Emerging evidences have indicated the role of ferroptosis in the progression of metabolic-associated fatty liver disease (MAFLD); thus, inhibiting ferroptosis is a promising strategy for the development of MAFLD therapeutics. Recent studies have demonstrated the antioxidative effect of the gut commensal bacterium Akkermansia muciniphila (A. muc); however, whether it can alleviate ferroptosis remains unclear. The current study indicates A. muc intervention efficiently reversed high-fat high-fructose diet (HFHFD)-induced lipid peroxidation and ferroptosis in the liver. These beneficial effects were mediated by activation of the hepatic AMPK/SIRT1/PGC-1α axis, as evidenced by the finding that AMPK deficiency abrogated the amelioration of lipid peroxidation in vitro and in vivo. Furthermore, the short-chain fatty acids (SCFAs) were enriched upon A. muc treatment, and acetate was identified as a key activator of hepatic AMPK signalling. Mechanistically, microbiota-derived acetate was transported to the liver and metabolized to adenosine monophosphate (AMP), which triggered AMPK activation. Furthermore, a colonization assay in germ-free mice confirmed that A. muc mediated antiferroptotic effects in the absence of other microbes. These data indicated that A. muc exerts antiferroptotic effects against MAFLD, at least partially by producing acetate, which activates the hepatic AMPK/SIRT1/PGC-1α axis to alleviate ferroptosis via the inhibition of polyunsaturated fatty acid (PUFA) synthesis.

The cardioprotective effects of histone deacetylase (HDAC) inhibitors (HDIs) are at odds with the deleterious effects of HDAC depletion. Here, we use HDAC3 as a prototype HDAC to address this contradiction. We show that adult-onset cardiac-specific depletion of HDAC3 in mice causes cardiac hypertrophy and contractile dysfunction on a high-fat diet (HFD), excluding developmental disruption as a major reason for the contradiction. Genetically abolishing HDAC3 enzymatic activity without affecting its protein level does not cause cardiac dysfunction on HFD. HDAC3 depletion causes robust downregulation of lipid oxidation/bioenergetic genes and upregulation of antioxidant/anti-apoptotic genes. In contrast, HDAC3 enzyme activity abolishment causes much milder changes in far fewer genes. The abnormal gene expression is cardiomyocyte-autonomous and can be rescued by an enzyme-dead HDAC3 mutant but not by an HDAC3 mutant (Δ33-70) that lacks interaction with the nuclear-envelope protein lamina-associated polypeptide 2β (LAP2β). Tethering LAP2β to the HDAC3 Δ33-70 mutant restored its ability to rescue gene expression. Finally, HDAC3 depletion, not loss of HDAC3 enzymatic activity, exacerbates cardiac contractile functions upon aortic constriction. These results suggest that the cardiac function of HDAC3 in adults is not attributable to its enzyme activity, which has implications for understanding the cardioprotective effects of HDIs.

Gene therapy, harnessing the power of CRISPR-Cas9 and/or DNAzyme systems, stands as a pivotal approach in cancer therapy, enabling the meticulous manipulation of genes pivotal to tumorigenesis and immunity. However, the pursuit of precise gene therapy encounters formidable hurdles. Herein, a near-infrared upconversion theranostic nanomachine is devised and tailors for CRISPR-Cas9/DNAzyme systems mediate precise gene therapy. An ingenious logic DNAzyme system consists of Chain 1 (C1)/Chain 2 (C2) and endogenous lncRNA is designed. We employ manganese modified upconversion nanoparticles for carrying ultraviolet-responsive C1-PC linker-C2 (C₂P) chain and Cas9 ribonucleoprotein (RNP), with outermost coats with hyaluronic acid. Upon reaching tumor microenvironment (TME), the released Mn²⁺ ions orchestrate a trifecta: facilitating endosomal escape, activating cGAS-STING signaling, and enabling T1-magnetic resonance imaging. Under near-infrared irradiation, Cas9 RNP/C₂P complex dissociates, releasing Cas9 RNP into the nucleus to perform gene editing of Ptpn2, while C1/C2 chains self-assemble with endogenous lncRNA to form a functional DNAzyme system, targeting PD-L1 mRNA for gene silencing. This strategy remodels the TME by activating cGAS-STING signaling and dual immune checkpoints blockade, thus realizing tumor elimination. Our theranostic nanomachine armed with the CRISPR-Cas9/DNAzyme logic systems, represents a resourceful and promising strategy for advancing cancer systemic immunotherapy and precise gene therapy.

