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Predictive value of serum apolipoprotein panel (ApoA1 / ApoA2 / ApoA4) as a biomarker for individual radiosensitivity
No full textAbstract Background Significant interindividual variability in radiosensitivity poses a major challenge to conventional radiation protection and radiotherapy. Current prediction strategies relying on DNA damage or genomic analysis have inherent limitations, underscoring the need for minimally invasive serum biomarkers. While serum apolipoproteins are crucial regulators of lipid transport, metabolism, and cellular stress response, their role as biomarkers for radiosensitivity remains largely unexplored. Methods A 7.3 Gy ⁶⁰Co γ-ray whole-body irradiation mouse model (with training and independent validation cohorts) was established to assess individual radiosensitivity. Pre-irradiation peripheral serum samples underwent high-throughput proteomics analysis to identify differential proteins (DEPs) linked to 30-day post-irradiation survival. KEGG and GO enrichment analyses were conducted to characterize DEP-associated pathways. An XGBoost machine learning model was built using candidate biomarkers, with SHAP analysis to define their predictive contributions; Cox proportional hazards and Pearson correlation analyses were applied to evaluate survival associations. Results DIA-based proteomics identified 580 DEPs in the training cohort and 449 in the validation cohort. KEGG and GO enrichment analyses confirmed that these DEPs were predominantly enriched in the cholesterol metabolism and reverse cholesterol transport pathways. The predictive model based on an apolipoprotein panel (ApoA1/ApoA2/ApoA4), established using the XGBoost algorithm, exhibited exceptional performance in the training cohort (AUC = 1) and maintained robust generalizability in an independent validation cohort (AUC = 0.833). Compared with non-survivors, survivors exhibited significantly elevated serum levels of ApoA1 and ApoA2 but markedly reduced levels of ApoA4. Cox proportional hazards regression analysis established ApoA1 and ApoA2 as independent protective factors, whereas high ApoA4 expression was an adverse prognostic indicator. Notably, ApoA4 levels also demonstrated a strong negative correlation with post-irradiation survival time. Conclusion The serum apolipoprotein profile (ApoA1/ApoA2/ApoA4) serves not only as a promising minimally invasive biomarker for predicting individual radiosensitivity in mice but also reveals a critical link between the cholesterol metabolic pathway and radiation response. This finding lays a theoretical foundation for translating predictive, cholesterol metabolism-related biomarkers to support radiation response assessments. Given the limitations of animal models, subsequent studies are required to validate the clinical applicability of this panel in human cohorts, with the aim of offering an effective tool for personalized radiation protection and precise radiotherapy
Association between the cumulative atherogenic index of plasma and cardiometabolic multimorbidity: the mediating effects of the TyG index and body mass index
No full textAbstract Background Cardiometabolic multimorbidity (CMM) poses a significant global health challenge. The atherogenic index of plasma (AIP) is a promising biomarker for cardiometabolic risk, but there is limited information on its cumulative effect on CMM and the underlying mechanisms. This study investigated the association of cumulative AIP exposure with CMM risk, and explored the mediating roles of the triglyceride glucose (TyG) index and body mass index (BMI). Methods This study was based on data from 5,454 participants from the China Health and Retirement Longitudinal Study (CHARLS, 2011 baseline). The participants were stratified into tertiles of cumulative AIP (cuAIP) and classified into three distinct AIP trajectory groups using k-means clustering. Associations between cuAIP levels, AIP trajectories, and CMM incidence were assessed using logistic. The relationship between cuAIP and CMM was further examined using receiver operating characteristic (ROC) curve analysis and restricted cubic splines (RCS). Structural equation modeling was used to evaluate the mediating roles of the TyG index and BMI. Finally, subgroup and sensitivity analyses were conducted to validate the results. Results A total of 385 CMM cases were observed during the 7-year follow-up. Cluster analysis revealed the highest CMM incidence (12.1%) in the persistently high AIP trajectory group. Logistic regression models indicated that the highest cuAIP group (OR 2.81, 95% CI: 1.95–4.14) and high AIP trajectory group (OR 2.34, 95% CI: 1.68–3.28) had the highest CMM risk, with consistent results in sensitivity analyses and most subgroups. The AUC of cuAIP for predicting CMM was 0.648, and RCS curves demonstrated increasing CMM incidence with rising cuAIP levels. Mediation analysis indicated that the TyG index and BMI mediated 74% and 26% of the total effect, respectively. Conclusion This study establishes AIP as an independent predictor of CMM, whereby its effect is primarily mediated by the TyG index and BMI. These findings support the implementation of integrated clinical strategies to effectively prevent CMM and its associated diseases
