Peripheral blood metabolome predicts mood change-related activity in mouse model of bipolar disorder.

Bipolar disorder is a major mental illness characterized by severe swings in mood and activity levels which occur with variable amplitude and frequency. Attempts have been made to identify mood states and biological features associated with mood changes to compensate for current clinical diagnosis, which is mainly based on patients' subjective reports. Here, we used infradian (a cycle > 24 h) cyclic locomotor activity in a mouse model useful for the study of bipolar disorder as a proxy for mood changes. We show that metabolome patterns in peripheral blood could retrospectively predict the locomotor activity levels. We longitudinally monitored locomotor activity in the home cage, and subsequently collected peripheral blood and performed metabolomic analyses. We then constructed cross-validated linear regression models based on blood metabolome patterns to predict locomotor activity levels of individual mice. Our analysis revealed a significant correlation between actual and predicted activity levels, indicative of successful predictions. Pathway analysis of metabolites used for successful predictions showed enrichment in mitochondria metabolism-related terms, such as "Warburg effect" and "citric acid cycle." In addition, we found that peripheral blood metabolome patterns predicted expression levels of genes implicated in bipolar disorder in the hippocampus, a brain region responsible for mood regulation, suggesting that the brain-periphery axis is related to mood-change-associated behaviors. Our results may serve as a basis for predicting individual mood states through blood metabolomics in bipolar disorder and other mood disorders and may provide potential insight into systemic metabolic activity in relation to mood changes.

Genomic Basis of Circannual Rhythm in the European Corn Borer Moth.

Synchronizing the annual timing of physiological, morphological, and behavioral transitions with seasons enables survival in temperate environments [1]. The capacity to adjust life history timing and track local seasonal cycles can facilitate geographic expansion [2], adaptation [3], and tolerance [4-6] during rapid environmental change. Understanding the proximate causes of variation in seasonal timing improves prediction of future response and persistence [7, 8]. However, relatively little is known about the molecular basis generating this diversity [9], particularly in Lepidoptera, a group with many species in decline [10, 11]. In insects, the stress-tolerant physiological state of diapause enables coping with seasonal challenges [1, 12-15]. Seasonal changes in photoperiod and temperature are used to synchronize diapause with winter, and timing of diapause transitions varies widely within and among species [9, 16]. Changes in spring diapause termination in the European corn borer moth (Ostrinia nubilalis) have allowed populations to respond to shorter winters and emerge ∼3 weeks earlier in the year [17]. Multiple whole-genome approaches suggest two circadian clock genes, period (per) and pigment-dispersing factor receptor (Pdfr), underlie this polymorphism. Per and Pdfr are within interacting quantitative trait loci (QTL) and differ in allele frequency among individuals that end diapause early or late, with alleles maintained in high linkage disequilibrium. Our results provide testable hypotheses about the physiological role of circadian clock genes in the circannual timer. We predict these gene candidates will be targets of selection for future adaptation under continued global climate change [18].