Impacts of Nutlin-3a and exercise on murine double minute 2-enriched glioma treatment.

JOURNAL/nrgr/04.03/01300535-202504000-00029/figure1/v/2024-07-06T104127Z/r/image-tiff Recent research has demonstrated the impact of physical activity on the prognosis of glioma patients, with evidence suggesting exercise may reduce mortality risks and aid neural regeneration. The role of the small ubiquitin-like modifier (SUMO) protein, especially post-exercise, in cancer progression, is gaining attention, as are the potential anti-cancer effects of SUMOylation. We used machine learning to create the exercise and SUMO-related gene signature (ESLRS). This signature shows how physical activity might help improve the outlook for low-grade glioma and other cancers. We demonstrated the prognostic and immunotherapeutic significance of ESLRS markers, specifically highlighting how murine double minute 2 (MDM2), a component of the ESLRS, can be targeted by nutlin-3. This underscores the intricate relationship between natural compounds such as nutlin-3 and immune regulation. Using comprehensive CRISPR screening, we validated the effects of specific ESLRS genes on low-grade glioma progression. We also revealed insights into the effectiveness of Nutlin-3a as a potent MDM2 inhibitor through molecular docking and dynamic simulation. Nutlin-3a inhibited glioma cell proliferation and activated the p53 pathway. Its efficacy decreased with MDM2 overexpression, and this was reversed by Nutlin-3a or exercise. Experiments using a low-grade glioma mouse model highlighted the effect of physical activity on oxidative stress and molecular pathway regulation. Notably, both physical exercise and Nutlin-3a administration improved physical function in mice bearing tumors derived from MDM2-overexpressing cells. These results suggest the potential for Nutlin-3a, an MDM2 inhibitor, with physical exercise as a therapeutic approach for glioma management. Our research also supports the use of natural products for therapy and sheds light on the interaction of exercise, natural products, and immune regulation in cancer treatment.

Beyond Glycolysis: Aldolase A is a Novel Effector in Reelin Mediated Dendritic Development.

Reelin, a secreted glycoprotein, plays a crucial role in guiding neocortical neuronal migration, dendritic outgrowth and arborization, and synaptic plasticity in the adult brain. Reelin primarily operates through the canonical lipoprotein receptors apolipoprotein E receptor 2 (Apoer2) and very low-density lipoprotein receptor (Vldlr). Reelin also engages with non-canonical receptors and unidentified co-receptors; however, the effects of which are less understood. Using high-throughput tandem mass tag LC-MS/MS-based proteomics and gene set enrichment analysis, we identified both shared and unique intracellular pathways activated by Reelin through its canonical and non-canonical signaling in primary murine neurons of either sex during dendritic growth and arborization. We observed pathway crosstalk related to regulation of cytoskeleton, neuron projection development, protein transport, and actin filament-based process. We also found enriched gene sets exclusively by the non-canonical Reelin pathway including protein translation, mRNA metabolic process and ribonucleoprotein complex biogenesis suggesting Reelin fine-tunes neuronal structure through distinct signaling pathways. A key discovery is the identification of aldolase A, a glycolytic enzyme and actin binding protein, as a novel effector of Reelin signaling. Reelin induced de novo translation and mobilization of aldolase A from the actin cytoskeleton. We demonstrated that aldolase A is necessary for Reelin-mediated dendrite growth and arborization in primary murine neurons and mouse brain cortical neurons. Interestingly, the function of aldolase A in dendrite development is independent of its known role in glycolysis. Altogether, our findings provide new insights into the Reelin-dependent signaling pathways and effector proteins that are crucial for dendritic development.Significance Statement Reelin is an extracellular glycoprotein that exerts its function primarily by binding to the canonical lipoprotein receptors Apoer2 and Vldlr. Reelin is best known for its role in neuronal migration during prenatal brain development. Reelin also signals through a non-canonical pathway outside of Apoer2/Vldlr; however, these receptors and signal transduction pathways are less defined. Here, we examined Reelin's role during dendritic outgrowth in primary murine neurons and identified shared and distinct pathways activated by canonical and non-canonical Reelin signaling. We also found aldolase A as a novel effector of Reelin signaling, that functions independently of its known metabolic role, highlighting Reelin's influence on neuronal structure and growth.

