Biased allosteric activation of ketone body receptor HCAR2 suppresses inflammation.

Hydroxycarboxylic acid receptor 2 (HCAR2), modulated by endogenous ketone body ß-hydroxybutyrate and exogenous niacin, is a promising therapeutic target for inflammation-related diseases. HCAR2 mediates distinct pathophysiological events by activating Gi/o protein or ß-arrestin effectors. Here, we characterize compound 9n as a Gi-biased allosteric modulator (BAM) of HCAR2 and exhibit anti-inflammatory efficacy in RAW264.7 macrophages via a specific HCAR2-Gi pathway. Furthermore, four structures of HCAR2-Gi complex bound to orthosteric agonists (niacin or monomethyl fumarate), compound 9n, and niacin together with compound 9n simultaneously reveal a common orthosteric site and a unique allosteric site. Combined with functional studies, we decipher the action framework of biased allosteric modulation of compound 9n on the orthosteric site. Moreover, co-administration of compound 9n with orthosteric agonists could enhance anti-inflammatory effects in the mouse model of colitis. Together, our study provides insight to understand the molecular pharmacology of the BAM and facilitates exploring the therapeutic potential of the BAM with orthosteric drugs.

Ligand recognition and G-protein coupling of trace amine receptor TAAR1.

Trace-amine-associated receptors (TAARs), a group of biogenic amine receptors, have essential roles in neurological and metabolic homeostasis1. They recognize diverse endogenous trace amines and subsequently activate a range of G-protein-subtype signalling pathways2,3. Notably, TAAR1 has emerged as a promising therapeutic target for treating psychiatric disorders4,5. However, the molecular mechanisms underlying its ability to recognize different ligands remain largely unclear. Here we present nine cryo-electron microscopy structures, with eight showing human and mouse TAAR1 in a complex with an array of ligands, including the endogenous 3-iodothyronamine, two antipsychotic agents, the psychoactive drug amphetamine and two identified catecholamine agonists, and one showing 5-HT1AR in a complex with an antipsychotic agent. These structures reveal a rigid consensus binding motif in TAAR1 that binds to endogenous trace amine stimuli and two extended binding pockets that accommodate diverse chemotypes. Combined with mutational analysis, functional assays and molecular dynamic simulations, we elucidate the structural basis of drug polypharmacology and identify the species-specific differences between human and mouse TAAR1. Our study provides insights into the mechanism of ligand recognition and G-protein selectivity by TAAR1, which may help in the discovery of ligands or therapeutic strategies for neurological and metabolic disorders.

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.

Structure-based identification of a G protein-biased allosteric modulator of cannabinoid receptor CB1.

Cannabis sativa is known for its therapeutic benefit in various diseases including pain relief by targeting cannabinoid receptors. The primary component of cannabis, Δ9-tetrahydrocannabinol (THC), and other agonists engage the orthosteric site of CB1, activating both Gi and ß-arrestin signaling pathways. The activation of diverse pathways could result in on-target side effects and cannabis addiction, which may hinder therapeutic potential. A significant challenge in pharmacology is the design of a ligand that can modulate specific signaling of CB1. By leveraging insights from the structure-function selectivity relationship (SFSR), we have identified Gi signaling-biased agonist-allosteric modulators (ago-BAMs). Further, two cryoelectron microscopy (cryo-EM) structures reveal the binding mode of ago-BAM at the extrahelical allosteric site of CB1. Combining mutagenesis and pharmacological studies, we elucidated the detailed mechanism of ago-BAM-mediated biased signaling. Notably, ago-BAM CB-05 demonstrated analgesic efficacy with fewer side effects, minimal drug toxicity and no cannabis addiction in mouse pain models. In summary, our finding not only suggests that ago-BAMs of CB1 provide a potential nonopioid strategy for pain management but also sheds light on BAM identification for GPCRs.

Entropy drives the ligand recognition in G-protein-coupled receptor subtypes.

