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The key role of RNA-binding proteins (RBPs) in posttranscriptional regulation of gene expression is intimately tied to their subcellular localization. Here, we show a subcellular-specific RNA labeling method for efficient enrichment and deep profiling of nuclear and cytoplasmic RBPs. A total of 1221 nuclear RBPs and 1333 cytoplasmic RBPs were enriched and identified using nuclear/cytoplasm targeting enrichment probes, representing an increase of 54.4% and 85.7% compared with previous reports. The probes were further applied in the omics-level investigation of subcellular-specific RBP-RNA interactions upon ferroptosis induction. Interestingly, large-scale RBPs display enhanced interaction with RNAs in nucleus but reduced association with RNAs in cytoplasm during ferroptosis process. Furthermore, we discovered dozens of nucleoplasmic translocation candidate RBPs upon ferroptosis induction and validated representative ones by immunofluorescence imaging. The enrichment of Tricarboxylic acid cycle in the translocation candidate RBPs may provide insights for investigating their possible roles in ferroptosis induced metabolism dysregulation. The reported assay shows a subcellular-specific RNA labeling method for efficient enrichment and deep profiling of nuclear and cytoplasmic RBPs, the authors apply this to investigate changes of subcellular-specific RBP-RNA interactions in ferroptosis.

Traditional centralized highway energy systems exhibit significant resilience shortcomings in the face of climate change mitigation requirements and increasingly frequent extreme weather events. Meanwhile, prevailing microgrid control strategies remain predominantly focused on economic optimization under normal conditions, lacking the flexibility to address dynamic risks or the interdependencies between transportation and power systems. This study proposes an adaptive, risk-driven control framework that holistically coordinates power generation infrastructures, microgrids, demand-side loads, energy storage systems, and transport dynamics through continuous risk assessment. This enables the system to dynamically shift operational priorities—from cost-efficiency in stable periods to robustness during emergencies. A multi-objective optimization model is established, integrating infrastructure resilience, operational costs, and traffic impacts. It is solved using an enhanced evolutionary algorithm that combines the non-dominated sorting genetic algorithm II with differential evolution (NSGA-II-DE). Extensive simulations under extreme weather scenarios validate the framework’s ability to autonomously reconfigure operations, achieving 92.5% renewable energy utilization under low-risk conditions while elevating critical load assurance to 98.8% under high-risk scenarios. This strategy provides both theoretical and technical guarantees for securing highway renewable energy system operations.

Mammalian organs and tissues are composed of heterogeneously distributed cells, which interact with each other and the extracellular matrix surrounding them in a spatially defined way. Therefore, spatially resolved gene expression profiling is crucial for determining the function and phenotypes of these cells. While genome mutations and transcriptome alterations act as drivers of diseases, the proteins that they encode regulate essentially all biological functions and constitute the majority of biomarkers and drug targets for disease diagnostics and treatment. However, unlike transcriptomics, which has a recent explosion in high-throughput spatial technologies with deep coverage, spatial proteomics capable of reaching bulk tissue-level coverage is still rare in the field, due to the non-amplifiable nature of proteins and sensitivity limitation of mass spectrometry (MS). More importantly, due to the limited multiplexing capability of the current proteomics methods, whole-tissue slice mapping with high spatial resolution requires a formidable amount of MS matching time. To achieve spatially resolved, deeply covered proteome mapping for centimeter-sized samples, we developed a s parse s ampling s trategy for s patial p roteomics (S4P) using computationally assisted image reconstruction methods, which is potentially capable of reducing the number of samples by tens to thousands of times depending on the spatial resolution. In this way, we generated the largest spatial proteome to date, mapping more than 9000 proteins in the mouse brain, and discovered potential new regional or cell type markers. Considering its advantage in sensitivity and throughput, we expect that the S4P strategy will be applicable to a wide range of tissues in future studies.

