Human microglial state dynamics in Alzheimer's disease progression.

Altered microglial states affect neuroinflammation, neurodegeneration, and disease but remain poorly understood. Here, we report 194,000 single-nucleus microglial transcriptomes and epigenomes across 443 human subjects and diverse Alzheimer's disease (AD) pathological phenotypes. We annotate 12 microglial transcriptional states, including AD-dysregulated homeostatic, inflammatory, and lipid-processing states. We identify 1,542 AD-differentially-expressed genes, including both microglia-state-specific and disease-stage-specific alterations. By integrating epigenomic, transcriptomic, and motif information, we infer upstream regulators of microglial cell states, gene-regulatory networks, enhancer-gene links, and transcription-factor-driven microglial state transitions. We demonstrate that ectopic expression of our predicted homeostatic-state activators induces homeostatic features in human iPSC-derived microglia-like cells, while inhibiting activators of inflammation can block inflammatory progression. Lastly, we pinpoint the expression of AD-risk genes in microglial states and differential expression of AD-risk genes and their regulators during AD progression. Overall, we provide insights underlying microglial states, including state-specific and AD-stage-specific microglial alterations at unprecedented resolution.

Epigenomic dissection of Alzheimer's disease pinpoints causal variants and reveals epigenome erosion.

Recent work has identified dozens of non-coding loci for Alzheimer's disease (AD) risk, but their mechanisms and AD transcriptional regulatory circuitry are poorly understood. Here, we profile epigenomic and transcriptomic landscapes of 850,000 nuclei from prefrontal cortexes of 92 individuals with and without AD to build a map of the brain regulome, including epigenomic profiles, transcriptional regulators, co-accessibility modules, and peak-to-gene links in a cell-type-specific manner. We develop methods for multimodal integration and detecting regulatory modules using peak-to-gene linking. We show AD risk loci are enriched in microglial enhancers and for specific TFs including SPI1, ELF2, and RUNX1. We detect 9,628 cell-type-specific ATAC-QTL loci, which we integrate alongside peak-to-gene links to prioritize AD variant regulatory circuits. We report differential accessibility of regulatory modules in late AD in glia and in early AD in neurons. Strikingly, late-stage AD brains show global epigenome dysregulation indicative of epigenome erosion and cell identity loss.

Nutrition impact on ILC3 maintenance and function centers on a cell-intrinsic CD71-iron axis.

Iron metabolism is pivotal for cell fitness in the mammalian host; however, its role in group 3 innate lymphoid cells (ILC3s) is unknown. Here we show that transferrin receptor CD71 (encoded by Tfrc)-mediated iron metabolism cell-intrinsically controls ILC3 proliferation and host protection against Citrobacter rodentium infection and metabolically affects mitochondrial respiration by switching of oxidative phosphorylation toward glycolysis. Iron deprivation or Tfrc ablation in ILC3s reduces the expression and/or activity of the aryl hydrocarbon receptor (Ahr), a key ILC3 regulator. Genetic ablation or activation of Ahr in ILC3s leads to CD71 upregulation or downregulation, respectively, suggesting Ahr-mediated suppression of CD71. Mechanistically, Ahr directly binds to the Tfrc promoter to inhibit transcription. Iron overload partially restores the defective ILC3 compartment in the small intestine of Ahr-deficient mice, consistent with the compensatory upregulation of CD71. These data collectively demonstrate an under-appreciated role of the Ahr-CD71-iron axis in the regulation of ILC3 maintenance and function.

Multisensory gamma stimulation promotes glymphatic clearance of amyloid.

The glymphatic movement of fluid through the brain removes metabolic waste1-4. Noninvasive 40 Hz stimulation promotes 40 Hz neural activity in multiple brain regions and attenuates pathology in mouse models of Alzheimer's disease5-8. Here we show that multisensory gamma stimulation promotes the influx of cerebrospinal fluid and the efflux of interstitial fluid in the cortex of the 5XFAD mouse model of Alzheimer's disease. Influx of cerebrospinal fluid was associated with increased aquaporin-4 polarization along astrocytic endfeet and dilated meningeal lymphatic vessels. Inhibiting glymphatic clearance abolished the removal of amyloid by multisensory 40 Hz stimulation. Using chemogenetic manipulation and a genetically encoded sensor for neuropeptide signalling, we found that vasoactive intestinal peptide interneurons facilitate glymphatic clearance by regulating arterial pulsatility. Our findings establish novel mechanisms that recruit the glymphatic system to remove brain amyloid.

