Microglia modulate general anesthesia through P2Y12 receptor.

General anesthesia (GA) is an unconscious state produced by anesthetic drugs, which act on neurons to cause overall suppression of neuronal activity in the brain. Recent studies have revealed that GA also substantially enhances the dynamics of microglia, the primary brain immune cells, with increased process motility and territory surveillance. However, whether microglia are actively involved in GA modulation remains unknown. Here, we report a previously unrecognized role for microglia engaging in multiple GA processes. We found that microglial ablation reduced the sensitivity of mice to anesthetics and substantially shortened duration of loss of righting reflex (LORR) or unconsciousness induced by multiple anesthetics, thereby promoting earlier emergence from GA. Microglial repopulation restored the regular anesthetic recovery, and chemogenetic activation of microglia prolonged the duration of LORR. In addition, anesthesia-accompanying analgesia and hypothermia were also attenuated after microglial depletion. Single-cell RNA sequencing analyses showed that anesthesia prominently affected the transcriptional levels of chemotaxis and migration-related genes in microglia. By pharmacologically targeting different microglial motility pathways, we found that blocking P2Y12 receptor (P2Y12R) reduced the duration of LORR of mice. Moreover, genetic ablation of P2Y12R in microglia also promoted quicker recovery in mice from anesthesia, verifying the importance of microglial P2Y12R in anesthetic regulation. Our work presents the first evidence that microglia actively participate in multiple processes of GA through P2Y12R-mediated signaling and expands the non-immune roles of microglia in the brain.

Fecal microbiota transplantation ameliorates type 2 diabetes via metabolic remodeling of the gut microbiota in db/db mice.

INTRODUCTION: Gut microbiome (GM) deregulation has been implicated in major conditions such as obesity and type 2 diabetes (T2DM). Our previous prospective study indicated that fecal microbiota transplantation (FMT) successfully improved patients with T2DM. We hypothesized that FMT may be a potential therapeutic method for T2DM, but its precise mechanisms in T2DM remains to be elucidated. RESEARCH DESIGN AND METHODS: Eight db/m mice were FMT donors and control mice, and 16 genetically diabetic db/db mice were equally divided into two groups (db/db+phosphate-buffered saline (PBS) group, db/db+FMT group). The db/db+FMT group was administered fresh fecal suspension (0.2 mL/mice) daily for 4 weeks. Analysis of the GM and serum metabolome was carried out by 16S ribosomal RNA sequencing and liquid chromatogram-mass spectrometry, respectively. Effects of FMT on the gut barrier and pancreas were assessed using protein assays, messenger RNA, immunohistology and clinical indicators testing. RESULTS: Our results showed that FMT treatment of db/db mice relieves a series of clinical indicators, including fasting plasma glucose, serum insulin and oral glucose tolerance test among others. Compared with non-diabetic control mice, db/db+PBS mice exhibited decreased abundance of Ruminococaceae, Porphyromonadaceae and increased abundance of Rikenellaceae and Lactobacillaceae. FMT treatment reversed this effect on the microbiome. Eleven metabolites were changed between the db/db+PBS and db/db+FMT groups. Correlation analysis showed that the structural changes of the GM were correlated with host metabolite levels. We further showed that FMT treatment of db/db mice improved intestinal barrier function, reduced inflammation and caused an alteration in the number of circulating immune cells. CONCLUSIONS: FMT-mediated changes in the GM, serum metabolites, intestinal epithelial barrier, inflammation and circulating immune cells play an important role in the efficacy of FMT on T2DM disease progression.

Transcriptomics Dissection of Calorie Restriction and Exercise Training in Brown Adipose Tissue and Skeletal Muscle.

