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Recently, PGs have gained attention as having important roles in brain and additional behaviors, e.g., anxiety-like behavior (Arentsen et al., 2018), brain development (Arentsen et al., 2017), thermoregulation (Gabanyi et al., 2022), and feeding (Gabanyi et al., 2022), yet a mechanistic understanding of microbe-host dynamics and characterization of the molecules involved requires assessment of brain area changes in PG associated with physiological variation. DESeq2 is suited to analysis of datasets with the noted limitations and provides a statistical approach that is based on pooling of information for multiple genes and uses assumptions of gene variance measured within a single experiment to provide a quantitative approach, highlighting the strength of differential expression as opposed to simply the presence of differentially expressed genes (Love et al., 2014). Sampling times were selected based on the 24 h, rest-activity patterns of wildtype mice, ZT0, the transition from the dark to light period and also the transition from active to inactive times of day, ZT3, corresponding to consolidated rest, i.e., immediately following a dramatic increase in sleep (from ZT0 to 3), ZT6, the rest period midpoint, ZT12, the transition from the light to dark period, and the transition from the primary rest to primary active times of day, and ZT15, corresponding to consolidated activity (Figures 1A, B). Multiple reviews and primary data publications address these molecular mechanisms (e.g., Krueger et al., 2008, Krueger et al., 2013, Imeri and Opp, 2009, Krueger et al., 1984, Rehman et al., 2001, Oles et al., 2020, Krueger and Majde, 2017, Pabst et al., 1999, Lee et al., 2025, Wheeler et al., 2023, Chedid et al., 1984, Bhusal et al., 2024, Shoham et al., 1989, Dinarello and Krueger, 1986, Churchill et al., 2008, Yasuda et al., 2005, Opp, 2005, Laman et al., 2020, Tosoni et al., 2019, Wheeler and Gomperts Boneca, 2024).