RNA modifications constitute a crucial class of post-transcriptional chemical alterations that profoundly influence RNA stability and translational efficiency, thereby shaping cellular protein expression profiles. These diverse chemical marks are ubiquitously involved in key biological processes, including cell proliferation, differentiation, apoptosis, and metastatic potential, and they exert precise regulatory control over these functions. A major advance in the field is the recognition that RNA modifications do not act in isolation. Instead, they participate in complex, dynamic interactions—through synergistic enhancement, antagonism, competitive binding, and functional crosstalk—forming what is now termed the “RNA modification interactome” or “RNA modification interaction network.” The formation and functional operation of this interactome rely on a multilayered regulatory framework orchestrated by RNA-modifying enzymes—commonly referred to as “writers,” “erasers,” and “readers.” These enzymes exhibit hierarchical organization within signaling cascades, often functioning in upstream-downstream sequences and converging at critical regulatory nodes. Their integration is further mediated through shared regulatory elements or the assembly into multi-enzyme complexes. This intricate enzymatic network directly governs and shapes the interdependent relationships among various RNA modifications. This review systematically elucidates the molecular mechanisms underlying both direct and indirect interactions between RNA modifications. Building upon this foundation, we introduce novel quantitative assessment frameworks and predictive disease models designed to leverage these interaction patterns. Importantly, studies across multiple disease contexts have identified core downstream signaling axes driven by specific constellations of interacting RNA modifications. These findings not only deepen our understanding of how RNA modification crosstalk contributes to disease initiation and progression, but also highlight its translational potential. This potential is exemplified by the discovery of diagnostic biomarkers based on interaction signatures and the development of therapeutic strategies targeting pathogenic modification networks. Together, these insights provide a conceptual framework for understanding the dynamic and multidimensional regulatory roles of RNA modifications in cellular systems. In conclusion, the emerging concept of RNA modification crosstalk reveals the extraordinary complexity of post-transcriptional regulation and opens new research avenues. It offers critical insights into the central question of how RNA-modifying enzymes achieve substrate specificity—determining which nucleotides within specific RNA transcripts are selectively modified during defined developmental or pathological stages. Decoding these specificity determinants, shaped in large part by the modification interactome, is essential for fully understanding the biological and pathological significance of the epitranscriptome.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

A combination of the "disease-syndrome-symptom" approach was used to study the syndrome characterization and features of dampness-heat obstruction syndrome in papain-induced knee osteoarthritis(KOA) model rats during the disease process. Forty-eight male SD rats were randomly divided into sham and model groups. The KOA model was established by injecting a mixture of papain and L-cysteine into the joint cavity on days 1, 3, and 5. During the 8 weeks following model establishment, the rats were assessed weekly for the plantar mechanical pain threshold, knee joint diameter, local skin temperature of the knee joint, weight-bearing difference between the two hind feet, and the modified Lequesne MG score of the knee joint. Samples were collected at 1, 2, 4, 6, and 8 weeks after model establishment to observe the gross lesions in cartilage and synovium. Histopathological changes in joint tissues were examined using hematoxylin-eosin, Masson's trichrome, and Senna red O-solid green staining. ELISA and immunohistochemical analysis were performed to detect the levels of interleukin(IL)-1β, IL-6, tumor necrosis factor(TNF)-α, prostaglandin E2(PGE2), and the expression of aquaporins(AQP) 1 and 3 in serum and synovium. The results showed that the ink score of articular cartilage in the model group significantly increased from 4 to 8 weeks, the cartilage Mankin's score and the percentage of Masson-positive area in cartilage increased significantly from 1 to 8 weeks. The percentage of red-stained area for cartilage proteoglycans decreased significantly from 1 to 8 weeks. The synovitis score from 1 to 6 weeks and the percentage of blue-stained collagen fibers in the synovium from 1 to 8 weeks increased significantly, with statistically significant differences compared to the sham group. The mechanical pain threshold in the model group significantly decreased from 1 to 8 weeks, the knee joint diameter significantly increased from 1 to 6 weeks, and the local skin temperature of the knee joint, the weight-bearing difference between the two hind feet, and the modified Lequesne MG score from 1 to 5 weeks significantly increased, all with statistically significant differences compared to the sham group. The levels of IL-1β, IL-6, TNF-α, and PGE2 in serum and synovium of the model group significantly increased from 1 to 6 weeks. Serum TNF-α and PGE2, and synovial IL-1β, also significantly increased at 8 weeks. The levels of cartilage AQP1 and AQP3 significantly increased from 1 to 4 weeks, while synovial AQP1 and AQP3 increased significantly from 1 to 6 weeks, with all differences statistically significant compared to the sham group. In conclusion, papain-induced KOA rats exhibited pathological changes, including articular cartilage degeneration and synovial inflammation, within 1 week of induction. The KOA rats showed characteristics of dampness-heat obstruction syndrome, such as joint pain, swelling, elevated skin temperature, and decreased function, as well as increased inflammatory factors and AQP1、AQP3 in serum and joint tissues within 5 to 6 weeks of disease onset. These results provide an experimental model for studying the syndromes of KOA with dampness-heat obstruction syndrome.