Calorie restriction (CR) and exercise training (EX) are two critical lifestyle interventions for the prevention and treatment of metabolic diseases, such as obesity and diabetes. Brown adipose tissue (BAT) and skeletal muscle are two important organs for the generation of heat. Here, we undertook detailed transcriptional profiling of these two thermogenic tissues from mice treated subjected to CR and/or EX. We found transcriptional reprogramming of BAT and skeletal muscle as a result of CR but little from EX. Consistent with this, CR induced alterations in the expression of genes encoding adipokines and myokines in BAT and skeletal muscle, respectively. Deconvolution analysis showed differences in the subpopulations of myogenic cells, mesothelial cells and endogenic cells in BAT and in the subpopulations of satellite cells, immune cells and endothelial cells in skeletal muscle as a result of CR or EX. NicheNet analysis, exploring potential inter-organ communication, indicated that BAT and skeletal muscle could mutually regulate their fatty acid metabolism and thermogenesis through ligands and receptors. These data comprise an extensive resource for the study of thermogenic tissue molecular responses to CR and/or EX in a healthy state.

Discovery of natural-product-derived sequanamycins as potent oral anti-tuberculosis agents.

The emergence of drug-resistant tuberculosis has created an urgent need for new anti-tubercular agents. Here, we report the discovery of a series of macrolides called sequanamycins with outstanding in vitro and in vivo activity against Mycobacterium tuberculosis (Mtb). Sequanamycins are bacterial ribosome inhibitors that interact with the ribosome in a similar manner to classic macrolides like erythromycin and clarithromycin, but with binding characteristics that allow them to overcome the inherent macrolide resistance of Mtb. Structures of the ribosome with bound inhibitors were used to optimize sequanamycin to produce the advanced lead compound SEQ-9. SEQ-9 was efficacious in mouse models of acute and chronic TB as a single agent, and it demonstrated bactericidal activity in a murine TB infection model in combination with other TB drugs. These results support further investigation of this series as TB clinical candidates, with the potential for use in new regimens against drug-susceptible and drug-resistant TB.

Amino acid sensing and lysosomal signaling complexes.

Amino acid pools in the cell are monitored by dedicated sensors, whose structures are now coming into view. The lysosomal Rag GTPases are central to this pathway, and the regulation of their GAP complexes, FLCN-FNIP and GATOR1, have been worked out in detail. For FLCN-FNIP, the entire chain of events from the arginine transporter SLC38A9 to substrate-specific mTORC1 activation has been visualized. The structure GATOR2 has been determined, hinting at an ordering of amino acid signaling across a larger size scale than anticipated. The centerpiece of lysosomal signaling, mTORC1, has been revealed to recognize its substrates by more nuanced and substrate-specific mechanisms than previous appreciated. Beyond the well-studied Rag GTPase and mTORC1 machinery, another lysosomal amino acid sensor/effector system, that of PQLC2 and the C9orf72-containing CSW complex, is coming into structural view. These developments hold promise for further insights into lysosomal physiology and lysosome-centric therapeutics.

Structure of the lysosomal mTORC1-TFEB-Rag-Ragulator megacomplex.

The transcription factor TFEB is a master regulator of lysosomal biogenesis and autophagy1. The phosphorylation of TFEB by the mechanistic target of rapamycin complex 1 (mTORC1)2-5 is unique in its mTORC1 substrate recruitment mechanism, which is strictly dependent on the amino acid-mediated activation of the RagC GTPase activating protein FLCN6,7. TFEB lacks the TOR signalling motif responsible for the recruitment of other mTORC1 substrates. We used cryogenic-electron microscopy to determine the structure of TFEB as presented to mTORC1 for phosphorylation, which we refer to as the 'megacomplex'. Two full Rag-Ragulator complexes present each molecule of TFEB to the mTOR active site. One Rag-Ragulator complex is bound to Raptor in the canonical mode seen previously in the absence of TFEB. A second Rag-Ragulator complex (non-canonical) docks onto the first through a RagC GDP-dependent contact with the second Ragulator complex. The non-canonical Rag dimer binds the first helix of TFEB with a RagCGDP-dependent aspartate clamp in the cleft between the Rag G domains. In cellulo mutation of the clamp drives TFEB constitutively into the nucleus while having no effect on mTORC1 localization. The remainder of the 108-amino acid TFEB docking domain winds around Raptor and then back to RagA. The double use of RagC GDP contacts in both Rag dimers explains the strong dependence of TFEB phosphorylation on FLCN and the RagC GDP state.

Dual interfacial engineering of a Chevrel phase electrode material for stable hydrogen evolution at 2500 mA cm-2.

