MicroRNA-142-3p alleviated high salt-induced cardiac fibrosis via downregulating optineurin-mediated mitophagy.

High salt can induce cardiac damage. The aim of this present study was to explore the effect and the mechanism of microRNA (miR)-142-3p on the cardiac fibrosis induced by high salt. Rats received high salt diet to induce cardiac fibrosis in vivo, and neonatal rat cardiac fibroblasts (NRCF) treated with sodium chloride (NaCl) to induce fibrosis in vitro. The fibrosis and mitochondrial autophagy levels were increased the heart and NRCF treated with NaCl, which were alleviated by miR-142-3p upregulation. The fibrosis and mitochondrial autophagy levels were elevated in NRCF after treating with miR-142-3p antagomiR. Optineurin (OPTN) expression was increased in the mitochondria of NRCF induced by NaCl, which was attenuated by miR-142-3p agomiR. OPTN downregulation inhibited the increases of fibrosis and mitochondrial autophagy levels induced by NaCl in NRCF. These results miR-142-3p could alleviate high salt-induced cardiac fibrosis via downregulation of OPTN to reduce mitophagy.

Antidepressant effects of activation of infralimbic cortex via upregulation of BDNF and ß-catenin in an estradiol withdrawal model.

RATIONALE: Clinical and preclinical studies have demonstrated that estradiol withdrawal after delivery is one of important factors involved in the pathogenesis of postpartum depression (PPD). The infralimbic cortex (IL) is related to anxiety and mood disorders. Whether IL neurons mediate PPD is still unclear. OBJECTIVES: This study was to observe the antidepressant effect and expression of BDNF and ß-catenin in IL by allopregnanolone (ALLO) treatment or the selective activation or inhibition of IL neurons using a chemogenetic approach in a pseudopregnancy model of PPD. METHODS: Administration of estradiol combined with progesterone and the abrupt withdrawal of estradiol simulated the pregnancy and early postpartum periods to induce depression in ovariectomized rats. The relative expression levels of ß-catenin and BDNF were observed by western blotting. RESULTS: Immobility time was significantly increased in the forced swim test and open-arm movement was reduced in the elevated plus maze test in the estradiol-withdrawn rats. After ALLO treatment, the immobility time were lower and open-arm traveling times higher than those of the estradiol-withdrawn rats. Meanwhile, the expression level of BDNF or ß-catenin in the IL was reduced significantly in estradiol-withdrawn rats, which was prevented by treatment with ALLO. The hM3Dq chemogenetic activation of pyramidal neurons in the IL reversed the immobility and open-arm travel time trends in the estradiol-withdrawal rat model, but chemogenetic inhibition of IL neurons failed to affect this. Upregulated BDNF and ß-catenin expression and increased c-Fos in the basolateral amygdala were found following IL neuron excitation in model rats. CONCLUSIONS: Our results demonstrated that pseudopregnancy and estradiol withdrawal produced depressive-like behavior and anxiety. ALLO treatment or specific excitement of IL pyramidal neurons relieved abnormal behaviors and upregulated BDNF and ß-catenin expression in the IL in the PPD model, suggesting that hypofunction of IL neurons may be involved in the pathogenesis of PPD.

Genetic Causal Relationship Between Alanine Aminotransferase Levels and Risk of Gestational Diabetes Mellitus: Mendelian Randomization Analysis Based on Two Samples.

Gestational diabetes mellitus (GDM) is a frequent complication of pregnancy. The specific mechanisms underlying GDM have not yet been fully elucidated. Contemporary research indicates a potential association between liver enzyme irregularities and an increased risk of metabolic disorders, including diabetes. The alanine aminotransferase (ALT) level is recognized as a sensitive marker of liver injury. An increase in ALT levels is hypothesized to be linked to the pathogenesis of insulin resistance and diabetes. Nonetheless, the definitive causal link between ALT levels and GDM still needs to be determined. This investigation utilized two-sample Mendelian randomization (MR) to examine the genetic causation between alanine aminotransferase (ALT) and GDM. We acquired alanine aminotransferase (ALT)-related GWAS summary data from the UK Biobank, Million Veteran Program, Rotterdam Study, and Lifeline Study. Gestational diabetes data were obtained from the FinnGen Consortium. We employed various MR analysis techniques, including inverse-variance weighted (IVW), MR Egger, weighted median, simple, and weighted weighting. In addition to MR-Egger intercepts, Cochrane's Q test was also used to assess heterogeneity in the MR data, and the MR-PRESSO test was used to assess horizontal pleiotropy. To assess the association's sensitivity, a leave-one-out approach was employed. The IVW results confirmed the independent risk factor for GDM development, as indicated by the ALT level (p = .011). As shown by leave-one-out analysis, horizontal pleiotrophy did not significantly skew the causative link (p > .05). Our dual-sample MR analysis provides substantiated evidence of a genetic causal relationship between alanine aminotransferase (ALT) levels and gestational diabetes.