The influences of ApoE isoforms on endothelial adherens junctions and actin cytoskeleton responding to mCRP.

Apolipoprotein E4 (ApoE4) plays an important role responding to monomeric C-reactive protein (mCRP) via binding to CD31 leading to cerebrovascular damage and Alzheimer's disease (AD). Using phosphor-proteomic profiling, we found altered cytoskeleton proteins in the microvasculature of AD brains, including increased levels of hyperphosphorylated tau (pTau) and the actin-related protein, LIMA1. To address the hypothesis that cytoskeletal changes serve as early pathological signatures linked with CD31 in brain endothelia in ApoE4 carriers, ApoE4 knock-in mice intraperitoneal injected with mCRP revealed that mCRP increased the expressions of phosphorylated CD31 (pCD31) and LIMA1, and facilitate the binding of pCD31 to LIMA1. mCRP combined with recombinant APOE4 protein decreased interaction of CD31 and VE-Cadherin at adherens junctions (AJs), along with altered the expression of various actin cytoskeleton proteins, causing microvasculature damage. Notably, the APOE2 protein attenuated these changes. Overall, our study demonstrates that ApoE4 responds to mCRP to disrupt the endothelial AJs which link with the actin cytoskeleton and this pathway could play a key role in the barrier dysfunction leading to AD risk.

Pantothenate-encapsulated liposomes combined with exercise for effective inhibition of CRM1-mediated PKM2 translocation in Alzheimer's therapy.

Alzheimer's disease (AD) is a complex neurodegenerative condition characterized by metabolic imbalances and neuroinflammation, posing a formidable challenge in medicine due to the lack of effective treatments. Despite considerable research efforts, a cure for AD remains elusive, with current therapies primarily focused on symptom management rather than addressing the disease's underlying causes. This study initially discerned, through Mendelian randomization analysis that elevating pantothenate levels significantly contributes to the prophylaxis of Alzheimer's disease. We explore the therapeutic potential of pantothenate encapsulated in liposomes (Pan@TRF@Liposome NPs), targeting the modulation of CRM1-mediated PKM2 nuclear translocation, a critical mechanism in AD pathology. Additionally, we investigate the synergistic effects of exercise, proposing a combined approach to AD treatment. Exercise-induced metabolic alterations share significant similarities with those associated with dementia, suggesting a potential complementary effect. The Pan@TRF@Liposome NPs exhibit notable biocompatibility, showing no liver or kidney toxicity in vivo, while demonstrating stability and effectiveness in modulating CRM1-mediated PKM2 nuclear translocation, thereby reducing neuroinflammation and neuronal apoptosis. The combined treatment of exercise and Pan@TRF@Liposome NP administration in an AD animal model leads to improved neurofunctional outcomes and cognitive performance. These findings highlight the nanoparticles' role as effective modulators of CRM1-mediated PKM2 nuclear translocation, with significant implications for mitigating neuroinflammation and neuronal apoptosis. Together with exercise, this dual-modality approach could offer new avenues for enhancing cognitive performance and neurofunctional outcomes in AD, marking a promising step forward in developing treatment strategies for this challenging disorder.

PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis.

High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.

Vancomycin trough concentration in adult patients with periprosthetic joint infection: A machine learning-based covariate model.