Constructing stable electrodes which function over long timescales at large current density is essential for the industrial realization and implementation of water electrolysis. However, rapid gas bubble detachment at large current density usually results in peeling-off of electrocatalysts and performance degradation, especially for long term operations. Here we construct a mechanically-stable, all-metal, and highly active CuMo6S8/Cu electrode by in-situ reaction between MoS2 and Cu. The Chevrel phase electrode exhibits strong binding at the electrocatalyst-support interface with weak adhesion at electrocatalyst-bubble interface, in addition to fast hydrogen evolution and charge transfer kinetics. These features facilitate the achievement of large current density of 2500 mA cm-2 at a small overpotential of 334 mV which operate stably at 2500 mA cm-2 for over 100 h. In-situ total internal reflection imaging at micrometer level and mechanical tests disclose the relationships of two interfacial forces and performance of electrocatalysts. This dual interfacial engineering strategy can be extended to construct stable and high-performance electrodes for other gas-involving reactions.

Interplay between an ATP-binding cassette F protein and the ribosome from Mycobacterium tuberculosis.

EttA, energy-dependent translational throttle A, is a ribosomal factor that gates ribosome entry into the translation elongation cycle. A detailed understanding of its mechanism of action is limited due to the lack of high-resolution structures along its ATPase cycle. Here we present the cryo-electron microscopy (cryo-EM) structures of EttA from Mycobacterium tuberculosis (Mtb), referred to as MtbEttA, in complex with the Mtb 70S ribosome initiation complex (70SIC) at the pre-hydrolysis (ADPNP) and transition (ADP-VO4) states, and the crystal structure of MtbEttA alone in the post-hydrolysis (ADP) state. We observe that MtbEttA binds the E-site of the Mtb 70SIC, remodeling the P-site tRNA and the ribosomal intersubunit bridge B7a during the ribosomal ratcheting. In return, the rotation of the 30S causes conformational changes in MtbEttA, forcing the two nucleotide-binding sites (NBSs) to alternate to engage each ADPNP in the pre-hydrolysis states, followed by complete engagements of both ADP-VO4 molecules in the ATP-hydrolysis transition states. In the post-hydrolysis state, the conserved ATP-hydrolysis motifs of MtbEttA dissociate from both ADP molecules, leaving two nucleotide-binding domains (NBDs) in an open conformation. These structures reveal a dynamic interplay between MtbEttA and the Mtb ribosome, providing insights into the mechanism of translational regulation by EttA-like proteins.

A Deep Learning Approach for the Estimation of Glomerular Filtration Rate.

An accurate estimation of glomerular filtration rate (GFR) is clinically crucial for kidney disease diagnosis and predicting the prognosis of chronic kidney disease (CKD). Machine learning methodologies such as deep neural networks provide a potential avenue for increasing accuracy in GFR estimation. We developed a novel deep learning architecture, a deep and shallow neural network, to estimate GFR (dlGFR for short) and examined its comparative performance with estimated GFR from Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations. The dlGFR model jointly trains a shallow learning model and a deep neural network to enable both linear transformation from input features to a log GFR target, and non-linear feature embedding for stage of kidney function classification. We validate the proposed methods on the data from multiple studies obtained from the NIDDK Central Database Repository. The deep learning model predicted values of GFR within 30% of measured GFR with 88.3% accuracy, compared to the 87.1% and 84.7% of the accuracy achieved by CKD-EPI and MDRD equations (p = 0.051 and p < 0.001, respectively). Our results suggest that deep learning methods are superior to equations resulting from traditional statistical methods in estimating glomerular filtration rate. Based on these results, an end-to-end predication system has been deployed to facilitate use of the proposed dlGFR algorithm.

Central 5-HTR2C in the Control of Metabolic Homeostasis.

The 5-hydroxytryptamine 2C receptor (5-HTR2C) is a class G protein-coupled receptor (GPCR) enriched in the hypothalamus and the brain stem, where it has been shown to regulate energy homeostasis, including feeding and glucose metabolism. Accordingly, 5-HTR2C has been the target of several anti-obesity drugs, though the associated side effects greatly curbed their clinical applications. Dissecting the specific neural circuits of 5-HTR2C-expressing neurons and the detailed molecular pathways of 5-HTR2C signaling in metabolic regulation will help to develop better therapeutic strategies towards metabolic disorders. In this review, we introduced the regulatory role of 5-HTR2C in feeding behavior and glucose metabolism, with particular focus on the molecular pathways, neural network, and its interaction with other metabolic hormones, such as leptin, ghrelin, insulin, and estrogens. Moreover, the latest progress in the clinical research on 5-HTR2C agonists was also discussed.