Single-cell dissection of the human brain vasculature.

Despite the importance of the cerebrovasculature in maintaining normal brain physiology and in understanding neurodegeneration and drug delivery to the central nervous system1, human cerebrovascular cells remain poorly characterized owing to their sparsity and dispersion. Here we perform single-cell characterization of the human cerebrovasculature using both ex vivo fresh tissue experimental enrichment and post mortem in silico sorting of human cortical tissue samples. We capture 16,681 cerebrovascular nuclei across 11 subtypes, including endothelial cells, mural cells and three distinct subtypes of perivascular fibroblast along the vasculature. We uncover human-specific expression patterns along the arteriovenous axis and determine previously uncharacterized cell-type-specific markers. We use these human-specific signatures to study changes in 3,945 cerebrovascular cells from patients with Huntington's disease, which reveal activation of innate immune signalling in vascular and glial cell types and a concomitant reduction in the levels of proteins critical for maintenance of blood-brain barrier integrity. Finally, our study provides a comprehensive molecular atlas of the human cerebrovasculature to guide future biological and therapeutic studies.

The epithelial barrier: The gateway to allergic, autoimmune, and metabolic diseases and chronic neuropsychiatric conditions.

Since the 1960 s, our health has been compromised by exposure to over 350,000 newly introduced toxic substances, contributing to the current pandemic in allergic, autoimmune and metabolic diseases. The "Epithelial Barrier Theory" postulates that these diseases are exacerbated by persistent periepithelial inflammation (epithelitis) triggered by exposure to a wide range of epithelial barrier-damaging substances as well as genetic susceptibility. The epithelial barrier serves as the body's primary physical, chemical, and immunological barrier against external stimuli. A leaky epithelial barrier facilitates the translocation of the microbiome from the surface of the afflicted tissues to interepithelial and even deeper subepithelial locations. In turn, opportunistic bacterial colonization, microbiota dysbiosis, local inflammation and impaired tissue regeneration and remodelling follow. Migration of inflammatory cells to susceptible tissues contributes to damage and inflammation, initiating and aggravating many chronic inflammatory diseases. The objective of this review is to highlight and evaluate recent studies on epithelial physiology and its role in the pathogenesis of chronic diseases in light of the epithelial barrier theory.

A radiomics model to predict γδ T-cell abundance and overall survival in head and neck squamous cell carcinoma.

γδ T cells are becoming increasingly popular because of their attractive potential for antitumor immunotherapy. However, the role and assessment of γδ T cells in head and neck squamous cell carcinoma (HNSCC) are not well understood. We aimed to explore the prognostic value of γδ T cell and predict its abundance using a radiomics model. Computer tomography images with corresponding gene expression data and clinicopathological data were obtained from online databases. After outlining the volumes of interest manually, the radiomic features were screened using maximum melevance minimum redundancy and recursive feature elimination algorithms. A radiomics model was developed to predict γδ T-cell abundance using gradient boosting machine. Kaplan-Meier survival curves and univariate and multivariate Cox regression analyses were used for the survival analysis. In this study, we confirmed that γδ T-cell abundance was an independent predictor of favorable overall survival (OS) in patients with HNSCC. Moreover, a radiomics model was built to predict the γδ T-cell abundance level (the areas under the operating characteristic curves of 0.847 and 0.798 in the training and validation sets, respectively). The calibration and decision curves analysis demonstrated the fitness of the model. The high radiomic score was an independent protective factor for OS. Our results indicated that γδ T-cell abundance was a promising prognostic predictor in HNSCC, and the radiomics model could discriminate its abundance levels and predict OS. The noninvasive radiomics model provided a potentially powerful prediction tool to aid clinical judgment and antitumor immunotherapy.

Metabolic heterogeneity affects trastuzumab response and survival in HER2-positive advanced gastric cancer.