AIMS: Although there are various model-based approaches to individualized vancomycin (VCM) administration, few have been reported for adult patients with periprosthetic joint infection (PJI). This work attempted to develop a machine learning (ML)-based model for predicting VCM trough concentration in adult PJI patients. METHODS: The dataset of 287 VCM trough concentrations from 130 adult PJI patients was split into a training set (229) and a testing set (58) at a ratio of 8:2, and an independent external 32 concentrations were collected as a validation set. A total of 13 covariates and the target variable (VCM trough concentration) were included in the dataset. A covariate model was respectively constructed by support vector regression, random forest regression and gradient boosted regression trees and interpreted by SHapley Additive exPlanation (SHAP). RESULTS: The SHAP plots visualized the weight of the covariates in the models, with estimated glomerular filtration rate and VCM daily dose as the 2 most important factors, which were adopted for the model construction. Random forest regression was the optimal ML algorithm with a relative accuracy of 82.8% and absolute accuracy of 67.2% (R2 =.61, mean absolute error = 2.4, mean square error = 10.1), and its prediction performance was verified in the validation set. CONCLUSION: The proposed ML-based model can satisfactorily predict the VCM trough concentration in adult PJI patients. Its construction can be facilitated with only 2 clinical parameters (estimated glomerular filtration rate and VCM daily dose), and prediction accuracy can be rationalized by SHAP values, which highlights a profound practical value for clinical dosing guidance and timely treatment.

High-Resolution Spatiotemporal Modeling for PM2.5 Major Components in the Pearl River Delta and Its Implications for Epidemiological Studies.

Distinguishing the effects of different fine particulate matter components (PMCs) is crucial for mitigating their effects on human health. However, the sparse distribution of locations where PM is collected for component analysis makes it challenging to investigate the relevant health effects. This study aimed to investigate the agreement between data-fusion-enhanced exposure assessment and site monitoring data in estimating the effects of PMCs on gestational diabetes mellitus (GDM). We first improved the spatial resolution and accuracy of exposure assessment for five major PMCs (EC, OM, NO3-, NH4+, and SO42-) in the Pearl River Delta region by a data fusion model that combined inputs from multiple sources using a random forest model (10-fold cross-validation R2: 0.52 to 0.61; root mean square error: 0.55 to 2.26 µg/m3). Next, we compared the associations between exposures to PMCs during pregnancy and GDM in a hospital-based cohort of 1148 pregnant women in Heshan, China, using both site monitoring data and data-fusion model estimates. The comparative analysis showed that the data-fusion-based exposure generated stronger estimates of identifying statistical disparities. This study suggests that data-fusion-enhanced estimates can improve exposure assessment and potentially mitigate the misclassification of population exposure arising from the utilization of site monitoring data.

A Preoperative Prediction Model for Lymph Node Metastasis in Patients with Gastric Cancer Using a Machine Learning-based Ultrasomics Approach.

OBJECTIVE: This study aimed to develop an ultrasomics model for predicting lymph node metastasis preoperative in patients with gastric cancer (GC). METHODS: This study enrolled GC patients who underwent preoperative ultrasound examination. Manual segmentation of the region of interest (ROI) was performed by an experienced radiologist to extract radiomics features using the Pyradiomics software. The Z-score algorithm was used for feature normalization, followed by the Wilcoxon test to identify the most informative features. Linear prediction models were constructed using the least absolute shrinkage and selection operator (LASSO). The performance of the ultrasomics model was evaluated using the area under curve (AUC), sensitivity, specificity, and the corresponding 95% confidence intervals (CIs). RESULTS: A total of 464 GC patients (mean age: 60.4 years ±11.3 [SD]; 328 men [70.7%]) were analyzed, of whom 291 had lymph node metastasis. The patients were randomly assigned to either the training (n=324) or test (n=140) sets, using a 7:3 ratio. An ultrasomics model that consisted of 19 radiomics features was developed using Wilcoxon and LASSO algorithms in the training set. Our ultrasomics model showed moderate performance for lymph node metastasis prediction in both the training (AUC: 0.802, 95%CI: 0.752-0.851, P<0.001) and test sets (AUC: 0.802, 95%CI: 0.724-0.879, P<0.001). The calibration curve analysis indicated good agreement between the predicted probabilities of ultrasomics and actual lymph node metastasis status. CONCLUSION: Our study highlights the potential of a machine learning-based ultrasomics model in predicting lymph node metastasis in GC patients, offering implications for personalized therapy approaches.

