Frontiers in Molecular Biosciences最新文献

Introduction: Acute myocardial infarction is a leading cause of global morbidity and mortality. Galectin-3, a β-galactoside-binding lectin, has been implicated as a key mediator in the pathophysiology following AMI. This review aims to synthesize the evidence on the multifaceted role of galectin-3, spanning from molecular mechanisms to clinical applications.

Methods: A comprehensive literature review was conducted to synthesize current evidence on the molecular functions, biomarker utility, and therapeutic targeting of galectin-3 in AMI. The analysis focused on studies investigating its signaling pathways, clinical correlations, and preclinical interventional models.

Results: Our synthesis demonstrates that galectin-3 acts as a damage-associated molecular pattern that drives critical post-AMI pathologies. Mechanistically, it amplifies inflammation via NF-κB activation and macrophage polarization, promotes fibrosis through synergy with the TGF-β/Smad pathway and fibroblast activation, and regulates cardiomyocyte apoptosis and oxidative/endoplasmic reticulum stress. Clinically, its dynamic expression correlates with infarct size, adverse ventricular remodeling, and poor outcomes. As a biomarker, elevated circulating galectin-3 predicts major adverse cardiovascular events, heart failure, and mortality, improving risk stratification in multi-marker panels. Serial measurements indicate treatment response, with declining levels post-PCI or statin therapy associated with improved prognosis. Therapeutically, both genetic ablation and pharmacological inhibition of galectin-3 attenuate inflammation, fibrosis, and cardiac dysfunction in preclinical models.

Discussion: Galectin-3 occupies a critical position at the intersection of AMI pathogenesis, diagnosis, and therapy. Targeting the galectin-3 pathway represents a promising therapeutic strategy to improve post-AMI outcomes, although its clinical translation requires further investigation. This review underscores the potential of integrating galectin-3 assessment and inhibition into future AMI management strategies.

SR proteins are RNA-binding proteins with one or two RNA recognition motif (RRM)-type RNA-binding domains and a C-terminal region rich in arginine-serine dipeptides. They function in cellular processes ranging from transcription to translation. The best-known SR protein, SRSF1, modulates RNA splicing by stabilizing the binding of constitutive splicing factors, but there is also evidence that it participates in constitutive splicing reactions and is present in spliceosomal complexes. It has been shown recently that it interacts with DDX23, an RNA helicase that triggers the transition from complex pre-B to complex B during activation of the spliceosome. To identify in which other steps of spliceosome assembly and reaction it might be present, we have used split-APEX with SRSF1 and a number of helicases, each of the latter being involved in a particular step. Peroxidase activity should only be reconstituted if SRSF1 and the helicase were in contact, and the consequent biotinylation should reveal proteins in the vicinity. Our results show that all the helicases tested can complement SRSF1, but that the proximal proteins are very similar in all cases. Moreover, the proteins identified fall into two major classes: splicing-related proteins and ribosomal proteins. The results raise the possibility that SRSF1 and the canonical helicases have hitherto unsuspected collaborative roles in ribosomal assembly or translation.

This review explores the critical function of the evolutionarily conserved ChaC-like enzyme family as central regulators of intracellular glutathione (GSH) homeostasis, focusing on the mammalian isoforms CHAC1 and CHAC2. We detail how these γ-glutamylcyclotransferases degrade GSH, thereby modulating cellular redox balance and integrating diverse stress signaling pathways. CHAC1 emerges as a key stress-responsive effector, transcriptionally upregulated via the ATF4-CHOP axis during endoplasmic reticulum stress and amino acid deprivation. Its role is especially crucial in the induction of ferroptosis, an iron-dependent cell death pathway, positioning it as a context-dependent modulator of cancer progression, neurotoxicity and neurodegeneration. Furthermore, we examine the opposing roles of CHAC1 and CHAC2 in stem cell fate decisions via NOTCH1 signaling and development. The complex duality of CHAC1 in oncology, acting as both a tumor suppressor by promoting ferroptosis and a potential oncogene in TP53-mutant backgrounds, alongside its functions in neuroprotection and immunity, underscores its therapeutic potential.