Achieving ligand subtype selectivity within highly homologous subtypes of G-protein-coupled receptor (GPCR) is critical yet challenging for GPCR drug discovery, primarily due to the unclear mechanism underlying ligand subtype selectivity, which hampers the rational design of subtype-selective ligands. Herein, we disclose an unusual molecular mechanism of entropy-driven ligand recognition in cannabinoid (CB) receptor subtypes, revealed through atomic-level molecular dynamics simulations, cryoelectron microscopy structure, and mutagenesis experiments. This mechanism is attributed to the distinct conformational dynamics of the receptor's orthosteric pocket, leading to variations in ligand binding entropy and consequently, differential binding affinities, which culminate in specific ligand recognition. We experimentally validated this mechanism and leveraged it to design ligands with enhanced or ablated subtype selectivity. One such ligand demonstrated favorable pharmacokinetic properties and significant efficacy in rodent inflammatory analgesic models. More importantly, it is precisely due to the high subtype selectivity obtained based on this mechanism that this ligand does not show addictive properties in animal models. Our findings elucidate the unconventional role of entropy in CB receptor subtype selectivity and suggest a strategy for rational design of ligands to achieve entropy-driven subtype selectivity for many pharmaceutically important GPCRs.

Estimating the genome-wide mutation rate from thousands of unrelated individuals.

We provide a method for estimating the genome-wide mutation rate from sequence data on unrelated individuals by using segments of identity by descent (IBD). The length of an IBD segment indicates the time to shared ancestor of the segment, and mutations that have occurred since the shared ancestor result in discordances between the two IBD haplotypes. Previous methods for IBD-based estimation of mutation rate have required the use of family data for accurate phasing of the genotypes. This has limited the scope of application of IBD-based mutation rate estimation. Here, we develop an IBD-based method for mutation rate estimation from population data, and we apply it to whole-genome sequence data on 4,166 European American individuals from the TOPMed Framingham Heart Study, 2,996 European American individuals from the TOPMed My Life, Our Future study, and 1,586 African American individuals from the TOPMed Hypertension Genetic Epidemiology Network study. Although mutation rates may differ between populations as a result of genetic factors, demographic factors such as average parental age, and environmental exposures, our results are consistent with equal genome-wide average mutation rates across these three populations. Our overall estimate of the average genome-wide mutation rate per 108 base pairs per generation for single-nucleotide variants is 1.24 (95% CI 1.18-1.33).

Fast two-stage phasing of large-scale sequence data.

Haplotype phasing is the estimation of haplotypes from genotype data. We present a fast, accurate, and memory-efficient haplotype phasing method that scales to large-scale SNP array and sequence data. The method uses marker windowing and composite reference haplotypes to reduce memory usage and computation time. It incorporates a progressive phasing algorithm that identifies confidently phased heterozygotes in each iteration and fixes the phase of these heterozygotes in subsequent iterations. For data with many low-frequency variants, such as whole-genome sequence data, the method employs a two-stage phasing algorithm that phases high-frequency markers via progressive phasing in the first stage and phases low-frequency markers via genotype imputation in the second stage. This haplotype phasing method is implemented in the open-source Beagle 5.2 software package. We compare Beagle 5.2 and SHAPEIT 4.2.1 by using expanding subsets of 485,301 UK Biobank samples and 38,387 TOPMed samples. Both methods have very similar accuracy and computation time for UK Biobank SNP array data. However, for TOPMed sequence data, Beagle is more than 20 times faster than SHAPEIT, achieves similar accuracy, and scales to larger sample sizes.

Statistical design and analysis of controlled human malaria infection trials.

