Amino acid metabolic reprogramming in the tumor microenvironment and its implication for cancer therapy.

Amino acids are essential building blocks for proteins, crucial energy sources for cell survival, and key signaling molecules supporting the resistant growth of tumor cells. In tumor cells, amino acid metabolic reprogramming is characterized by the enhanced uptake of amino acids as well as their aberrant synthesis, breakdown, and transport, leading to immune evasion and malignant progression of tumor cells. This article reviews the altered amino acid metabolism in tumor cells and its impact on tumor microenvironment, and also provides an overview of the current clinical applications of amino acid metabolism. Innovative drugs targeting amino acid metabolism hold great promise for precision and personalized cancer therapy.

DrugMetric: quantitative drug-likeness scoring based on chemical space distance.

The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.

Exploring the Regulatory Effect of LPJZ-658 on Copper Deficiency Combined with Sugar-Induced MASLD in Middle-Aged Mice Based on Multi-Omics Analysis.

Globally, metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed nonalcoholic fatty liver disease (NAFLD), is one of the most common liver disorders and is strongly associated with copper deficiency. To explore the potential effects and mechanisms of Lactiplantibacillus plantarum LPJZ-658, copper deficiency combined with a high-sugar diet-induced MASLD mouse model was utilized in this study. We fed 40-week-old (middle-aged) male C57BL/6 mice a copper-deficient and high-sugar diet for 16 weeks (CuDS), with supplementary LPJZ-658 for the last 6 weeks (CuDS + LPJZ-658). In this study, we measured body weight, liver weight, and serum biochemical markers. Lipid accumulation, histology, lipidomics, and sphingolipid metabolism-related enzyme expression were investigated to analyze liver function. Untargeted metabolomics was used to analyze the serum and the composition and abundance of intestinal flora. In addition, the correlation between differential liver lipid profiles, serum metabolites, and gut flora at the genus level was measured. The results show that LPJZ-658 significantly improves abnormal liver function and hepatic steatosis. The lipidomics analyses and metabolic pathway analysis identified sphingolipid, retinol, and glycerophospholipid metabolism as the most relevant metabolic pathways that characterized liver lipid dysregulation in the CuDS group. Consistently, RT-qPCR analyses revealed that the enzymes catalyzing sphingolipid metabolism that were significantly upregulated in the CuDS group were downregulated by the LPJZ-658 treatment. In addition, the serum metabolomics results indicated that the linoleic acid, taurine and hypotaurine, and ascorbate and aldarate metabolism pathways were associated with CuDS-induced MASLD. Notably, we found that treatment with LPJZ-658 partially reversed the changes in the differential serum metabolites. Finally, LPJZ-658 effectively regulated intestinal flora abnormalities and was significantly correlated with differential hepatic lipid species and serum metabolites. In conclusion, we elucidated the function and potential mechanisms of LPJZ-658 in alleviating copper deficiency combined with sugar-induced middle-aged MASLD and hope this will provide possible treatment strategies for improving MASLD.

Organic acids released by submerged macrophytes with damaged leaves alter the denitrification microbial community in rhizosphere.

