Malignant tumors represent a major threat to global health. Conventional anti-tumor pharmacotherapy often encounters challenges such as drug resistance, highlighting an urgent need for the development of novel therapeutic strategies. Fatty acid synthase (FASN), the key enzyme catalyzing de novo fatty acid synthesis, is subject to precise regulation at multiple levels, including transcriptional control, various post-translational modifications such as ubiquitination and phosphorylation, as well as modulation by diverse signaling pathways. Recent studies have revealed that FASN is aberrantly overexpressed in various malignant tumors and is closely associated with tumor progression and poor patient prognosis. FASN is a homodimer composed of seven functional domains that catalyzes the NADPH-dependent condensation of acetyl-CoA and malonyl-CoA to generate saturated fatty acids, primarily palmitic acid. Its stability is regulated by multiple ubiquitin ligases and deubiquitinating enzymes. Additionally, FASN is subject to upstream regulation via neural precursor cell-expressed developmentally downregulated 8 (Nedd8) modification and the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) pathway, thereby establishing a metabolic-signaling positive feedback loop. As a core executor of metabolic reprogramming, FASN promotes tumorigenesis through dual mechanisms. First, its fatty acid synthesis product, palmitate, participates in membrane phospholipid synthesis, lipid raft formation, and protein palmitoylation, thereby activating several key oncogenic signaling pathways, including PI3K/AKT/mTOR, wingless-type MMTV integration site family member (Wnt)/β‑catenin, and signal transducer and activator of transcription 3 (STAT3)/matrix metalloproteinase (MMP), leading to tumor development and progression. Second, FASN plays a pivotal role in modulating the anti-tumor functions of immune cells and remodeling the tumor immune microenvironment. Specifically, FASN enhances immune checkpoint inhibition by inducing programmed death-ligand 1 (PD-L1) palmitoylation, suppresses the activation of cytotoxic T lymphocytes and natural killer cells, and promotes the polarization of M2-type macrophages, consequently facilitating tumor immune evasion and malignant progression. Precisely due to its significant overexpression in tumor cells, its critical functional role, and its differential expression compared to normal cells, FASN has emerged as a highly promising target for anti-tumor drug development. Highly selective small-molecule inhibitors, notably represented by TVB-2640, have advanced to clinical trial stages and demonstrated favorable anti-tumor activity. Furthermore, the combination of FASN inhibitors with other chemotherapeutic agents or targeted drugs can overcome the limitations of monotherapy through synergistic effects or by resensitizing tumor cells to conventional drugs, achieving a “1+1>2” therapeutic outcome. With the advancement of modern traditional Chinese medicine (TCM), numerous active ingredients derived from TCM have been confirmed to exert anti-tumor effects by modulating FASN-related pathways. This integrated approach leverages the precision of Western medicine while simultaneously harnessing the holistic regulatory benefits of TCM to alleviate the side effects of radiotherapy and chemotherapy. Despite the promising prospects of FASN-targeted therapies, challenges remain, including tumor cell metabolic plasticity, tumor context-dependent responses, and heterogeneity. This review systematically summarizes the molecular structure, physiological functions, and mechanisms of FASN in tumorigenesis, as well as recent advances in targeted therapies. Future directions—including the precise identification of responsive patient populations using spatial transcriptomics, the development of novel combination regimens, and the active exploration of integrative strategies combining traditional Chinese and Western medicine—will facilitate the clinical translation of FASN-targeted therapies and open new avenues for improving the quality of life and prognosis of cancer patients.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

Isoliquiritigenin (ISL) is a chalcone compound isolated from licorice, known for its anti-diabetic, anti-cancer, and antioxidant properties. Our previous study has demonstrated that ISL effectively lowers blood glucose levels in type 2 diabetes mellitus (T2DM) mice and improves disturbances in glucolipid and energy metabolism induced by T2DM. This study aims to further investigate the effects of ISL on alleviating abnormal endoplasmic reticulum stress (ERS) caused by T2DM and to elucidate its molecular mechanisms. In vivo experiments were conducted using 8-week-old SPF male C57BL/6J mice. The T2DM animal model was established by high-fat and high-sugar diet combined with intraperitoneal injections of streptozotocin (STZ), in compliance with the ethical guidelines set by the Animal Welfare Committee of Beijing University of Chinese Medicine (approval number: BUCM-2022021503-1134). In vitro experiments employed human liver cancer HepG2 cells, which were induced with tunicamycin (TM) to establish the ERS cell model. Transcriptomic sequencing was used to analyze changes in gene expression in the liver samples of T2DM mice following ISL treatment. Real-time quantitative polymerase chain reaction (RT-qPCR) was employed to assess the regulatory effects of ISL on key ERS genes. Enzyme-linked immunosorbent assay (ELISA), Western blot (WB), and immunofluorescence techniques were used to evaluate ISL's effects on ERS-related proteins. Results indicate that ISL significantly downregulates the expression of ERS-related genes, reduces the level of glucose-regulated protein 78 (GRP78), and inhibits the phosphorylation of protein kinase RNA-like endoplasmic reticulum kinase (PERK), thereby alleviating abnormal ERS induced by T2DM. Additionally, ISL increases the protein levels of insulin receptor substrate (IRS) 1 and IRS2 and enhances the phosphorylation of protein kinase B (Akt), thereby improving insulin sensitivity. In conclusion, ISL is able to alleviate T2DM associated symptoms by improving abnormal ERS and enhancing insulin sensitivity.

