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.