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.

Atlas of Circadian Metabolism Reveals System-wide Coordination and Communication between Clocks.

Metabolic diseases are often characterized by circadian misalignment in different tissues, yet how altered coordination and communication among tissue clocks relate to specific pathogenic mechanisms remains largely unknown. Applying an integrated systems biology approach, we performed 24-hr metabolomics profiling of eight mouse tissues simultaneously. We present a temporal and spatial atlas of circadian metabolism in the context of systemic energy balance and under chronic nutrient stress (high-fat diet [HFD]). Comparative analysis reveals how the repertoires of tissue metabolism are linked and gated to specific temporal windows and how this highly specialized communication and coherence among tissue clocks is rewired by nutrient challenge. Overall, we illustrate how dynamic metabolic relationships can be reconstructed across time and space and how integration of circadian metabolomics data from multiple tissues can improve our understanding of health and disease.

Lung Adenocarcinoma Distally Rewires Hepatic Circadian Homeostasis.

The circadian clock controls metabolic and physiological processes through finely tuned molecular mechanisms. The clock is remarkably plastic and adapts to exogenous "zeitgebers," such as light and nutrition. How a pathological condition in a given tissue influences systemic circadian homeostasis in other tissues remains an unanswered question of conceptual and biomedical importance. Here, we show that lung adenocarcinoma operates as an endogenous reorganizer of circadian metabolism. High-throughput transcriptomics and metabolomics revealed unique signatures of transcripts and metabolites cycling exclusively in livers of tumor-bearing mice. Remarkably, lung cancer has no effect on the core clock but rather reprograms hepatic metabolism through altered pro-inflammatory response via the STAT3-Socs3 pathway. This results in disruption of AKT, AMPK, and SREBP signaling, leading to altered insulin, glucose, and lipid metabolism. Thus, lung adenocarcinoma functions as a potent endogenous circadian organizer (ECO), which rewires the pathophysiological dimension of a distal tissue such as the liver. PAPERCLIP.

Partitioning circadian transcription by SIRT6 leads to segregated control of cellular metabolism.

Circadian rhythms are intimately linked to cellular metabolism. Specifically, the NAD(+)-dependent deacetylase SIRT1, the founding member of the sirtuin family, contributes to clock function. Whereas SIRT1 exhibits diversity in deacetylation targets and subcellular localization, SIRT6 is the only constitutively chromatin-associated sirtuin and is prominently present at transcriptionally active genomic loci. Comparison of the hepatic circadian transcriptomes reveals that SIRT6 and SIRT1 separately control transcriptional specificity and therefore define distinctly partitioned classes of circadian genes. SIRT6 interacts with CLOCK:BMAL1 and, differently from SIRT1, governs their chromatin recruitment to circadian gene promoters. Moreover, SIRT6 controls circadian chromatin recruitment of SREBP-1, resulting in the cyclic regulation of genes implicated in fatty acid and cholesterol metabolism. This mechanism parallels a phenotypic disruption in fatty acid metabolism in SIRT6 null mice as revealed by circadian metabolome analyses. Thus, genomic partitioning by two independent sirtuins contributes to differential control of circadian metabolism.

Reprogramming of the circadian clock by nutritional challenge.

Circadian rhythms and cellular metabolism are intimately linked. Here, we reveal that a high-fat diet (HFD) generates a profound reorganization of specific metabolic pathways, leading to widespread remodeling of the liver clock. Strikingly, in addition to disrupting the normal circadian cycle, HFD causes an unexpectedly large-scale genesis of de novo oscillating transcripts, resulting in reorganization of the coordinated oscillations between coherent transcripts and metabolites. The mechanisms underlying this reprogramming involve both the impairment of CLOCK:BMAL1 chromatin recruitment and a pronounced cyclic activation of surrogate pathways through the transcriptional regulator PPARγ. Finally, we demonstrate that it is specifically the nutritional challenge, and not the development of obesity, that causes the reprogramming of the clock and that the effects of the diet on the clock are reversible.

PMechDB: A Public Database of Elementary Polar Reaction Steps.

Most online chemical reaction databases are not publicly accessible or are fully downloadable. These databases tend to contain reactions in noncanonicalized formats and often lack comprehensive information regarding reaction pathways, intermediates, and byproducts. Within the few publicly available databases, reactions are typically stored in the form of unbalanced, overall transformations with minimal interpretability of the underlying chemistry. These limitations present significant obstacles to data-driven applications including the development of machine learning models. As an effort to overcome these challenges, we introduce PMechDB, a publicly accessible platform designed to curate, aggregate, and share polar chemical reaction data in the form of elementary reaction steps. Our initial version of PMechDB consists of over 100,000 such steps. In the PMechDB, all reactions are stored as canonicalized and balanced elementary steps, featuring accurate atom mapping and arrow-pushing mechanisms. As an online interactive database, PMechDB provides multiple interfaces that enable users to search, download, and upload chemical reactions. We anticipate that the public availability of PMechDB and its standardized data representation will prove beneficial for chemoinformatics research and education and the development of data-driven, interpretable models for predicting reactions and pathways. PMechDB platform is accessible online at https://deeprxn.ics.uci.edu/pmechdb.