Background Adherence to 24-h movement guidelines that include physical activity (PA), sedentary behaviour (SB), and sleep is linked to improved health outcomes, yet its impact on health-related quality of life (HRQoL) in older adults remains underexplored. Objective This study investigated the relationship between adherence to guidelines for 24-h movement behaviours and HRQoL in older adults. Methods A systematic search of four databases was conducted to identify studies published from the date of inception to January 2025. The Appraisal Tool for Cross-Sectional Studies was used to assess the risk of bias among the included studies. Random-effects models were used to conduct a meta-analysis involving the extracted proportions and odds ratios (OR) from each study. The Freeman–Tukey transformation was applied to stabilise the variance of proportional data before meta-analysis, and pooled proportions were back-transformed to their original scale for reporting. Heterogeneity was assessed using the I 2 statistic. Publication bias was evaluated using Begg’s test. A sensitivity analysis was conducted, which included changing the random-effects model to a fixed-effects model and adopting a leave-one-out analysis. Results Twenty-five cross-sectional articles were included, covering a total of 170,670 older adults aged ≥ 60 years. We found that 24.3% (95% confidence interval [CI]: 22.1%–26.5%, p  < 0.01) of older adults adhered to at least one movement guideline, among whom 23.0% (95% CI: 20.8%–25.2%, p  < 0.01, n  = 18) adhered to PA guidelines, 18.5% (95% CI: 15.3%–21.7%, p  < 0.01, n  = 7) to SB guidelines, and 40.0% (95% CI: 37.5%–42.5%, p  < 0.01, n  = 7) to sleep guidelines. Our meta-analysis suggests that compared with non-compliance, adherence to at least one 24-h movement guideline was associated with higher HRQoL among older adults (OR = 1.65, 95% CI: 1.44–1.89,  p  < 0.01, I^2 = 95.5%, n  = 15). Small-study effects were examined with Begg’s test; all subgroup analyses of individual guidelines were underpowered due to a small number of included studies ( n  < 10: PA n  = 7, SB n  = 5 and SL n  = 4). Conclusion Adherence to at least one 24-h movement guideline is associated with better HRQoL in older adults. However, the lack of available research—primarily due to an insufficient number of studies reporting data on multi-component adherence—limits our ability to conduct further analyses on the association between adherence to a combination of two to three movement guidelines and HRQoL.

Background Targeted protein degradation of neosubstrates plays a crucial role in hematological cancer treatment involving immunomodulatory imide drugs (IMiDs) therapy. Nevertheless, the persistence of inevitable drug resistance and hematological toxicities represents a significant obstacle to their clinical effectiveness. Methods Phenotypic profiling of a small molecule compounds library in multiple hematological cancer cell lines was conducted to screen for hit degraders. Molecular dynamic-based rational design and cell-based functional assays were conducted to develop more potent degraders. Multiple myeloma (MM) tumor xenograft models were employed to investigate the antitumor efficacy of the degraders as single or combined agents with standard of care agents. Unbiased proteomics was employed to identify multiple therapeutically relevant neosubstrates targeted by the degraders. MM patient-derived cell lines (PDCs) and a panel of solid cancer cell lines were utilized to investigate the effects of candidate degrader on different stage of MM cells and solid malignancies. Unbiased proteomics of IMiDs-resistant MM cells, cell-based functional assays and RT-PCR analysis of clinical MM specimens were utilized to explore the role of BRD9 associated with IMiDs resistance and MM progression. Results We identified a novel cereblon (CRBN)-dependent lead degrader with phthalazinone scaffold, MGD-4 , which induced the degradation of Ikaros proteins. We further developed a novel potent candidate, MGD-28 , significantly inhibited the growth of hematological cancer cells and induced the degradation of IKZF1/2/3 and CK1α with nanomolar potency via a Cullin-CRBN dependent pathway. Oral administration of MGD-4 and MGD-28 effectively inhibited MM tumor growth and exhibited significant synergistic effects with standard of care agents. MGD-28 exhibited preferentially profound cytotoxicity towards MM PDCs at different disease stages and broad antiproliferative activity in multiple solid malignancies. BRD9 modulated IMiDs resistance, and the expression of BRD9 was significant positively correlated with IKZF1/2/3 and CK1α in MM specimens at different stages. We also observed pronounced synergetic efficacy between the BRD9 inhibitor and MGD-28 for MM treatment. Conclusions Our findings present a strategy for the multi-targeted degradation of Ikaros proteins and CK1α against hematological cancers, which may be expanded to additional targets and indications. This strategy may enhance efficacy treatment against multiple hematological cancers and solid tumors.

