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Molecular insights into chronotype and time-of-day effects on decision-making.

ABSTRACT: Recent reports highlight that human decision-making is influenced by the time of day and whether one is a morning or evening person (i.e., chronotype). Here, we test whether these behavioral effects are associated with endogenous biological rhythms. We asked participants to complete two well-established decision-making tasks in the morning or evening: the matrix task (an ethical decision task) and the balloon analog risk task (BART; a risk-taking task), and we measured their chronotype in two ways. First, participants completed a self-report measure, the Horne-Östberg Morningness-Eveningness Questionnaire (MEQ). Second, we measured the expression of two circadian clock-regulated genes-Per3 and Nr1d2-from peripheral clock cells in participants' hair follicle samples. Using a cosinor model, we estimated the phase of the peripheral clock and assigned RNA chronotypes to participants with advanced (larks) or delayed (owls) phases. The behavioral data were analyzed independently for self-reported (MEQ) and RNA-based chronotypes. We find that significant chronotype and/or time-of-day effects between larks and owls in decision-making tasks occur only in RNA-based chronotypes. Our results provide evidence that time-of-day effects on decision-making can be explained by phase differences in oscillating clock genes and suggest that variation in the molecular clockwork may influence inter-individual differences in decision-making behavior.

SUBMITTER: Ingram KK

PROVIDER: S-EPMC4937423 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Molecular insights into chronotype and time-of-day effects on decision-making.

Ingram Krista K KK Ay Ahmet A Kwon Soo Bin SB Woods Kerri K Escobar Sue S Gordon Molly M Smith Isaac H IH Bearden Neil N Filipowicz Allan A Jain Kriti K

Scientific reports 20160708

Recent reports highlight that human decision-making is influenced by the time of day and whether one is a morning or evening person (i.e., chronotype). Here, we test whether these behavioral effects are associated with endogenous biological rhythms. We asked participants to complete two well-established decision-making tasks in the morning or evening: the matrix task (an ethical decision task) and the balloon analog risk task (BART; a risk-taking task), and we measured their chronotype in two wa ...[more]

PMID: 27388366

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Project description:In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect. Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak.

Project description:BackgroundHepatocellular carcinoma (HCC) is the fifth most common cancer and the third leading cause of cancer deaths worldwide, seriously affecting human community health and care. Emerging evidence has shown that aberrant glycosylation is associated with tumor progression and metastasis. However, the role of glycosylation-related genes in HCC has notbeen reported.MethodsWeighted gene coexpression network analysis and non-negative matrix factorization analysis were applied to identify functional modules and molecularm subtypes in HCC. The least absolute shrinkage and selection operator Cox regression was used to construct the glycosylation-related signature. The independent prognostic value of the risk model was confirmed and validated by systematic techniques, including principal component analysis, T-distributed random neighbor embedding analysis, Kaplan-Meier survival analysis, the ROC curve, multivariate Cox regression, the nomogram, and the calibration curve. The single-sample gene set enrichment analysis, gene set variation analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses were evaluated by the immune microenvironment and potential biological processes. The quantitative real-time polymerase chain reaction and immunohistochemistry analysis were used to verify the expression of five genes.ResultsWe identified the glycosylation-related genes with bioinformatics analysis to construct and validate a five-gene signature for the prognosis of HCC patients. Patients with HCC in the high-risk group had a worse prognosis. The risk score could be an independent factor and was associated with clinical features, such as the grade and stage. The nomogram exhibited an accurate score that included the risk score and clinical parameters. The infiltration levels of antitumor cells were upregulated in the low-risk group, including B_cells, Mast_cells, neutrophils, NK_cells, and T_helper_cells. Moreover, glycosylation was more sensitive to immunotherapy, and may play a critical role in the metabolic processes of HCC, such as bile acid metabolism and fatty acid metabolism. In addition, the five-gene messenger RNA (mRNA) and protein expression were overexpressed in HCC cells and tissues.ConclusionsThe glycosylation-related signature is effective for prognostic recognition, immune efficacy evaluation, and substance metabolism in HCC, providing a novel insight for therapeutic target prediction and clinical decision-making.

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Molecular insights into chronotype and time-of-day effects on decision-making.

Publications

Molecular insights into chronotype and time-of-day effects on decision-making.

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