Predicting self-intercepted medication ordering errors using machine learning.

Current approaches to understanding medication ordering errors rely on relatively small manually captured error samples. These approaches are resource-intensive, do not scale for computerized provider order entry (CPOE) systems, and are likely to miss important risk factors associated with medication ordering errors. Previously, we described a dataset of CPOE-based medication voiding accompanied by univariable and multivariable regression analyses. However, these traditional techniques require expert guidance and may perform poorly compared to newer approaches. In this paper, we update that analysis using machine learning (ML) models to predict erroneous medication orders and identify its contributing factors. We retrieved patient demographics (race/ethnicity, sex, age), clinician characteristics, type of medication order (inpatient, prescription, home medication by history), and order content. We compared logistic regression, random forest, boosted decision trees, and artificial neural network models. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). The dataset included 5,804,192 medication orders, of which 28,695 (0.5%) were voided. ML correctly classified voids at reasonable accuracy; with a positive predictive value of 10%, ~20% of errors were included. Gradient boosted decision trees achieved the highest AUROC (0.7968) and AUPRC (0.0647) among all models. Logistic regression had the poorest performance. Models identified predictive factors with high face validity (e.g., student orders), and a decision tree revealed interacting contexts with high rates of errors not identified by previous regression models. Prediction models using order-entry information offers promise for error surveillance, patient safety improvements, and targeted clinical review. The improved performance of models with complex interactions points to the importance of contextual medication ordering information for understanding contributors to medication errors.

Pseudotime Ordering Single-Cell Transcriptomic of ß Cells Pancreatic Islets in Health and Type 2 Diabetes.

ß cells are defined by the ability to produce and secret insulin. Recent studies have evaluated that human pancreatic ß cells are heterogeneous and demonstrated the transcript alterations of ß cell subpopulation in diabetes. Single-cell RNA sequence (scRNA-seq) analysis helps us to refine the cell types signatures and understand the role of the ß cells during metabolic challenges and diseases. Here, we construct the pseudotime trajectory of ß cells from publicly available scRNA-seq data in health and type 2 diabetes (T2D) based on highly dispersed and highly expressed genes using Monocle2. We identified three major states including 1) Normal branch, 2) Obesity-like branch and 3) T2D-like branch based on biomarker genes and genes that give rise to bifurcation in the trajectory. ß cell function-maintain-related genes, insulin expression-related genes, and T2D-related genes enriched in three branches, respectively. Continuous pseudotime spectrum might suggest that ß cells transition among different states. The application of pseudotime analysis is conducted to clarify the different cell states, providing novel insights into the pathology of ß cells in T2D. Supplementary Information: The online version contains supplementary material is available at 10.1007/s43657-021-00024-z.

Hierarchical natural move Monte Carlo refines flexible RNA structures into cryo-EM densities.

Ribonucleic acids (RNAs) play essential roles in living cells. Many of them fold into defined three-dimensional (3D) structures to perform functions. Recent advances in single-particle cryo-electron microscopy (cryo-EM) have enabled structure determinations of RNA to atomic resolutions. However, most RNA molecules are structurally flexible, limiting the resolution of their structures solved by cryo-EM. In modeling these molecules, several computational methods are limited by the requirement of massive computational resources and/or the low efficiency in exploring large-scale structural variations. Here we use hierarchical natural move Monte Carlo (HNMMC), which takes advantage of collective motions for groups of nucleic acid residues, to refine RNA structures into their cryo-EM maps, preserving atomic details in the models. After validating the method on a simulated density map of tRNA, we applied it to objectively obtain the model of the folding intermediate for the specificity domain of ribonuclease P from Bacillus subtilis and refine a flexible ribosomal RNA (rRNA) expansion segment from the Mycobacterium tuberculosis (Mtb) ribosome in different conformational states. Finally, we used HNMMC to model atomic details and flexibility for two distinct conformations of the complete genomic RNA (gRNA) inside MS2, a single-stranded RNA virus, revealing multiple pathways for its capsid assembly.

