Microelement strontium and human health: comprehensive analysis of the role in inflammation and non-communicable diseases (NCDs).

Strontium (Sr), a trace element with a long history and a significant presence in the Earth's crust, plays a critical yet often overlooked role in various biological processes affecting human health. This comprehensive review explores the multifaceted implications of Sr, especially in the context of non-communicable diseases (NCDs) such as cardiovascular diseases, osteoporosis, hypertension, and diabetes mellitus. Sr is predominantly acquired through diet and water and has shown promise as a clinical marker for calcium absorption studies. It contributes to the mitigation of several NCDs by inhibiting oxidative stress, showcasing antioxidant properties, and suppressing inflammatory cytokines. The review delves deep into the mechanisms through which Sr interacts with human physiology, emphasizing its uptake, metabolism, and potential to prevent chronic conditions. Despite its apparent benefits in managing bone fractures, hypertension, and diabetes, current research on Sr's role in human health is not exhaustive. The review underscores the need for more comprehensive studies to solidify Sr's beneficial associations and address the gaps in understanding Sr intake and its optimal levels for human health.

Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials.

High energy and low sensitivity have been the focus of developing new energetic materials (EMs). However, there has been a lack of a quick and accurate method for evaluating the stability of diverse EMs. Here, we develop a machine learning prediction model with high accuracy for bond dissociation energy (BDE) of EMs. A reliable and representative BDE dataset of EMs is constructed by collecting 778 experimental energetic compounds and quantum mechanics calculation. To sufficiently characterize the BDE of EMs, a hybrid feature representation is proposed by coupling the local target bond into the global structure characteristics. To alleviate the limitation of the low dataset, pairwise difference regression is utilized as a data augmentation with the advantage of reducing systematic errors and improving diversity. Benefiting from these improvements, the XGBoost model achieves the best prediction accuracy with R2 of 0.98 and MAE of 8.8 kJ mol-1, significantly outperforming competitive models.

Computational insights into diverse binding modes of the allosteric modulator and their regulation on dopamine D1 receptor.

Allosteric drugs hold the promise of addressing many challenges in the current drug development of GPCRs. However, the molecular mechanism underlying their allosteric modulations remain largely elusive. The dopamine D1 receptor (DRD1), a member of Class A GPCRs, is critical for treating psychiatric disorders, and LY3154207 serves as its promising positive allosteric modulator (PAM). In the work, we utilized extensive Gaussian-accelerated molecular dynamics simulations (a total of 41µs) for the first time probe the diverse binding modes of the allosteric modulator and their regulation effects, based on the DRD1 and LY3154207 as representative. Our simulations identify four binding modes of LY3154207 (one boat mode, two metastable vertical modes and a novel cleft-anchored mode), in which the boat mode is the most stable while there three modes are similar in the stability. However, it is interesting to observed that the most stable boat mode inversely exhibits the weakest positive allosteric effect on influencing the orthosteric ligand binding and maintaining the activity of the transducer binding site. It should result from its induced weaker correlation between the allosteric site and the orthosteric site, and between the orthosteric site and the transducer binding site than the other three binding modes, as well as its weakened interaction between a crucial activation-related residue (S2025.46) and the orthosteric ligand (dopamine). Overall, the work offers atomic-level information to advance our understanding of the complex allosteric regulation on GPCRs, which is beneficial to the allosteric modulator design and development.

Influence of labor migration on rural household food waste in China: Application of propensity score matching (PSM).

Food waste has emerged as a critical global concern, with households identified as major contributors to this pressing issue. As the world grapples with sustainability challenges, addressing food waste in the context of rural labor migration is crucial for achieving broader sustainable development goals. However, there is still limited research regarding the relationship between labor migration and food waste. We utilized propensity score matching to analyze cross-sectional data collected from 1270 rural households in China. Labor migration led to significant increases of 37% in overall food waste and 35% in plant-based food waste, respectively. Furthermore, households with labor migration exhibited 29%, 31%, and 30 % higher energy, protein, and carbohydrate waste, respectively, compared to non-migration households. Regarding micronutrients, migration led to a 39% increase in iron waste, a 42% increase in zinc waste, and a 47% increase in selenium waste. The results of the categorical analysis indicate variations in the impact of labor migration on food wastage within rural households. Food wastage in rural households with chronic illness patients responds differently to labor migration. Moreover, labor migration predominantly affects households without courier services in villages, where dietary diversity plays a significant role. Understanding these variations is essential for crafting targeted interventions and policies to address food waste in different rural contexts. The policy implications of our study are crucial for addressing food waste and advancing sustainable development in rural China, where labor migration plays a significant role.