Submerged macrophytes have important impacts on the denitrification and anaerobic ammonia-oxidizing (anammox) processes. Leaf damage in these plants probably changes the rhizosphere environment, affecting organic acid release and denitrifying bacteria. However, there is a lack of comprehensive understanding of the specific changes. This study investigated these changes in the rhizosphere of Potamogeton crispus with four degrees of leaf excision. When 0 %, 30 %, 50 % and 70 % of leaves were excised, the concentrations of total organic acid were 31.45, 32.67, 38.26, and 35.16 mg/L, respectively. The abundances of nirS-type denitrifying bacteria were 2.10 × 1010, 1.59 × 1010, 2.54 × 1010, and 4.67 × 1010 copies/g dry sediment, respectively. The abundances of anammox bacteria were 7.58 × 109, 4.59 × 109, 3.81 × 109, and 3.90 × 109 copies/g dry sediment, respectively. The concentration of total organic acids and the abundance of two denitrification microorganisms in the rhizosphere zone were higher than those in the root zone and non-rhizosphere zone. With increasing leaf damage, the number of OTUs in the Pseudomonas genus of nirS-type denitrifying bacteria first increased and then decreased, while that of the Thauera genus was relatively stable. The overall increase in the OTU number of anammox bacteria indicated that leaf damage promotes root exudates release, thereby leading to an increase in their diversity. The co-occurrence network revealed that the two denitrification microorganisms had about 60.52 % positive connections in rhizosphere while 64.73 % negative connections in non-rhizosphere. The abundance and community composition of both denitrification microorganisms were positively correlated with the concentrations of various substances such as oxalic acid, succinic acid, total organic acids and NO2--N. These findings demonstrate that submerged plant damage has significantly impacts on the structure of denitrification microbial community in the rhizosphere, which may alter the nitrogen cycling process in the deposit sediment. SYNOPSIS: This study reveals leaf damage of macrophyte changed the rhizosphere denitrification microbial community, which is helpful to further understand the process of nitrogen cycle in water.

Artificial Channels Based on Bottlebrush Polymers: Enhanced Ion Transport Through Polymer Topology Control.

Synthetic structures mimicking the transport function of natural ion channel proteins have a wide range of applications, including therapeutic treatments, separation membranes, sensing, and biotechnologies. However, the development of polymer-based artificial channels has been hampered due to the limitation on available models. In this study, we demonstrate the great potential of bottlebrush polymers as accessible and versatile molecular scaffolds for developing efficient artificial ion channels. Adopting the bottlebrush configuration enhanced ion transport activity of the channels compared to their linear analogs. Matching the structure of lipid bilayers, the bottlebrush channel with a hydrophilic-hydrophobic-hydrophilic triblock architecture exhibited the highest activity among the series. Functionalized with urea groups, these channels displayed high anion selectivity. Additionally, we illustrated that the transport properties could be fine-tuned by modifying the chemistry of ion binding sites. This work not only highlights the importance of polymer topology control in channel design, but also reveals the great potential for further developing bottlebrush channels with customized features and diverse functionalities.

Exploring the nexus between green finance and energy efficiency: Unravelling the impact through green technology innovation and energy structure.

This study delves into the intricate relationship between green finance and energy efficiency, focusing on how green technology innovation and energy structure transformations contribute to this dynamic. Utilizing panel data from China's provinces over the period 2015-2022, the research aims to uncover the nuances of how green finance can serve as a catalyst for enhancing energy efficiency across different regions. The objective is to quantify the impact of green finance on energy efficiency, considering the mediating roles of green technology innovation and shifts in energy structure. The analysis employs a sophisticated panel entropy weighting technique to analyze the data, ensuring a robust examination of the relationships between these variables. The results reveal a significant positive impact of green finance on energy efficiency, mediated by advances in green technology and modifications in the energy structure towards more sustainable forms. Specifically, regions with higher engagement in green finance initiatives demonstrated marked improvements in energy efficiency, attributed to substantial investments in green technologies and a gradual shift away from traditional, inefficient energy sources. These findings underscore the pivotal role of green finance in driving the transition towards a more energy-efficient and sustainable economic model. Policy implications drawn from this study suggest that targeted financial policies promoting green investments can significantly bolster energy efficiency.

The impact of left-behind experience on urban identity of new-generation migrant workers.

We investigate the impact of left-behind experiences on the urban identity of new-generation migrant workers using data from the 2017 China Migrants Dynamic Survey. The results show the following: (1) The left-behind experience is an important factor undermining the urban identity of new-generation migrant workers, and the conclusion remains consistent after robustness checks, such as propensity score matching. (2) Left-behind experiences of both parents away from home had the most significant negative impact on urban identity. (3) The results of the mechanism tests indicate that the left-behind experience exerts an adverse impact on urban identity through the pathways of poorer physical health, more frequent migration, more challenging job search, and stronger dependence on preexisting social networks. The findings of this study also offer policy suggestions for promoting the urban identity of new-generation migrant workers.