Animals ; Male ; Rats, Sprague-Dawley ; Rats ; Osteoarthritis, Knee/physiopathology* ; Disease Models, Animal ; Humans ; Interleukin-1beta/metabolism* ; Interleukin-6/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Knee Joint/pathology*

The aim of this study was to investigate the improvement effect of Wenpi Pills(WPP) on non-alcoholic fatty liver disease(NAFLD). The experiment was divided into two parts, using C57BL/6 mouse models induced by a high-fat diet(HFD) and a methionine and choline deficiency diet(MCD). The HFD-induced experiment lasted for 16 weeks, while the MCD-induced experiment lasted for 6 weeks. Mice in both parts were divided into four groups: control group, model group, low-dose WPP group(3.875 g·kg~(-1), WPP＿L), and high-dose WPP group(15.5 g·kg~(-1), WPP＿H). After sample collection from the HFD-induced mice, lipid content in the serum and liver, liver function indexes in the serum, and hepatic pathology were examined. Real-time fluorescent quantitative reverse transcription PCR(qRT-PCR) was used to detect the expression of lipid-related genes. After sample collection from the MCD-induced mice, serum liver function indexes and inflammatory factors were measured, and hepatic pathology and lipid changes were analyzed by hematoxylin-eosin(HE) staining and widely targeted lipidomic profiling, respectively. The results from the HFD-induced experiment showed that, compared with the HFD group, WPP administration significantly reduced the levels of aspartate aminotransferase(AST), alanine aminotransferase(ALT), triglyceride(TG), and total cholesterol(TC) in the serum, with the WPP＿H group showing the most significant improvement. HE staining results indicated that, compared with the HFD group, WPP treatment improved the morphology of white adipocytes, reducing their size, and alleviated hepatic steatosis and lipid droplet accumulation. The qRT-PCR results suggested that WPP might increase the mRNA expression of liver cholesterol-converting genes, such as liver X receptor α(LXRα) and cytochrome P450 family 27 subfamily A member 1(CYP27A1), as well as lipid consumption genes like peroxisome proliferator-activated receptor α(PPARα) and adenosine mono-phosphate-activated protein kinase(AMPK). Meanwhile, WPP decreased the mRNA expression of lipid synthesis genes, including fatty acid synthetase(FAS), stearoyl-CoA desaturase 1(SCD1), and sterol regulatory element-binding protein 1c(SREBP-1c), thereby reducing liver lipid accumulation. The results from the MCD-induced experiment showed that, compared with the MCD group, WPP administration reduced the levels of ALT, AST, and inflammatory factors in the serum, thereby alleviating liver injury and the inflammatory response. HE staining of liver tissue indicated that WPP effectively improved hepatic steatosis. Non-targeted lipidomics analysis showed that WPP improved lipid metabolism disorders in the liver, mainly by affecting the metabolism of TG and cholesterol esters. In conclusion, WPP can improve hepatic lipid accumulation in NAFLD mice induced by both HFD and MCD. This beneficial effect is primarily achieved by alleviating liver injury and inflammation, as well as regulating lipid metabolism.