BACKGROUND: Malaria is a potentially life-threatening disease caused by Plasmodium protozoa transmitted by infected Anopheles mosquitoes. Controlled human malaria infection (CHMI) trials are used to assess the efficacy of interventions for malaria elimination. The operating characteristics of statistical methods for assessing the ability of interventions to protect individuals from malaria is uncertain in small CHMI studies. This paper presents simulation studies comparing the performance of a variety of statistical methods for assessing efficacy of intervention in CHMI trials. METHODS: Two types of CHMI designs were investigated: the commonly used single high-dose design (SHD) and the repeated low-dose design (RLD), motivated by simian immunodeficiency virus (SIV) challenge studies. In the context of SHD, the primary efficacy endpoint is typically time to infection. Using a continuous time survival model, five statistical tests for assessing the extent to which an intervention confers partial or full protection under single dose CHMI designs were evaluated. For RLD, the primary efficacy endpoint is typically the binary infection status after a specific number of challenges. A discrete time survival model was used to study the characteristics of RLD versus SHD challenge studies. RESULTS: In a SHD study with the continuous time survival model, log-rank test and t-test are the most powerful and provide more interpretable results than Wilcoxon rank-sum tests and Lachenbruch tests, while the likelihood ratio test is uniformly most powerful but requires knowledge of the underlying probability model. In the discrete time survival model setting, SHDs are more powerful for assessing the efficacy of an intervention to prevent infection than RLDs. However, additional information can be inferred from RLD challenge designs, particularly using a likelihood ratio test. CONCLUSIONS: Different statistical methods can be used to analyze controlled human malaria infection (CHMI) experiments, and the choice of method depends on the specific characteristics of the experiment, such as the sample size allocation between the control and intervention groups, and the nature of the intervention. The simulation results provide guidance for the trade off in statistical power when choosing between different statistical methods and study designs.

Estimating the Genome-wide Mutation Rate with Three-Way Identity by Descent.

The two primary methods for estimating the genome-wide mutation rate have been counting de novo mutations in parent-offspring trios and comparing sequence data between closely related species. With parent-offspring trio analysis it is difficult to control for genotype error, and resolution is limited because each trio provides information from only two meioses. Inter-species comparison is difficult to calibrate due to uncertainty in the number of meioses separating species, and it can be biased by selection and by changing mutation rates over time. An alternative class of approaches for estimating mutation rates that avoids these limitations is based on identity by descent (IBD) segments that arise from common ancestry within the past few thousand years. Existing IBD-based methods are limited to highly inbred samples, or lack robustness to genotype error and error in the estimated demographic history. We present an IBD-based method that uses sharing of IBD segments among sets of three individuals to estimate the mutation rate. Our method is applicable to accurately phased genotype data, such as parent-offspring trio data phased using Mendelian rules of inheritance. Unlike standard parent-offspring analysis, our method utilizes distant relationships and is robust to genotype error. We apply our method to data from 1,307 European-ancestry individuals in the Framingham Heart Study sequenced by the NHLBI TOPMed project. We obtain an estimate of 1.29 × 10-8 mutations per base pair per meiosis with a 95% confidence interval of [1.02 × 10-8, 1.56 × 10-8].

POPdemog: visualizing population demographic history from simulation scripts.

Summary: We present POPdemog, an R package which converts coalescent simulation program input parameters into a visual representation of the demographic model. This package is useful for preparing figures, for checking that demographic simulation parameters have been correctly specified, and for understanding demographic models that other researchers have used to simulate genetic data. The POPdemog package supports the ms, msa, msHot, MaCS, msprime, scrm and Cosi2 programs, and includes options for customizing the output figures. Availability and implementation: The POPdemog package and its tutorial can be freely downloaded from https://github.com/YingZhou001/POPdemog. Supplementary information: Supplementary data are available at Bioinformatics online.

Dipyridyl-Fused Quinoxalineimide (DPQI): A Strong Electron-Withdrawing Building Block for n-Type Polymer Semiconductors.

