Gut Microbiome Composition and Its Association with Sleep in Major Psychiatric Disorders.

INTRODUCTION: Sleep disturbances are highly prevalent across most major psychiatric disorders. Alterations in the hypothalamic-pituitary-adrenal axis, neuroimmune mechanisms, and circadian rhythm disturbances partially explain this connection. The gut microbiome is also suspected to play a role in sleep regulation, and recent studies suggest that certain probiotics, prebiotics, synbiotics, and fecal microbiome transplantation can improve sleep quality. METHODS: We aimed to assess the relationship between gut-microbiota composition, psychiatric disorders, and sleep quality in this cross-sectional, cross-disorder study. We recruited 103 participants, 63 patients with psychiatric disorders (major depressive disorder [n = 31], bipolar disorder [n = 13], psychotic disorder [n = 19]) along with 40 healthy controls. Sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI). The fecal microbiome was analyzed using 16S rRNA sequencing, and groups were compared based on alpha and beta diversity metrics, as well as differentially abundant species and genera. RESULTS: A transdiagnostic decrease in alpha diversity and differences in beta diversity indices were observed in psychiatric patients, compared to controls. Correlation analysis of diversity metrics and PSQI score showed no significance in the patient and control groups. However, three species, Ellagibacter isourolithinifaciens, Senegalimassilia faecalis, and uncultured Blautia sp., and two genera, Senegalimassilia and uncultured Muribaculaceae genus, were differentially abundant in psychiatric patients with good sleep quality (PSQI >8), compared to poor-sleep quality patients (PSQI ≤8). CONCLUSION: In conclusion, this study raises important questions about the interconnection of the gut microbiome and sleep disturbances.

An approach to high throughput measurement of accurate retention data in liquid chromatography.

Efforts to model and simulate various aspects of liquid chromatography (LC) separations (e.g., retention, selectivity, peak capacity, injection breakthrough) depend on experimental retention measurements to use as the basis for the models and simulations. Often these modeling and simulation efforts are limited by datasets that are too small because of the cost (time and money) associated with making the measurements. Other groups have demonstrated improvements in throughput of LC separations by focusing on "overhead" associated with the instrument itself - for example, between-analysis software processing time, and autosampler motions. In this paper we explore the possibility of using columns with small volumes (i.e., 5 mm x 2.1 mm i.d.) compared to conventional columns (e.g., 100 mm x 2.1 mm i.d.) that are typically used for retention measurements. We find that isocratic retention factors calculated for columns with these dimensions are different by about 20%; we attribute this difference - which we interpret as an error in measurements based on data from the 5 mm column - to extra-column volume associated with inlet and outlet frits. Since retention factor is a thermodynamic property of the mobile/stationary phase system under study, it should be independent of the dimensions of the column that is used for the measurement. We propose using ratios of retention factors (i.e., selectivities) to translate retention measurements between columns of different dimensions, so that measurements made using small columns can be used to make predictions for separations that involve conventional columns. We find that this approach reduces the difference in retention factors (5 mm compared to 100 mm columns) from an average of 18% to an average absolute difference of 1.7% (all errors less than 8%). This approach will significantly increase the rate at which high quality retention data can be collected to thousands of measurements per instrument per day, which in turn will likely have a profound impact on the quality of models and simulations that can be developed for many aspects of LC separations.