Physiologically-based pharmacokinetic modeling to predict the exposure and provide dosage regimens of Ustekinumab in pediatric patients with inflammatory bowel disease.

Ustekinumab (UST), a fully human immunoglobulin G1 κ monoclonal antibody, exhibiting high affinity for the p40 subunit shared by IL-12 and IL-23, which play key roles in the pathogenesis of inflammatory bowel disease (IBD). By scaling the physiologically-based pharmacokinetic modeling (PBPK) model of UST in adult patients with IBD, we aim to predict effective dosages for UST in pediatric patients, thereby offering a more practical dosing regimen for real-world applications. In this work, a PBPK model for UST in adult patients with IBD has been developed using PK-Sim and Mobi. Advanced ontogeny model has been incorporated to extrapolate the model to pediatric patients. The simulation results showed that the fold errors of the predicted and observed values of the area under the curve (AUC) and peak plasma concentration (Cmax) were between 0.79 and 1.73. For children aged 6-18, it is recommended to administer the drug per kilogram of body weight, at the model-recommended dose, to achieve a median AUC similar to that of the adult reference population post-administration. This comprehensive model construction enables us to comprehensively and extensively explore the pharmacokinetic characteristics of UST in pediatric patients of different age groups, providing robust support for clinical applications and personalized drug therapy.

Modeling an evaluation of the efficacy of the novel neuroanalgesic drug mirogabalin for diabetic peripheral neuropathic pain and postherpetic neuralgia therapy.

Diabetic peripheral neuropathic pain (DPNP) and postherpetic neuralgia (PHN) are challenging and often intractable complex medical conditions, with a substantial impact on the quality of life. Mirogabalin, a novel voltage-gated Ca2+ channel α2δ ligand, was approved for the indication of DPNP and PHN. However, the time course of effects has not yet been clarified.We aimed to establish pharmacodynamic and placebo effect models of mirogabalin and pregabalin in DPNP and PHN, and to quantitatively compare the efficacy characteristics (maximum efficacy, onset time, and other pharmacodynamic parameters) and safety of mirogabalin and pregabalin. Public databases were comprehensively searched for randomized placebo-controlled clinical trials. A model-based meta-analysis (MBMA) was developed to describe the time course of drug efficacy and placebo effects. Adverse events were compared using a fixed-effects meta-analysis. Sixteen studies including 5,147 participants were eligible for this study. The placebo effect was relatively high and gradually increased with time, and it required at least eight weeks to reach a plateau. The pharmacodynamic model revealed that the maximum pure efficacy for mirogabalin and pregabalin was approximately -7.85 % and -8.86 %, respectively; the efficacy of mirogabalin to relieve DPNP and PHN was not superior to that of pregabalin, and both drugs had similar safety. While the rate constant of the onset rate of pregabalin was approximately thrice as high as that of mirogabalin. In addition, the baseline level of pain was an important factor affecting pregabalin efficacy. These findings are helpful in evaluating the clinical extension value of mirogabalin. They suggest that the high placebo effect and the baseline level of pain should be considered when grouping patients in future research and development of voltage-gated Ca2+ channel neuroanalgesic.

The association between PM2.5 components and blood pressure changes in late pregnancy: A combined analysis of traditional and machine learning models.