Exploration of new electron-withdrawing building blocks plays a key role in the development of n-type organic semiconductors. Herein, a strong electron-withdrawing building block, dipyridyl-fused quinoxalineimide (DPQI), was successfully designed and synthesized. Single-crystal structure reveals that DPQI molecule possesses a completely planar backbone, which is beneficial for charge transport. For comparison, dibenzo-fused quinoxalineimide (DBQI) was also synthesized. The frontier molecular orbital (FMO) energy levels downshift with the incorporation of nitrogen atoms onto the π-conjugated backbone of quinoxalineimide. Two acceptor-acceptor (or all-acceptor) polymers P(BTI-DBQI) and P(BTI-DPQI) based on DBQI and DPQI were synthesized, respectively. Two polymers exhibit deep lowest-unoccupied molecular orbital (LUMO) levels (~-3.5 eV). Additionally, P(BTI-DPQI) exhibits unipolar n-type charge transport with µe of 1.4×10-4  cm2  V-1 s-1 in the organic field-effect transistors (OFET), which render them highly attractive for developing n-type semiconductors device. This work demonstrates that DPQI is a promising building block for constructing n-type polymer semiconductors.

Activation of Dopamine Receptor D1 and Downstream Cellular Functions by Polydopamine.

Polydopamine is a remarkable molecule that has gained considerable attention for its role in material surface modification, leading to an abundance of research in the biomaterial domain. While its widespread use is well documented, the molecule's potential cellular interactions have been less explored. In particular, dopamine serves as a neurotransmitter and a hormone that interacts with dopamine receptors in cells. Our study sheds light on the previously unexamined interaction between polydopamine and dopamine receptor D1 (DRD1). We discovered that polydopamine, along with its derivatives, such as levodopa and catechol, can activate DRD1─a function previously attributed solely to dopamine. Moreover, we found that polydopamine has the ability to influence cell behavior through the cAMP/PKA pathway, thereby affecting RhoA activity and stress fiber formation. These observations invite further consideration regarding the biological safety of polydopamine in biomedical contexts and also open avenues for new research directions in designing bioactive functional materials.

[Epidemiology survey of gastrointestinal stromal tumor in Shanxi Province in 2011].

OBJECTIVE: To explore the incidence and distribution of gastrointestinal stromal tumor (GIST) in Shanxi Province. METHODS: Newly diagnosed and suspected GIST cases of Shanxi Province on January 1, 2011 to December 31, 2011 were collected from medical insurance records and hospital surveys. All specimens were sent to the Department of Pathology at Shanxi Provincial Tumor Hospital for examinations. And the data were analyzed by SPSS statistical analysis software. RESULTS: There were 153 newly discovered cases of GIST in Shanxi Province in 2011. And its distribution was scattered in different regions. The incidence was 4.3 per million (153/35 932 786) . The high-risk areas were Taiyuan (n = 25) and Changzhi (n = 25). There were 83 (54.2%) males and 70 (45.8%) females. And the incidence of males was not different from that of females ( (4.5 vs 4.0 )per million, P > 0.05). The median onset age was 59 (24-79) years. A high incidence of GIST occurred at an age range of 50-59 years (n = 33). Among the 139 patients, the tumor locations were stomach (n = 88, 63.3%), small intestine (n = 21, 15.1%), colon (n = 7, 5.0%), duodenum (n = 6, 4.3%), esophagus (n = 3, 2.2%) and extra-gastrointestinal (n = 14, 10.1%). And 113 cases had a record of tumor size. The median diameter was 5.78 (0.3-25.0) cm. The largest diameter was ≤ 2 cm (n = 30, 26.5%), > 2-5 cm (n = 33, 29.2%), > 5-10 cm (n = 36, 31.9%) and >10 cm (n = 14, 12.4%). The cell types of 141 cases were spindle cell (n = 112, 79.4%), epithelial (n = 11, 7.8%) and mixed (n = 18, 12.8%). CONCLUSIONS: Shanxi Province has a low incidence of GIST. And no statistically significant difference exists in the incidence between males and females. Taiyuan and Changzhi are relatively more prevalent.

Research on Optical Fiber Ring Resonator Q Value and Coupling Efficiency Optimization.