Animals ; Non-alcoholic Fatty Liver Disease/genetics* ; Lipid Metabolism/drug effects* ; Mice ; Mice, Inbred C57BL ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Diet, High-Fat/adverse effects* ; Liver/drug effects* ; Humans ; Disease Models, Animal ; Methionine

Proanthocyanidin B_2(PAC-B_2), a polyphenolic dimeric compound comprising two epicatechin molecules linked by a C-C bond, is extensively found in traditional Chinese medicines, with anti-tumor and anti-oxidant activities. Given the limited bioavailability, a thorough investigation and comprehensive understanding of PAC-B_2 metabolism in vivo are essential for elucidating therapeutic forms and mechanisms. In the present study, ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF-MS) in the negative ion mode was employed to acquire the MS/MS information of PAC-B_2 and metabolites in urine and feces samples of the rats administrated with PAC-B_2. Online energy-resolved MS(ER-MS) was applied as supplementary to obtain the full collision energy ramp-MS~2 spectra(FCER-MS~2) of isomers-of-interest, which implied comprehensive MS~2 information of targeted compounds. Finally, the possible metabolic pathways of PAC-B_2 in rats were proposed. The primary fragmentation behaviors of PAC-B_2 in the negative ion mode included quinone methide fission between C_4-C_8 bond, retro Diels-Alder cracking of F-ring, heterocyclic ring fission of C-ring, and neutral loss of small molecules such as H_2O. A total of 25 metabolites were tentatively elucidated in urine and feces samples of rats administrated with PAC-B_2 by fragmentation pattern and reported literature. Two groups of isomers, M3/M4/M5 and M9/M11, were confirmatively differentiated based on the relationships between optimal collision energy provided by FCER-MS~2 and bond properties, including bond length and bond dissociation energy. In addition to the ring-opening and methylation, PAC-B_2 could also be metabolized into epicatechin and low molecular weight phenolic acids, which were subsequently subjected to dehydroxylation, ring-opening, methylation, sulfation, and glucuronidation. The structural information provided by online ER-MS and FCER-MS~2 enabled the differentiation of isomers and improved the identification confidence. More importantly, the present study deeply analyzes the in vivo metabolic pathways of PAC-B_2, providing a basis for the research on the pharmacological mechanism of this compound.
Animals ; Proanthocyanidins/urine* ; Rats ; Male ; Drugs, Chinese Herbal/chemistry* ; Rats, Sprague-Dawley ; Tandem Mass Spectrometry ; Chromatography, High Pressure Liquid ; Feces/chemistry* ; Molecular Structure

OBJECTIVE: To explore the epidemiological characteristics of knee osteoarthritis (KOA) among the elderly in the community, and its correlation with bone mass loss. METHODS: A cross-sectional study was conducted on elderly community population over 50 year old from 12 community health service centers in Zhejiang province. Their gender, age, body mass index (BMI), T value and KOA diagnosis were collected using face to face questionnaire survey. Univariate regression was used to analyze the influence of age, gender, BMI and bone loss on KOA. Logistic multivariate regression model was used to analyze the independent effect of bone mass loss on KOA. RESULTS: Among 4 173 subjects in this study, 1 710 of them were had a KOA. The prevalence rate was 40.9%. The mean age, the proportion of females and the mean BMI in KOA patients were (65.5±3.8) years old, 67.7%(1 158/1 710) and(24.59±1.28) kg·m^-2, respectively, which were significantly higher than (58.5±3.2) years old, 51.3%(1 263/2 463), and (23.48±1.25) kg·m^-2 in non-KOA subjects (P<0.001). In the population aged from 60 to 69 years old, the influence of osteopenia and osteoporosis on the prevalence of KOA was[OR=1.21, 95%CI(1.00, 1.46), P=0.053 2], [OR=1.42, 95%CI(1.14, 1.78), P=0.002 2]. The influence of male and female osteoporosis on the prevalence of KOA was [OR=1.52, 95%CI(1.16, 1.99), P=0.002 7] and [OR=1.87, 95%CI(1.51, 2.32), P<0.000 1], respectively. In the population of 24 kg·m^-2≤BMI<28 kg·m^-2, the influence of osteopenia and osteoporosis on the prevalence of KOA was [OR=1.47, 95%CI(1.21, 1.80), P=0.000 1], [OR=2.69, 95%CI(2.11, 3.42), P<0.000 1], respectively. After controlling the confounding factors of age, gender and BMI, compared with people with normal bone mass, the effect of osteopenia on the prevalence of KOA was [OR=1.34, 95%CI(1.08, 1.67), P=0.009 2], and the effect of osteoporosis on the prevalence of KOA was [OR=1.38, 95%CI(1.06, 1.79), P=0.017 9]. CONCLUSION Elderly overweight women are more likely to develop KOA. Bone mass loss is an independent risk factor for KOA, which will significantly increase the prevalence of KOA in people overweight or aged 60 to 69 years old.