Bioorthogonal Quinone Methide Decaging Enables Live-Cell Quantification of Tumor-Specific Immune Interactions.

Effective antitumor immunity hinges on the specific engagement between tumor and cytotoxic immune cells, especially cytotoxic T cells. Although investigating these intercellular interactions is crucial for characterizing immune responses and guiding immunotherapeutic applications, direct and quantitative detection of tumor-T cell interactions within a live-cell context remains challenging. We herein report a photocatalytic live-cell interaction labeling strategy (CAT-Cell) relying on the bioorthogonal decaging of quinone methide moieties for sensitive and selective investigation and quantification of tumor-T cell interactions. By developing quinone methide-derived probes optimized for capturing cell-cell interactions (CCIs), we demonstrated the capacity of CAT-Cell for detecting CCIs directed by various types of receptor-ligand pairs (e.g., CD40-CD40L, TCR-pMHC) and further quantified the strengths of tumor-T cell interactions that are crucial for evaluating the antitumor immune responses. We further applied CAT-Cell for ex vivo quantification of tumor-specific T cell interactions on splenocyte and solid tumor samples from mouse models. Finally, the broad compatibility and utility of CAT-Cell were demonstrated by integrating it with the antigen-specific targeting system as well as for tumor-natural killer cell interaction detection. By leveraging the bioorthogonal photocatalytic decaging chemistry on quinone methide, CAT-Cell provides a sensitive, tunable, universal, and noninvasive toolbox for unraveling and quantifying the crucial but delicate tumor-immune interactions under live-cell settings.

R-MFE-TCN: A correlation prediction model between body surface and tumor during respiratory movement.

BACKGROUND: 2D CT image-guided radiofrequency ablation (RFA) is an exciting minimally invasive treatment that can destroy liver tumors without removing them. However, CT images can only provide limited static information, and the tumor will move with the patient's respiratory movement. Therefore, how to accurately locate tumors under free conditions is an urgent problem to be solved at present. PURPOSE: The purpose of this study is to propose a respiratory correlation prediction model for mixed reality surgical assistance system, Riemannian and Multivariate Feature Enhanced Temporal Convolutional Network (R-MFE-TCN), and to achieve accurate respiratory correlation prediction. METHODS: The model adopts a respiration-oriented Riemannian information enhancement strategy to expand the diversity of the dataset. A new Multivariate Feature Enhancement module (MFE) is proposed to retain respiratory data information, so that the network can fully explore the correlation of internal and external data information, the dual-channel is used to retain multivariate respiratory feature, and the Multi-headed Self-attention obtains respiratory peak-to-valley value periodic information. This information significantly improves the prediction performance of the network. At the same time, the PSO algorithm is used for hyperparameter optimization. In the experiment, a total of seven patients' internal and external respiratory motion trajectories were obtained from the dataset, and the first six patients were selected as the training set. The respiratory signal collection frequency was 21 Hz. RESULTS: A large number of experiments on the dataset prove the good performance of this method, which improves the prediction accuracy while also having strong robustness. This method can reduce the delay deviation under long window prediction and achieve good performance. In the case of 400 ms, the average RMSE and MAE are 0.0453  and 0.0361 mm, respectively, which is better than other research methods. CONCLUSION: The R-MFE-TCN can be extended to respiratory correlation prediction in different clinical situations, meeting the accuracy requirements for respiratory delay prediction in surgical assistance.

Characterization of PANoptosis-related genes and the immune landscape in moyamoya disease.