BACKGROUND: PM2.5 is a harmful mixture of various chemical components that pose a challenge in determining their individual and combined health effects due to multicollinearity issues with traditional linear regression models. This study aimed to develop an analytical methodology combining traditional and novel machine learning models to evaluate PM2.5's combined effects on blood pressure (BP) and identify the most toxic components. METHODS: We measured late-pregnancy BP of 1138 women from the Heshan cohort while simultaneously analyzing 31 PM2.5 components. We utilized multiple linear regression modeling to establish the relationship between PM2.5 components and late-pregnancy BP and applied Random Forest (RF) and generalized Weighted Quantile Sum (gWQS) regression to identify the most toxic components contributing to elevated BP and to quantitatively evaluate the cumulative effect of the PM2.5 component mixtures. RESULTS: The results revealed that 16 PM2.5 components, such as EC, OC, Ti, Fe, Mn, Cu, Cd, Mg, K, Pb, Se, Na+, K+, Cl-, NO3-, and F-, contributed to elevated systolic blood pressure (SBP), while 26 components, including two carbon components (EC, OC), fourteen metallics (Ti, Fe, Mn, Cr, Mo, Co, Cu, Zn, Cd, Na, Mg, Al, K, Pb), one metalloid (Se), and nine water-soluble ions (Na+, K+, Mg2+, Ca2+, NH4+, Cl-, NO3-, SO42-, F-), contributed to elevated diastolic blood pressure (DBP). Mn and Cr were the most toxic components for elevated SBP and DBP, respectively, as analyzed by RF and gWQS models and verified against each other. Exposure to PM2.5 component mixtures increased SBP by 1.04 mmHg (95% CI: 0.33-1.76) and DBP by 1.13 mmHg (95% CI: 0.47-1.78). CONCLUSIONS: Our study highlights the effectiveness of combining traditional and novel models as an analytical strategy to quantify the health effects of PM2.5 constituent mixtures.

Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas.

PURPOSE: Preoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved. METHODS: Contrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets. The diagnostic accuracy of MRI was determined by consistency with postoperative histopathological examination. T1-weighted (T1-WI), T2-weighted (T2-WI) and contrast-enhanced T1-weighted (CE-T1) MR images of the sagittal plane containing the tumour mass were selected for analysis. For each sequence, patient MRI data were randomly allocated to 5 groups that further underwent fivefold cross-validation to evaluate the diagnostic efficacy of the CNN models. An additional 34 pairs of cases were used as an external test dataset to validate the CNN classifiers. RESULTS: After comparing multiple backbone CNN models, we developed a diagnostic system using Inception-v3. In the external test dataset, the per-examination combined sensitivities were 0.78 (0.71-0.84, 95% CI) based on T1-weighted images, 0.79 (0.72-0.84, 95% CI) for T2-weighted images, 0.88 (0.83-0.92, 95% CI) for CE-T1 images, and 0.88 (0.83-0.92, 95% CI) for all weighted images. The combined specificities were 0.72 based on T1-WI (0.66-0.78, 95% CI), 0.84 (0.78-0.89, 95% CI) based on T2-WI, 0.74 (0.67-0.80, 95% CI) for CE-T1, and 0.81 (0.76-0.86, 95% CI) for all weighted images. After all three MRI modalities were merged, the receiver operating characteristic (ROC) curve was calculated, and the area under the curve (AUC) was 0.93, with an accuracy of 0.87. CONCLUSIONS: CNN based MRI analysis has the potential to accurately differentiate ependymomas from schwannomas in the lumbar segment.

Electric Field Switching of Magnon Spin Current in a Compensated Ferrimagnet.

Manipulation of directional magnon propagation, known as magnon spin current, is essential for developing magnonic devices featuring nonvolatile functionalities and ultralow power consumption. Magnon spin current can usually be modulated by magnetic field or current-induced spin torques. However, these approaches may lead to energy dissipation due to Joule heating. Electric-field switching of magnon spin current without charge current is highly preferred but challenging to realize. By integrating magnonic and piezoelectric materials, the manipulation of the magnon spin current generated by the spin Seebeck effect in the ferrimagnetic insulator Gd3Fe5O12 (GdIG) film on a piezoelectric substrate is demonstrated. Reversible electric-field switching of magnon polarization without applied charge current is observed. Through strain-mediated magnetoelectric coupling, the electric field induces the magnetic compensation transition between two magnetic states of the GdIG, resulting in its magnetization reversal and the simultaneous switching of magnon spin current. This work establishes a prototype material platform that paves the way for developing magnon logic devices characterized by all electric field reading and writing and reveals the underlying physics principles of their functions.