Maternal health during pregnancy has a direct impact on the risk and severity of neurodevelopmental disorders (NDDs) in the offspring, especially in the case of drug exposure. However, little progress has been made to assess the risk of drug exposure during pregnancy due to ethical constraints and drug use factors. We collected and manually curated sub-pathways and pathways (sub-/pathways) and drug information to propose an analytical framework for predicting drug candidates. This framework linked sub-/pathway activity and drug response scores derived from gene transcription data and was applied to human fetal brain development and six NDDs. Further, specific and pleiotropic sub-/pathways/drugs were identified using entropy, and sex bias was analyzed in conjunction with logistic regression and random forest models. We identified 19 disorder-associated and 256 regionally pleiotropic and specific candidate drugs that targeted risk sub-/pathways in NDDs, showing temporal or spatial changes across fetal development. Moreover, 5443 differential drug-sub-/pathways exhibited sex-biased differences after filling in the gender labels. A user-friendly NDDP visualization website ( https://ndd-lab.shinyapps.io/NDDP ) was developed to allow researchers and clinicians to access and retrieve data easily. Our framework overcame data gaps and identified numerous pleiotropic and specific candidates across six disorders and fetal developmental trajectories. This could significantly contribute to drug discovery during pregnancy and can be applied to a wide range of traits.
Humans ; Female ; Pregnancy ; Neurodevelopmental Disorders/metabolism* ; Male ; Prenatal Exposure Delayed Effects ; Fetal Development/drug effects* ; Drug Discovery/methods* ; Brain/metabolism*

Identification of disease-specific cell subtypes (DSCSs) has profound implications for understanding disease mechanisms, preoperative diagnosis, and precision therapy. However, achieving unified annotation of DSCSs in heterogeneous single-cell datasets remains a challenge. In this study, we developed the gPRINT algorithm (generalized approach for cell subtype identification with single cell's voicePRINT). Inspired by the principles of speech recognition in noisy environments, gPRINT transforms gene position and gene expression information into voiceprints based on ordered and clustered gene expression phenomena, obtaining unique "gene print" patterns for each cell. Then, we integrated neural networks to mitigate the impact of background noise on cell identity label mapping. We demonstrated the reproducibility of gPRINT across different donors, single-cell sequencing platforms, and disease subtypes, and its utility for automatic cell subtype annotation across datasets. Moreover, gPRINT achieved higher annotation accuracy of 98.37% when externally validated based on the same tissue, surpassing other algorithms. Furthermore, this approach has been applied to fibrosis-associated diseases in multiple tissues throughout the body, as well as to the annotation of fibroblast subtypes in a single tissue, tendon, where fibrosis is prevalent. We successfully achieved automatic prediction of tendinopathy-specific cell subtypes, key targets, and related drugs. In summary, gPRINT provides an automated and unified approach for identifying DSCSs across datasets, facilitating the elucidation of specific cell subtypes under different disease states and providing a powerful tool for exploring therapeutic targets in diseases.
Humans ; Algorithms ; Single-Cell Analysis ; Databases, Genetic ; Molecular Sequence Annotation

Protozoan infections (e.g., malaria, trypanosomiasis, and toxoplasmosis) pose a considerable global burden on public health and socioeconomic problems, leading to high rates of morbidity and mortality. Due to the limited arsenal of effective drugs for these diseases, which are associated with devastating side effects and escalating drug resistance, there is an urgent need for innovative antiprotozoal drugs. The emergence of drug repurposing offers a low-cost approach to discovering new therapies for protozoan diseases. In this review, we summarize recent advances in drug repurposing for various human protozoan diseases and explore cost-effective strategies to identify viable new treatments. We highlight the cross-applicability of repurposed drugs across diverse diseases and harness common chemical motifs to provide new insights into drug design, facilitating the discovery of new antiprotozoal drugs. Challenges and opportunities in the field are discussed, delineating novel directions for ongoing and future research.