Clinical Knowledge and Reasoning Abilities of AI Large Language Models in Anesthesiology: A Comparative Study on the American Board of Anesthesiology Examination.

BACKGROUND: Over the past decade, artificial intelligence (AI) has expanded significantly with increased adoption across various industries, including medicine. Recently, AI-based large language models such as Generative Pretrained Transformer-3 (GPT-3), Bard, and Generative Pretrained Transformer-3 (GPT-4) have demonstrated remarkable language capabilities. While previous studies have explored their potential in general medical knowledge tasks, here we assess their clinical knowledge and reasoning abilities in a specialized medical context. METHODS: We studied and compared the performance of all 3 models on both the written and oral portions of the comprehensive and challenging American Board of Anesthesiology (ABA) examination, which evaluates candidates' knowledge and competence in anesthesia practice. RESULTS: Our results reveal that only GPT-4 successfully passed the written examination, achieving an accuracy of 78% on the basic section and 80% on the advanced section. In comparison, the less recent or smaller GPT-3 and Bard models scored 58% and 47% on the basic examination, and 50% and 46% on the advanced examination, respectively. Consequently, only GPT-4 was evaluated in the oral examination, with examiners concluding that it had a reasonable possibility of passing the structured oral examination. Additionally, we observe that these models exhibit varying degrees of proficiency across distinct topics, which could serve as an indicator of the relative quality of information contained in the corresponding training datasets. This may also act as a predictor for determining which anesthesiology subspecialty is most likely to witness the earliest integration with AI. CONCLUSIONS: GPT-4 outperformed GPT-3 and Bard on both basic and advanced sections of the written ABA examination, and actual board examiners considered GPT-4 to have a reasonable possibility of passing the real oral examination; these models also exhibit varying degrees of proficiency across distinct topics.

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.

An atlas of posttranslational modifications on RNA binding proteins.

RNA structure and function are intimately tied to RNA binding protein recognition and regulation. Posttranslational modifications are chemical modifications which can control protein biology. The role of PTMs in the regulation RBPs is not well understood, in part due to a lacking analysis of PTM deposition on RBPs. Herein, we present an analysis of posttranslational modifications (PTMs) on RNA binding proteins (RBPs; a PTM RBP Atlas). We curate published datasets and primary literature to understand the landscape of PTMs and use protein-protein interaction data to understand and potentially provide a framework for understanding which enzymes are controlling PTM deposition and removal on the RBP landscape. Intersection of our data with The Cancer Genome Atlas also provides researchers understanding of mutations that would alter PTM deposition. Additional characterization of the RNA-protein interface provided from in-cell UV crosslinking experiments provides a framework for hypotheses about which PTMs could be regulating RNA binding and thus RBP function. Finally, we provide an online database for our data that is easy to use for the community. It is our hope our efforts will provide researchers will an invaluable tool to test the function of PTMs controlling RBP function and thus RNA biology.

SSpro/ACCpro 6: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, deep learning and structural similarity.

MOTIVATION: Accurately predicting protein secondary structure and relative solvent accessibility is important for the study of protein evolution, structure and an early-stage component of typical protein 3D structure prediction pipelines. RESULTS: We present a new improved version of the SSpro/ACCpro suite of predictors for the prediction of protein secondary structure (in three and eight classes) and relative solvent accessibility. The changes include improved, TensorFlow-trained, deep learning predictors, a richer set of profile features (232 features per residue position) and sequence-only features (71 features per position), a more recent Protein Data Bank (PDB) snapshot for training, better hyperparameter tuning and improvements made to the HOMOLpro module, which leverages structural information from protein segment homologs in the PDB. The new SSpro 6 outperforms the previous version (SSpro 5) by 3-4% in Q3 accuracy and, when used with HOMOLPRO, reaches accuracy in the 95-100% range. AVAILABILITY AND IMPLEMENTATION: The predictors' software, data and web servers are available through the SCRATCH suite of protein structure predictors at http://scratch.proteomics.ics.uci.edu. To maximize comptatibility and ease of use, the deep learning predictors are re-implemented as pure Python/numpy code without TensorFlow dependency. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RMechDB: A Public Database of Elementary Radical Reaction Steps.