Clinicopathological characteristics and gene mutations in 11 patients with lipoprotein glomerulopathy.

OBJECTIVE: Lipoprotein glomerulopathy (LPG) is a rare disorder characterized by the development of glomerular lipoprotein thrombosis. LPG exhibits familial aggregation, with mutations in the apolipoprotein E (APOE) gene identified as the leading cause of this disease. This study aimed to investigate APOE gene mutations and the clinicopathological features in eleven LPG patients. METHODS: Clinicopathological and follow-up data were obtained by extracting DNA, followed by APOE coding region sequencing analysis. This study analyzed clinical and pathological manifestations, gene mutations, treatment and prognosis. RESULTS: The mean age of the eleven patients was 33.82 years. Among them, five had a positive family history for LPG, ten presented with proteinuria, four exhibited nephrotic syndrome, and six presented with microscopic hematuria. Dyslipidemia was identified in ten patients. In all renal specimens, there was evident dilation of glomerular capillary lumens containing lipoprotein thrombi, and positive oil red O staining was observed in frozen sections of all samples. APOE gene testing revealed that one patient had no mutations, while the remaining ten patients exhibited mutations in the APOE gene, with three patients presenting with multiple mutations simultaneously. Following the confirmation of LPG diagnosis, treatment with angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor blocker (ARB) was initiated, and the disease progressed slowly. CONCLUSION: LPG is histologically characterized by lamellated lipoprotein thrombi in glomeruli, and kidney biopsy is essential for diagnosis. Mutations in the APOE gene are the leading cause of LPG. This study revealed clinicopathological characteristics and APOE gene mutations in patients with LPG, which helps us better understand the disease.

Dietary sodium acetate and sodium butyrate attenuate intestinal damage and improve lipid metabolism in juvenile largemouth bass (Micropterus salmoides) fed a high carbohydrate diet by reducing endoplasmic reticulum stress.

High-carbohydrate (HC) diets decrease the intestinal levels of sodium acetate (SA) and sodium butyrate (SB) and impair the gut health of largemouth bass; however, SA and SB have been shown to enhance immunity and improve intestinal health in farmed animals. Thus, the present study was to investigate the effects of dietary SA and SB on HC diet-induced intestinal injury and the potential mechanisms in juvenile largemouth bass. The experiment set five isonitrogenous and isolipidic diets, including a low-carbohydrate diet (9% starch) (LC), a high carbohydrate diet (18% starch) (HC), and the HC diet supplemented with 2 g/kg SA (HCSA), 2 g/kg SB (HCSB) or a combination of 1 g/kg SA and 1 g/kg SB (HCSASB). The feeding experiment was conducted for 8 weeks. A total of 525 juvenile largemouth bass with an initial body weight of 7.00 ± 0.20 g were used. The results showed that dietary SA and SB improved the weight gain rate and specific growth rate (P < 0.05) and ameliorated serum parameters (alkaline phosphatase, acid phosphatase, glutamate transaminase, and glutamic oxaloacetic transaminase) (P < 0.05). And, importantly, dietary SA and SB repaired the intestinal barrier by increasing the expression levels of zonula occludens-1, occludin, and claudin-7 (P < 0.05), reduced HC-induced intestinal damage, and alleviated intestinal inflammation and cell apoptosis by attenuating HC-induced intestinal endoplasmic reticulum stress (P < 0.05). Further results revealed that dietary SA and SB reduced HC-induced intestinal fat deposition by inhibiting adipogenesis and promoting lipolysis (P < 0.05). In summary, this study demonstrated that dietary SA and SB attenuated HC-induced intestinal damage and reduced excessive intestinal fat deposition in largemouth bass.

Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data.

BACKGROUND: Gliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this circumstance, genotyping is an effective means of categorising gliomas. The Ki67 proliferation index, a widely used marker of cellular proliferation in clinical contexts, has demonstrated potential for predicting tumour classification and prognosis. In particular, magnetic resonance imaging (MRI) plays a vital role in the diagnosis of brain tumours. Using MRI to extract glioma-related features and construct a machine learning model offers a viable avenue to classify and predict the level of Ki67 expression. METHODS: This study retrospectively collected MRI data and postoperative immunohistochemical results from 613 glioma patients from the First Affliated Hospital of Nanjing Medical University. Subsequently, we performed registration and skull stripping on the four MRI modalities: T1-weighted (T1), T2-weighted (T2), T1-weighted with contrast enhancement (T1CE), and Fluid Attenuated Inversion Recovery (FLAIR). Each modality's segmentation yielded three distinct tumour regions. Following segmentation, a comprehensive set of features encompassing texture, first-order, and shape attributes were extracted from these delineated regions. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) algorithm with subsequent sorting to identify the most important features. These selected features were further analysed using correlation analysis to finalise the selection for machine learning model development. Eight models: logistic regression (LR), naive bayes, decision tree, gradient boosting tree, and support vector classification (SVM), random forest (RF), XGBoost, and LightGBM were used to objectively classify Ki67 expression. RESULTS: In total, 613 patients were enroled in the study, and 24,455 radiomic features were extracted from each patient's MRI. These features were eventually reduced to 36 after LASSO screening, RF importance ranking, and correlation analysis. Among all the tested machine learning models, LR and linear SVM exhibited superior performance. LR achieved the highest area under the curve score of 0.912 ± 0.036, while linear SVM obtained the top accuracy with a score of 0.884 ± 0.031. CONCLUSION: This study introduced a novel approach for classifying Ki67 expression levels using MRI, which has been proven to be highly effective. With the LR model at its core, our method demonstrated its potential in signalling a promising avenue for future research. This innovative approach of predicting Ki67 expression based on MRI features not only enhances our understanding of cell activity but also represents a significant leap forward in brain glioma research. This underscores the potential of integrating machine learning with medical imaging to aid in the diagnosis and prognosis of complex diseases.

Machine learning combined with single-cell analysis reveals predictive capacity and immunotherapy response of T cell exhaustion-associated lncRNAs in uterine corpus endometrial carcinoma.

BACKGROUND: The exhaustion of T-cells is a primary factor contributing to immune dysfunction in cancer. Long non-coding RNAs (lncRNAs) play a significant role in the advancement, survival, and treatment of Uterine Corpus Endometrial Carcinoma (UCEC). Nevertheless, there has been no investigation into the involvement of lncRNAs associated with T-cell exhaustion (TEXLs) in UCEC. The goal of this work is to establish predictive models for TEXLs in UCEC and study their related immune features. METHODS: Using transcriptome and single-cell sequencing data from The Cancer Genome Atlas and Gene Expression Omnibus databases, we employed co-expression analysis and univariate Cox regression to identify prognostic-associated TEXLs (pTEXLs). The prognostic model was developed using the Least Absolute Contraction and Selection Operator. The immunotherapy characteristics of the prognostic model risk score were studied. Then molecular subgroups were identified through non-negative Matrix Factorization based on pTEXLs. The identification of co-expressed genes was done using a weighted correlation network analysis. Subsequently, a diagnostic model for UCEC was created. In-depth investigations, both in vitro and in vivo, were carried out to elucidate the molecular mechanism of the key gene within the diagnostic model. RESULTS: Receiver operating characteristic curve, calibration curve, and decision curve analysis proved the validity of the predictive models established according to pTEXLs. The subgroup with lower risk scores in the prognostic model has better responses to blocking immune checkpoint therapy. Single-cell analysis suggests that the expression level of MIEN1 is relatively high in immune cells among diagnostic genes. Furthermore, the targeted suppression of MIEN1 via sh-MIEN1 diminishes the proliferative, migratory, and invasive capacities of UCEC cells, potentially associated with CD8+ T cell exhaustion. CONCLUSIONS: The association between TEXLs and UCEC was methodically elucidated by our investigation. A stable pTEXLs risk prediction model and a diagnosis model for UCEC were also established.

