Systat

Two-way analysis of variance (ANOVA) was conducted on all variables (SYSTAT; SPSS Inc.). Where there were no significant interactions, post hoc tests, specifically least significant difference (LSD), were undertaken. Where significant interactions were found, one-way ANOVAs and/or t-tests were conducted on the variables, and these results, and associated LSDs, were reported. Data for stem and branch number were log-transformed to normalize the data for analysis. Untransformed data are presented in the tables.

For each physiological test, we conducted a nested repeated measures analysis of variance (ANOVA). The independent variables were adapted state (parental, 16 °C-adapted, and 28 °C-adapted) and strain origin (AK, MT, and NV), with strain origin nested within adapted state. Incubation temperature was the repeated measure. The dependent variable was spore production, biomass, or MSR. Kolmogorov-Smirnov post hoc tests were used to assess pairwise differences. Significant interactions between adapted state and incubation temperature would support our hypotheses if the 16 °C- or 28 °C-adapted strains also displayed significantly higher sporulation, less biomass, and lower MSR than the parental strains when all were incubated at the selective temperature. Differences were considered significant when P < 0.05. We ranked all data, because they did not conform to assumptions for normality or homogeneity of variances. All statistical analyses were done using the statistical program R (www.r-project.org) and SYSTAT (SPSS, Evanston, IL).

Derived (normalized) values from RTqPCR, ChIP, ChSP and dual luciferase assays were log₁₀ transformed before analysis by one-way ANOVA followed by the Holm-Sidak multiple comparison test, or by unpaired Student’s t-test using SYSTAT (version 13; SPSS Inc., Chicago, IL). Data are reported as the mean ± standard error of the mean (SEM). When non-parametric tests were required, the Kruskal-Wallace one-way analysis of variance or Mann–Whitney U-Test were used. Results of the statistical analyses are reported in the figure legends.

Quantitative variables are reported as mean ± SD, unless specified. Categorical variables are reported as percentage and differences in the distribution and were tested by χ² test. Differences across groups were tested by ANCOVA, adjusting for age and sex. Intra-individual comparisons of baseline and follow-up variables were performed with paired t-tests. Multivariable logistic regression was used to predict the likelihood of transitioning from healthy to unhealthy metabolic status. The reproducibility of metabolically healthy OwOb was evaluated using kappa statistics, according to Cohen’s method using linear weight [20 (link)]. The standard error and 95% confidence interval were calculated according to Fleiss et al. [21 ]. The strength of agreement was defined as poor if kappa was <0.20, fair if kappa was 0.21–0.40, moderate if kappa was 0.41–0.60, good if kappa was 0.61–0.80, and very good if kappa was 0.81–1.00 [22 ]. A two-tailed probability value ≤ 0.05 was considered significant. Analyses were performed using Systat version 12 (SPSS Inc., Evanston, IL, USA), and MedCalc version 20.218 (MedCalc Software Ltd., Ostend, Belgium).

All results were subjected to one-way analysis of variance (ANOVA) with the aid of Systat (version 12.0 for Windows) from SPSS Science (17.0.1, Chicago, IL, USA, 2008), and represent the means ± SD of 20 seeds per group. Differences in the mean value between groups were assessed by a two-tailed Student’s t-test and a p < 0.05 was considered statistically significant.

Normoglycemic and hyperglycemic samples were evaluated to analyze the changes in protein expression. A one-way analysis of variance (ANOVA, parametric) was conducted for the group comparisons using the Bonferroni correction for multiple post hoc analyses. All the statistical analysis was performed using SPSS software (Systat version 13.0, SPSS Inc., Chicago, IL, USA), and a p-value ≤ 0.05 was considered to be statistically significant. The results were demonstrated as mean ± SEM.