Journal of advanced research最新文献

Background: Osteoarthritis (OA) and neurodegenerative disorders such as Alzheimer's and Parkinson's diseases are prevalent age-related conditions that severely impair quality of life in the elderly. Growing evidence suggests shared pathological drivers, including chronic low-grade inflammation, oxidative stress, mitochondrial dysfunction, and cellular senescence, link the degeneration of articular cartilage with neuronal loss in the aging brain.

Aim of review: This review introduces and defines the emerging concept of the "brain-joint axis" as a bidirectional communication network integrating neural, endocrine, and immune signaling between the central nervous system (CNS) and joint tissues. By consolidating evidence of systemic crosstalk between OA and neurodegenerative disorders, we highlight how aging-related biological processes simultaneously influence joint and brain homeostasis.

Key scientific concepts of review: We discuss how proinflammatory cytokines (e.g., interleukin-1β, tumor necrosis factor-α) and metabolic stress mediators propagate systemic inflammation that exacerbates both cartilage degradation and neuronal damage. Furthermore, we propose that OA is not merely a localized musculoskeletal disorder but part of a broader systemic neuro-immuno-endocrine network whose dysfunction contributes to neurodegeneration. Indeed, this work delineates the mechanistic framework of the brain-joint axis, emphasizing its translational potential as a therapeutic target to simultaneously mitigate OA progression and neurodegenerative decline.

Introduction: Type 2 diabetes (T2D) significantly increases dementia risk, yet the molecular mechanisms underlying this association remain unclear.

Objectives: This study aimed to identify protein signatures that distinguish dementia risk in T2D patients, develop a proteomic prediction model, and elucidate biological pathways connecting T2D and dementia.

Methods: We analyzed 2,920 plasma proteins from 52,958 participants (including 3,292 with T2D) in the UK Biobank Pharma Proteomics Project with a median follow-up of 14.6 years. Cox regression models with interaction terms identified T2D-specific protein associations with dementia risk. Machine learning models were developed to predict dementia in T2D patients. Pathway analysis and weighted gene co-expression network analysis identified biological mechanisms linking T2D and dementia.

Results: We identified 471 proteins with significant interaction effects between T2D and dementia risk. In non-T2D individuals, elevated levels of neuronal pentraxin receptor (NPTXR, HR = 0.74, 95 %CI:0.66-0.83) and carbonic anhydrase 14 (CA14, HR = 0.67, 95 %CI:0.60-0.75) were exclusively associated with decreased dementia risk. Conversely, in T2D patients, elevated rho guanine nucleotide exchange factor 12 (ARHGEF12, HR = 1.45, 95 %CI:1.10-1.91) was specifically associated with increased dementia risk. A 51-protein model accurately predicted 15-year dementia risk in T2D patients (AUC = 0.835, C-index = 0.829), outperforming conventional clinical risk scores and maintaining high accuracy for Alzheimer's disease and vascular dementia. Pathway analysis revealed enrichment of IL6-JAK-STAT3 signaling in T2D-related dementia, while dysregulation of fatty acid metabolism was specific to T2D-associated Alzheimer's disease.

Conclusions: This large-scale proteomic analysis identifies specific molecular signatures that differentiate dementia risk in diabetic and non-diabetic populations, with potential applications for early risk stratification and targeted interventions. The identified pathways provide novel insights into the pathophysiological processes connecting T2D and dementia and suggest potential therapeutic targets.

Introduction: Sepsis is a life-threatening condition with a high mortality rate, yet its underlying mechanisms remain incompletely understood.

Objectives: This study investigates the role of the cleaved extracellular domain of signal regulatory protein alpha (SIRPα-ex) in the pathogenesis of sepsis.

Methods: The presence of shed SIRPα-ex was examined in the circulation of septic mice and patients. Functional assays involved neutralization of SIRPα-ex with an anti-SIRPα antibody and administration of recombinant SIRPα-ex-Fc in a murine sepsis model. Genetic models, including Sirpα^-/- and Sirpα-ct^-/- mice, were used to assess the contribution of SIRPα signaling.

Results: We found that Sirpα^-/^- mice were protected from sepsis despite marked hyperinflammation, suggesting a cytokine-independent protective mechanism. Circulating SIRPα-ex was elevated in both septic mice and patients. Neutralization of SIRPα-ex significantly attenuated lipopolysaccharide (LPS)-induced sepsis, whereas administration of SIRPα-ex-Fc exacerbated disease severity. Mechanistically, SIRPα-ex was cleaved by the metalloproteinase ADAM10 and subsequently bound to erythrocyte CD47, triggering nitric oxide (NO) release. Inhibition of ADAM10 reduced plasma NO levels and vascular permeability in septic mice.

Conclusion: These findings identify shedding SIRPα-ex as a key exacerbating factor in sepsis via NO-mediated vascular dysfunction. Targeting SIRPα-ex shedding offers a potential therapeutic strategy for mitigating sepsis.

