Defining the Independence of the Liver Circadian Clock.

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.

Brain histone beta-hydroxybutyrylation couples metabolism with gene expression.

Little is known about the impact of metabolic stimuli on brain tissue at a molecular level. The ketone body beta-hydroxybutyrate (BHB) can be a signaling molecule regulating gene transcription. Thus, we assessed lysine beta-hydroxybutyrylation (K-bhb) levels in proteins extracted from the cerebral cortex of mice undergoing a ketogenic metabolic challenge (48 h fasting). We found that fasting enhanced K-bhb in a variety of proteins including histone H3. ChIP-seq experiments showed that K9 beta-hydroxybutyrylation of H3 (H3K9-bhb) was significantly enriched by fasting on more than 8000 DNA loci. Transcriptomic analysis showed that H3K9-bhb on enhancers and promoters correlated with active gene expression. One of the most enriched functional annotations both at the epigenetic and transcriptional level was "circadian rhythms''. Indeed, we found that the diurnal oscillation of specific transcripts was modulated by fasting at distinct zeitgeber times both in the cortex and suprachiasmatic nucleus. Moreover, specific changes in locomotor activity daily features were observed during re-feeding after 48-h fasting. Thus, our results suggest that fasting remarkably impinges on the cerebral cortex transcriptional and epigenetic landscape, and BHB acts as a powerful epigenetic molecule in the brain through direct and specific histone marks remodeling in neural tissue cells.

CircadiOmics: circadian omic web portal.

Circadian rhythms are a foundational aspect of biology. These rhythms are found at the molecular level in every cell of every living organism and they play a fundamental role in homeostasis and a variety of physiological processes. As a result, biomedical research of circadian rhythms continues to expand at a rapid pace. To support this research, CircadiOmics (http://circadiomics.igb.uci.edu/) is the largest annotated repository and analytic web server for high-throughput omic (e.g. transcriptomic, metabolomic, proteomic) circadian time series experimental data. CircadiOmics contains over 290 experiments and over 100 million individual measurements, across >20 unique tissues/organs, and 11 different species. Users are able to visualize and mine these datasets by deriving and comparing periodicity statistics for oscillating molecular species including: period, amplitude, phase, P-value and q-value. These statistics are obtained from BIO_CYCLE and JTK_CYCLE and are intuitively aggregated and displayed for comparison. CircadiOmics is the most up-to-date and cutting-edge web portal for searching and analyzing circadian omic data and is used by researchers around the world.

Reshaping circadian metabolism in the suprachiasmatic nucleus and prefrontal cortex by nutritional challenge.

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.

MiR-29 coordinates age-dependent plasticity brakes in the adult visual cortex.

Visual cortical circuits show profound plasticity during early life and are later stabilized by molecular "brakes" limiting excessive rewiring beyond a critical period. The mechanisms coordinating the expression of these factors during the transition from development to adulthood remain unknown. We found that miR-29a expression in the visual cortex dramatically increases with age, but it is not experience-dependent. Precocious high levels of miR-29a blocked ocular dominance plasticity and caused an early appearance of perineuronal nets. Conversely, inhibition of miR-29a in adult mice using LNA antagomirs activated ocular dominance plasticity, reduced perineuronal nets, and restored their juvenile chemical composition. Activated adult plasticity had the typical functional and proteomic signature of critical period plasticity. Transcriptomic and proteomic studies indicated that miR-29a manipulation regulates the expression of plasticity brakes in specific cortical circuits. These data indicate that miR-29a is a regulator of the plasticity brakes promoting age-dependent stabilization of visual cortical connections.

Medical Informatics Operating Room Vitals and Events Repository (MOVER): a public-access operating room database.

Objectives: Artificial intelligence (AI) holds great promise for transforming the healthcare industry. However, despite its potential, AI is yet to see widespread deployment in clinical settings in significant part due to the lack of publicly available clinical data and the lack of transparency in the published AI algorithms. There are few clinical data repositories publicly accessible to researchers to train and test AI algorithms, and even fewer that contain specialized data from the perioperative setting. To address this gap, we present and release the Medical Informatics Operating Room Vitals and Events Repository (MOVER). Materials and Methods: This first release of MOVER includes adult patients who underwent surgery at the University of California, Irvine Medical Center from 2015 to 2022. Data for patients who underwent surgery were captured from 2 different sources: High-fidelity physiological waveforms from all of the operating rooms were captured in real time and matched with electronic medical record data. Results: MOVER includes data from 58 799 unique patients and 83 468 surgeries. MOVER is available for download at https://doi.org/10.24432/C5VS5G, it can be downloaded by anyone who signs a data usage agreement (DUA), to restrict traffic to legitimate researchers. Discussion: To the best of our knowledge MOVER is the only freely available public data repository that contains electronic health record and high-fidelity physiological waveforms data for patients undergoing surgery. Conclusion: MOVER is freely available to all researchers who sign a DUA, and we hope that it will accelerate the integration of AI into healthcare settings, ultimately leading to improved patient outcomes.