Urine is an attractive and non-invasive alternative source to tissue, blood or other biofluids for biomarker screening in clinical research. In normal human adult urine, 48% of the total urinary protein is in the sediment, 49% is soluble and the remaining 3% is contained in urinary extracellular vesicles (EVs). The soluble proteins and EV proteins in urine have attracted particular attention in recent years as cancer diagnostics. Furthermore, considering the important role of N-glycoproteins in practically all physiological processes, including regulating receptor-ligand binding, cell-cell interactions, inflammatory response and tumour progression, N-glycoproteome in human urine is an invaluable target for monitoring the physiological status and pathological changes of the kidney and urinary tract. Given the different origins of the soluble proteins and EV proteins in the urine, different N-glycoproteome patterns exist. Therefore, isolating the soluble N-glycoproteins and EV N-glycoproteins for separate analysis will provide a more specific and comprehensive view and provide a deeper understanding of human urinary N-glycoproteome. In this work, we developed a sequential separation method that isolates urinary soluble proteins and EV proteins via stepwise ultrafiltration based on their obvious size difference. A facile and reproducible protein isolation was achieved using this strategy. Subsequent N-glycoproteome enrichment and identification revealed distinct patterns in the two sub-proteomes of urine with more than 60% differential N-glycopeptides. A more comprehensive picture of the urinary N-glycoproteome with close to 1800 identified N-glycopeptides was obtained by this new analysis strategy, therefore making it advantageous for urinary biomarker screening.

Cytochrome P450 (CYP450) and 5′-diphosphate glucuronosyltransferases (UGT) are the two major families of drug-metabolizing enzymes in the human liver microsome (HLM). As a result of their frequent abundance fluctuation among populations, the accurate quantification of these enzymes in different individuals is important for designing patient-specific dosage regimens in the framework of precision medicine. The preparation and quantification of internal standards is an essential step for the quantitative analysis of enzymes. However, the commonly employed stable isotope labeling-based strategy (QconCAT) suffers from requiring very expensive isotopic reagents, tedious experimental procedures, and long labeling times. Furthermore, arginine-to-proline conversion during metabolic isotopic labeling compromises the quantification accuracy. Therefore, we present a new strategy that replaces stable isotope-labeled amino acids with lanthanide labeling for the preparation and quantification of QconCAT internal standard peptides, which leads to a threefold reduction in the reagent costs and a fivefold reduction in the time consumed. The absolute amount of trypsin-digested QconCAT peptides can be obtained by lanthanide labeling and inductively coupled plasma–optical emission spectrometry (ICP-OES) analysis with a high quantification accuracy (%RE < 20%). By taking advantage of the highly selective and facile ICP-OES procedure and multiplexed large-scale absolute target protein quantification using biological mass spectrometry, this strategy was successfully used for the absolute quantification of drug-metabolizing enzymes. We obtained good linearity (correlation coefficient > 0.95) over concentrations spanning 2.5 orders of magnitude with improved sensitivity (limit of quantification = 2 fmol) in nine HLM samples, indicating the potential of this method for large-scale absolute target protein quantification in clinical samples.