BACKGROUND: Trastuzumab is the only first-line treatment targeted against the human epidermal growth factor receptor 2 (HER2) approved for patients with HER2-positive advanced gastric cancer. The impact of metabolic heterogeneity on trastuzumab treatment efficacy remains unclear. METHODS: Spatial metabolomics via high mass resolution imaging mass spectrometry was performed in pretherapeutic biopsies of patients with HER2-positive advanced gastric cancer in a prospective multicentre observational study. The mass spectra, representing the metabolic heterogeneity within tumour areas, were grouped by K-means clustering algorithm. Simpson's diversity index was applied to compare the metabolic heterogeneity level of individual patients. RESULTS: Clustering analysis revealed metabolic heterogeneity in HER2-positive gastric cancer patients and uncovered nine tumour subpopulations. High metabolic heterogeneity was shown as a factor indicating sensitivity to trastuzumab (p = 0.008) and favourable prognosis at trend level. Two of the nine tumour subpopulations associated with favourable prognosis and trastuzumab sensitivity, and one subpopulation associated with poor prognosis and trastuzumab resistance. CONCLUSIONS: This work revealed that tumour metabolic heterogeneity associated with prognosis and trastuzumab response based on tissue metabolomics of HER2-positive gastric cancer. Tumour metabolic subpopulations may provide an association with trastuzumab therapy efficacy. CLINICAL TRIAL REGISTRATION: The patient cohort was conducted from a multicentre observational study (VARIANZ;NCT02305043).

MRI versus Dual-Energy CT in Local-Regional Staging of Gastric Cancer.

Background Preoperative local-regional tumor staging of gastric cancer (GC) is critical for appropriate treatment planning. The comparative accuracy of multiparametric MRI (mpMRI) versus dual-energy CT (DECT) for staging of GC is not known. Purpose To compare the diagnostic accuracy of personalized mpMRI with that of DECT for local-regional T and N staging in patients with GC receiving curative surgical intervention. Materials and Methods Patients with GC who underwent gastric mpMRI and DECT before gastrectomy with lymphadenectomy were eligible for this single-center prospective noninferiority study between November 2021 and September 2022. mpMRI comprised T2-weighted imaging, multiorientational zoomed diffusion-weighted imaging, and extradimensional volumetric interpolated breath-hold examination dynamic contrast-enhanced imaging. Dual-phase DECT images were reconstructed at 40 keV and standard 120 kVp-like images. Using gastrectomy specimens as the reference standard, the diagnostic accuracy of mpMRI and DECT for T and N staging was compared by six radiologists in a pairwise blinded manner. Interreader agreement was assessed using the weighted κ and Kendall W statistics. The McNemar test was used for head-to-head accuracy comparisons between DECT and mpMRI. Results This study included 202 participants (mean age, 62 years ± 11 [SD]; 145 male). The interreader agreement of the six readers for T and N staging of GC was excellent for both mpMRI (κ = 0.89 and 0.85, respectively) and DECT (κ = 0.86 and 0.84, respectively). Regardless of reader experience, higher accuracy was achieved with mpMRI than with DECT for both T (61%-77% vs 50%-64%; all P < .05) and N (54%-68% vs 51%-58%; P = .497-.005) staging, specifically T1 (83% vs 65%) and T4a (78% vs 68%) tumors and N1 (41% vs 24%) and N3 (64% vs 45%) nodules (all P < .05). Conclusion Personalized mpMRI was superior in T staging and noninferior or superior in N staging compared with DECT for patients with GC. Clinical trial registration no. NCT05508126 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Méndez and Martín-Garre in this issue.

The TaSnRK1-TabHLH489 module integrates brassinosteroid and sugar signalling to regulate the grain length in bread wheat.

Regulation of grain size is a crucial strategy for improving the crop yield and is also a fundamental aspect of developmental biology. However, the underlying molecular mechanisms governing grain development in wheat remain largely unknown. In this study, we identified a wheat atypical basic helix-loop-helix (bHLH) transcription factor, TabHLH489, which is tightly associated with grain length through genome-wide association study and map-based cloning. Knockout of TabHLH489 and its homologous genes resulted in increased grain length and weight, whereas the overexpression led to decreased grain length and weight. TaSnRK1α1, the α-catalytic subunit of plant energy sensor SnRK1, interacted with and phosphorylated TabHLH489 to induce its degradation, thereby promoting wheat grain development. Sugar treatment induced TaSnRK1α1 protein accumulation while reducing TabHLH489 protein levels. Moreover, brassinosteroid (BR) promotes grain development by decreasing TabHLH489 expression through the transcription factor BRASSINAZOLE RESISTANT1 (BZR1). Importantly, natural variations in the promoter region of TabHLH489 affect the TaBZR1 binding ability, thereby influencing TabHLH489 expression. Taken together, our findings reveal that the TaSnRK1α1-TabHLH489 regulatory module integrates BR and sugar signalling to regulate grain length, presenting potential targets for enhancing grain size in wheat.