Background: In the realm of biomedical research and clinical practice, the identification and accurate detection of biomarkers have become increasingly critical. Biomarkers serve as key indicators for disease diagnosis, prognosis evaluation, and drug efficacy monitoring, playing a pivotal role in advancing personalized medicine. However, the complexity and diversity of biomarkers pose significant challenges to their detection and imaging. Traditional methods, such as immunoassays, nucleic acid detection, and mass spectrometry, often fall short in terms of sensitivity, specificity, and efficiency. Consequently, there is an urgent need for more precise and efficient technologies to enhance the detection of biomarkers.

Aim of review: This review aims to provide a comprehensive overview of the latest advancements in biomaterials for biomarker imaging and detection. It seeks to highlight the critical role of biomarkers in disease diagnosis and management, while exploring the potential of newly developed biomaterials to overcome the limitations of conventional detection methods.

Key scientific concepts of review: The review delves into the unique physicochemical properties of biomaterials, such as nanoparticles, quantum dots (QDs), and biopolymers, which enable highly sensitive, specific, and high-resolution biomarker detection. This review finds that by integrating their molecular recognition mechanisms with advanced imaging technologies, these biomaterials demonstrate significant advantages in detecting biomarkers for major diseases such as cancer, cardiovascular diseases (CVDs), and neurodegenerative diseases. For instance, nanoparticle-based probes can detect tumor markers at extremely low concentrations, while QD imaging techniques enable high-resolution imaging at the cellular and tissue levels. Additionally, this review provides an in-depth discussion of the numerous challenges confronting biomaterial-based detection technologies during clinical translation and proposes future research directions. We emphasize the necessity of accelerating the development of innovative materials, optimizing imaging and detection technologies, and facilitating clinical application translation.

Introduction: Lactate, a glycolysis byproduct, has been implicated in the fibrotic process, while transforming growth factor-beta 1 (TGF-β1) plays a central role in promoting fibrosis. Air pollution, particularly fine particulate matter (PM2.5), represents a significant environmental risk factor for the development of pulmonary fibrosis. However, the role of lactate and the underlying mechanisms by which it acts in PM2.5-induced pulmonary fibrosis remain poorly understood.

Objectives: This study aimed to identify the cell types contributing to lactate accumulation in lung tissue during PM2.5-induced pulmonary fibrosis and elucidate the mechanism by which lactate regulates TGF-β1.

Methods: Seven types of lung cells from PM2.5-exposed mice were isolated using fluorescence-activated cell sorting to determine their lactate production. Immunoprecipitation and immunoblotting were performed to assess the impact of lactate on TGF-β1 stability. The effect of histone lactylation on Stub1 gene expression was investigated by chromatin immunoprecipitation assays.

Results: Macrophages exhibited elevated lactate production during PM2.5-induced pulmonary fibrosis. Elevated intracellular lactate levels in macrophages suppressed the expression of carboxyl terminus of Hsc70-interacting protein (CHIP, encoded by Stub1) via the enrichment of lactylated H3K18 at the Stub1 promoter locus. Consequently, reduced CHIP expression impeded TGF-β1 degradation, promoted enhanced TGF-β1 secretion by macrophages, and exacerbated pulmonary fibrosis symptoms. Moreover, the inhibition of lactate production significantly alleviated the pulmonary fibrosis phenotype in PM2.5-exposed mice.

Conclusion: Elevated lactate production in macrophages induced by PM2.5 inhibits the ubiquitination and degradation of TGF-β1 through the suppression of CHIP expression, thereby enhancing TGF-β1 secretion and exacerbating pulmonary fibrosis.

Background: Fingernail metabolomics provides a novel, non-invasive platform that captures long-term biochemical fluctuations for identifying reliable biomarkers for dementia and mild cognitive impairment (MCI) due to Alzheimer's disease (AD).

Methods: A total of 199 participants were enrolled and stratified according to Clinical Dementia Rating (CDR) scores (0, 0.5, 1, 2, and 3). Fingernail clippings were collected and analysed using gas chromatography-mass spectrometry (GC-MS) based metabolomic. Differentially expressed metabolites (DEMs) across cognitive groups were identified using clustering, ordinal logistic regression, and machine learning approaches. Pathway enrichment and correlation analyses were conducted to explore underlying disease mechanisms and clinical relevance.