The coupling efficiency of the fiber ring resonator has an important influence on the scale factor of the resonant fiber gyroscope. In order to improve the scale factor of the gyroscope, the coupling efficiency of the fiber ring resonator and its influential factors on the scale factor of the gyroscope are analyzed and tested. The results show that the coupling efficiency is affected by both the splitting ratio of the coupler and the loss in the cavity. When the coupling efficiency approaches 0.75 at the under-coupling state, the scaling factor of the gyroscope is the highest. This provides a theoretical reference and an experimental basis for the enhancement of the scaling factor of the resonant fiber gyroscope with the fiber ring resonator as the sensitive unit, providing options for multiple applications such as sea, land, sky and space.

Orthosteric ligand selectivity and allosteric probe dependence at Hydroxycarboxylic acid receptor HCAR2.

Hydroxycarboxylic acid receptor 2 (HCAR2), a member of Class A G-protein-coupled receptor (GPCR) family, plays a pivotal role in anti-lipolytic and anti-inflammatory effects, establishing it as a significant therapeutic target for treating dyslipidemia and inflammatory diseases. However, the mechanism underlying the signaling of HCAR2 induced by various types of ligands remains elusive. In this study, we elucidate the cryo-electron microscopy (cryo-EM) structure of Gi-coupled HCAR2 in complex with a selective agonist, MK-6892, resolved to a resolution of 2.60 Å. Our structural analysis reveals that MK-6892 occupies not only the orthosteric binding pocket (OBP) but also an extended binding pocket (EBP) within HCAR2. Pharmacological assays conducted in this study demonstrate that the OBP is a critical determinant for ligand selectivity among the HCARs subfamily. Moreover, we investigate the pharmacological properties of the allosteric modulator compound 9n, revealing its probe-dependent behavior on HCAR2 in response to varying orthosteric agonists. Collectively, our findings provide invaluable structural insights that contribute to a deeper understanding of the regulatory mechanisms governing HCAR2 signaling transduction mediated by both orthosteric and allosteric ligands.

Structure, function and drug discovery of GPCR signaling.

G protein-coupled receptors (GPCRs) are versatile and vital proteins involved in a wide array of physiological processes and responses, such as sensory perception (e.g., vision, taste, and smell), immune response, hormone regulation, and neurotransmission. Their diverse and essential roles in the body make them a significant focus for pharmaceutical research and drug development. Currently, approximately 35% of marketed drugs directly target GPCRs, underscoring their prominence as therapeutic targets. Recent advances in structural biology have substantially deepened our understanding of GPCR activation mechanisms and interactions with G-protein and arrestin signaling pathways. This review offers an in-depth exploration of both traditional and recent methods in GPCR structure analysis. It presents structure-based insights into ligand recognition and receptor activation mechanisms and delves deeper into the mechanisms of canonical and noncanonical signaling pathways downstream of GPCRs. Furthermore, it highlights recent advancements in GPCR-related drug discovery and development. Particular emphasis is placed on GPCR selective drugs, allosteric and biased signaling, polyphamarcology, and antibody drugs. Our goal is to provide researchers with a thorough and updated understanding of GPCR structure determination, signaling pathway investigation, and drug development. This foundation aims to propel forward-thinking therapeutic approaches that target GPCRs, drawing upon the latest insights into GPCR ligand selectivity, activation, and biased signaling mechanisms.

Prospect of acromegaly therapy: molecular mechanism of clinical drugs octreotide and paltusotine.

Somatostatin receptor 2 (SSTR2) is highly expressed in neuroendocrine tumors and represents as a therapeutic target. Several peptide analogs mimicking the endogenous ligand somatostatin are available for clinical use, but poor therapeutic effects occur in a subset of patients, which may be correlated with subtype selectivity or cell surface expression. Here, we clarify the signal bias profiles of the first-generation peptide drug octreotide and a new-generation small molecule paltusotine by evaluating their pharmacological characteristics. We then perform cryo-electron microscopy analysis of SSTR2-Gi complexes to determine how the drugs activate SSTR2 in a selective manner. In this work, we decipher the mechanism of ligand recognition, subtype selectivity and signal bias property of SSTR2 sensing octreotide and paltusotine, which may aid in designing therapeutic drugs with specific pharmacological profiles against neuroendocrine tumors.