Attention Mechanism-Based Graph Neural Network Model for Effective Activity Prediction of SARS-CoV-2 Main Protease Inhibitors: Application to Drug Repurposing as Potential COVID-19 Therapy.

Compared to de novo drug discovery, drug repurposing provides a time-efficient way to treat coronavirus disease 19 (COVID-19) that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 main protease (Mpro) has been proved to be an attractive drug target due to its pivotal involvement in viral replication and transcription. Here, we present a graph neural network-based deep-learning (DL) strategy to prioritize the existing drugs for their potential therapeutic effects against SARS-CoV-2 Mpro. Mpro inhibitors were represented as molecular graphs ready for graph attention network (GAT) and graph isomorphism network (GIN) modeling for predicting the inhibitory activities. The result shows that the GAT model outperforms the GIN and other competitive models and yields satisfactory predictions for unseen Mpro inhibitors, confirming its robustness and generalization. The attention mechanism of GAT enables to capture the dominant substructures and thus to realize the interpretability of the model. Finally, we applied the optimal GAT model in conjunction with molecular docking simulations to screen the Drug Repurposing Hub (DRH) database. As a result, 18 drug hits with best consensus prediction scores and binding affinity values were identified as the potential therapeutics against COVID-19. Both the extensive literature searching and evaluations on adsorption, distribution, metabolism, excretion, and toxicity (ADMET) illustrate the premium drug-likeness and pharmacokinetic properties of the drug candidates. Overall, our work not only provides an effective GAT-based DL prediction tool for inhibitory activity of SARS-CoV-2 Mpro inhibitors but also provides theoretical guidelines for drug discovery in the COVID-19 treatment.

HOPEXGB: A Consensual Model for Predicting miRNA/lncRNA-Disease Associations Using a Heterogeneous Disease-miRNA-lncRNA Information Network.

Predicting disease-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) is crucial to find new biomarkers for the prevention, diagnosis, and treatment of complex human diseases. Computational predictions for miRNA/lncRNA-disease associations are of great practical significance, since traditional experimental detection is expensive and time-consuming. In this paper, we proposed a consensual machine-learning technique-based prediction approach to identify disease-related miRNAs and lncRNAs by high-order proximity preserved embedding (HOPE) and eXtreme Gradient Boosting (XGB), named HOPEXGB. By connecting lncRNA, miRNA, and disease nodes based on their correlations and relationships, we first created a heterogeneous disease-miRNA-lncRNA (DML) information network to achieve an effective fusion of information on similarities, correlations, and interactions among miRNAs, lncRNAs, and diseases. In addition, a more rational negative data set was generated based on the similarities of unknown associations with the known ones, so as to effectively reduce the false negative rate in the data set for model construction. By 10-fold cross-validation, HOPE shows better performance than other graph embedding methods. The final consensual HOPEXGB model yields robust performance with a mean prediction accuracy of 0.9569 and also demonstrates high sensitivity and specificity advantages compared to lncRNA/miRNA-specific predictions. Moreover, it is superior to other existing methods and gives promising performance on the external testing data, indicating that integrating the information on lncRNA-miRNA interactions and the similarities of lncRNAs/miRNAs is beneficial for improving the prediction performance of the model. Finally, case studies on lung, stomach, and breast cancers indicate that HOPEXGB could be a powerful tool for preclinical biomarker detection and bioexperiment preliminary screening for the diagnosis and prognosis of cancers. HOPEXGB is publicly available at https://github.com/airpamper/HOPEXGB.

The status of the global food waste mitigation policies: experience and inspiration for China.