OBJECTIVE: To assess the cardioprotective effect and impact of Qishen Granules (QSG) on different ischemic areas of the myocardium in heart failure (HF) rats by evaluating its metabolic pattern, substrate utilization, and mechanistic modulation. METHODS: In vivo, echocardiography and histology were used to assess rat cardiac function; positron emission tomography was performed to assess the abundance of glucose metabolism in the ischemic border and remote areas of the heart; fatty acid metabolism and ATP production levels were assessed by hematologic and biochemical analyses. The above experiments evaluated the cardioprotective effect of QSG on left anterior descending ligation-induced HF in rats and the mode of energy metabolism modulation. In vitro, a hypoxia-induced H9C2 model was established, mitochondrial damage was evaluated by flow cytometry, and nuclear translocation of hypoxia-inducible factor-1 α (HIF-1 α) was observed by immunofluorescence to assess the mechanism of energy metabolism regulation by QSG in hypoxic and normoxia conditions. RESULTS: QSG regulated the pattern of glucose and fatty acid metabolism in the border and remote areas of the heart via the HIF-1 α pathway, and improved cardiac function in HF rats. Specifically, QSG promoted HIF-1 α expression and entry into the nucleus at high levels of hypoxia (P<0.05), thereby promoting increased compensatory glucose metabolism; while reducing nuclear accumulation of HIF-1 α at relatively low levels of hypoxia (P<0.05), promoting the increased lipid metabolism. CONCLUSIONS QSG regulates the protein stability of HIF-1 α, thereby coordinating energy supply balance between the ischemic border and remote areas of the myocardium. This alleviates the energy metabolism disorder caused by ischemic injury.
Animals ; Myocardial Infarction/physiopathology* ; Male ; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism* ; Rats, Sprague-Dawley ; Glucose/metabolism* ; Drugs, Chinese Herbal/therapeutic use* ; Energy Metabolism/drug effects* ; Rats ; Fatty Acids/metabolism* ; Myocardium/pathology*

OBJECTIVE: To evaluate the clinical effect and safety of acupuncture manipulation on treatment of intractable facial paralysis (IFP), and verify the practicality and precision of the Anzhong Facial Paralysis Precision Scale (Eyelid Closure Grading Scale, AFPPS-ECGS). METHODS: A multicentre, single-blind, randomized controlled trial was conducted from October 2022 to June 2024. Eighty-nine IFP participants were randomly assigned to an ordinary acupuncture group (OAG, 45 cases) and a characteristic acupuncture group (CAG, 44 cases) using a random number table method. The main acupoints selected included Yangbai (GB 14), Quanliao (SI 18), Yingxiang (LI 20), Shuigou (GV 26), Dicang (ST 4), Chengjiang (CV 24), Taiyang (EX-HN 5), Jiache (ST 6), Fengchi (GB 20), and Hegu (LI 4). The OAG patients received ordinary acupuncture manipulation, while the CAG received characteristic acupuncture manipulation. Both groups received acupuncture treatment 3 times a week, with 10 times per course, lasting for 10 weeks. Facial recovery was assessed at baseline and after the 1st, 2nd and 3rd treatment course by AFPPS-ECGS and the House-Brackmann (H-B) Grading Scale. Infrared thermography technology was used to observe the temperature difference between healthy and affected sides in various facial regions. Adverse events and laboratory test abnormalities were recorded. The correlation between the scores of the two scales was analyzed using Pearson correlation coefficient. RESULTS: After the 2nd treatment course, the two groups showed statistically significant differences in AFPPS-ECGS scores (P<0.05), with even greater significance after the 3rd course (P<0.01). Similarly, H-B Grading Scale scores demonstrated significant differences between groups following the 3rd treatment course (P<0.05). Regarding temperature measurements, significant differences in temperatures of frontal and ocular areas were observed after the 2nd course (P<0.05), becoming more pronounced after the 3rd course (P<0.01). Additionally, mouth corner temperature differences reached statistical significance by the 3rd course (P<0.05). No safety-related incidents were observed during the study. Correlation analysis revealed that the AFPPS-ECGS and the H-B Grading Scale were strongly correlated (r=0.86, 0.91, 0.93, and 0.91 at baseline, and after 1st, 2nd, and 3rd treatment course, respectively, all P<0.01). CONCLUSIONS Acupuncture is an effective treatment for IFP, and the characteristic acupuncture manipulation enhances the therapeutic effect. The use of the AFPPS-ECGS can more accurately reflect the recovery status of patients with IFP. (Trial registration No. ChiCTR2200065442).
Humans ; Acupuncture Therapy/methods* ; Facial Paralysis/therapy* ; Female ; Male ; Middle Aged ; Adult ; Treatment Outcome ; Acupuncture Points ; Aged