ACACA reduces lipid accumulation through dual regulation of lipid metabolism and mitochondrial function via AMPK- PPARα- CPT1A axis.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a multifaceted metabolic disorder, whose global prevalence is rapidly increasing. Acetyl CoA carboxylases 1 (ACACA) is the key enzyme that controls the rate of fatty acid synthesis. Hence, it is crucial to investigate the function of ACACA in regulating lipid metabolism during the progress of NAFLD. METHODS: Firstly, a fatty liver mouse model was established by high-fat diet at 2nd, 12th, and 20th week, respectively. Then, transcriptome analysis was performed on liver samples to investigate the underlying mechanisms and identify the target gene of the occurrence and development of NAFLD. Afterwards, lipid accumulation cell model was induced by palmitic acid and oleic acid (PA ∶ OA molar ratio = 1∶2). Next, we silenced the target gene ACACA using small interfering RNAs (siRNAs) or the CMS-121 inhibitor. Subsequently, experiments were performed comprehensively the effects of inhibiting ACACA on mitochondrial function and lipid metabolism, as well as on AMPK- PPARα- CPT1A pathway. RESULTS: This data indicated that the pathways significantly affected by high-fat diet include lipid metabolism and mitochondrial function. Then, we focus on the target gene ACACA. In addition, the in vitro results suggested that inhibiting of ACACA in vitro reduces intracellular lipid accumulation, specifically the content of TG and TC. Furthermore, ACACA ameliorated mitochondrial dysfunction and alleviate oxidative stress, including MMP complete, ATP and ROS production, as well as the expression of mitochondria respiratory chain complex (MRC) and AMPK proteins. Meanwhile, ACACA inhibition enhances lipid metabolism through activation of PPARα/CPT1A, leading to a decrease in intracellular lipid accumulation. CONCLUSION: Targeting ACACA can reduce lipid accumulation by mediating the AMPK- PPARα- CPT1A pathway, which regulates lipid metabolism and alleviates mitochondrial dysfunction.

Enhancement of in situ detection and imaging of phytohormones in plant tissues by MALDI-MSI using 2,4-dihydroxy-5-nitrobenzoic acid as a novel matrix.

Phytohormones possess unique chemical structures, and their physiological effects are regulated through intricate interactions or crosstalk among multiple phytohormones. MALDI-MSI enables the simultaneous detection and imaging of multiple hormones. However, its application for tracing phytohormones is currently restricted by low abundance of hormone in plant and suboptimal matrix selection. 2,4-Dihydroxy-5-nitrobenzoic acid (DHNBA) was reported as a new MALDI matrix for the enhanced detection and imaging of multiple phytohormones in plant tissues. DHNBA demonstrates remarkable sensitivity improvement when compared to the commonly used matrix, 2,5-dihydroxybenzoic acid (DHB), in the detection of isoprenoid cytokinins (trans-zeatin (tZ), dihy-drozeatin (DHZ), meta-topolin (mT), and N6-(Δ2-isopentenyl) adenine (iP)), jasmonic acid (JA), abscisic acid (ABA), and 1-aminocyclo-propane-1-carboxylic acid (ACC) standards. The distinctive properties of DHNBA (i.e. robust UV absorption, uniform matrix deposition, negligible background interference, and high ionization efficiency of phytohormones) make it as an ideal matrix for enhanced detection and imaging of phytohormones, including tZ, DHZ, ABA, indole-3-acetic acid (IAA), and ACC, by MALDI-MSI in various plant tissues, for example germinating seeds, primary/lateral roots, and nodules. Employing DHNBA significantly enhances our capability to concurrently track complex phytohormone biosynthesis pathways while providing precise differentiation of the specific roles played by individual phytohormones within the same category. This will propel forward the comprehensive exploration of phytohormonal functions in plant science.

HCC EV ECG score: An extracellular vesicle-based protein assay for detection of early-stage hepatocellular carcinoma.

