Modeling a Superorganism - Considerations Regarding the Use of "Dirty" Mice in Biomedical Research
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An ever-expanding body of evidence in both humans and animal models demonstrates the influence of the resident gut microbiota on host health and disease susceptibility. However, as unwanted bacterial, viral, protozoal, and parasitic agents have gradually been eliminated from colonies of purpose-bred laboratory mice, the resident microbiota has lost richness and complexity. Recent studies have shown that the ultra-hygienic environment of traditional laboratory mice and lack of antigenic exposure during development results in mice with an immune system more akin to that of a neonate than an adult human. In contrast, wild mice or mice purchased from pet stores are exposed to much greater antigen burdens and their immune system reflects this with significantly greater numbers of memory T cells and more robust vaccine responses. The current review explores the use of alternative sources for research rodents, with an emphasis on the differences in resident gut microbiota and pathogen burden between wild mice, pet store-origin mice, and traditional laboratory mice. Specifically, the literature is compared and contrasted to our own data reflecting the endogenous gut microbiota and pathogen load of wild and pet store mice, as well as the changes in both during and after procedures intended to eliminate certain zoonotic agents present in pet store mice. These data demonstrate that, while alternative sources of research rodents will likely provide models that are more translatable to the human condition, there are also several real-world considerations for scientists including contamination of research facilities and human health risks such as zoonotic diseases.

Automated sleep classification with chronic neural implants in freely behaving canines.

Objective.Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function.Approach.Here we develop and validate an automated iEEG-based sleep-wake classifier for canines using expert sleep labels derived from simultaneous video, accelerometry, scalp electroencephalography (EEG) and iEEG monitoring. The video, scalp EEG, and accelerometry recordings were manually scored by a board-certified sleep expert into sleep-wake state categories: awake, rapid-eye-movement (REM) sleep, and three non-REM sleep categories (NREM1, 2, 3). The expert labels were used to train, validate, and test a fully automated iEEG sleep-wake classifier in freely behaving canines.Main results. The iEEG-based classifier achieved an overall classification accuracy of 0.878 ± 0.055 and a Cohen's Kappa score of 0.786 ± 0.090. Subsequently, we used the automated iEEG-based classifier to investigate sleep over multiple weeks in freely behaving canines. The results show that the dogs spend a significant amount of the day sleeping, but the characteristics of daytime nap sleep differ from night-time sleep in three key characteristics: during the day, there are fewer NREM sleep cycles (10.81 ± 2.34 cycles per day vs. 22.39 ± 3.88 cycles per night;p< 0.001), shorter NREM cycle durations (13.83 ± 8.50 min per day vs. 15.09 ± 8.55 min per night;p< 0.001), and dogs spend a greater proportion of sleep time in NREM sleep and less time in REM sleep compared to night-time sleep (NREM 0.88 ± 0.09, REM 0.12 ± 0.09 per day vs. NREM 0.80 ± 0.08, REM 0.20 ± 0.08 per night;p< 0.001).Significance.These results support the feasibility and accuracy of automated iEEG sleep-wake classifiers for canine behavior investigations.