Ovarian tumor (OT) is the most lethal form of gynecologic malignancy, with minimal improvements in patient outcomes over the past several decades. Metastasis is the leading cause of ovarian cancer-related deaths, yet the underlying mechanisms remain poorly understood. Psychological stress is known to activate the glucocorticoid receptor (NR3C1), a factor associated with poor prognosis in OT patients. However, the precise mechanisms linking NR3C1 signaling and metastasis have yet to be fully elucidated. In this study, we demonstrate that chronic restraint stress accelerates epithelial-mesenchymal transition (EMT) and metastasis in OT through an NR3C1-dependent mechanism involving nuclear protein 1 (NUPR1). Mechanistically, NR3C1 directly regulates the transcription of NUPR1, which in turn increases the expression of snail family transcriptional repressor 2 (SNAI2), a key driver of EMT. Clinically, elevated NR3C1 positively correlates with NUPR1 expression in OT patients, and both are positively associated with poorer prognosis. Overall, our study identified the NR3C1/NUPR1 axis as a critical regulatory pathway in psychological stress-induced OT metastasis, suggesting a potential therapeutic target for intervention in OT metastasis.

Hemorrhagic shock is a common clinical emergency that can aggravate cell injury after resuscitation. Astrocytes are crucial for the survival of neurons because they regulate the surrounding ionic microenvironment of neurons. Although hemorrhagic shock and resuscitation (HSR) injury can impair cognition, it remains unclear how this insult directly affects astrocytes. In this study, we established an HSR model by bleeding and re-transfusion in mice. The social interaction test and new object recognition test were applied to evaluate post-operative cognitive changes, and the results suggest that mice experience cognitive impairment following exposure to HSR. In the HSR group, the power spectral density of β and γ oscillations decreased, and the coupling of the θ oscillation phase and γ oscillation amplitude was abnormal, which indicated abnormal neuronal oscillation and cognitive impairment after HSR exposure. In brief, cognitive impairment in mice is strongly correlated with Ca²⁺ signal strength in lateral septum astrocytes following HSR.
Animals ; Astrocytes/metabolism* ; Shock, Hemorrhagic/metabolism* ; Resuscitation/adverse effects* ; Male ; Mice ; Calcium Signaling/physiology* ; Mice, Inbred C57BL ; Septal Nuclei/metabolism* ; Cognitive Dysfunction/etiology* ; Disease Models, Animal ; Cognition Disorders/etiology*

Hepatocellular carcinoma (HCC) expresses abundant glycolytic enzymes and displays comprehensive glucose metabolism reprogramming. Aldolase A (ALDOA) plays a prominent role in glycolysis; however, little is known about its role in HCC development. In the present study, we aim to explore how ALDOA is involved in HCC proliferation. HCC proliferation was markedly suppressed both in vitro and in vivo following ALDOA knockout, which is consistent with ALDOA overexpression encouraging HCC proliferation. Mechanistically, ALDOA knockout partially limits the glycolytic flux in HCC cells. Meanwhile, ALDOA translocated to nuclei and directly interacted with c-Jun to facilitate its Thr93 phosphorylation by P21-activated protein kinase; ALDOA knockout markedly diminished c-Jun Thr93 phosphorylation and then dampened c-Jun transcription function. A crucial site Y364 mutation in ALDOA disrupted its interaction with c-Jun, and Y364S ALDOA expression failed to rescue cell proliferation in ALDOA deletion cells. In HCC patients, the expression level of ALDOA was correlated with the phosphorylation level of c-Jun (Thr93) and poor prognosis. Remarkably, hepatic ALDOA was significantly upregulated in the promotion and progression stages of diethylnitrosamine-induced HCC models, and the knockdown of A ldoa strikingly decreased HCC development in vivo. Our study demonstrated that ALDOA is a vital driver for HCC development by activating c-Jun-mediated oncogene transcription, opening additional avenues for anti-cancer therapies.