DataColor: unveiling biological data relationships through distinctive color mapping.

In the era of rapid advancements in high-throughput omics technologies, the visualization of diverse data types with varying orders of magnitude presents a pressing challenge. To bridge this gap, we introduce DataColor, an all-encompassing software solution meticulously crafted to address this challenge. Our aim is to empower users with the ability to handle a wide array of data types through an assortment of tools, while simultaneously streamlining parameter selection for rapid insights and detailed enhancements. DataColor stands as a robust toolkit, encompassing 23 distinct tools coupled with over 600 parameters. The defining characteristic of this toolkit is its adept utilization of the color spectrum, allowing for the representation of data spanning diverse types and magnitudes. Through the integration of advanced algorithms encompassing data clustering, normalization, squarified layouts, and customizable parameters, DataColor unveils an abundance of insights that lay hidden within the intricate relationships embedded in the data. Whether you find yourself navigating the analysis of expansive datasets or embarking on the quest to visualize intricate patterns, DataColor stands as the comprehensive and potent solution. We extend the availability of DataColor to all users at no cost, accessible through the following link: https://github.com/frankgenome/DataColor.

Corrigendum: Integrated metabolomics and proteomics reveal biomarkers associated with hemodialysis in end-stage kidney disease.

[This corrects the article DOI: 10.3389/fphar.2023.1243505.].

Beyond Glycolysis: Aldolase A is a Novel Effector in Reelin Mediated Dendritic Development.

Reelin, a secreted glycoprotein, plays a crucial role in guiding neocortical neuronal migration, dendritic outgrowth and arborization, and synaptic plasticity in the adult brain. Reelin primarily operates through the canonical lipoprotein receptors apolipoprotein E receptor 2 (Apoer2) and very low-density lipoprotein receptor (Vldlr). Reelin also engages with non-canonical receptors and unidentified co-receptors; however, the effects of which are less understood. Using high-throughput tandem mass tag LC-MS/MS-based proteomics and gene set enrichment analysis, we identified both shared and unique intracellular pathways activated by Reelin through its canonical and non-canonical signaling in primary murine neurons during dendritic growth and arborization. We observed pathway crosstalk related to regulation of cytoskeleton, neuron projection development, protein transport, and actin filament-based process. We also found enriched gene sets exclusively by the non-canonical Reelin pathway including protein translation, mRNA metabolic process and ribonucleoprotein complex biogenesis suggesting Reelin fine-tunes neuronal structure through distinct signaling pathways. A key discovery is the identification of aldolase A, a glycolytic enzyme and actin binding protein, as a novel effector of Reelin signaling. Reelin induced de novo translation and mobilization of aldolase A from the actin cytoskeleton. We demonstrated that aldolase A is necessary for Reelin-mediated dendrite growth and arborization in primary murine neurons and mouse brain cortical neurons. Interestingly, the function of aldolase A in dendrite development is independent of its known role in glycolysis. Altogether, our findings provide new insights into the Reelin-dependent signaling pathways and effector proteins that are crucial for actin remodeling and dendritic development. Significance: Reelin is an extracellular glycoprotein and exerts its function primarily by binding to the canonical lipoprotein receptors Apoer2 and Vldlr. Reelin is best known for its role in neuronal migration during prenatal brain development. Reelin also signals through a non-canonical pathway outside of Apoer2/Vldlr; however, these receptors and signal transduction pathways are less defined. Here, we examined Reelin's role during dendritic outgrowth in primary murine neurons and identified shared and distinct pathways activated by canonical and non-canonical Reelin signaling. We also found aldolase A as a novel effector of Reelin signaling, that functions independently of its known metabolic role, highlighting Reelin's influence on actin dynamics and neuronal structure and growth.

Integrated metabolomics and proteomics reveal biomarkers associated with hemodialysis in end-stage kidney disease.