Association of HDL subclass components with all-cause and cardiovascular mortality: a prospective cohort study based on the ChinaHEART project
No full textAbstract Background While the U-shaped association between high-density lipoprotein cholesterol (HDL-C) levels and the risk of all-cause and cardiovascular mortality is well-established, the underlying contributions of HDL subclasses remain poorly understood. This study aimed to comprehensively analyze the variations of HDL subclass components across different HDL-C levels and assess their associations with the risk of all-cause and cardiovascular mortality. Methods This study enrolled 1,585 participants aged 35–75 years from China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) (2014–2023). Lipoprotein parameters were measured by nuclear magnetic resonance, with a focus on triglycerides (TG), cholesterol (CH), free cholesterol (FC), phospholipids (PL), apolipoprotein A1 (Apo-A1) and apolipoprotein A2 (Apo-A2) within four density-separated HDL subclasses (HDL1–HDL4). Between-group comparisons were performed using analysis of variance with post-hoc least significant difference tests. Cox proportional hazards regression models and competing risk models were used to assess the association of HDL subclass components with all-cause and cardiovascular mortality. Potential nonlinear associations were examined using models with restricted cubic splines (RCS). Results During a median follow-up of 7.6 years, 84 all-cause (5.3%) and 23 (1.5%) cardiovascular deaths were documented. As HDL-C concentration increased, most HDL subclass components (including CH, FC, PL, and Apo-A1) also increased across low (≤ 30 mg/dL), intermediate (50–60 mg/dL), and high (≥ 100 mg/dL) HDL-C groups. Regression models showed that components in larger, more buoyant HDL subclasses (such as H1TG, H2TG, H1CH, H1FC, H1PL, H1A1, H1A2 and H2A2) were positively associated with all-cause mortality, whereas smaller, denser ones (including H4CH, H4FC, H4PL, H4A1 and H4A2) exhibited protective effects. H1PL, H1A1 and H1A2 also emerged as independent risk factors for cardiovascular mortality. The RCS analysis revealed positive linear associations of H1CH and H1A1 with all-cause mortality, while H4CH and H4A1 were inversely associated. Conclusions Larger, more buoyant HDL subclasses showed a positive association with all-cause mortality, whereas smaller, denser ones were protectively associated. The U-shaped association between HDL-C and mortality may be primarily explained by lower levels of H4CH at very low HDL-C concentrations and higher levels of H1CH at extremely high HDL-C levels. Similar explanations could also account for the association between Apo-A1 and mortality. Trial registration ClinicalTrials.gov, NCT02536456. Registered 24 August 2015
Applications and potential mechanisms of transcranial magnetic stimulation in autism spectrum disorders
No full textAbstract Background Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique widely applied in clinical interventions for various neurological disorders. Its safety profile, ease of operation, and potential therapeutic value have prompted exploration in autism spectrum disorder (ASD). However, TMS efficacy in ASD exhibits marked heterogeneity, primarily due to the lack of robust scientific theoretical support for existing therapeutic approaches—this severely hinders the standardisation of TMS in ASD clinical practice and the improvement of therapeutic outcomes. Main Body The present narrative review first provides an in-depth synthesis of existing TMS research in ASD, focusing on the safety validation of different stimulation protocols, the scientific rationale for protocol selection, and the observed differences in efficacy across recent studies. It also explores the suitability of stimulation coil types and the rationality of target localisation, offering practical guidance for harnessing TMS’s therapeutic potential. Building on this, the core of the review focuses on summarising both potential and proposed mechanisms of TMS in ASD, encompassing key dimensions such as ion channels, excitatory-inhibitory imbalance, synaptic plasticity, neural oscillations, neuroinflammation, and the gut microbiome, while elucidating the interrelationships among these factors. Conclusion This narrative review systematically synthesises the proposed mechanisms by which TMS may affect ASD, aiming to provide a foundation for optimising TMS-based therapeutic regimens for ASD and advancing the development of TMS as a more effective and reliable treatment option
SCMO: a deep learning model integrating the single-cell resolution TME ecosystem and multi-omics for survival prediction in CRC patients