BACKGROUND AND AIMS: The sensitivity of current surveillance methods for detecting early-stage hepatocellular carcinoma (HCC) is suboptimal. Extracellular vesicles (EVs) are promising circulating biomarkers for early cancer detection. In this study, we aim to develop an HCC EV-based surface protein assay for early detection of HCC. APPROACH AND RESULTS: Tissue microarray was used to evaluate four potential HCC-associated protein markers. An HCC EV surface protein assay, composed of covalent chemistry-mediated HCC EV purification and real-time immuno-polymerase chain reaction readouts, was developed and optimized for quantifying subpopulations of EVs. An HCC EV ECG score, calculated from the readouts of three HCC EV subpopulations ( E pCAM + CD63 + , C D147 + CD63 + , and G PC3 + CD63 + HCC EVs), was established for detecting early-stage HCC. A phase 2 biomarker study was conducted to evaluate the performance of ECG score in a training cohort ( n  = 106) and an independent validation cohort ( n  = 72).Overall, 99.7% of tissue microarray stained positive for at least one of the four HCC-associated protein markers (EpCAM, CD147, GPC3, and ASGPR1) that were subsequently validated in HCC EVs. In the training cohort, HCC EV ECG score demonstrated an area under the receiver operating curve (AUROC) of 0.95 (95% confidence interval [CI], 0.90-0.99) for distinguishing early-stage HCC from cirrhosis with a sensitivity of 91% and a specificity of 90%. The AUROCs of the HCC EV ECG score remained excellent in the validation cohort (0.93; 95% CI, 0.87-0.99) and in the subgroups by etiology (viral: 0.95; 95% CI, 0.90-1.00; nonviral: 0.94; 95% CI, 0.88-0.99). CONCLUSION: HCC EV ECG score demonstrated great potential for detecting early-stage HCC. It could augment current surveillance methods and improve patients' outcomes.

Prognostic value of plasma microRNAs for non-small cell lung cancer based on data mining models.

BACKGROUND: As biomarkers, microRNAs (miRNAs) are closely associated with the occurrence, progression, and prognosis of non-small cell lung cancer (NSCLC). However, the prognostic predictive value of miRNAs in NSCLC has rarely been explored. In this study, the value in prognosis prediction of NSCLC was mined based on data mining models using clinical data and plasma miRNAs biomarkers. METHODS: A total of 69 patients were included in this prospective cohort study. After informed consent, they filled out questionnaires and had their peripheral blood collected. The expressions of plasma miRNAs were examined by quantitative polymerase chain reaction (qPCR). The Whitney U test was used to analyze non-normally distributed data. Kaplan-Meier was used to plot the survival curve, the log-rank test was used to compare with the overall survival curve, and the Cox proportional hazards model was used to screen the factors related to the prognosis of lung cancer. Data mining techniques were utilized to predict the prognostic status of patients. RESULTS: We identified that smoking (HR = 2.406, 95% CI = 1.256-4.611), clinical stage III + IV (HR = 5.389, 95% CI = 2.290-12.684), the high expression group of miR-20a (HR = 4.420, 95% CI = 1.760-11.100), the high expression group of miR-197 (HR = 3.828, 95% CI = 1.778-8.245), the low expression group of miR-145 ( HR = 0.286, 95% CI = 0.116-0.709), and the low expression group of miR-30a (HR = 0.307, 95% CI = 0.133-0.706) was associated with worse prognosis. Among the five data mining models, the decision trees (DT) C5.0 model performs the best, with accuracy and Area Under Curve (AUC) of 93.75% and 0.929 (0.685, 0.997), respectively. CONCLUSION: The results showed that the high expression level of miR-20a and miR-197, the low expression level of miR-145 and miR-30a were strongly associated with poorer prognosis in NSCLC patients, and the DT C5.0 model may serve as a novel, accurate, method for predicting prognosis of NSCLC.

BHAFT: Bayesian heredity-constrained accelerated failure time models for detecting gene-environment interactions in survival analysis.

