation, which together with positive cooperative ligand-binding increases the potency of GR agonists and shifts the bio-character of partial GR agonists. Clearly GR-levels are a major factor in determining the sensitivity to GCs and a critical factor regulating transcriptional programs

The stressed brain of humans and rodents

After stress, the brain is exposed to waves of stress mediators, including corticosterone (in rodents) and cortisol (in humans). Corticosteroid hormones affect neuronal physiology in two time-domains: rapid, non-genomic actions primarily via mineralocorticoid receptors; and delayed genomic effects via glucocorticoid receptors. In parallel, cognitive processing is affected by stress hormones. Directly after stress, emotional behaviour involving the amygdala is strongly facilitated with cognitively a strong emphasis on the now and self, at the cost of higher cognitive processing. This enables the organism to quickly and adequately respond to the situation at hand. Several hours later, emotional circuits are dampened while functions related to the prefrontal cortex and hippocampus are promoted. This allows the individual to rationalize the stressful event and place it in the right context, which is beneficial in the long run. The brain's response to stress depends on an individual's genetic background in interaction with life events. Studies in rodents point to the possibility to prevent or reverse long-term consequences of early life adversity on cognitive processing, by normalizing the balance between the two receptor types for corticosteroid hormones at a critical moment just before the onset of puberty

Effects of early life stress on biochemical indicators of the dopaminergic system:A 3 level meta-analysis of rodent studies

Adverse early life events are a well-established risk factor for the precipitation of behavioral disorders characterized by anomalies in the dopaminergic system, such as schizophrenia and addiction. The correlation between early life conditions and the dopaminergic system has been causally investigated in more than 90 rodent publications. Here, we tested the validity of the hypothesis that early life stress (ELS) alters dopamine signaling by performing an extensive 3-level mixed effect meta-analysis. We included several ELS models and biochemical indicators of the dopaminergic system in a variety of brain areas, for a total of 1009 comparisons. Contrary to our expectations, only a few comparisons displayed a significant effect. Specifically, the striatal area was the most vulnerable, displaying decreased dopamine precursor and increased metabolites after ELS. To make all data openly accessible, we created MaDEapp (https://osf.io/w25m4/), a tool to explore data of the meta-analysis with the intent to guide future (pre)clinical research and allow power calculations. All in all, ELS induces a few yet robust changes on biochemical indicators of the dopaminergic system

Increasing the statistical power of animal experiments with historical control data

Low statistical power reduces the reliability of animal research; yet, increasing sample sizes to increase statistical power is problematic for both ethical and practical reasons. We present an alternative solution using Bayesian priors based on historical control data, which capitalizes on the observation that control groups in general are expected to be similar to each other. In a simulation study, we show that including data from control groups of previous studies could halve the minimum sample size required to reach the canonical 80% power or increase power when using the same number of animals. We validated the approach on a dataset based on seven independent rodent studies on the cognitive effects of early-life adversity. We present an open-source tool, RePAIR, that can be widely used to apply this approach and increase statistical power, thereby improving the reliability of animal experiments.</p