Deep-learning model for predicting 30-day postoperative mortality.

BACKGROUND: Postoperative mortality occurs in 1-2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. We sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality. METHODS: We constructed a multipath convolutional neural network model using patient characteristics, co-morbid conditions, preoperative laboratory values, and intraoperative numerical data from patients undergoing surgery with tracheal intubation at a single medical centre. Data for 60 min prior to a randomly selected time point were utilised. Model performance was compared with a deep neural network, a random forest, a support vector machine, and a logistic regression using predetermined summary statistics of intraoperative data. RESULTS: Of 95 907 patients, 941 (1%) died within 30 days. The multipath convolutional neural network predicted postoperative 30-day mortality with an area under the receiver operating characteristic curve of 0.867 (95% confidence interval [CI]: 0.835-0.899). This was higher than that for the deep neural network (0.825; 95% CI: 0.790-0.860), random forest (0.848; 95% CI: 0.815-0.882), support vector machine (0.836; 95% CI: 0.802-870), and logistic regression (0.837; 95% CI: 0.803-0.871). CONCLUSIONS: A deep-learning time-series model improves prediction compared with models with simple summaries of intraoperative data. We have created a model that can be used in real time to detect dynamic changes in a patient's risk for postoperative mortality.

Structural basis for the adsorption of a single-stranded RNA bacteriophage.

Single-stranded RNA bacteriophages (ssRNA phages) infect Gram-negative bacteria via a single maturation protein (Mat), which attaches to a retractile pilus of the host. Here we present structures of the ssRNA phage MS2 in complex with the Escherichia coli F-pilus, showing a network of hydrophobic and electrostatic interactions at the Mat-pilus interface. Moreover, binding of the pilus induces slight orientational variations of the Mat relative to the rest of the phage capsid, priming the Mat-connected genomic RNA (gRNA) for its release from the virions. The exposed tip of the attached Mat points opposite to the direction of the pilus retraction, which may facilitate the translocation of the gRNA from the capsid into the host cytosol. In addition, our structures determine the orientation of the assembled F-pilin subunits relative to the cell envelope, providing insights into the F-like type IV secretion systems.

Novel Histidine Kinase Gene HisK2301 from Rhodosporidium kratochvilovae Contributes to Cold Adaption by Promoting Biosynthesis of Polyunsaturated Fatty Acids and Glycerol.

Hybrid histidine kinase (HHKs) are widespread in fungi, but their roles in the regulation of fungal adaptation to environmental stresses remain largely unclear. To elucidate this, we cloned HisK2301 from Rhodosporidium kratochvilovae strain YM25235, characterized HisK2301 as a novel HHK, and further investigated the role of HisK2301 by overexpressing it in YM25235. Our results revealed that HisK2301 can promote adaptation of YM25235 to cold, osmotic, and salt stresses. During cold stress, HisK2301 significantly enhanced the biosynthesis of polyunsaturated fatty acids (PUFA) and intracellular glycerol. HisK2301 also augmented the expression levels of Δ12/Δ15 fatty acid desaturase ( RKD12) and glycerol-3-phosphate dehydrogenase1 ( GPD1), which are responsible for PUFA and glycerol biosynthesis, respectively. To conclude, our findings give the first insight into the defense and mechanisms of HisK2301 in fungi against cold stress and suggest the potential use of the novel cold-adapted HHK HisK2301 in industrial processes, such as large-scale production of PUFA.

A Factored Generalized Additive Model for Clinical Decision Support in the Operating Room.

Logistic regression (LR) is widely used in clinical prediction because it is simple to deploy and easy to interpret. Nevertheless, being a linear model, LR has limited expressive capability and often has unsatisfactory performance. Generalized additive models (GAMs) extend the linear model with transformations of input features, though feature interaction is not allowed for all GAM variants. In this paper, we propose a factored generalized additive model (F-GAM) to preserve the model interpretability for targeted features while allowing a rich model for interaction with features fixed within the individual. We evaluate F-GAM on prediction of two targets, postoperative acute kidney injury and acute respiratory failure, from a single-center database. We find superior model performance of F-GAM in terms of AUPRC and AUROC compared to several other GAM implementations, random forests, support vector machine, and a deep neural network. We find that the model interpretability is good with results with high face validity.