Primary ciliary dyskinesia (PCD) is a genetically heterogeneous disorder characterized by impaired motility of cilia and flagella. Mutations in cilia- and flagella-associated protein 300 ( CFAP300 ) are associated with human PCD and male infertility; however, the underlying pathogenic mechanisms remain poorly understood. In a consanguineous Chinese family, we identified a homozygous CFAP300 loss-of-function variant (c.304delC) in a proband presenting with classical PCD symptoms and severe sperm abnormalities, including dynein arm deficiency and acrosomal malformation, as confirmed by transmission electron microscopy (TEM). Histological analysis revealed multiple morphological abnormalities of the sperm flagella in CFAP300 -mutant individual, whereas immunofluorescence demonstrated markedly reduced CFAP300 expression in the spermatozoa of the proband. Furthermore, tandem mass tag (TMT)-based quantitative proteomics showed that the CFAP300 mutation reduced key spermatogenesis proteins (e.g., sperm flagellar 2 [SPEF2], solute carrier family 25 member 31 [SLC25A31], and A-kinase anchoring protein 3 [AKAP3]) and mitochondrial ATP synthesis factors (e.g., SLC25A31, cation channel sperm-associated 3 [CATSPER3]). It also triggered abnormal increases in autophagy-related proteins and signaling mediator phosphorylation. These molecular alterations are likely to contribute to progressive deterioration of sperm ultrastructure and function. Notably, successful pregnancy was achieved via intracytoplasmic sperm injection (ICSI) using the proband's sperm. Overall, this study expands the known CFAP300 mutational spectrum and offers novel mechanistic insights into its role in spermatogenesis.
Humans ; Male ; Infertility, Male/pathology* ; Acrosome/pathology* ; Sperm Tail/pathology* ; Pedigree ; Spermatozoa ; Adult ; Loss of Function Mutation ; Ciliary Motility Disorders/genetics* ; Spermatogenesis/genetics* ; Female

OBJECTIVE: To investigate the effect of irradiation dose rate of ⁶⁰Co γ-ray on hematopoietic and immune cells in total body irradiation (TBI) mice. METHODS: After TBI with 8 Gy ⁶⁰Co γ-ray at three irradiation dose rates of 0.027, 0.256 and 0.597 Gy/min, the survival and change of body weight of C57BL/6J mice were observed within 30 days. The peripheral blood parameters were examined at each time point within 30 days post-irradiation. The hematopoietic stem/progenitor cell counts of mice were examined on the 10^th and 30^th day post-irradiation by flow cytometry, as well as the proportions of immune cells in peripheral blood, bone marrow and spleen of mice on the 30^th day post-irradiation. RESULTS: After TBI with 8 Gy ⁶⁰Co γ-ray, the 30-day survival rate of high dose-rate group was 0, which was significantly lower than 90% of medium dose-rate group and 100% of low dose-rate group (both P < 0.001). The peripheral blood parameters of all three groups showed a sharp decline → low value → gradually recovering trend. The count of white blood cell, neutrophil, lymphocyte, red blood cell, platelet and hemoglobin level in the high dose-rate and medium dose-rate groups were significantly lower than those in the low dose-rate group on day 7-18 post-irradiation (all P < 0.05), but there were no significant differences between the high dose-rate and medium dose-rate groups (P >0.05). On the 10^th day after irradiation, the proportion and number of bone marrow hematopoietic stem/progenitor cells (including LK, LSK, LT-HSC, ST-HSC, and MPP cells) in the low dose-rate and medium dose-rate groups were significantly decreased compared to those in the normal group (all P < 0.05), but there were no significant differences between the two groups (P >0.05). On the 30^th day after irradiation, LSK, LT-HSC, ST-HSC and MPP cells in the low dose-rate group recovered to normal levels, while those in the medium dose-rate group were still significantly lower than those in the low dose-rate group (all P < 0.001). The results of bone marrow and peripheral immune cell tests on the 30^th day after irradiation showed that the ratios of T and B lymphocytes in the low dose-rate and medium dose-rate groups were reduced compared to that in the normal group (both P < 0.05), while the ratio of neutrophils was increased (P < 0.01). The trend of changes in the spleen and peripheral blood was consistent. CONCLUSION The degree of hematopoietic and immune cell damage in mice after TBI with 8 Gy ⁶⁰Co γ-ray is related to the dose rate, and low dose-rate irradiation can reduce the damage in the animal model. Therefore, choosing the appropriate dose rate of irradiation is a key factor in establishing an objective and reliable experimental animal model of irradiation.
Animals ; Mice ; Whole-Body Irradiation ; Gamma Rays ; Mice, Inbred C57BL ; Hematopoietic Stem Cells/radiation effects* ; Cobalt Radioisotopes ; Dose-Response Relationship, Radiation ; Male