Background: Osteosarcoma remains lethal for many patients with metastatic or relapsed disease. Post-translational modifications (PTMs) regulate protein signaling and may shape the tumor microenvironment and clinical behavior in osteosarcoma, but PTM-anchored transcriptomic programs are as yet not well defined.

Methods: We integrated single-cell RNA sequencing from GSE162454 with curated PTM and immune gene sets to build a PTM-related framework for osteosarcoma. Tumor cell differentially expressed genes were intersected with PTM and immune repertoires to derive candidates. A PTM-related prognostic score was trained in TARGET-OS and validated in GSE21257 and GSE16091. Immune infiltration and microenvironment features were profiled using ssGSEA, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression (ESTIMATE) data, and Tumor Immune Dysfunction and Exclusion (TIDE) scores. Model interpretation used SHapley Additive exPlanations (SHAP) and single-cell localization. GRN was prioritized for exploration of immune correlations and in vitro loss-of-function assays in U2OS and HOS cells.

Results: The three-way intersection yielded 298 genes. The PTM-related score stratified overall survival in training and validation cohorts and remained independent of clinical covariates. High scores aligned with an immunosuppressed, stroma-rich microenvironment, with lower ImmuneScores and ESTIMATE scores, enrichment of myeloid and regulatory lineages, higher dysfunction and exclusion by TIDE, and reduced cytolytic, interferon, and antigen-presentation programs. SHAP highlighted a compact driver set enriched in malignant and stromal compartments. GRN showed strong contribution and consistent single-cell localization. Elevated GRN correlated with plasmacytoid dendritic cells, myeloid-derived suppressor cells (MDSCs), macrophages, regulatory T cells (Tregs), and multiple inhibitory checkpoints and with diminished immune effector functions. GRN silencing reduced proliferation, clonogenicity, migration, and invasion in osteosarcoma cells.

Conclusion: A PTM-anchored transcriptomic signature captures prognostic heterogeneity in osteosarcoma and links adverse outcome to an immunosuppressed microenvironment. GRN emerges as a tumor- and stroma-intrinsic mediator of immune suppression and malignant traits and represents a biologically grounded target for future mechanistic and therapeutic studies.

Background: MicroRNA-155 (miR-155) is a key regulator of macrophage function, and its abnormal expression is closely associated with the pathogenesis of tuberculosis (TB)-a disease where impaired macrophage autophagy weakens anti-mycobacterial immunity. Exosomes are promising nucleic acid carriers due to their biocompatibility and cell-targeting ability. Here, we constructed exosome-based miR-155 delivery systems (Exo-miR155-ago/Exo-miR155-antago; "ago" = agomir, a miR-155 agonist that enhances its expression; "antago" = antagomir, a miR-155 antagonist that inhibits its expression) to modulate macrophage autophagy and remold anti-TB immune responses.

Methods: Exosomes were isolated from the supernatant of bone marrow mesenchymal stem cells using differential centrifugation. The miR155-5p agomir and antagomir were transfected into exosomes via the Exosome Transfection Kit, followed by co-incubation with macrophages. Transcriptomics and proteomics were employed to screen for differentially expressed genes and proteins. Western blot was employed to detect autophagy-related proteins and phosphorylated proteins in signaling pathways (p- denotes phosphorylation, a key post-translational modification regulating protein activity). Techniques including transmission electron microscopy (TEM), Monodansylcadaverine (MDC) staining, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) were applied to detect the autophagic level of macrophages.