MOVER: Medical Informatics Operating Room Vitals and Events Repository.

Artificial Intelligence (AI) holds great promise for transforming the healthcare industry. However, despite its potential, AI is yet to see widespread deployment in clinical settings in significant part due to the lack of publicly available clinical data and the lack of transparency in the published AI algorithms. There are few clinical data repositories publicly accessible to researchers to train and test AI algorithms, and even fewer that contain specialized data from the perioperative setting. To address this gap, we present and release the Medical Informatics Operating Room Vitals and Events Repository, which includes data from 58,799 unique patients and 83,468 surgeries collected from the UCI Medical Center over a period of seven years. MOVER is freely available to all researchers who sign a data usage agreement, and we hope that it will accelerate the integration of AI into healthcare settings, ultimately leading to improved patient outcomes.

Liver and muscle circadian clocks cooperate to support glucose tolerance in mice.

Physiology is regulated by interconnected cell and tissue circadian clocks. Disruption of the rhythms generated by the concerted activity of these clocks is associated with metabolic disease. Here we tested the interactions between clocks in two critical components of organismal metabolism, liver and skeletal muscle, by rescuing clock function either in each organ separately or in both organs simultaneously in otherwise clock-less mice. Experiments showed that individual clocks are partially sufficient for tissue glucose metabolism, yet the connections between both tissue clocks coupled to daily feeding rhythms support systemic glucose tolerance. This synergy relies in part on local transcriptional control of the glucose machinery, feeding-responsive signals such as insulin, and metabolic cycles that connect the muscle and liver. We posit that spatiotemporal mechanisms of muscle and liver play an essential role in the maintenance of systemic glucose homeostasis and that disrupting this diurnal coordination can contribute to metabolic disease.

Bioinformatics and Systems Biology of Circadian Rhythms: BIO_CYCLE and CircadiOmics.

Circadian rhythms are fundamental to biology and medicine and today these can be studied at the molecular level in high-throughput fashion using various omic technologies. We briefly present two resources for the study of circadian omic (e.g. transcriptomic, metabolomic, proteomic) time series. First, BIO_CYCLE is a deep-learning-based program and web server that can analyze omic time series and statistically assess their periodic nature and, when periodic, accurately infer the corresponding period, amplitude, and phase. Second, CircadiOmics is the larges annotated repository of circadian omic time series, containing over 260 experiments and 90 million individual measurements, across multiple organs and tissues, and across 9 different species. In combination, these tools enable powerful bioinformatics and systems biology analyses. The are currently being deployed in a host of different projects where they are enabling significant discoveries: both tools are publicly available over the web at: http://circadiomics.ics.uci.edu/ .

The hidden link between circadian entropy and mental health disorders.

The high overlapping nature of various features across multiple mental health disorders suggests the existence of common psychopathology factor(s) (p-factors) that mediate similar phenotypic presentations across distinct but relatable disorders. In this perspective, we argue that circadian rhythm disruption (CRD) is a common underlying p-factor that bridges across mental health disorders within their age and sex contexts. We present and analyze evidence from the literature for the critical roles circadian rhythmicity plays in regulating mental, emotional, and behavioral functions throughout the lifespan. A review of the literature shows that coarse CRD, such as sleep disruption, is prevalent in all mental health disorders at the level of etiological and pathophysiological mechanisms and clinical phenotypical manifestations. Finally, we discuss the subtle interplay of CRD with sex in relation to these disorders across different stages of life. Our perspective highlights the need to shift investigations towards molecular levels, for instance, by using spatiotemporal circadian "omic" studies in animal models to identify the complex and causal relationships between CRD and mental health disorders.

Tryptophan metabolism is a physiological integrator regulating circadian rhythms.