We introduce RMechDB, an open-access platform for aggregating, curating, and distributing reliable data about elementary radical reaction steps for computational radical reaction modeling and prediction. RMechDB contains over 5,300 elementary radical reaction steps, each with a single transition state at or around room temperature. These elementary step reactions are manually curated plausible arrow-pushing steps for organic radical reactions. The steps were taken from a variety of sources. Over 2,000 mechanistic steps were extracted from textbooks and/or constructed from research publications. Another 3,000 were taken from gas-phase atmospheric reactions of isoprene and other organic molecules on the MCM (Master Chemical Mechanism) Web site. Reactions are encoded in the SMIRKS format with accurate atom mapping and annotations for arrow-pushing mechanisms. At its core, RMechDB consists of a database schema with an online interactive search interface and a request portal for downloading the raw form of elementary step reactions with their metadata. It also offers an interface for submitting new reactions to RMechDB and expanding the data set through community contributions. Although there are several applications for RMechDB, it is primarily designed as a central platform of radical elementary steps with a unified and structured representation. We believe that this open access to this data and platform enables the extension of data-driven models for chemical reaction predictions and other chemoinformatics predictive tasks.

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.

Structure, self-assembly, and properties of a truncated reflectin variant.

Naturally occurring and recombinant protein-based materials are frequently employed for the study of fundamental biological processes and are often leveraged for applications in areas as diverse as electronics, optics, bioengineering, medicine, and even fashion. Within this context, unique structural proteins known as reflectins have recently attracted substantial attention due to their key roles in the fascinating color-changing capabilities of cephalopods and their technological potential as biophotonic and bioelectronic materials. However, progress toward understanding reflectins has been hindered by their atypical aromatic and charged residue-enriched sequences, extreme sensitivities to subtle changes in environmental conditions, and well-known propensities for aggregation. Herein, we elucidate the structure of a reflectin variant at the molecular level, demonstrate a straightforward mechanical agitation-based methodology for controlling this variant's hierarchical assembly, and establish a direct correlation between the protein's structural characteristics and intrinsic optical properties. Altogether, our findings address multiple challenges associated with the development of reflectins as materials, furnish molecular-level insight into the mechanistic underpinnings of cephalopod skin cells' color-changing functionalities, and may inform new research directions across biochemistry, cellular biology, bioengineering, and optics.

Combining Allograft Adipose and Fascia Matrix as an Off-the-Shelf Scaffold for Adipose Tissue Engineering Stimulates Angiogenic Responses and Activates a Proregenerative Macrophage Profile in a Rodent Model.

OBJECTIVE: Bioscaffolds for treating soft tissue defects have limitations. As a bioscaffold, allograft adipose matrix (AAM) is a promising approach to treat soft tissue defects. Previously, we revealed that combining superficial adipose fascia matrix with AAM, components of the hypodermis layer of adipose tissue, improved volume retention, adipogenesis, and angiogenesis in rats 8 weeks after it was implanted compared with AAM alone. Here, we modified the fascia matrix and AAM preparation, examined the tissue over 18 weeks, and conducted a deeper molecular investigation. We hypothesized that the combined matrices created a better scaffold by triggering angiogenesis and proregenerative signals. METHODS: Human AAM and fascia matrix were implanted (4 [1 mL] implants/animal) into the dorsum of male Fischer rats (6-8 weeks old; ~140 g) randomly as follows: AAM, fascia, 75/25 (AAM/fascia), 50/50, and 50/50 + hyaluronic acid (HA; to improve extrudability) (n = 4/group/time point). After 72 hours, as well as 1, 3, 6, 9, 12, and 18 weeks, graft retention was assessed by a gas pycnometer. Adipogenesis (HE), angiogenesis (CD31), and macrophage infiltration (CD80 and CD163) were evaluated histologically at all time points. The adipose area and M1/M2 macrophage ratio were determined using ImageJ. RNA sequencing (RNA-seq) and bioinformatics were conducted to evaluate pathway enrichments. RESULTS: By 18 weeks, the adipose area was 2365% greater for 50/50 HA (281.6 ± 21.6) than AAM (11.4 ± 0.9) (P < 0.001). The M1/M2 macrophage ratio was significantly lower for 50/50 HA (0.8 ± 0.1) than AAM (0.9 ± 0.1) at 6 weeks (16%; P < 0.05). This inversely correlated with adipose area (r = -0.6; P > 0.05). The RNA-seq data revealed that upregulated adipogenesis, angiogenesis, and macrophage-induced tissue regeneration genes were temporally different between the groups. CONCLUSIONS: Combining the fascia matrix with AAM creates a bioscaffold with an improved retention volume that supports M2 macrophage-mediated angiogenesis and adipogenesis. This bioscaffold is worthy of further investigation.

Local features determine Ty3 targeting frequency at RNA polymerase III transcription start sites.