Results: Transcriptome sequencing identified 704 differentially expressed genes, with significant enrichment in TNF and NF-κB pathways, differential expression of NF-κB target genes (e.g., autophagy core gene Beclin1), and expression changes in key genes of the energy metabolism-related AMPK/mTOR pathway; proteomic analysis found 164 differentially expressed proteins, including key molecules of the "Pathogen Recognition-TLR4-NF-κB-Autophagy-Related Gene Transcription" pathway (TLR4, p-p65) and core proteins of the AMPK/mTOR pathway (p-AMPK, p-mTOR); functional verification showed the Exo-miR155-ago group had more autophagosomes (TEM), higher autophagic vacuole accumulation (MDC staining), upregulated mRNA/protein of autophagy-related molecules (LC3B, Beclin1), downregulated mRNA/protein of p62 (RT-qPCR/Western blot), activated p-p65 (NF-κB pathway), and increased p-AMPK with decreased p-mTOR (AMPK/mTOR pathway), and all results confirmed Exo-miR155-ago promotes macrophage autophagy via the synergistic effect of the two pathways.

Conclusion: This study provides multi-omics evidence for autophagy modulation mediated by the exosomal nucleic acid delivery system, verifies that this system regulates macrophage autophagy by controlling the TLR4-NF-κB pathway and AMPK/mTOR pathway, and clarifies the application potential of this system in tuberculosis (TB) and other macrophage-associated.

Telomeres are nucleoprotein complexes at chromosome ends, composed of tandemly repeated specific DNA sequences along with associated proteins. In somatic cells, telomeres progressively shorten with each cell division, making telomere length a key biomarker of cellular aging. Moreover, alterations in telomeric attrition are characteristic of numerous lifestyle factors, age-related diseases, and cancers, establishing telomere length as both a pivotal biomarker and a central focus in contemporary biomedical research. Strong interest in this area drives the continuous development of new methods for telomere length measurement and improvements to existing ones. Currently, over two dozen such methods have been developed, making the ability to select the most appropriate one essential for addressing specific research objectives. This review provides a state-of-the-art survey of all existing methods, highlighting their advantages, limitations, and applications. Special attention is focused on the rapidly evolving field of adapting long-read sequencing technologies to enhance the efficiency of telomere length measurement, along with novel insights into the structure and diversity of telomeric sequences uncovered by this approach.

The 'one-size-fits-all' therapeutic model is inadequate to address individual patient variability, creating an urgent need for an integrative framework for precision medicine. The 'Three Causes Tailored Treatment' (TCTT) principle from traditional Chinese medicine offers a time-tested, holistic blueprint that simultaneously considers the individual, temporal, and environmental dimensions of health. Here, we argue that the synergy of artificial intelligence (AI) and multi-omics technologies is the key to transforming this ancient wisdom into a modern, quantitative clinical paradigm. We demonstrate how multi-omics data provides the foundational layers to quantify the TCTT principle-for instance, using integrated omics (e.g., genomics, proteomics, microbiome) to establish the individual's molecular baseline ("Who"); chronomics to capture temporal fluxes ("When"); and the exposome to decipher the internalized environmental imprint ("Where")-while AI-powered multimodal integration models their complex interactions. By synthesizing evidence across the disease continuum, this review provides a translational roadmap for building dynamic clinical decision-support systems, thereby charting a course toward truly personalized, time-sensitive, and context-aware healthcare.

Background: Chaihu-Longgu-Muli decoction (CLMD) is a traditional Chinese medicine formula that shows promise in alleviating symptoms related to premenstrual syndrome (PMS). However, the underlying mechanism remains unclear. This study uses a machine learning-assisted framework integrated with network pharmacology and experimental validation to elucidate the key targets and signaling pathways involved in the therapeutic effects of CLMD on PMS.

Methods: We developed an integrative research framework that incorporates network pharmacology, machine learning, molecular dynamics, and in vitro validation. First, we built an overlap network by intersecting disease-related gene sets with data from the TCMSP, BATMAN-TCM, and other relevant databases. We subsequently performed GO and KEGG enrichment analyses. Second, we generated a protein‒protein interaction (PPI) network and screened key targets via machine learning algorithms. Third, we evaluated key active components and targets for ligand‒receptor binding stability via molecular docking and 200 ns MD simulations. Finally, we validated the proposed mechanism by assessing the ability of CLMD to modulate the inflammatory microenvironment using Raw264.7 cells as the experimental model.