Moyamoya disease (MMD) is a cerebrovascular narrowing and occlusive condition characterized by progressive stenosis of the terminal portion of the internal carotid artery and the formation of an abnormal network of dilated, fragile perforators at the base of the brain. However, the role of PANoptosis, an apoptotic mechanism associated with vascular disease, has not been elucidated in MMD. In our study, a total of 40 patients' genetic data were included, and a total of 815 MMD-related differential genes were screened, including 215 upregulated genes and 600 downregulated genes. Among them, DNAJA3, ESR1, H19, KRT18 and STK3 were five key genes. These five key genes were associated with a variety of immune cells and immune factors. Moreover, GSEA (gene set enrichment analysis) and GSVA (gene set variation analysis) showed that the different expression levels of the five key genes affected multiple signaling pathways associated with MMD. In addition, they were associated with the expression of MMD-related genes. Then, based on the five key genes, a transcription factor regulatory network was constructed. In addition, targeted therapeutic drugs against MMD-related genes were obtained by the Cmap drug prediction method: MST-312, bisacodyl, indirubin, and tropanyl-3,5-dimethylbenzoate. These results suggest that the PANoptosis-related genes may contribute to the pathogenesis of MMD through multiple mechanisms.

MulStack: An ensemble learning prediction model of multilabel mRNA subcellular localization.

Subcellular localization of mRNA is related to protein synthesis, cell polarity, cell movement and other biological regulation mechanisms. The distribution of mRNAs in subcellulars is similar to that of proteins, and most mRNAs are distributed in multiple subcellulars. Recently, some computational methods have been designed to predict the subcellular localization of mRNA. However, these methods only employed a sin-gle level of mRNA features and did not employ the position encoding of nucleotides in mRNA. In this paper, an ensemble learning prediction model is proposed, named MulStack, which is based on random forest and deep learning for multilabel mRNA subcellular localization. The proposed method employs two levels of mRNA features, including sequence-level and residue-level features, and position encoding is employed for the first time in the field of subcellular localization of mRNA. Random forest is employed to learn mRNA sequence-level feature, deep learning is employed to learn mRNA sequence-level feature and mRNA residue-level combined with position encoding. And the outputs of random forest and deep learning model will be weighted sum as the prediction probability. Compared with existing methods, the results show that MulStack is the best in the localization of the nucleus, cytosol and exosome. In addition, position weight matrices (PWMs) are extracted by convolutional neural networks (CNNs) that can be matched with known RNA binding protein motifs. Gene ontology (GO) enrichment analysis shows biological processes, molecular functions and cellular components of mRNA genes. The prediction web server of MulStack is freely accessible at http://bliulab.net/MulStack.

A partially flipped physiology classroom improves the deep learning approach of medical students.

This study aimed to compare the impact of the partially flipped physiology classroom (PFC) and the traditional lecture-based classroom (TLC) on students' learning approaches. The study was conducted over 5 mo at Xiangya School of Medicine from February to July 2022 and comprised 71 students majoring in clinical medicine. The experimental group (n = 32) received PFC teaching, whereas the control group (n = 39) received TLC. The Revised Two-Factor Study Process Questionnaire (R-SPQ-2F) was used to assess the impact of different teaching methods on students' learning approaches. After the PFC, students got significantly higher scores on deep learning approach (Z = -3.133, P < 0.05). Conversely, after the TLC students showed significantly higher scores on surface learning approach (Z = -2.259, P < 0.05). After the course, students in the PFC group scored significantly higher in deep learning strategy than those in the TLC group (Z = -2.196, P < 0.05). The PFC model had a positive impact on deep learning motive and strategy, leading to an improvement in the deep approach, which is beneficial for the long-term development of students. In contrast, the TLC model only improved the surface learning approach. The study implies that educators should consider implementing PFC to enhance students' learning approaches.NEW & NOTEWORTHY In this article, we compare the impact of the partially flipped classroom (PFC) and the traditional lecture classroom (TLC) in a physiology course on medical students' learning approaches. We found that the PFC benefited students by significantly enhancing their deep learning motive, strategy, and approach, which was good for them. However, the TLC model only improved the surface learning motive and approach.