Background: We hypothesize that the poor survival outcomes of end-stage kidney disease (ESKD) patients undergoing hemodialysis are associated with a low filtering efficiency and selectivity. The current gold standard criteria using single or several markers show an inability to predict or disclose the treatment effect and disease progression accurately. Methods: We performed an integrated mass spectrometry-based metabolomic and proteomic workflow capable of detecting and quantifying circulating small molecules and proteins in the serum of ESKD patients. Markers linked to cardiovascular disease (CVD) were validated on human induced pluripotent stem cell (iPSC)-derived cardiomyocytes. Results: We identified dozens of elevated molecules in the serum of patients compared with healthy controls. Surprisingly, many metabolites, including lipids, remained at an elevated blood concentration despite dialysis. These molecules and their associated physical interaction networks are correlated with clinical complications in chronic kidney disease. This study confirmed two uremic toxins associated with CVD, a major risk for patients with ESKD. Conclusion: The retained molecules and metabolite-protein interaction network address a knowledge gap of candidate uremic toxins associated with clinical complications in patients undergoing dialysis, providing mechanistic insights and potential drug discovery strategies for ESKD.

Loss of ADAR1 in macrophages in combination with interferon gamma suppresses tumor growth by remodeling the tumor microenvironment.

BACKGROUND: ADAR1, the major enzyme for RNA editing, has emerged as a tumor-intrinsic key determinant for cancer immunotherapy efficacy through modulating interferon-mediated innate immunity. However, the role of ADAR1 in innate immune cells such as macrophages remains unknown. METHODS: We first analyzed publicly accessible patient-derived single-cell RNA-sequencing and perturbed RNA sequencing data to elucidate the ADAR1 expression and function in macrophages. Subsequently, we evaluated the combined effects of ADAR1 conditional knockout in macrophages and interferon (IFN)-γ treatment on tumor growth in three distinct disease mouse models: LLC for lung cancer, B16-F10 for melanoma, and MC38 for colon cancer. To gain the mechanistic insights, we performed human cytokine arrays to identify differentially secreted cytokines in response to ADAR1 perturbations in THP-1 cells. Furthermore, we examined the effects of ADAR1 loss and IFN-γ treatment on vessel formation through immunohistochemical staining of mouse tumor sections and tube-forming experiments using HUVEC and SVEC4-10 cells. We also assessed the effects on CD8+ T cells using immunofluorescent and immunohistochemical staining and flow cytometry. To explore the translational potential, we examined the consequences of injecting ADAR1-deficient macrophages alongside IFN-γ treatment on tumor growth in LLC-tumor-bearing mice. RESULTS: Our analysis on public data suggests that ADAR1 loss in macrophages promotes antitumor immunity as in cancer cells. Indeed, ADAR1 loss in macrophages combined with IFN-γ treatment results in tumor regression in diverse disease mouse models. Mechanistically, the loss of ADAR1 in macrophages leads to the differential secretion of key cytokines: it inhibits the translation of CCL20, GDF15, IL-18BP, and TIM-3 by activating PKR/EIF2α signaling but increases the secretion of IFN-γ through transcriptional upregulation and interleukin (IL)-18 due to the 5'UTR uORF. Consequently, decreased CCL20 and GDF15 and increased IFN-γ suppress angiogenesis, while decreased IL-18BP and TIM-3 and increased IL-18 induce antitumor immunity by enhancing cytotoxicity of CD8+ T cells. We further demonstrate that combination therapy of injecting ADAR1-deficient macrophages and IFN-γ effectively suppresses tumors in vivo. CONCLUSION: This study provides a comprehensive elucidation of how ADAR1 loss within macrophages contributes to the establishment of an antitumor microenvironment, suggesting the therapeutic potential of targeting ADAR1 beyond the scope of cancer cells.

Physiological Properties of Perennial Rice Regenerating Cultivation in Two Years with Four Harvests.