Mechanism of activation and biased signaling in complement receptor C5aR1.

The complement system plays an important role in the innate immune response to invading pathogens. The complement fragment C5a is one of its important effector components and exerts diverse physiological functions through activation of the C5a receptor 1 (C5aR1) and associated downstream G protein and ß-arrestin signaling pathways. Dysfunction of the C5a-C5aR1 axis is linked to numerous inflammatory and immune-mediated diseases, but the structural basis for activation and biased signaling of C5aR1 remains elusive. Here, we present cryo-electron microscopy structures of the activated wild-type C5aR1-Gi protein complex bound to each of the following: C5a, the hexapeptidic agonist C5apep, and the G protein-biased agonist BM213. The structures reveal the landscape of the C5a-C5aR1 interaction as well as a common motif for the recognition of diverse orthosteric ligands. Moreover, combined with mutagenesis studies and cell-based pharmacological assays, we deciphered a framework for biased signaling using different peptide analogs and provided insight into the activation mechanism of C5aR1 by solving the structure of C5aR1I116A mutant-Gi signaling activation complex induced by C089, which exerts antagonism on wild-type C5aR1. In addition, unusual conformational changes in the intracellular end of transmembrane domain 7 and helix 8 upon agonist binding suggest a differential signal transduction process. Collectively, our study provides mechanistic understanding into the ligand recognition, biased signaling modulation, activation, and Gi protein coupling of C5aR1, which may facilitate the future design of therapeutic agents.

Preparation of carboxymethyl cellulose/graphene composite aerogel beads and their adsorption for methylene blue.

Carboxymethyl cellulose/ graphene composite aerogel beads (CMC/GAs) were prepared by the easily scaling-up method, i.e., wet spinning- environmental pressure drying method. The influences of the type and concentration of coagulating bath on the formation of aerogel beads were discussed, and the forming mechanism was analyzed. The CMC/GAs was characterized through SEM, XRD, FI-IR, Raman, XPS, electronic universal testing machine and other methods. The CMC/GAs-30 has an average particle size and a mean pore diameter of 3.83 mm and 82 µm, respectively. The analysis results indicated that the adsorption mechanisms of CMC/GAs on methylene blue (MB) are mainly through the electrostatic interaction. The adsorption process conforms to the Langmuir model (R2 = 0.9964) and pseudo-second-order kinetic model (R2 is higher than 0.99). When the particle size of CMC/GAs-30 decreases, the equilibrium adsorption capacity for MB increases. Under the experimental conditions explored, the Langmuir maximum adsorption capacity of CMC/GAs-30 for MB is 222.72 mg.g-1. The CMC/GAs-30 show good recycle performance in MB adsorption. The removal rate of MB from water by CMC/GAs-30 remained at about 90% after 30-times adsorption- regeneration cycles.

Analysis of the factors influencing the college students' employment willingness under the strategy of "strengthening the provincial capital".

University graduates can inject strong impetus into the development of cities. However, China's "Strengthening the Provincial Capital" strategy attracts many college graduates, resulting in many limitations in attracting college students to employment in non-provincial capital cities. This paper systematically studies college students' work and entrepreneurial intention factors. We conduct a social survey of employment and entrepreneurial intentions among university graduates in Xiangtan City, Hunan Province, and obtain 12,897 questionnaires. Then, we construct an index analysis system (IAS) and analyze the 12897 questionnaires systematically based on IAS. The results show that urban characteristics and personal orientation most directly affect college graduates' employment and entrepreneurial willingness. Based on the statistical conclusions, we put forward corresponding policy recommendations. Colleges should build a "government-enterprise-school" cooperation mechanism, implement strategically focused attraction policies following local conditions and local conditions, and promote the employment of college students to promote employment and entrepreneurship in non-provincial capitals.