No full textAbstract Background Colorectal cancer (CRC) remains a leading cause of global cancer mortality, highlighting the need for precise survival prediction to guide clinical decisions. Although tissue-level multi-omics is widely utilized for survival prediction, its limited resolution cannot capture tumor heterogeneity. Single-cell RNA sequencing (scRNA-seq) enables dissection of the tumor microenvironment (TME) at cellular resolution, supporting personalized prognostic assessment. Methods We collected 213 CRC scRNA-seq samples and established a CRC-specific TME atlas comprising 339,060 cells. Using this atlas as a reference, we deconvolved bulk RNA-seq data from TCGA–CRC cohort with the EcoTyper algorithm to reconstruct TME features. Clinical, genomic, and transcriptomic data were obtained from the Xena platform; microbial data were sourced from the BIC database. We integrated TME and multi-omics features through a self-normalizing neural network to construct a deep learning model (single-cell resolution TME ecosystem with multi-omics data [SCMO]) for survival prediction. To enhance interpretability, we utilized the Integrated Gradients algorithm and spatial transcriptomic data to analyze multi-omics and TME features. We performed anticancer drug screening with tumor necrosis factor receptor-associated protein 1 (TRAP1), a critical feature according to the Integrated Gradients algorithm, as a potential target. Results We identified 13 survival-related TME features from the CRC-specific atlas: 12 cell states and one multi-cellular ecosystem. SCMO, which combined TME and multi-omics features, improved survival prediction and outperformed existing methods, achieving a concordance index of 0.762. The SCMO demonstrated robust performance for long-term predictions, achieving areas under the curve (AUCs) of 0.752, 0.772, and 0.869 for 1-, 3-, and 5-year predictions in the training set, with corresponding test set AUCs of 0.639, 0.756, and 0.772. TME features from the SCMO model revealed that ecosystem density increased with CRC malignancy. Multi-omics features included TRAP1 as a potential drug target. Drug screening identified saikosaponin A as a novel TRAP1 inhibitor, and its anticancer activity was validated in vitro. We developed SCMO-Lite, a simplified model incorporating 12 high-attribution-weight multi-omics features, which demonstrated robust risk stratification. Conclusions SCMO combines analytical precision with biological interpretability, offering novel insights for oncology survival prediction. Graphical Abstrac
Retraction Note: FXR activation alleviates tacrolimus-induced post-transplant diabetes mellitus by regulating renal gluconeogenesis and glucose uptake
No full textTargeting the mitochondrial metabolite-dynamics-MDVs-MitoEVs axis: a new frontier in osteoarthritis management
No full textAbstract Background Osteoarthritis (OA) is characterized by progressive cartilage degradation, osteophyte formation, and synovitis. OA progression is linked to mitochondrial metabolic dysfunction, marked by tricarboxylic acid (TCA) cycle imbalance and impaired oxidative phosphorylation (OXPHOS). Key scientific concepts of review Three critical factors regulate mitochondrial metabolism: mitochondrial metabolites, mitochondrial dynamics, and mitochondrial-derived vesicles (MDVs). Mitochondrial metabolites such as fumarate and succinate exacerbate OA pathogenesis by mediating mitochondrial dysfunction, whereas itaconate, α-ketoglutarate (α-KG), and fumarate derivatives confer protective effects. Imbalanced mitochondrial dynamics drive cartilage degradation through oxidative stress. Inflammatory MDVs may accelerate OA by transferring mitochondrial damage-associated molecular patterns (mtDAMPs) into the extracellular space through mitochondrial-derived extracellular vesicles (mitoEVs). Given the interplay of mitochondrial metabolites, mitochondrial dynamics, and MDVs/mitoEVs, we propose that the metabolite-dynamics-MDVs-mitoEVs axis represents a pivotal mechanism driving OA progression and a potential target for mitochondrial-directed therapies. Conclusion Future efforts should prioritize advancing mitochondrial metabolic modulators and MSC-mitoEVs, with validation through synovial fluid biomarkers and support from crucial preclinical safety and delivery studies
RHOJ derived peptide promotes chemosensitivity by inhibiting glutamine metabolism in gastric cancer
No full textAbstract Background Chemoresistance is a cause of chemotherapy failure in gastric cancer (GC) treatment. Recent studies have shown that the dysregulation of glutamine metabolism plays a pivotal role in promoting chemoresistance. While small-molecule inhibitors targeting glutamine metabolism have been investigated, peptide-based compounds have attracted increasing attention because of their high specificity and low toxicity. Endogenous or rationally designed peptides have shown potential for inducing apoptosis, disrupting cancer-related signaling pathways, and overcoming drug resistance in various cancers. However, the potential of functional peptides to target glutamine metabolism and reverse drug resistance in the context of GC has not been thoroughly explored. Methods We performed proteomic profiling to identify upregulated proteins in cisplatin-sensitive GC cells, from which peptides were derived for functional screening. A RHOJ-derived peptide (peptide 1) was identified and validated as a candidate chemosensitizer. Untargeted metabolomics, flow cytometry, molecular docking, molecular dynamics simulations, fluorescence imaging, and a subcutaneous xenograft model were used to investigate the mechanism through which peptide 1 modulates GLUL-mediated glutamine metabolism and reverses cisplatin resistance. Results In this study, we