Crop perennialization has garnered global attention recently due to its role in sustainable agriculture. However, there is still a lack of detailed information regarding perennial rice's regenerative characteristics and physiological mechanisms in crop ratooning systems with different rice stubble heights. In addition, the response of phytohormones to varying stubble heights and how this response influences the regenerative characteristics of ratoon rice remains poorly documented. Here, we explored the regenerative characteristics and physiological mechanisms of an annual hybrid rice, AR2640, and a perennial rice, PR25, subjected to different stubble heights (5, 10, and 15 cm). The response of phytohormones to varying stubble heights and how this response influences the regenerative characteristics of ratoon rice were also investigated. The results show that PR25 overwintered successfully and produced the highest yield, especially in the second ratoon season, mainly due to its extended growth duration, higher number of mother stems, tillers at the basal nodes, higher number of effective panicles, and heavier grain weight when subjected to lower stubble heights. Further analysis revealed that PR25 exhibited a higher regeneration rate from the lower-position nodes in the stem with lower stubble heights. this was primarily due to the higher contents of phytohormones, especially auxin (IAA) and gibberellin (GA3) at an early stage and abscisic acid (ABA) at a later stage after harvesting of the main crop. Our findings reveal how ratoon rice enhances performance based on different stubble heights, which provides valuable insights and serves as crucial references for delving deeper into cultivating high-yielding perennial rice.

Gestational age modifies the association between exposure to fine particles and fetal death: findings from a nationwide epidemiological study in the contiguous United States.

BACKGROUNDS: The vulnerability of fetuses differs at different developmental stages, in response to environmental stressors such as fine particulate matter (PM2.5), a ubiquitous air pollutant. Whether gestational age (GA) modifies the association between prenatal fine particulate matter (PM2.5) exposure and fetal death remains unclear. METHODS: We selected approximately 47.8 million eligible United States (US) livebirth and fetal death (defined as a termination at a GA of 20-43 weeks) records from 1989 to 2004. For each record, we took the level of prenatal exposure to PM2.5 as the average concentration in the mother's residential county during the entire gestational period, or a specific trimester (i.e., GA-specific exposure), according to well-established estimates of monthly levels across the contiguous US. First, we evaluated the associations between PM2.5 exposure and fetal death at a specific GA (i.e., GA-specific outcome) using five different logit models (unadjusted, covariate-adjusted, propensity-score, double robust, and diagnostic-score models). Double robust model was selected as the main model due to its advantages in causal inference. Then, we conducted meta-analyses to pool the estimated GA-specific associations, and explored how the pooled estimates varied with GA. RESULTS: According to the meta-analysis, all models suggested gestational PM2.5 exposure was associated with fetal death. However, there was slight heterogeneity in the estimated effects, as different models revealed a range of 3.6-10.7% increase in the odds of fetal death per 5-µg/m3 increment of PM2.5. Each 5-µg/m3 increase in PM2.5 exposure during the entire gestation period significantly increased the odds of fetal death, by 8.1% (95% confidence interval [CI]: 5.1-11.2%). In terms of GA-specific outcomes, the odds of fetal death at a GA of 20-27, 28-36, or ≥ 37 weeks increased by 11.0% (5.9-16.4%), 5.2% (0.4-10.1%), and 8.3% (2.5-14.5%), respectively. In terms of GA-specific exposure, the odds of fetal death increased by 6.0% (3.9-8.2%), 4.1% (3.9-8.2%), and 4.3% (0.5-8.2%) with 5-µg/m3 increases in PM2.5 exposure during the first, second, and third trimester, respectively. The association had the largest effect size (odds ratio = 1.098, 95% CI: 1.061-1.137) between PM2.5 exposure during early gestation (i.e., first trimester) and early fetal death (i.e., 20-27 weeks). CONCLUSIONS: Prenatal exposure to PM2.5 was significantly associated with an increased risk of fetal death. The association was varied by gestational-age-specific exposures or outcomes, suggesting gestation age as a potential modifier on the effect of PM2.5. The fetus was most vulnerable during the early stage of development to death associated with PM2.5 exposure.