OBJECTIVE: The circadian clock aligns physiology with the 24-hour rotation of Earth. Light and food are the main environmental cues (zeitgebers) regulating circadian rhythms in mammals. Yet, little is known about the interaction between specific dietary components and light in coordinating circadian homeostasis. Herein, we focused on the role of essential amino acids. METHODS: Mice were fed diets depleted of specific essential amino acids and their behavioral rhythms were monitored and tryptophan was selected for downstream analyses. The role of tryptophan metabolism in modulating circadian homeostasis was studied using isotope tracing as well as transcriptomic- and metabolomic- analyses. RESULTS: Dietary tryptophan depletion alters behavioral rhythms in mice. Furthermore, tryptophan metabolism was shown to be regulated in a time- and light- dependent manner. A multi-omics approach and combinatory diet/light interventions demonstrated that tryptophan metabolism modulates temporal regulation of metabolism and transcription programs by buffering photic cues. Specifically, tryptophan metabolites regulate central circadian functions of the suprachiasmatic nucleus and the core clock machinery in the liver. CONCLUSIONS: Tryptophan metabolism is a modulator of circadian homeostasis by integrating environmental cues. Our findings propose tryptophan metabolism as a potential point for pharmacologic intervention to modulate phenotypes associated with disrupted circadian rhythms.

Atlas of exercise metabolism reveals time-dependent signatures of metabolic homeostasis.

Tissue sensitivity and response to exercise vary according to the time of day and alignment of circadian clocks, but the optimal exercise time to elicit a desired metabolic outcome is not fully defined. To understand how tissues independently and collectively respond to timed exercise, we applied a systems biology approach. We mapped and compared global metabolite responses of seven different mouse tissues and serum after an acute exercise bout performed at different times of the day. Comparative analyses of intra- and inter-tissue metabolite dynamics, including temporal profiling and blood sampling across liver and hindlimb muscles, uncovered an unbiased view of local and systemic metabolic responses to exercise unique to time of day. This comprehensive atlas of exercise metabolism provides clarity and physiological context regarding the production and distribution of canonical and novel time-dependent exerkine metabolites, such as 2-hydroxybutyrate (2-HB), and reveals insight into the health-promoting benefits of exercise on metabolism.

Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality.

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.

Integration of feeding behavior by the liver circadian clock reveals network dependency of metabolic rhythms.

The mammalian circadian clock, expressed throughout the brain and body, controls daily metabolic homeostasis. Clock function in peripheral tissues is required, but not sufficient, for this task. Because of the lack of specialized animal models, it is unclear how tissue clocks interact with extrinsic signals to drive molecular oscillations. Here, we isolated the interaction between feeding and the liver clock by reconstituting Bmal1 exclusively in hepatocytes (Liver-RE), in otherwise clock-less mice, and controlling timing of food intake. We found that the cooperative action of BMAL1 and the transcription factor CEBPB regulates daily liver metabolic transcriptional programs. Functionally, the liver clock and feeding rhythm are sufficient to drive temporal carbohydrate homeostasis. By contrast, liver rhythms tied to redox and lipid metabolism required communication with the skeletal muscle clock, demonstrating peripheral clock cross-talk. Our results highlight how the inner workings of the clock system rely on communicating signals to maintain daily metabolism.

Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing.

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.

S-adenosyl-l-homocysteine hydrolase links methionine metabolism to the circadian clock and chromatin remodeling.

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.

FKBP10 Regulates Protein Translation to Sustain Lung Cancer Growth.

Cancer therapy is limited, in part, by lack of specificity. Thus, identifying molecules that are selectively expressed by, and relevant for, cancer cells is of paramount medical importance. Here, we show that peptidyl-prolyl-cis-trans-isomerase (PPIase) FK506-binding protein 10 (FKBP10)-positive cells are present in cancer lesions but absent in the healthy parenchyma of human lung. FKBP10 expression negatively correlates with survival of lung cancer patients, and its downregulation causes a dramatic diminution of lung tumor burden in mice. Mechanistically, our results from gain- and loss-of-function assays show that FKBP10 boosts cancer growth and stemness via its PPIase activity. Also, FKBP10 interacts with ribosomes, and its downregulation leads to reduction of translation elongation at the beginning of open reading frames (ORFs), particularly upon insertion of proline residues. Thus, our data unveil FKBP10 as a cancer-selective molecule with a key role in translational reprogramming, stem-like traits, and growth of lung cancer.

Time of Exercise Specifies the Impact on Muscle Metabolic Pathways and Systemic Energy Homeostasis.

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, HIF1α, 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.