Data and statistical analysis
The study design included a priori power analysis to determine the sample size for each experiment/subgroup. We used data from prior/similar experiments to determine the effect sizes and SDs. These were used in a power analysis with the software G*Power (Faul, Erdfelder, Lang & Buchner). To determine the necessary sample sizes, we assumed a type I error of α = 5% and a statistical power of at least 80%. Mice were allocated to the experimental groups by a different person than the experimenter and this person also regenotyped the mice after killing and allocated the genotype to the mouse number. A fully blinded experiment is not possible, because the experienced experimenter has a high chance of correctly guessing the genotype based on the different phenotypes of knockout and wild-type mice. Sample sizes were calculated as biological replicates and were allocated equally to the experimental groups. Studies were designed to generate groups of equal size; however, accidental deaths (spontaneous or euthanasia due to disease) occurred during the preparatory period (transfer of the mice after group assignment to an animal experiment laboratory, one week obligatory acclimatization, removal of PEG laxative at day 1 of the experimental period), resulting in slightly uneven numbers in theCftr-/- groups. The data were analyzed with GraphPad Prism Version 8.0.2, adhering to the British Journal of Pharmacology’s recommendations for experimental design and analysis in pharmacology (Curtis et al., 2018). All results are presented as mean ± SEM. The body weight, stool water content and GI transit time are averaged for each time point. Unpaired Students’t-test (for parametric data with normal distribution) or non-parametric Mann-Whitney U-test was used for the comparisons between vehicle and tenapanor treated groups and/or within genotype. Area under the curve was used to analyze the daily body weight and stool water content. Obstruction and survival curve was analysed by the Kaplan-Meier log-rank test. For multiple comparisons between the genotypes, the one-way ANOVA (for parametric data with normal distribution) was used with the Bonferroni post hoc analysis only if the F value of the ANOVA reached significance. If the normality test failed with one or both groups, the non-parametric Kruskal-Wallis test was performed. Significant differences are indicated as: # and *p < 0.05.