Food waste has become a significant challenge faced by the community with a shared future for mankind, and it has also caused a considerable impact on China's food security. Scholars across disciplines, international organizations, and especially policymakers are increasingly interested in food waste. Policies are seen as a powerful factor in reducing food waste, but current research on related policies is more scattered. This paper summarizes and analyzes the experiences of food waste policy development and implementation by systematically reviewing the studies on food waste reduction policies. The results of this paper's analysis show that current global food waste policies are focused at the national strategic level, with approaches such as legislation, food donation, waste recycling, awareness and education, and data collection. At the same time, we find that the current experience of developed countries in policy formulation and implementation is beneficial for policy formulation in developing countries. And taking China as an example, we believe that developing countries can improve food waste policies in the future by improving legislation, guiding the development of food banks, promoting social governance, and strengthening scientific research projects. These policies will all contribute strongly to global environmental friendliness. In addition, we discuss some of the factors that influence the development of food waste policies and argue that in the future, more consideration needs to be given to the effects of policy implementation and that case studies should focus more on developing countries. This will contribute to the global sustainable development process. Supplementary Information: The online version contains supplementary material available at 10.1007/s10668-023-03132-0.

Multi-omics integration analysis of GPCRs in pan-cancer to uncover inter-omics relationships and potential driver genes.

G protein-coupled receptors (GPCRs) are the largest drug target family. Unfortunately, applications of GPCRs in cancer therapy are scarce due to very limited knowledge regarding their correlations with cancers. Multi-omics data enables systematic investigations of GPCRs, yet their effective integration remains a challenge due to the complexity of the data. Here, we adopt two types of integration strategies, multi-staged and meta-dimensional approaches, to fully characterize somatic mutations, somatic copy number alterations (SCNAs), DNA methylations, and mRNA expressions of GPCRs in 33 cancers. Results from the multi-staged integration reveal that GPCR mutations cannot well predict expression dysregulation. The correlations between expressions and SCNAs are primarily positive, while correlations of the methylations with expressions and SCNAs are bimodal with negative correlations predominating. Based on these correlations, 32 and 144 potential cancer-related GPCRs driven by aberrant SCNA and methylation are identified, respectively. In addition, the meta-dimensional integration analysis is carried out by using deep learning models, which predict more than one hundred GPCRs as potential oncogenes. When comparing results between the two integration strategies, 165 cancer-related GPCRs are common in both, suggesting that they should be prioritized in future studies. However, 172 GPCRs emerge in only one, indicating that the two integration strategies should be considered concurrently to complement the information missed by the other such that obtain a more comprehensive understanding. Finally, correlation analysis further reveals that GPCRs, in particular for the class A and adhesion receptors, are generally immune-related. In a whole, the work is for the first time to reveal the associations between different omics layers and highlight the necessity of combing the two strategies in identifying cancer-related GPCRs.

A consensual machine-learning-assisted QSAR model for effective bioactivity prediction of xanthine oxidase inhibitors using molecular fingerprints.

Xanthine oxidase inhibitors (XOIs) have been widely studied due to the promising potential as safe and effective therapeutics in hyperuricemia and gout. Currently, available XOI molecules have been developed from different experiments but they are with the wide structure diversity and significant varying bioactivities. So it is of great practical significance to present a consensual QSAR model for effective bioactivity prediction of XOIs based on a systematic compiling of these XOIs across different experiments. In this work, 249 XOIs belonging to 16 scaffolds were collected and were integrated into a consensual dataset by introducing the concept of IC50 values relative to allopurinol (RIC50). Here, extended connectivity fingerprints (ECFPs) were employed to represent XOI molecules. By performing effective feature selection by machine-learning method, 54 crucial fingerprints were indicated to be valuable for predicting the inhibitory potency (IP) of XOIs. The optimal predictor yields the promising performance by different cross-validation tests. Besides, an external validation of 43 XOIs and a case study on febuxostat also provide satisfactory results, indicating the powerful generalization of our predictor. Here, the predictor was interpreted by shapely additive explanation (SHAP) method which revealed several important substructures by mapping the featured fingerprints to molecular structures. Then, 15 new molecules were designed and predicted by our predictor to show superior IP than febuxostat. Finally, molecular docking simulation was performed to gain a deep insight into molecular binding mode with xanthine oxidase (XO) enzyme, showing that molecules with selenazole moiety, cyano group and isopropyl group tended to yield higher IP. The absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction results further enhanced the potential of these novel XOIs as drug candidates. Overall, this work presents a QSAR model for accurate prediction of IP of XOIs, and is expected to provide new insights for further structure-guided design of novel XOIs.