Predicting Hospital Readmission via Cost-Sensitive Deep Learning.

With increased use of electronic medical records (EMRs), data mining on medical data has great potential to improve the quality of hospital treatment and increase the survival rate of patients. Early readmission prediction enables early intervention, which is essential to preventing serious or life-threatening events, and act as a substantial contributor to reduce healthcare costs. Existing works on predicting readmission often focus on certain vital signs and diseases by extracting statistical features. They also fail to consider skewness of class labels in medical data and different costs of misclassification errors. In this paper, we recur to the merits of convolutional neural networks (CNN) to automatically learn features from time series of vital sign, and categorical feature embedding to effectively encode feature vectors with heterogeneous clinical features, such as demographics, hospitalization history, vital signs, and laboratory tests. Then, both learnt features via CNN and statistical features via feature embedding are fed into a multilayer perceptron (MLP) for prediction. We use a cost-sensitive formulation to train MLP during prediction to tackle the imbalance and skewness challenge. We validate the proposed approach on two real medical datasets from Barnes-Jewish Hospital, and all data is taken from historical EMR databases and reflects the kinds of data that would realistically be available at the clinical prediction system in hospitals. We find that early prediction of readmission is possible and when compared with state-of-the-art existing methods used by hospitals, our methods perform significantly better. For example, using the general hospital wards data for 30-day readmission prediction, the area under the curve (AUC) for the proposed model was 0.70, significantly higher than all the baseline methods. Based on these results, a system is being deployed in hospital settings with the proposed forecasting algorithms to support treatment.

Development of a yeast internal-subunit eGFP labeling strategy and its application in subunit identification in eukaryotic group II chaperonin TRiC/CCT.

Unambiguous subunit assignment in a multicomponent complex is critical for thorough understanding of the machinery and its functionality. The eukaryotic group II chaperonin TRiC/CCT folds approximately 10% of cytosolic proteins and is important for the maintenance of cellular homeostasis. TRiC consists of two rings and each ring has eight homologous but distinct subunits. Unambiguous subunit identification of a macromolecular machine such as TRiC through intermediate or low-resolution cryo-EM map remains challenging. Here we present a yeast internal-subunit eGFP labeling strategy termed YISEL, which can quickly introduce an eGFP tag in the internal position of a target subunit by homologous recombination, and the tag labeled protein can be expressed in endogenous level. Through this method, the labeling efficiency and tag-occupancy is ensured, and the inserted tag is usually less mobile compared to that fused to the terminus. It can also be used to bio-engineer other tag in the internal position of a protein in yeast. By applying our YISEL strategy and combined with cryo-EM 3D reconstruction, we unambiguously identified all the subunits in the cryo-EM map of TRiC, demonstrating the potential for broad application of this strategy in accurate and efficient subunit identification in other challenging complexes.

Structural insights into species-specific features of the ribosome from the human pathogen Mycobacterium tuberculosis.

Ribosomes from Mycobacterium tuberculosis (Mtb) possess species-specific ribosomal RNA (rRNA) expansion segments and ribosomal proteins (rProtein). Here, we present the near-atomic structures of the Mtb 50S ribosomal subunit and the complete Mtb 70S ribosome, solved by cryo-electron microscopy. Upon joining of the large and small ribosomal subunits, a 100-nt long expansion segment of the Mtb 23S rRNA, named H54a or the 'handle', switches interactions from with rRNA helix H68 and rProtein uL2 to with rProtein bS6, forming a new intersubunit bridge 'B9'. In Mtb 70S, bridge B9 is mostly maintained, leading to correlated motions among the handle, the L1 stalk and the small subunit in the rotated and non-rotated states. Two new protein densities were discovered near the decoding center and the peptidyl transferase center, respectively. These results provide a structural basis for studying translation in Mtb as well as developing new tuberculosis drugs.