Results: By constructing an intersecting network of CLMD-active ingredient-disease targets, we identified 204 representative active components and nearly 300 potential targets. Intersecting these genes with PMS-related genes yielded 46 key targets. The PPI network built in Cytoscape/STRING, together with multiple machine learning algorithms (LASSO, SVM-RFE, and random forest), was used to select key targets, including IL6, TNF, and IL1B. At the molecular level, the key active components (quercetin, kaempferol, and wogonin) showed strong docking affinities to these targets (binding energies <-5.0 kcal/mol) and exhibited stable MD conformations. CLMD intervention significantly downregulated IL6, TNF, and IL1B, reduced reactive oxygen species (ROS) accumulation, and promoted macrophage polarization from the proinflammatory M1 phenotype to the reparative M2 phenotype. Consequently, the experimental findings corroborate the network pharmacology predictions.

Conclusion: CLMD exerts its therapeutic effects through multicomponent-multitarget-multipathway synergy that modulates the inflammatory microenvironment, which provides mechanistic insight into relieving the multidimensional symptoms of PMS.

Background: Lynch syndrome is an inherited cancer predisposition caused by pathogenic variants in mismatch repair (MMR) genes. Large genomic rearrangements (LGRs) in MLH1 are often underestimated due to detection challenges. Functional analyses of specific variants such as MLH1 exon 13 deletion (MLH1-EX13 Del) remain scarce.

Methods: A three-generation Chinese family with Lynch syndrome was investigated. Targeted next-generation sequencing identified MLH1-EX13 Del in the proband, which was validated by qPCR in family members. Cancer patients underwent MMR immunohistochemistry (IHC) and microsatellite instability (MSI) testing. Data-independent acquisition proteomics was performed on four paired tumor and adjacent tissues, followed by Gene Ontology and KEGG enrichment analyses.

Results: Six malignant tumors were diagnosed in the family. All tested carriers harbored MLH1-EX13 Del. IHC showed loss of MLH1 and PMS2, occasionally with focal MLH1 positivity or concurrent MSH2 loss. All tumors tested were MSI-H. Proteomics revealed systemic downregulation of oxidative phosphorylation across mitochondrial respiratory complexes, whereas ribosome biogenesis proteins were upregulated, indicating enhanced protein synthesis. Immune pathway analysis revealed activation of neutrophil-mediated immunity and upregulation of inflammatory markers (S100A8/A9, MPO, ELANE), consistent with an inflamed tumor phenotype.

Conclusion: This study provides the first proteomic evidence linking MLH1-EX13 Del to suppressed mitochondrial metabolism and immune activation. These findings highlight metabolic vulnerability and an inflammatory microenvironment as potential therapeutic targets, offering new insights into Lynch syndrome-associated colorectal cancer.

Introduction: Kinases are essential for cellular regulation and drug development. Predicting the quantitative binding affinity between small-molecule compounds and kinases remains a challenge because of data complexity.

Method: We developed DeepKinome, a 20-layer convolutional neural network-based deep learning (DL) regression model, to predict quantitative binding affinity. Given the continuous nature of binding affinity, the root mean square error (RMSE), the coefficient of determination (R²), the Pearson's correlation coefficient (PCC) between actual and predicted values, and the acceptance interval ratio (AIR) were evaluated. Trained using data from 234 kinases and 163 compounds from the L1000 database.

Results: DeepKinome outperformed five DL and four machine learning models, achieving an RMSE of 1.157, an R² of 0.535, a PCC of 0.743, and an AIR of 0.570. Explainable artificial intelligence analysis revealed key amino acid sequences that influenced the predictions aligned with known kinase phosphorylation sites.

Conclusion: DeepKinome offers a promising approach for understanding kinase inhibition and compound binding.