OBJECTIVE: To investigate the association between ABO blood types and gestational diabetes mellitus (GDM) risk. METHODS: A prospective birth cohort study was conducted. ABO blood types were determined using the slide method. GDM diagnosis was based on a 75-g, 2-h oral glucose tolerance test (OGTT) according to the criteria of the International Association of Diabetes and Pregnancy Study Groups. Logistic regression was applied to calculate the odds ratios ( ORs) and 95% confidence intervals ( CIs) between ABO blood types and GDM risk. RESULTS: A total of 30,740 pregnant women with a mean age of 31.81 years were enrolled in this study. The ABO blood types distribution was: type O (30.99%), type A (26.58%), type B (32.20%), and type AB (10.23%). GDM was identified in 14.44% of participants. Using blood type O as a reference, GDM risk was not significantly higher for types A ( OR = 1.05) or B ( OR = 1.04). However, women with type AB had a 19% increased risk of GDM ( OR = 1.19, 95% CI = 1.05-1.34; P < 0.05), even after adjusting for various factors. This increased risk for type AB was consistent across subgroup and sensitivity analyses. CONCLUSION The ABO blood types may influence GDM risk, with type AB associated with a higher risk. Incorporating it-either as a single risk factor or in combination with other known factors-could help identify individuals at risk for GDM before or during early pregnancy.
Humans ; Female ; Pregnancy ; Diabetes, Gestational/etiology* ; ABO Blood-Group System ; Adult ; Prospective Studies ; Risk Factors ; Young Adult

ObjectiveTo investigate the application value of a multimodal model integrating radiomics features， deep learning features， and clinical structured data in predicting severe acute pancreatitis （SAP）， and to provide more accurate tools for the early identification of SAP in clinical practice. MethodsThe patients with acute pancreatitis （AP） who attended The First Affiliated Hospital of Soochow University， Jintan Hospital Affiliated to Jiangsu University， and Suzhou Yongding Hospital from January 1， 2017 to December 31， 2023 were included. Related data were collected， including demographic information， previous medical history， etiology， laboratory test data， and systemic inflammatory response syndrome （SIRS） within 24 hours after admission， as well as imaging data within 72 hours after admission， while related scores were calculated， including Ranson score， modified CT severity index （MCTSI）， bedside index for severity in acute pancreatitis （BISAP）， and systemic inflammatory response syndrome， albumin， blood urea nitrogen and pleural effusion （SABP） score. The model was constructed in the following process： （1） three-dimensional CT images were used to extract and identify radiomics features， and a radiomics classification model was established based on the extreme gradient Boost （XGBoost） algorithm； （2） U-Net is used to perform semantic segmentation of three-dimensional CT images， and then the results of segmentation were imported into 3D ResNet50 to construct a deep learning classification model； （3） the predicted values of the above two models were integrated with clinical structured data to establish a multimodal model based on the XGBoost algorithm. The variable importance plot and local interpretability plot were used to perform visual interpretation of the model. The independent samples t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test or Fisher’s exact test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was plotted for each model and existing scoring systems， and the area under the ROC curve （AUC） was calculated to assess their performance； the Delong test was used for comparison of AUC. ResultsA total of 609 patients who met the criteria were included， among whom 114 （18.7%） developed SAP. In this study， the data of 426 patients from The First Affiliated Hospital of Soochow University was used as the training set， and the data of 183 patients from Jintan Hospital Affiliated to Jiangsu University and Suzhou Yongding Hospital were used as the independent test set. The multimodal model had an AUC of 0.914 in the test set， which was significantly higher than the AUC of traditional scoring systems such as MCTSI （AUC=0.827）， Ranson score （AUC=0.675）， BISAP （AUC=0.791）， and SABP score （AUC=0.648）； in addition， the multimodal model showed a significant improvement in performance compared with the radiomics classification model （AUC=0.739） and the deep learning classification model （AUC=0.685） （the Delong test： Z=-3.23， -4.83， -3.48， -4.92， -4.31， and -4.59， all P <0.01）. The top 10 variables in terms of importance in the multimodal model were pleural effusion， predicted value of the deep learning model， predicted value of the radiomics model， triglycerides， calcium ions， SIRS， white blood cell count， age， platelets， and C-reactive protein， suggesting that the above variables had significant contributions to the performance of the model in predicting SAP. ConclusionBased on structured data， radiomic features， and deep learning features， this study constructs a multicenter prediction model for SAP based on the XGBoost algorithm， which has a better predictive performance than existing traditional scoring systems and unimodal models.