SUMMARY Mammals rely on a network of circadian clocks to control daily systemic metabolism and physiology. The central pacemaker in the suprachiasmatic nucleus (SCN) is considered hierarchically dominant over peripheral clocks, whose degree of independence, or tissue level autonomy, has never been ascertained in vivo. Using arrhythmic Bmal1-null mice, we generated animals with reconstituted circadian expression of BMAL1 exclusively in the liver (Liver-RE). High-throughput transcriptomics and metabolomics show that the liver has independent circadian functions, specific for metabolic processes such as the NAD+ salvage pathway and glycogen turnover. However, although BMAL1 occupies chromatin at most genomic targets in Liver-RE mice, circadian expression is restricted to ~ 10% of normally rhythmic transcripts. Finally, rhythmic clock gene expression is lost in Liver-RE mice under constant darkness. Hence, full circadian function in the liver depends on signals emanating from other clocks and light contributes to tissue-autonomous clock function.

While the timing of food intake is important, it is unclear whether the effects of exercise on energy metabolism are restricted to unique time windows. As circadian regulation is key to controlling metabolism, understanding the impact of exercise performed at different times of the day is relevant for physiology and homeostasis. Using high-throughput transcriptomic and metabolomic approaches, we identify distinct responses of metabolic oscillations that characterize exercise in either the early rest phase or the early active phase in mice. Notably, glycolytic activation is specific to exercise at the active phase. At the molecular level, HIF1a, a central regulator of glycolysis during hypoxia, is selectively activated in a time-dependent manner upon exercise, resulting in carbohydrate exhaustion, usage of alternative energy sources, and adaptation of systemic energy expenditure. Our findings demonstrate that the time of day is a critical factor to amplify the beneficial impact of exercise on both metabolic pathways within skeletal muscle and systemic energy homeostasis.

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The inner workings of the clock system rely on communicating signals between distal tissues to maintain daily metabolism.

Rapid antimicrobial susceptibility testing (AST) is an integral tool to mitigate the unnecessary use of powerful and broad-spectrum antibiotics that leads to the proliferation of multi-drug-resistant bacteria. Using a sensor platform composed of surface-enhanced Raman scattering (SERS) sensors with control of nanogap chemistry and machine learning algorithms for analysis of complex spectral data, bacteria metabolic profiles post antibiotic exposure are correlated with susceptibility. Deep neural network models are able to discriminate the responses of Escherichia coli and Pseudomonas aeruginosa to antibiotics from untreated cells in SERS data in 10 min after antibiotic exposure with greater than 99% accuracy. Deep learning analysis is also able to differentiate responses from untreated cells with antibiotic dosages up to 10-fold lower than the minimum inhibitory concentration observed in conventional growth assays. In addition, analysis of SERS data using a generative model, a variational autoencoder, identifies spectral features in the P. aeruginosa lysate data associated with antibiotic efficacy. From this insight, a combinatorial dataset of metabolites is selected to extend the latent space of the variational autoencoder. This culture-free dataset dramatically improves classification accuracy to select effective antibiotic treatment in 30 min. Unsupervised Bayesian Gaussian mixture analysis achieves 99.3% accuracy in discriminating between susceptible versus resistant to antibiotic cultures in SERS using the extended latent space. Discriminative and generative models rapidly provide high classification accuracy with small sets of labeled data, which enormously reduces the amount of time needed to validate phenotypic AST with conventional growth assays. Thus, this work outlines a promising approach toward practical rapid AST.