In addition to considering the main effects, understanding gene-environment (G × E) interactions is imperative for determining the etiology of diseases and the factors that affect their prognosis. In the existing statistical framework for censored survival outcomes, there are several challenges in detecting G × E interactions, such as handling high-dimensional omics data, diverse environmental factors, and algorithmic complications in survival analysis. The effect heredity principle has widely been used in studies involving interaction identification because it incorporates the dependence of the main and interaction effects. However, Bayesian survival models that incorporate the assumption of this principle have not been developed. Therefore, we propose Bayesian heredity-constrained accelerated failure time (BHAFT) models for identifying main and interaction (M-I) effects with novel spike-and-slab or regularized horseshoe priors to incorporate the assumption of effect heredity principle. The R package rstan was used to fit the proposed models. Extensive simulations demonstrated that BHAFT models had outperformed other existing models in terms of signal identification, coefficient estimation, and prognosis prediction. Biologically plausible G × E interactions associated with the prognosis of lung adenocarcinoma were identified using our proposed model. Notably, BHAFT models incorporating the effect heredity principle could identify both main and interaction effects, which are highly useful in exploring G × E interactions in high-dimensional survival analysis. The code and data used in our paper are available at https://github.com/SunNa-bayesian/BHAFT.

Mediation analysis method review of high throughput data.

High-throughput technologies have made high-dimensional settings increasingly common, providing opportunities for the development of high-dimensional mediation methods. We aimed to provide useful guidance for researchers using high-dimensional mediation analysis and ideas for biostatisticians to develop it by summarizing and discussing recent advances in high-dimensional mediation analysis. The method still faces many challenges when extended single and multiple mediation analyses to high-dimensional settings. The development of high-dimensional mediation methods attempts to address these issues, such as screening true mediators, estimating mediation effects by variable selection, reducing the mediation dimension to resolve correlations between variables, and utilizing composite null hypothesis testing to test them. Although these problems regarding high-dimensional mediation have been solved to some extent, some challenges remain. First, the correlation between mediators are rarely considered when the variables are selected for mediation. Second, downscaling without incorporating prior biological knowledge makes the results difficult to interpret. In addition, a method of sensitivity analysis for the strict sequential ignorability assumption in high-dimensional mediation analysis is still lacking. An analyst needs to consider the applicability of each method when utilizing them, while a biostatistician could consider extensions and improvements in the methodology.

Long Noncoding RNA Gpr137b-ps Promotes Advanced Atherosclerosis via the Regulation of Autophagy in Macrophages.

BACKGROUND: Current therapies cannot completely reverse advanced atherosclerosis. High levels of amino acids, induced by Western diet, stimulate mTORC1 (mammalian target of rapamycin complex 1)-autophagy defects in macrophages, accelerating atherosclerotic plaque progression. In addition, autophagy-lysosomal dysfunction contributes to plaque necrotic core enlargement and lipid accumulation. Therefore, it is essential to investigate the novel mechanism and molecules to reverse amino acid-mTORC1-autophagy signaling dysfunction in macrophages of patients with advanced atherosclerosis. METHODS: We observed that Gpr137b-ps (G-protein-coupled receptor 137B, pseudogene) was upregulated in advanced atherosclerotic plaques. The effect of Gpr137b-ps on the progression of atherosclerosis was studied by generating advanced plaques in ApoE-/- mice with cardiac-specific knockout of Gpr137b-ps. Bone marrow-derived macrophages and mouse mononuclear macrophage cell line RAW264.7 cells were subjected to starvation or amino acid stimulation to study amino acid-mTORC1-autophagy signaling. Using both gain- and loss-of-function approaches, we explored the mechanism of Gpr137b-ps-regulated autophagy. RESULTS: Our results demonstrated that Gpr137b-ps deficiency led to enhanced autophagy in macrophages and reduced atherosclerotic lesions, characterized by fewer necrotic cores and less lipid accumulation. Knockdown of Gpr137b-ps increased autophagy and prevented amino acid-induced mTORC1 signaling activation. As the downstream binding protein of Gpr137b-ps, HSC70 (heat shock cognate 70) rescued the impaired autophagy induced by Gpr137b-ps. Furthermore, Gpr137b-ps interfered with the HSC70 binding to G3BP (Ras GTPase-activating protein-binding protein), which tethers the TSC (tuberous sclerosis complex) complex to lysosomes and suppresses mTORC1 signaling. In addition to verifying that the NTF2 (nuclear transport factor 2) domain of G3BP binds to HSC70 by in vitro protein synthesis, we further demonstrated that HSC70 binds to the NTF2 domain of G3BP through its W90-F92 motif by using computational modeling. CONCLUSIONS: These findings reveal that Gpr137b-ps plays an essential role in the regulation of macrophage autophagy, which is crucial for the progression of advanced atherosclerosis. Gpr137b-ps impairs the interaction of HSC70 with G3BP to regulate amino acid-mTORC1-autophagy signaling, and these results provide a new potential therapeutic direction for the treatment of advanced atherosclerosis.