Aminocyclopropenium as a novel hydrogen bonding organocatalyst for cycloaddition of carbon disulfide and epoxide to prepare cyclic dithiocarbonate.

The smallest Hückel aromatic ring cyclopropenium substituted by electron-donating C-amino groups produced a aminocyclopropenium electron-rich cation. A bifunctional aminocyclopropenium halide catalyst installed with bis-(hydroxyethyl) functions on the amino group was then designed. A typical (diethanolamino)cyclopropenium halide catalyst C5·I was screened optimally for the cycloaddition of carbon disulfide into an epoxide to produce cyclic dithiocarbonate with an excellent conversion (95%) and high selectivity (92%). The electrostatic enhancement of alkyl C-H HBD capability was implemented via vicinal positive charges on the cyclopropenium core, and the acidity of the terminal O-H hydrogen proton increased by intramolecular H-bonding between the two hydroxy groups on the diethanolamino group (O-H⋯O-H). Then, a hybrid H-bond donor comprising enhanced alkyl C-H and hydroxy O-H was formed. The hybrid HBD offered by aminocyclopropenium was vital in activating the epoxide and stabilizing the intermediate, resulting in reduced O/S scrambling. Moreover, weakly coordinated iodide anion served as a nucleophilic reagent to open the ring of the epoxide. The cooperative catalytic mechanism of the HBD cation and halide anion was supported by NMR titrations and control experiments. Eleven epoxides with various substituents were converted into the corresponding cyclic thiocarbonate with high conversion and selectivity under mild conditions (25 °C, 6 h) without a solvent. The cycloaddition of carbon disulfide with epoxides catalyzed by aminocyclopropenium developed a new working model for hydrogen bonding organocatalysis.

Integrative analysis of the bladder cancer from a cell cycle NCAM1 perspective at both single cell and bulk resolution.

INTRODUCTION: Bladder cancer (BLCA) is a prevalent and deadly form of urinary cancer, and there is a need for effective therapies, particularly for muscle-invasive bladder cancer (MIBC). Cell cycle inhibitors show promise in restoring control of the cell cycle in BLCA cells, but their clinical prognosis evaluation is limited. METHODS: Transcriptome and scRNA-seq data were collected from the Cancer Genome Atlas Program (TCGA)-BLCA and GSE190888 cohort, respectively. R software and the Seurat package were used for data analysis, including cell quality control, dimensionality reduction, and identification of differentially expressed genes. Genes related to the cell cycle were obtained from the genecards website, and a protein-protein interaction network analysis was performed using cytoscape software. Functional enrichment analysis, immune infiltration analysis, drug sensitivity analysis, and molecular docking were conducted using various tools and packages. BLCA cell lines were cultured and transfected for in vitro experimental assays, including RT-qPCR analysis, and CCK-8 cell viability assays. RESULTS: We identified 32 genes as independent risk or protective factors for BLCA prediction. Functional enrichment analysis revealed their involvement in cell cycle regulation, apoptosis, and various signaling pathways. Using these genes, we developed a nomogram for predicting BLCA survival, which displayed high prognosis stratification efficacy in BLCA patients. Four cell cycle associated key genes identified, including NCAM1, HBB, CKD6, and CTLA4. We also identified the main cell types in BLCA patients and investigated the functional differences between epithelial cells based on their expression levels of key genes. Furthermore, we observed a high positive correlative relationship between the infiltration of cancer-associated fibroblasts and the risk score value. Finally, we conducted in vitro experiments to demonstrate the suppressive role of NCAM1 in BLCA cell proliferation. CONCLUSION: These findings suggest that cell cycle associated genes could serve as potential biomarkers for predicting BLCA prognosis and may represent therapeutic targets for the development of more effective therapies. Hopefully, these findings provide valuable insights into the molecular mechanisms and potential therapeutic targets in BLCA from the perspective of cell cycle. Moreover, NCAM1 was a novel cell proliferation suppressor in the BLCA carcinogenesis.