Circadian gene expression driven by transcription activators CLOCK and BMAL1 is intimately associated with dynamic chromatin remodeling. However, how cellular metabolism directs circadian chromatin remodeling is virtually unexplored. We report that the S-adenosylhomocysteine (SAH) hydrolyzing enzyme adenosylhomocysteinase (AHCY) cyclically associates to CLOCK-BMAL1 at chromatin sites and promotes circadian transcriptional activity. SAH is a potent feedback inhibitor of S-adenosylmethionine (SAM)–dependent methyltransferases, and timely hydrolysis of SAH by AHCY is critical to sustain methylation reactions. We show that AHCY is essential for cyclic H3K4 trimethylation, genome-wide recruitment of BMAL1 to chromatin, and subsequent circadian transcription. Depletion or targeted pharmacological inhibition of AHCY in mammalian cells markedly decreases the amplitude of circadian gene expression. In mice, pharmacological inhibition of AHCY in the hypothalamus alters circadian locomotor activity and rhythmic transcription within the suprachiasmatic nucleus. These results reveal a previously unappreciated connection between cellular metabolism, chromatin dynamics, and circadian regulation.

While deep neural networks (DNNs) and other machine learning models often have higher accuracy than simpler models like logistic regression (LR), they are often considered to be “black box” models and this lack of interpretability and transparency is considered a challenge for clinical adoption. In healthcare, intelligible models not only help clinicians to understand the problem and create more targeted action plans, but also help to gain the clinicians’ trust. One method of overcoming the limited interpretability of more complex models is to use Generalized Additive Models (GAMs). Standard GAMs simply model the target response as a sum of univariate models. Inspired by GAMs, the same idea can be applied to neural networks through an architecture referred to as Generalized Additive Models with Neural Networks (GAM-NNs). In this manuscript, we present the development and validation of a model applying the concept of GAM-NNs to allow for interpretability by visualizing the learned feature patterns related to risk of in-hospital mortality for patients undergoing surgery under general anesthesia. The data consists of 59,985 patients with a feature set of 46 features extracted at the end of surgery to which we added previously not included features: total anesthesia case time (1 feature); the time in minutes spent with mean arterial pressure (MAP) below 40, 45, 50, 55, 60, and 65 mmHg during surgery (6 features); and Healthcare Cost and Utilization Project (HCUP) Code Descriptions of the Primary current procedure terminology (CPT) codes (33 features) for a total of 86 features. All data were randomly split into 80% for training (n = 47,988) and 20% for testing (n = 11,997) prior to model development. Model performance was compared to a standard LR model using the same features as the GAM-NN. The data consisted of 59,985 surgical records, and the occurrence of in-hospital mortality was 0.81% in the training set and 0.72% in the testing set. The GAM-NN model with HCUP features had the highest area under the curve (AUC) 0.921 (0.895–0.95). Overall, both GAM-NN models had higher AUCs than LR models, however, had lower average precisions. The LR model without HCUP features had the highest average precision 0.217 (0.136–0.31). To assess the interpretability of the GAM-NNs, we then visualized the learned contributions of the GAM-NNs and compared against the learned contributions of the LRs for the models with HCUP features. Overall, we were able to demonstrate that our proposed generalized additive neural network (GAM-NN) architecture is able to (1) leverage a neural network’s ability to learn nonlinear patterns in the data, which is more clinically intuitive, (2) be interpreted easily, making it more clinically useful, and (3) maintain model performance as compared to previously published DNNs.

Food is a powerful entrainment cue for circadian clocks in peripheral tissues, and changes in the composition of nutrients have been demonstrated to metabolically reprogram peripheral clocks. However, how food challenges may influence circadian metabolism of the master clock in the suprachiasmatic nucleus (SCN) or in other brain areas is poorly understood. Using high-throughput metabolomics, we studied the circadian metabolome profiles of the SCN and medial prefrontal cortex (mPFC) in lean mice compared with mice challenged with a high-fat diet (HFD). Both the mPFC and the SCN displayed a robust cyclic metabolism, with a strikingly high sensitivity to HFD perturbation in an area-specific manner. The phase and amplitude of oscillations were drastically different between the SCN and mPFC, and the metabolic pathways impacted by HFD were remarkably region-dependent. Furthermore, HFD induced a significant increase in the number of cycling metabolites exclusively in the SCN, revealing an unsuspected susceptibility of the master clock to food stress.