A new molecular subclassification and in silico predictions for diagnosis and prognosis of papillary thyroid cancer by alternative splicing profile.

Introduction: Papillary thyroid cancer (PTC) is the most common endocrine malignancy. However, different PTC variants reveal high heterogeneity at histological, cytological, molecular and clinicopathological levels, which complicates the precise diagnosis and management of PTC. Alternative splicing (AS) has been reported to be potential cancer biomarkers and therapeutic targets. Method: Here, we aim to find a more sophisticated molecular subclassification and characterization for PTC by integrating AS profiling. Based on six differentially expressed alternative splicing (DEAS) events, a new molecular subclassification was proposed to reclassify PTC into three new groups named as Cluster0, Cluster1 and Cluster2 respectively. Results: An in silico prediction was performed for accurate recognition of new groups with the average accuracy of 91.2%. Moreover, series of analyses were implemented to explore the differences of clinicopathology, molecular and immune characteristics across them. It suggests that there are remarkable differences among them, but Cluster2 was characterized by poor prognosis, higher immune heterogeneity and more sensitive to anti-PD1 therapy. The splicing correlation networks proved the complicated regulation relationships between AS events and splicing factors (SFs). An independent prognostic indicator for PTC overall survival (OS) was established. Finally, three compounds (orantinib, tyrphostin-AG-1295 and AG-370) were discovered to be the potential therapeutic agents. Discussion: Overall, the six DEAS events are not only potential biomarkers for precise diagnosis of PTC, but also the probable prognostic predictors. This research would be expected to highlight the effect of AS events on PTC characterization and also provide new insights into refining precise subclassification and improving medical therapy for PTC patients.

Choroidal Microvasculature Dropout in Glaucoma.

Glaucoma is a group of diseases characterized by distinctive visual field defect and optic nerve atrophy usually associated with elevated intraocular pressure (IOP). It is one of the most serious visual disorders and the leading cause of irreversible blindness worldwide. As a multifactorial disease, the pathogenesis of glaucoma is complicated and has been far from fully understood, where vascular factors are recognized to play an important role in its development and progression of glaucoma. Empirical researches have shown that parapapillary choroidal microvasculature dropout (CMvD) is closely associated with the impairment of optic nerve head (ONH) perfusion, probably accelerating the progression of glaucoma. Accordingly, it is necessary to explore the details regarding the relationship between CMvD and glaucoma progress, hoping to enhance the understanding of pathogenesis of glaucoma. In this review, we aimed to establish comprehensive understanding of the relationship between CMvD and glaucoma with generally going through relevant up-to-date literatures. Among the events that are closely associated with CMvD, we summarized the ones specifically involved in the term of glaucomatous pathological process, including thickness of retinal nerve fiber layer (RNFL) thickness, lamina cribrosa (LC) morphology, cricumpapillary vessel density (cpVD) and visual function such as visual field (VF) defect as well as the prognosis of glaucoma. Although researchers have made great advances, there are still many issues need to be addressed particularly concerning the pathogenic role of CMvD in glaucoma development and its clinical implications with respect to glaucoma prognosis.

General Graph Neural Network-Based Model To Accurately Predict Cocrystal Density and Insight from Data Quality and Feature Representation.

Cocrystal engineering as an effective way to modify solid-state properties has inspired great interest from diverse material fields while cocrystal density is an important property closely correlated with the material function. In order to accurately predict the cocrystal density, we develop a graph neural network (GNN)-based deep learning framework by considering three key factors of machine learning (data quality, feature presentation, and model architecture). The result shows that different stoichiometric ratios of molecules in cocrystals can significantly influence the prediction performances, highlighting the importance of data quality. In addition, the feature complementary is not suitable for augmenting the molecular graph representation in the cocrystal density prediction, suggesting that the complementary strategy needs to consider whether extra features can sufficiently supplement the lacked information in the original representation. Based on these results, 4144 cocrystals with 1:1 stoichiometry ratio are selected as the dataset, supplemented by the data augmentation of exchanging a pair of coformers. The molecular graph is determined to learn feature representation to train the GNN-based model. Global attention is introduced to further optimize the feature space and identify important atoms to realize the interpretability of the model. Benefited from the advantages, our model significantly outperforms three competitive models and exhibits high prediction accuracy for unseen cocrystals, showcasing its robustness and generality. Overall, our work not only provides a general cocrystal density prediction tool for experimental investigations but also provides useful guidelines for the machine learning application. All source codes are freely available at https://github.com/Xiao-Gua00/CCPGraph.