Results: Thirty DEMs were identified across the five CDR categories. Among them, Dodecanoic Acid demonstrated a marked and progressive decline from cognitively normal individuals (CDR = 0) to those with advanced AD (CDR = 3). After adjustment for age, sex, education, body mass index, lifestyle factors, nutrition, and sleep quality, Dodecanoic Acid remained independently associated with disease severity (OR = 0.845, p = 0.019). Importantly, within each CDR category (0.5, 1, 2, and 3), Dodecanoic Acid levels showed no significant differences between individuals with and without 18F-AV45 PET-confirmed amyloid pathology (all p > 0.05). Correlation analysis revealed that lower levels of Dodecanoic Acid were linked to greater cognitive impairment (AVLT-IR: r = 0.29; ADAS-Cog: r = -0.32). Pathway enrichment analysis highlighted significant disruptions in fatty acid metabolism, suggesting deficits in energy regulation during AD progression. A machine learning model using 29 DEMs achieved an overall classification accuracy of 67.2 % for differentiating participants into normal cognition (NC), MCI, or AD dementia groups. The model yielded a micro-averaged AUC of 0.803, with one-vs-rest AUCs ranging from 0.71 to 0.87, indicating robust discriminatory performance. Notably, dodecanoic acid was the top contributor in the model, underscoring its potential as a diagnostic biomarker.

Conclusions: Dodecanoic Acid emerges as a key biomarker reflecting disrupted fatty acid metabolism during AD progression. By enabling long-term metabolic profiling, fingernail metabolomics presents a promising, scalable, and non-invasive strategy for early diagnosis, staging, and monitoring of neurodegenerative diseases.

Introduction: The limited regenerative capacity of the nervous system represents a significant clinical challenge in the context of peripheral nerve injuries. An innovative strategy for sciatic nerve repair has been developed using tissue-engineered nerve grafts (TENGs) composed of skin-derived precursor Schwann-like cells (SKP-SCs) and a silk fibroin-chitosan scaffold. However, the reason why SKP-SCs-TENG demonstrated superior enhanced nerve regeneration compared to the autograft and scaffold groups remains unclear.

Objectives: The present work aims to elucidate the superiority and molecular mechanisms underlying TENG repairs.

Methods: We conducted a comprehensive transcriptomic analysis of the rat dorsal root ganglia (DRG, L4-L6). A range of key processes were examined, including apoptosis, proliferation, migration, inflammation, the immune response, axonal outgrowth and myelination. To further elucidate the mechanism, LC-MS/MS analysis of SKP-SCs conditioned medium and RNA sequencing of cocultured DRG neurons were carried out.

Results: Post-implantation analyses demonstrated enhanced nerve regeneration, as evidenced by molecular data from gene set enrichment analysis and real-time PCR. A bioinformatics analysis including causal network analysis, upstream regulators prediction, and protein-protein interaction network analysis identified several candidate secreted proteins, including neurotrophic and pro-regenerative factors, which were mapped to key signaling pathways implicated in nerve repair. The results of the co-culture experiments with DRG neurons provided direct evidence of the paracrine effects of SKP-SCs, which enhanced neuronal survival and outgrowth. Bioinformatics analysis on RNA sequencing of DRG neurons further highlighted the molecular pathways that were modulated by the secreted factors of SKP-SCs.

Conclusion: This integrated approach demonstrates the potential of combining biomaterial scaffolds, cellular therapy, and omics technologies for developing effective strategies to repair peripheral nerve injuries. The findings provide a robust preclinical foundation for advancing TENG-based therapies toward clinical application.

Introduction: Cholesterol homeostasis has been identified as an essential downstream pathway of mutations in TP53. Esophageal cancer is one of the most prevalent malignancies exhibiting the mutation.

Objectives: To explore the significance of cholesterol metabolism-related characteristics in tumor phenotype and treatment outcomes of esophageal cancer.

Methods: We established a cholesterol metabolism-related gene set (CMGs) and performed Lasso-Cox analysis to identify prognostic signatures. Nomogram-based risk scores and clinical stages afterwards were constructed and evaluated. We simultaneously identified two metabolic subtypes based on the distinct features of the CMGs. We annotated the functional and pathway characteristics of differentially expressed genes between the clusters and compared the differences in clinical and immune characteristics. Finally, we assessed the prognostic value of signatures in the GSE53625 and two clinical cohorts using whole-exon sequencing and multiplex immunofluorescence.

Results: Our study identified five cholesterol prognosis-related genes (CRGs) that demonstrated superior prognostic efficacy in the training set compared to clinical staging, validated in independent public databases and two clinical cohorts. According to the different expression patterns of the signatures, patients were divided into two subtypes. The C1 group demonstrated poorer overall survival, response to immunotherapy, and downregulation of the p53 pathway. In the immune correlation analysis, we found that the risk score based on 5-signature model was significantly positively correlated with the abundance of suppressive immune cells and the immune checkpoints. Finally, we explored the impact of expression and genomic polymorphism of the signatures on the prognosis at the pan-cancer level.

Conclusions: Our findings underscore the distinct expression patterns of CRGs in esophageal cancer. These signatures are efficient to serve as prognostic indicators and assess the effectiveness of immunotherapy. They may also represent promising targets in other TP53 mutant malignancies.