Population identification and genetic diversity analysis of Fritillaria ussuriensis (Fritillaria) based on chloroplast genes atpF and petB.

The chloroplast genomes of five Fritillaria ussuriensis materials from different production areas were comparatively analyzed, atpF and petB were screened as specific DNA barcodes, and the population identification and genetic diversity of F. ussuriensis were analyzed based on them. The F. ussuriensis chloroplast genome showed a total length of 151 515-151 548 bp with a typical tetrad structure and encoded 130 genes. atpF and petB were used to amplify 183 samples from 13 populations, and they could identify 6 and 9 haplotypes, respectively. Joint analysis of the two sequences revealed 18 haplotypes, named H1-H18, with the most widely distributed and most abundant being H4. Ten haplotypes were unique for 7 populations that they could be used to distinguish from others. Haplotype diversity and nucleotide diversity were 0.99 and 2.09 × 10-3, respectively, indicating the genetic diversity was relatively rich. The results of the intermediary adjacency network showed that H5 was the oldest haplotype, and stellate radiation was centered around it, indicating that population expansion occurred in genuine production areas. This study lays a theoretical foundation for the population identification, genetic evolution, and breed selection of F. ussuriensis.

Ginsenosides Rg1 and Rg2 Activate Autophagy and Attenuate Oxidative Stress in Neuroblastoma Cells Overexpressing Aß(1-42).

Alzheimer's disease is a neurodegeneration with protein deposits, altered proteolysis, and inflammatory and oxidative processes as major hallmarks. Despite the continuous search for potential therapeutic treatments, no cure is available to date. The use of natural molecules as adjuvants in the treatment of Alzheimer's disease is a very promising strategy. In this regard, ginsenosides from ginseng root show a variety of biological effects. Here, we dissected the role of ginsenosides Rg1 and Rg2 in modulating autophagy and oxidative stress in neuroblastoma cells overexpressing Aß(1-42). Key hallmarks of these cellular processes were detected through immunomethods and fluorometric assays. Our findings indicate that ginsenosides are able to upregulate autophagy in neuronal cells as demonstrated by increased levels of LC3II and Beclin-1 proteins and decreased amounts of p62. Simultaneously, an activation of lysosomal hydrolases was observed. Furthermore, autophagy activation promoted the clearance of Aß(1-42). Rg1 and Rg2 also reduced oxidative stress sources and macromolecule oxidation, promoting NRF2 nuclear translocation and the expression of antioxidant enzymes. Our data further clarify the mechanisms of action of Rg1 and Rg2, indicating new insights into their role in the management of disorders like Alzheimer's disease.

Pd@Pt Core-Shell Nanocrystal-Decorated ZnO Nanosheets for ppt-Level NO2 Detection.

Bimetallic nanocrystals (NCs) have obtained significant attention due to their unique advantages of the intrinsic properties of individual metals and synergistic enhancements resulting from the electronic coupling between two constituent metals. In this work, Pd@Pt core-shell NCs were prepared through a facile one-pot solution-phase method, which had excellent dispersion and uniform size. Concurrently, ZnO nanosheets were prepared via a hydrothermal method. To explore their potential in nitrogen dioxide (NO2) gas sensing applications, sensitive materials based on ZnO nanosheets with varying mass percentages of Pd@Pt NCs were generated through an impregnation process. The sensor based on 0.3 wt % Pd@Pt-ZnO exhibited remarkable performance, demonstrating a substantial response (Rg/Ra = 60.3) to 50 ppb of NO2 at a low operating temperature of 80 °C. Notably, this sensor reached an outstanding low detection limit of 300 ppt. The enhancement in gas sensing capabilities can be attributed to the sensitization and synergistic effects imparted by the exceptional catalytic activity of Pd@Pt NCs, which significantly promoted the reaction. This research introduces a novel approach for the utilization of core-shell structured bimetallic nanocrystals as modifiers in metal-oxide-semiconductor (MOS) materials for NO2 detection.