Regulating the Electronic Structure of Metal-Organic Frameworks by Introducing Mn for Enhanced Oxygen Evolution Activity.

The construction of low-cost and highly efficient oxygen evolution electrocatalysts is paramount for clean and sustainable hydrogen energy. In recent years, metal-organic framework (MOF) OER electrocatalysts have attracted tremendous research attention. Herein, we report a simple and facile strategy to construct bimetallic MOFs (named CoMn0.01) for enhancing OER catalytic performance. Significantly, CoMn0.01 exhibited remarkable OER activity (255 mV at 10 mA cm-2) and a low Tafel slope of 66 mV dec-1, superior to those of commercial benchmark electrocatalysts (RuO2, 352 mV, 178 mV dec-1). Besides, the catalyst demonstrated outstanding longevity for 144 h at a current density of 100 mA cm -2. Mn doping can regulate the electronic structure of Co MOFs, which optimizes charge transfer capability and improves conductivity.

Machine Learning Models Identify Inhibitors of New Delhi Metallo-ß-lactamase.

The worldwide spread of the metallo-ß-lactamases (MBL), especially New Delhi metallo-ß-lactamase-1 (NDM-1), is threatening the efficacy of ß-lactams, which are the most potent and prescribed class of antibiotics in the clinic. Currently, FDA-approved MBL inhibitors are lacking in the clinic even though many strategies have been used in inhibitor development, including quantitative high-throughput screening (qHTS), fragment-based drug discovery (FBDD), and molecular docking. Herein, a machine learning-based prediction tool is described, which was generated using results from HTS of a large chemical library and previously published inhibition data. The prediction tool was then used for virtual screening of the NIH Genesis library, which was subsequently screened using qHTS. A novel MBL inhibitor was identified and shown to lower minimum inhibitory concentrations (MICs) of Meropenem for a panel of E. coli and K. pneumoniae clinical isolates expressing NDM-1. The mechanism of inhibition of this novel scaffold was probed utilizing equilibrium dialyses with metal analyses, native state electrospray ionization mass spectrometry, UV-vis spectrophotometry, and molecular docking. The uncovered inhibitor, compound 72922413, was shown to be 9-hydroxy-3-[(5-hydroxy-1-oxa-9-azaspiro[5.5]undec-9-yl)carbonyl]-4H-pyrido[1,2-a]pyrimidin-4-one.

Peritoneal Phosphate Clearance: Determinants and Association With Mortality.

BACKGROUND: Dialytic phosphate removal is a cornerstone of the management of hyperphosphatemia in peritoneal dialysis (PD) patients, but the influencing factors on peritoneal phosphate clearance (PPC) are incompletely understood. Our objective was to explore clinically relevant factors associated with PPC in patients with different PD modality and peritoneal transport status and the association of PPC with mortality. METHODS: This is a cross-sectional and prospective observational study. Four hundred eighty-five PD patients were enrolled and divided into 2 groups according to PPC. All-cause mortality was evaluated after followed-up for at least 3 months. RESULTS: High PPC group showed lower mortality compared with Low PPC group by Kaplan-Meier analysis and log-rank test. Both multivariate linear regression and multivariate logistic regression revealed that high transport status, total effluent dialysate volume per day, continuous ambulatory PD (CAPD), and protein in total effluent dialysate volume appeared to be positively correlated with PPC; body mass index (BMI) and the normalized protein equivalent of total nitrogen appearance (nPNA) were negatively correlated with PPC. Besides PD modality and membrane transport status, total effluent dialysate volume showed a strong relationship with PPC, but the correlation differed among PD modalities. CONCLUSIONS: Higher PPC was associated with lower all-cause mortality risk in PD patients. Higher PPC correlated with CAPD modality, fast transport status, higher effluent dialysate volume and protein content, and with lower BMI and nPNA.