Highly efficient protein digestion is one of the key issues in the “bottom-up” strategy-based proteomic studies. Compared with the time-consuming solution-based free protease digestion, immobilized protease digestion offers a promising alternative with obviously improved sample processing throughput. In this study, we proposed a new immobilized protease digestion strategy using two kinds of polymer-grafted graphene oxide (GO) conjugated trypsin. The polymer brush grafted GO was prepared using in situ polymer growth on initiator-functionalized GO using surface-initiated atom transfer radical polymerization (SI-ATRP) and characterized by AFM, TEM, TGA, and XPS. The polymer brush grafted GO supports three-dimensional trypsin immobilization, which not only increases the loading amount but also improves accessibility towards protein substrates. Both of the two types of immobilized trypsin provide 700 times shorter digestion time, while maintaining comparable protein/peptide identification scale compared with that of free trypsin digestion. More interestingly, combined application of the two types of immobilized trypsin with different surface-grafted polymers leads to at least 18.3/31.3% enhancement in protein/peptide identification compared with that obtained by digestion using a single type, indicating the potential of this digestion strategy for deeper proteome coverage using limited mass spectrometer machine hour. We expect these advantages may find valuable application in high throughput clinical proteomic studies, which often involve processing of a large number of samples. Graphical abstract Preparation of polymer brushes grafted and trypsin immobilized graphene oxide and its application in proteome digestion and mass spectrometry identification.

Glycans are complex biomolecules that encode rich information and regulate various biological processes, such as fertilization, host‐pathogen binding, and immune recognition, through interactions with glycan‐binding proteins. A key driving force for glycan‐protein recognition is the interaction between the π electron density of aromatic amino acid side chains and polarized C─H groups of the pyranose (termed the CH–π interaction). However, the relatively weak binding affinity between glycans and proteins has hindered the application of glycan detection and imaging. Here, computational modeling and molecular dynamics simulations are employed to design a chemical strategy that enhances the CH–π interaction between glycans and proteins by genetically incorporating electron‐rich tryptophan derivatives into a lectin PhoSL, which specifically recognizes core fucosylated N‐linked glycans. This significantly enhances the binding affinity of PhoSL with the core fucose ligand and enables sensitive detection and imaging of core fucosylated glycans in vitro and in xenograft tumors in mice. Further, the study showed that this strategy is applicable to improve the binding affinity of GafD lectin for N‐acetylglucosamine‐containing glycans. The approach thus provides a general and effective way to manipulate glycan‐protein recognition for glycoscience applications. Computational modeling is employed to identify the critical tryptophan residue on PhoSL for recognizing core fucosylated glycans. The tryptophan residue is replaced with electron‐rich derivatives of tryptophan using the genetic code expansion strategy to enhance CH–π interactions between PhoSL and glycans. The engineered lectin is able to detect and image core‐fucosylated glycans in cells and in mice with high sensitivity.

Oncogenic Kras-activated pancreatic ductal adenocarcinoma (PDAC) cells highly rely on an unconventional glutamine catabolic pathway to sustain cell growth. However, little is known about how this pathway is regulated. Here we demonstrate that Kras mutation induces cellular O-linked β-N-acetylglucosamine (O-GlcNAc), a prevalent form of protein glycosylation. Malate dehydrogenase 1 (MDH1), a key enzyme in the glutamine catabolic pathway, is positively regulated by O-GlcNAcylation on serine 189 (S189). Molecular dynamics simulations suggest that S189 glycosylation on monomeric MDH1 enhances the stability of the substrate-binding pocket and strengthens the substrate interactions by serving as a molecular glue. Depletion of O-GlcNAcylation reduces MDH1 activity, impairs glutamine metabolism, sensitizes PDAC cells to oxidative stress, decreases cell proliferation and inhibits tumor growth in nude mice. Furthermore, O-GlcNAcylation levels of MDH1 are elevated in clinical PDAC samples. Our study reveals that O-GlcNAcylation contributes to pancreatic cancer growth by regulating the metabolic activity of MDH1.Kras activation in pancreatic cancer cells induced O-GlcNAc modification of malate dehydrogenase 1, regulating glutamine metabolism and promoting tumor growth.