Emerging Role of Fibroblasts in Vitiligo: A Formerly Underestimated Rising Star.

Vitiligo is a disfiguring depigmentation disorder characterized by loss of melanocytes. Although numerous studies have been conducted on the pathogenesis of vitiligo, the underlying mechanisms remain unclear. Although most studies have focused on melanocytes and keratinocytes, growing evidence suggests the involvement of dermal fibroblasts, residing deeper in the skin. This review aims to elucidate the role of fibroblasts in both the physiological regulation of skin pigmentation and their pathological contribution to depigmentation, with the goal of shedding light on the involvement of fibroblasts in vitiligo. The topics covered in this review include alterations in the secretome, premature senescence, autophagy dysfunction, abnormal extracellular matrix, autoimmunity, and metabolic changes.

Bioorthogonal photocatalytic proximity labeling in primary living samples.

In situ profiling of subcellular proteomics in primary living systems, such as native tissues or clinic samples, is crucial for understanding life processes and diseases, yet challenging due to methodological obstacles. Here we report CAT-S, a bioorthogonal photocatalytic chemistry-enabled proximity labeling method, that expands proximity labeling to a wide range of primary living samples for in situ profiling of mitochondrial proteomes. Powered by our thioQM labeling warhead development and targeted bioorthogonal photocatalytic chemistry, CAT-S enables the labeling of mitochondrial proteins in living cells with high efficiency and specificity. We apply CAT-S to diverse cell cultures, dissociated mouse tissues as well as primary T cells from human blood, portraying the native-state mitochondrial proteomic characteristics, and unveiled hidden mitochondrial proteins (PTPN1, SLC35A4 uORF, and TRABD). Furthermore, CAT-S allows quantification of proteomic perturbations on dysfunctional tissues, exampled by diabetic mouse kidneys, revealing the alterations of lipid metabolism that may drive disease progression. Given the advantages of non-genetic operation, generality, and spatiotemporal resolution, CAT-S may open exciting avenues for subcellular proteomic investigations of primary samples that are otherwise inaccessible.

Comparison of Population-Weighted Exposure Estimates of Air Pollutants Based on Multiple Geostatistical Models in Beijing, China.

Various geostatistical models have been used in epidemiological research to evaluate ambient air pollutant exposures at a fine spatial scale. Few studies have investigated the performance of different exposure models on population-weighted exposure estimates and the resulting potential misclassification across various modeling approaches. This study developed spatial models for NO2 and PM2.5 and conducted exposure assessment in Beijing, China. It explored three spatial modeling approaches: variable dimension reduction, machine learning, and conventional linear regression. It compared their model performance by cross-validation (CV) and population-weighted exposure estimates. Specifically, partial least square (PLS) regression, random forests (RF), and supervised linear regression (SLR) models were developed based on an ordinary kriging (OK) framework for NO2 and PM2.5 in Beijing, China. The mean squared error-based R2 (R2mse) and root mean squared error (RMSE) in leave-one site-out cross-validation (LOOCV) were used to evaluate model performance. These models were used to predict the ambient exposure levels in the urban area and to estimate the misclassification of population-weighted exposure estimates in quartiles between them. The results showed that the PLS-OK models for NO2 and PM2.5, with the LOOCV R2mse of 0.82 and 0.81, respectively, outperformed the other models. The population-weighted exposure to NO2 estimated by the PLS-OK and RF-OK models exhibited the lowest misclassification in quartiles. For PM2.5, the estimates of potential misclassification were comparable across the three models. It indicated that the exposure misclassification made by choosing different modeling approaches should be carefully considered, and the resulting bias needs to be evaluated in epidemiological studies.