A new inhibitor of human peptide deformylase suppresses cell proliferation and induces cell apoptosis and autophagy in cancers.

Human peptide deformylase (hsPDF) has been found overexpressed in many cancer cells and its inhibitors exhibit antitumor activity. Studies were performed to validate that hsPDF is a good antitumor target. The inhibitory effect of PDF64 on hsPDF enzymatic activity was measured and confirmed by computation analysis. Antiproliferation activity was determined and in-vivo antitumor activity were analyzed in HCT116 and HL60 nude mice xenografts. Mitochondrial membrane potential (MMP), cell apoptosis, and autophagic cell death were analyzed by flow cytometry. ATP level was quantified using an ATP assay kit. Protein expression and kinase phosphorylation were determined by western blotting. A new hsPDF inhibitor PDF64 was identified. It showed evident antiproliferation activity in 10 cancer cells and significantly suppressed tumor growth in HCT116 and HL60 xenografts. It induced an obvious decrease in MMP and caused apparent cell apoptosis and autophagy in HCT116 and Jurkat cells. PDF64 treatment also led to an evident decrease in cellular ATP levels in these cells. Moreover, PDF64 downregulated c-Myc expression and had some effects on extracellular regulated protein kinases (ERK) and protein kinase B (Akt)/ mammalian target of rapamycin (mTOR) pathways. PDF64 exhibited good antitumor effects both in vivo and in vitro . It caused cell apoptosis and autophagic death in HCT116 and Jurkat cells. The effects may be mediated by inhibiting c-Myc expression and ERK or PI3K-Akt-mTOR pathway. Therefore, PDF64 may be a promising reagent for antitumor drug development, which further supports that hsPDF is a good antitumor drug target.

Pollution Characteristics, Spatial Distribution, and Evaluation of Heavy Metal(loid)s in Farmland Soils in a Typical Mountainous Hilly Area in China.

Heavy metal(loid)s pollution in farmland soil is not only a serious environmental but also a human health-related issue. Accurate understanding and evaluation of heavy metal pollution levels in the soil are very important for sustainable agricultural development and food safety. Mountainous and hilly areas have the dual functions of industrial development and agricultural production, and the farmland soil in these areas is more susceptible to heavy metal pollution. In this study, the single factor index, Nemerow index, geo-accumulation index, enrichment factor index, and potential ecological risk indices, which are mainly used to assess the contamination and risk of heavy metals in farmland soils. The sources of heavy metals in agricultural soils of the study area were analyzed using correlation analysis and principal component analysis. Finally, geostatistical methods were used to map the heavy metal contamination of farmland soils. An average concentration of all heavy metals (except As) in farmland soils of the study area exceeded the corresponding background values, as indicated by the obtained results. The results of the principal component analysis showed that the heavy metal sources in the soils of the study area can be classified into two groups. The five pollutant index methods all showed the most serious Hg pollution in the study area. The integrated pollutant mapping results showed that the risk of heavy metal pollution in the study area was mostly moderate, except for the western and central parts of the region. This study enhances understanding of the pollution levers of heavy metals in Yiyuan farmland soils, and also can facilitate the monitoring of heavy metal contaminants at the primary stage of the food chain and assess the risk of the presence of heavy metal contaminants in food, thus improving the health of the residents.

Function-oriented synthesis of Imidazo[1,2-a]pyrazine and Imidazo[1,2-b]pyridazine derivatives as potent PI3K/mTOR dual inhibitors.

The PI3K-Akt-mTOR signaling pathway is a highly frequently activated signal transduction pathway in human malignancies, which has been a hot target for anti-tumoral drug discovery. Based on our previous research, a function-oriented synthesis (FOS) of imidazo[1,2-a]pyrazines and imidazo[1,2-b]pyridazines was conducted, and their anticancer activities in vitro and in vivo were evaluated. Among them, compound 42 exhibited excellent dual PI3K/mTOR inhibitory activity, with IC50 values on PI3Kα and mTOR of 0.06 nM and 3.12 nM, respectively, much better than our previous reported compound 15a. Furthermore, compound 42 exhibited significant in vitro and in vivo anti-tumoral activities, great kinase selectivity, low hepatotoxicity, modest plasma clearance and acceptable oral bioavailability, which is a promising PI3K/mTOR targeted anti-cancer drug candidate.