found that glutamine metabolism was enhanced in cisplatin-resistant GC cells and that a peptide which derived from RHOJ (peptide 1) increased the sensitivity of resistant cells to chemotherapy. Molecular docking revealed that this peptide could bind to glutamine synthetase (GLUL), a key enzyme in the glutamine metabolism pathway. Mechanistically, peptide 1 inhibited glutamine production, increased ROS levels, induced DNA damage, and promoted apoptosis in resistant cells, ultimately restoring cisplatin sensitivity both in vitro and in vivo. Conclusions Our study demonstrated that glutamine metabolism plays a vital role in the chemoresistance of GC cells and that RHOJ-derived peptide 1 enhances the chemosensitivity of drug-resistant GC cells through targeting GLUL, depleting glutamine, inducing ROS accumulation, and promoting DNA damage. These mechanisms ultimately restores chemosensitivity in drug-resistant cells and highlight peptide 1 as a promising therapeutic strategy for overcoming chemoresistance in GC
Glutamine-driven upregulation of NPDC1 promotes colorectal cancer progression through PI3K/AKT signaling
No full textAbstract Background Colorectal cancer (CRC) ranks as one of the leading causes of cancer-related mortality globally. NPDC1 is a novel regulator involved in cell proliferation and is upregulated in CRC. However, the biological function and mechanism of NPDC1 driving CRC progression have not been investigated. Methods We integrated single-cell RNA-seq data and bulk RNA-seq cohorts to identify prognostic epithelial gene clusters. The R package “ClusterGVis” was employed to categorize six distinct gene clusters within epithelial cells, following Cox regression identifying poor prognosis genes (HR > 1) in the C1 cluster showing progressive upregulation across the four stages. NPDC1 expression was validated by quantitative real-time polymerase chain reaction (qRT-PCR), immunohistochemistry (IHC) and immunofluorescence (IF). Functional impacts on proliferation, metastasis, and immune microenvironment were assessed using CCK8 assays, EdU staining, colony formation, transwell assays and flow cytometry. Additionally, gene set enrichment analysis (GSEA) based on KEGG terms was performed to investigate the potential signaling pathways and biological functions associated with NPDC1 in CRC. The regulatory role of NPDC1 in tumor progression was assessed establishing subcutaneous xenograft tumor model and lung metastasis model of mouse CRC. Results NPDC1 is significantly upregulated in KRAS mutant CRC and correlates with poor prognosis. Functional experiments demonstrated that NPDC1 drives CRC proliferation in vitro and in vivo but does not affect apoptosis, migration, or invasion. Mechanistically, KRAS mutation-induced glutamine metabolism elevates NPDC1 expression via JUND, activating the PI3K-AKT pathway to promote tumor growth independently of immune modulation. Conclusion Collectively, our results reveal NPDC1 as a KRAS-glutamine axis effector that specifically regulates CRC proliferation via PI3K/AKT signaling, suggesting that NPDC1 could serve as a potential therapeutic target for CRC treatment, particularly in KRAS mutant CRC. Graphical Abstrac
Transcriptome changes in circulating immune cells of critical COVID-19 patients predict a specific metabolic and epigenetic imprint
No full textAbstract Background The progression to critical COVID-19 arises predominantly from a dysregulated host immune response although the underlying regulatory mechanisms still remain partially elusive. This limits a prompt prediction of the disease progression, reduces the therapeutic options and restrains our understanding of “long COVID”. Methods Here, we analyzed the transcriptome of peripheral blood mononuclear cells (PBMCs) collected from COVID-19 patients experiencing different degrees of the disease (mild and critical), and control patients enrolled in the clinical trial COntAGIouS as well as independent bulk RNA-seq, single-cell RNA-seq and proteomic datasets. Results In critical COVID-19 patients, the integrative analysis of transcriptomic data revealed an altered regulatory network involving microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and coding genes that control mRNA translation-related genes, epigenetics, and metabolism. In parallel, we observed an upregulation of tRNA aminoacylation genes in critical COVID-19 patients by the analysis of either bulk or single-cell RNA-seq data from publicly available independent cohorts. Additionally, we found increased expression of coding genes enriched for the cognate amino acids (glycine, alanine, isoleucine and tyrosine), all related to protein localization, post-translational modifications, and cell metabolism in our cohort. Similar alterations in amino acid frequency were found in an independent proteomic dataset. Conclusions Collectively, our findings indicate a broad perturbation of the gene expression landscape that characterizes the aberrant host immune response in critical COVID-19 patients and is potentially coordinated by miRNA and tRNA metabolism alterations. Trial registration COntAGIouS, NCT04327570. Registered 26 March 2020, https://clinicaltrials.gov/ct2/show/NCT04327570
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