Body weight was measured across multiple days within each mouse and thus a repeated measurement linear mixed effects model was used to describe the change in body weight across days and BCG-treatment groups. The model included the fixed

effects of BCG-treatment level (BCG0, BCG5, and BCG10), day (Day 0–5), the interaction among BCG treatment group and day and body weight at Day −1. Preliminary tests indicated that the repeated structure of the measurements was adequately modeled by an autoregressive order 1 structure including heterogeneity of variances across days and mouse was the experimental Staurosporine manufacturer unit. Univariate linear models were used to describe the change in weight between Day 0 and Day 2, the change in weight between Day 2 and Day 5, locomotor activity, rearing, immobility in the forced swim and tail suspension tests, and sucrose preference. These models included the classification fixed effect of BCG-treatment level and the covariate body weight at Day −1. Additional terms were included Roxadustat mouse in the models of specific indicators. Models describing sickness indicators included as covariates

depression-like indicators meanwhile models describing depression-like indicators included as covariates sickness indicators. This strategy enabled the study of the effect of BCG challenge on sickness or depression-like indicators adjusted for depression-like or sickness, respectively. Covariates were nested within BCG-treatment group to account for

the different trends of the covariates within group. Evaluation of the differences between Protein tyrosine phosphatase observed and predicted values enabled the identification of possible outliers and assessment of departures from the normality assumption. For the sample size available, the statistical significance of parametric tests was confirmed using a non-parametric resampling approach including 10,000 bootstrap samples. Resampling followed PROC MULTEST and the merBoot method in SAS and R, respectively. While univariate models describe one indicator at a time, multivariate models consider multiple response indicator variables. Multivariate models are advantageous when the response variables are correlated through the signal or noise components of the model. There is a compromise between the gains in precision to detect the relationship between the indicators and explanatory variables and the additional parameters in the multivariate relative to the univariate models (Stearns et al., 2005 and Serão et al., 2013). In a multivariate analysis, the test statistics available to assess the association between BCG-treatment group and behavioral indicators are equivalent when comparing two groups. Thus, results from one test, the Roy’s greatest characteristic root are presented. The multivariate models included the same cofactors and covariates used in the univariate models.