Does online food shopping boost dietary diversity? Application of an endogenous switching model with a count outcome variable.

Increasingly, rural households in developing countries are shopping for food online, and the COVID-19 pandemic has accelerated this trend. In parallel, dietary guidelines worldwide recommend eating a balanced and healthy diet. With this in mind, this study explores whether online food shopping boosts dietary diversity-defined as the number of distinct food groups consumed-among rural households in China. Because people choose to shop for food online, it is important to account for the self-selection bias inherent in online food shopping. Accordingly, we estimate the treatment effects of online food shopping on dietary diversity using the endogenous switching model with a count outcome variable. The results indicate that online food shopping increases dietary diversity by 7.34%. We also find that education, asset ownership, and knowing the government's dietary guidelines are the main factors driving rural households' decisions to shop for food online.

Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma.

Introduction: As a malignant brain tumor, glioblastoma (GBM) is characterized by intratumor heterogeneity, a worse prognosis, and highly invasive, lethal, and refractory natures. Immunotherapy has been becoming a promising strategy to treat diverse cancers. It has been known that there are highly heterogeneous immunosuppressive microenvironments among different GBM molecular subtypes that mainly include classical (CL), mesenchymal (MES), and proneural (PN), respectively. Therefore, an in-depth understanding of immune landscapes among them is essential for identifying novel immune markers of GBM. Methods and results: In the present study, based on collecting the largest number of 109 immune signatures, we aim to achieve a precise diagnosis, prognosis, and immunotherapy prediction for GBM by performing a comprehensive immunogenomic analysis. Firstly, machine-learning (ML) methods were proposed to evaluate the diagnostic values of these immune signatures, and the optimal classifier was constructed for accurate recognition of three GBM subtypes with robust and promising performance. The prognostic values of these signatures were then confirmed, and a risk score was established to divide all GBM patients into high-, medium-, and low-risk groups with a high predictive accuracy for overall survival (OS). Therefore, complete differential analysis across GBM subtypes was performed in terms of the immune characteristics along with clinicopathological and molecular features, which indicates that MES shows much higher immune heterogeneity compared to CL and PN but has significantly better immunotherapy responses, although MES patients may have an immunosuppressive microenvironment and be more proinflammatory and invasive. Finally, the MES subtype is proved to be more sensitive to 17-AAG, docetaxel, and erlotinib using drug sensitivity analysis and three compounds of AS-703026, PD-0325901, and MEK1-2-inhibitor might be potential therapeutic agents. Conclusion: Overall, the findings of this research could help enhance our understanding of the tumor immune microenvironment and provide new insights for improving the prognosis and immunotherapy of GBM patients.

Agouti-related protein as the glucose signaling sensor in the central melanocortin circuits in regulating fish food intake.

Agouti-related protein (AgRP) is a neuropeptide synthesized by AgRP/NPY neurons and transcribed as 132 amino acids in humans and 142 amino acids (AgRP1) in Japanese seabass (Lateolabrax maculatus) fish. AgRP neurons are activated by hormonal signals of energy deficits and inhibited by signals of energy surpluses and have been demonstrated to have the ability to sense the dynamics of blood glucose concentrations as the "glucose sensor" in mammals. It is widely recognized that AgRP is an endogenous antagonist of the melanocortin-3 and -4 receptors (MC3R and MC4R) in the hypothalamus, exhibiting potent orexigenic activity and control of energy homeostasis. Most fish, especially carnivorous fish, cannot make efficient use of carbohydrates. When carbohydrates like corn or wheat bran are added as energy sources, they often cause feeding inhibition and metabolic diseases. When fishmeal is replaced by plant protein, this does not completely eliminate carbs, limiting the utilization of carbohydrates and plant proteins in aquaculture. Our previous study showed that AgRP, and not neuropeptide Y (NPY) is the principal protein molecule that correlates well with feeding behavior in Japanese seabass from anorexia to adaptation. The Ghrelin/Leptin-mTOR-S6K1-NPY/AgRP/POMC feed intake regulatory pathway responds to the plant-oriented protein which contains glucose. However, its regulatory function and mechanism are still not clear. This review offers an integrative overview of how glucose signals converge on a molecular level in AgRP neurons of the arcuate nucleus of the hypothalamus. This is in order to control fish food intake and energy homeostasis.