Humans ; Female ; Male ; Aged ; Osteoarthritis, Knee/etiology* ; Middle Aged ; Cross-Sectional Studies ; Bone Density ; Aged, 80 and over ; Incidence ; Body Mass Index ; China/epidemiology* ; Osteoporosis/epidemiology*

OBJECTIVE: To explore the construction method of a resistant multiple myeloma (MM) patient-derived xenotransplantation (PDX) model. METHODS: 1.0×10⁷ MM patient-derived mononuclear cells (MNCs), 2.0×10⁶ MM.1S cells and 2.0×10⁶ NCI-H929 cells were respectively subcutaneously inoculated into NOD.CB17-Prkdc^scid Il2rg^tm1/Bcgen (B-NDG) mice with a volume of 100 μl per mouse to establish mouse model. The morphologic, phenotypic, proliferative and genetic characteristics of PDX tumor were studied by hematoxylin-eosin staining, immunohistochemical staining (IHC), cell cycle analysis, flow cytometry and fluorescence in situ hybridization (FISH). The sensitivity of PDX tumor to bortezomib and anlotinib monotherapy or in combination was investigated through cell proliferation, apoptosis and in vitro and in vivo experiments. The effects of anlotinib therapy on tumor blood vessel and cell apoptosis were analyzed by IHC, TUNEL staining and confocal fluorescence microscope. RESULTS: MM PDX model was successfully established by subcutaneously inoculating primary MNCs. The morphologic features of tumor cells from MM PDX model were similar to those of mature plasma cells. MM PDX tumor cells positively expressed CD138 and CD38, which presented 1q21 amplification, deletion of Rb1 and IgH rearrangement, and had a lower proliferative activity than MM cell lines. in vitro, PDX, MM.1S and NCI-H929 cells were treated by bortezomib and anlotinib for 24 hours, respectively. Cell viability assay showed that the IC₅₀ value of bortezomib were 5 716.486, 1.025 and 2.775 nmol/L, and IC₅₀ value of anlotinib were 5 5107.337, 0.706 and 5.13 μmol/L, respectively. Anlotinib treatment increased the apoptosis of MM.1S cells (P < 0.01), but did not affect PDX tumor cells (P >0.05). in vivo, there was no significant difference in PDX tumor growth between bortezomib monotherapy group and control group (P >0.05), while both anlotinib monotherapy and anlotinib combined with bortezomib effectively inhibited PDX tumor growth (both P < 0.05). The vascular perfusion and vascular density of PDX tumor were decreased in anlotinib treatment group (both P < 0.01). The apoptotic cells in anlotinib treatment group were increased compared with those in control group (P < 0.05). CONCLUSION Bortezomib-resistant MM PDX model can be successfully established by subcutaneous inoculation of MNCs from MM patients in B-NDG mice. This PDX model, which retains the basic biological characteristics of MM cells, can be used to study the novel therapies.
Animals ; Bortezomib ; Humans ; Multiple Myeloma/pathology* ; Mice ; Apoptosis ; Drug Resistance, Neoplasm ; Cell Line, Tumor ; Xenograft Model Antitumor Assays ; Mice, Inbred NOD ; Disease Models, Animal ; Cell Proliferation ; Transplantation, Heterologous

OBJECTIVE: To investigate the clinical features and prognosis of central nervous system (CNS) invasion in peripheral T-cell lymphoma (PTCL) and construct a risk prediction model for CNS invasion. METHODS: Clinical data of 395 patients with PTCL diagnosed and treated in the First Affiliated Hospital of Zhengzhou University from 1st January 2013 to 31st December 2022 were analyzed retrospectively. RESULTS: The median follow-up time of 395 PTCL patients was 24(1-143) months. There were 13 patients diagnosed CNS invasion, and the incidence was 3.3%. The risk of CNS invasion varied according to pathological subtype. The incidence of CNS invasion in patients with anaplastic large cell lymphoma (ALCL) was significantly higher than in patients with angioimmunoblastic T-cell lymphoma (AITL) (P <0.05). The median overall survival was significantly shorter in patients with CNS invasion than in those without CNS involvement, with a median survival time of 2.4(0.6-127) months after diagnosis of CNS invasion. The results of univariate and multivariate analysis showed that more than 1 extranodal involvement (HR=4.486, 95%CI : 1.166-17.264, P =0.029), ALCL subtype (HR=9.022, 95%CI : 2.289-35.557, P =0.002) and ECOG PS >1 (HR=15.890, 95%CI : 4.409-57.262, P <0.001) were independent risk factors for CNS invasion in PTCL patients. Each of these risk factors was assigned a value of 1 point and a new prediction model was constructed. It could stratify the patients into three distinct groups: low-risk group (0-1 point), intermediate-risk group (2 points) and high-risk group (3 points). The 1-year cumulative incidence of CNS invasion in the high-risk group was as high as 50.0%. Further evaluation of the model showed good discrimination and accuracy, and the consistency index was 0.913 (95%CI : 0.843-0.984). CONCLUSION The new model shows a precise risk assessment for CNS invasion prediction, while its specificity and sensitivity need further data validation.
Humans ; Lymphoma, T-Cell, Peripheral/pathology* ; Prognosis ; Retrospective Studies ; Central Nervous System Neoplasms/pathology* ; Neoplasm Invasiveness ; Male ; Female ; Central Nervous System/pathology* ; Middle Aged ; Adult