Retroelement integration into host genomes affects chromosome structure and function. A goal of a considerable number of investigations is to elucidate features influencing insertion site selection. The Saccharomyces cerevisiae Ty3 retrotransposon inserts proximal to the transcription start sites (TSS) of genes transcribed by RNA polymerase III (RNAP3). In this study, differential patterns of insertion were profiled genome-wide using a random barcode-tagged Ty3. Saturation transposition showed that tRNA genes (tDNAs) are targeted at widely different frequencies even within isoacceptor families. Ectopic expression of Ty3 integrase (IN) showed that it localized to targets independent of other Ty3 proteins and cDNA. IN, RNAP3, and transcription factor Brf1 were enriched at tDNA targets with high frequencies of transposition. To examine potential effects of cis-acting DNA features on transposition, targeting was tested on high-copy plasmids with restricted amounts of 5' flanking sequence plus tDNA. Relative activity of targets was reconstituted in these constructions. Weighting of genomic insertions according to frequency identified an A/T-rich sequence followed by C as the dominant site of strand transfer. This site lies immediately adjacent to the adenines previously implicated in the RNAP3 TSS motif (CAA). In silico DNA structural analysis upstream of this motif showed that targets with elevated DNA curvature coincide with reduced integration. We propose that integration mediated by the Ty3 intasome complex (IN and cDNA) is subject to inputs from a combination of host factor occupancy and insertion site architecture, and that this results in the wide range of Ty3 targeting frequencies.

Fold recognition by scoring protein maps using the congruence coefficient.

MOTIVATION: Protein fold recognition is a key step for template-based modeling approaches to protein structure prediction. Although closely related folds can be easily identified by sequence homology search in sequence databases, fold recognition is notoriously more difficult when it involves the identification of distantly related homologs. Recent progress in residue-residue contact and distance prediction opens up the possibility of improving fold recognition by using structural information contained in predicted distance and contact maps. RESULTS: Here we propose to use the congruence coefficient as a metric of similarity between maps. We prove that this metric has several interesting mathematical properties which allow one to compute in polynomial time its exact mean and variance over all possible (exponentially many) alignments between two symmetric matrices, and assess the statistical significance of similarity between aligned maps. We perform fold recognition tests by recovering predicted target contact/distance maps from the two most recent Critical Assessment of Structure Prediction editions and over 27 000 non-homologous structural templates from the ECOD database. On this large benchmark, we compare fold recognition performances of different alignment tools with their own similarity scores against those obtained using the congruence coefficient. We show that the congruence coefficient overall improves fold recognition over other methods, proving its effectiveness as a general similarity metric for protein map comparison. AVAILABILITY AND IMPLEMENTATION: The congruence coefficient software CCpro is available as part of the SCRATCH suite at: http://scratch.proteomics.ics.uci.edu/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Call for a Public Open Database of All Chemical Reactions.

Today there exists no public, freely downloadable, comprehensive database of all known chemical reactions and associated information. Such a database not only would serve chemical sciences and technologies around the world but also would enable the power of modern AI and machine learning methods to be unleashed on a host of fundamental problems. In time, this could lead to important scientific discoveries and economic developments for the benefit of humanity. While ideally such a repository ought to be created and maintained by an international consortium, in the near future, it may be easier to begin the process through governmental agencies such as the National Science Foundation or the National Institutes of Health. Working together, we could use a multipronged approach that could combine negotiations with commercial stakeholders, crowd-sourcing efforts, automated extraction methods, and legislative actions.

Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity.

There is a lack of scalable quantitative measures of reactivity that cover the full range of functional groups in organic chemistry, ranging from highly unreactive C-C bonds to highly reactive naked ions. Measuring reactivity experimentally is costly and time-consuming, and no single method has sufficient dynamic range to cover the astronomical size of chemical reactivity space. In previous quantum chemistry studies, we have introduced Methyl Cation Affinities (MCA*) and Methyl Anion Affinities (MAA*), using a solvation model, as quantitative measures of reactivity for organic functional groups over the broadest range. Although MCA* and MAA* offer good estimates of reactivity parameters, their calculation through Density Functional Theory (DFT) simulations is time-consuming. To circumvent this problem, we first use DFT to calculate MCA* and MAA* for more than 2,400 organic molecules thereby establishing a large data set of chemical reactivity scores. We then design deep learning methods to predict the reactivity of molecular structures and train them using this curated data set in combination with different representations of molecular structures. Using 10-fold cross-validation, we show that graph attention neural networks applied to a relational model of molecular structures produce the most accurate estimates of reactivity, achieving over 91% test accuracy for predicting the MCA* ± 3.0 or MAA* ± 3.0, over 50 orders of magnitude. Finally, we demonstrate the application of these reactivity scores to two tasks: (1) chemical reaction prediction and (2) combinatorial generation of reaction mechanisms. The curated data sets of MCA* and MAA* scores is available through the ChemDB chemoinformatics web portal at cdb.ics.uci.edu under Chemical Reactivities data sets.