Biased Activation Mechanism Induced by GPCR Heterodimerization: Observations from µOR/δOR Dimers.

GPCRs regulate multiple intracellular signaling cascades. Biasedly activating one signaling pathway over the others provides additional clinical utility to optimize GPCR-based therapies. GPCR heterodimers possess different functions from their monomeric states, including their selectivity to different transducers. However, the biased signaling mechanism induced by the heterodimerization remains unclear. Motivated by the issue, we select an important GPCR heterodimer (µOR/δOR heterodimer) as a case and use microsecond Gaussian accelerated molecular dynamics simulation coupled with potential of mean force and protein structure network (PSN) to probe mechanisms regarding the heterodimerization-induced constitutive ß-arrestin activity and efficacy change of the agonist DAMGO. The results show that only the lowest energy state of the µOR/δOR heterodimer, which adopts a slightly outward shift of TM6 and an ICL2 conformation close to the receptor core, can selectively accommodate ß-arrestins. PSN further reveals important roles of H8, ICL1, and ICL2 in regulating the constitutive ß-arrestin-biased activity for the apo µOR/δOR heterodimer. In addition, the heterodimerization can allosterically alter the binding mode of DAMGO mainly by means of W7.35. Consequently, DAMGO transmits the structural signal mainly through TM6 and TM7 in the dimer, rather than TM3 similar to the µOR monomer, thus changing the efficacy of DAMGO from a balanced agonist to the ß-arrestin-biased one. On the other side, the binding of DAMGO to the heterodimer can stabilize µOR/δOR heterodimers through a stronger interaction of TM1/TM1 and H8/H8, accordingly enhancing the interaction of µOR with δOR and the binding affinity of the dimer to the ß-arrestin. The agonist DAMGO does not change main compositions of the regulation network from the dimer interface to the transducer binding pocket of the µOR protomer, but induces an increase in the structural communication of the network, which should contribute to the enhanced ß-arrestin coupling. Our observations, for the first time, reveal the molecular mechanism of the biased signaling induced by the heterodimerization for GPCRs, which should be beneficial to more comprehensively understand the GPCR bias signaling.

Amino Acid Profiles and Nutritional Evaluation of Fresh Sweet-Waxy Corn from Three Different Regions of China.

This study conducted a comparative analysis of the amino acid compositions of Chinese Huangnuo 9 fresh sweet-waxy corn from three different provinces in China-Inner Mongolia, Jilin, and Heilongjiang Province. Moreover, we established a nutritive evaluation system based on amino acid profiles to evaluate, compare, and rank the fresh sweet-waxy corn planted in different regions. A total of 17 amino acids were quantified, and the amino acid composition of fresh sweet-waxy corn was analyzed and evaluated. The amino acid quality was determined by the amino acid pattern spectrum, chemical evaluations (including CS, AAS, EAAI, BV, U(a,u), NI, F, predict PER, and PDCAAS), flavor evaluation, amino acid matching degree evaluation, and the results of the factor analysis. The results showed that the protein content of fresh corn 1-1 from Inner Mongolia was the highest (40.26 ± 0.35 mg/g), but the factor analysis results, digestion, and absorption efficiency of fresh corn 1-2 were the best. The amino acid profile of fresh corn 1-1 was closest to each evaluation's model spectrum. The results of the diversity evaluations in fresh corn 3-2 were the best, and fresh corn 3-3 had the most essential amino acid content. A total of 17 amino acids in fresh corn were divided into three principal component factor analyses: functional principal components (Leu, Pro, Glu, His, Ile, Ser, Met, Val, Tyr, Thr), regulatory principal components (Lys, Gly, Ala, Asp, Arg, Trp), and protection principal components (Phe). The scores of the three principal components and the comprehensive score in fresh corn 1-2 were all the highest, followed by 3-3 and 1-1. The amino acid nutritional values of fresh corn 1-2 were the highest in 12 samples.