Ceramide enhanced the hepatic glucagon response through regulation of CREB activity.

BACKGROUND & AIMS: Hyperglycemia is associated with lipid disorders in patients with diabetes. Ceramides are metabolites involved in sphingolipid metabolism that accumulate during lipid disorders and exert deleterious effects on glucose and lipid metabolism. However, the effects of ceramide on glucagon-mediated hepatic gluconeogenesis remain largely unknown. This study was designed to investigate the impact of ceramides on gluconeogenesis in the context of the hepatic glucagon response, with the aim of finding new pharmacological interventions for hyperglycemia in diabetes. METHODS: Liquid chromatography-mass spectrometry was used to quantify ceramide content in the serum of patients with diabetes. Primary hepatocytes were isolated from male C57BL/6J mice to study the effects of ceramide on hepatic glucose production. Immunofluorescence staining was performed to view cAMP-responsive element-binding protein (CREB)- regulated transcription co-activator 2 (CRTC2) nuclear translocation in hepatocytes. Serine palmitoyl-transferase, long chain base subunit 2 (Sptlc2) knockdown mice were generated using an adeno-associated virus containing shRNA, and hepatic glucose production was assessed glucagon tolerance and pyruvate tolerance tests in mice fed a normal chow diet and high-fat diet. RESULTS: Increased ceramide levels were observed in the serum of patients newly diagnosed with type 2 diabetes. De novo ceramide synthesis was activated in mice with metabolic disorders. Ceramide enhanced hepatic glucose production in primary hepatocytes. In contrast, genetic silencing of Sptlc2 prevented this process. Mechanistically, ceramides de-phosphorylate CRTC2 (Ser 171) and facilitate its translocation into the nucleus for CREB activation, thereby augmenting the hepatic glucagon response. Hepatic Sptlc2 silencing blocked ceramide generation in the liver and thus restrained the hepatic glucagon response in mice fed a normal chow diet and high-fat diet. CONCLUSIONS: These data indicate that ceramide serves as an intracellular messenger that augments hepatic glucose production by regulating CRTC2/CREB activity in the context of the hepatic glucagon response, suggesting that CRTC2 phosphorylation might be a potential node for pharmacological interventions to restrain the hyperglycemic response during fasting in diabetes.

Differential contributions of the C5b-9 and C5a/C5aR pathways to microvascular and macrovascular thrombosis in complement-mediated thrombotic microangiopathy patients.

To clarify the role of the C5a/C5aR (C5a receptor) and C5b-9 pathways in macrovascular thrombosis (MAT) and renal microthrombosis (MIT), 73 renal biopsy-proven complement-mediated thrombotic microangiopathy (C-TMA) patients were enrolled; 9 patients with pure MAT and 13 patients with pure MIT were selected for further study. Twenty-five external C-TMA patients were selected as the validation cohort. Plasma C5a and sC5b-9 (soluble C5b-9) levels were significantly higher in patients with MAT than in those with MIT (P = 0.008, P = 0.041, respectively). The mean optical density of C5aR1 in the kidney was significantly higher in MAT patients than in those with MIT (P < 0.001). Both urinary sC5b-9 levels (MIT: P < 0.001, MAT: P = 0.004) and renal deposition of C5b-9 (MIT: P < 0.001, MAT: P = 0.001) were significantly higher in C-TMA patients compared to normal control, but were similar between MAT and MIT groups. In the correlation analysis within 22C-TMA patients, urinary sC5b-9 levels and renal deposition of C5b-9 were positively correlated to renal MIT formation (P = 0.009 and P = 0.031, respectively). Furthermore, the renal citrullinated histone H3 (CitH3)- and neutrophil elastase (NE)-positive area ratios were both significantly higher in the MAT group than in the MIT group (P = 0.006 and P = 0.020, respectively). Therefore, the local C5b-9 and C5a/C5aR1 pathways might have differential contributions to MIT and MAT formation in the disease.

S100a16 Deficiency Prevents Alcohol-induced Fatty Liver Injury via Inducing MANF Expression in Mice.

Alcoholic liver disease (ALD) encompasses conditions ranging from simple steatosis to cirrhosis and even liver cancer. It has gained significant global attention in recent years. Despite this, effective pharmacological treatments for ALD remain elusive, and the core mechanisms underlying the disease are not yet fully comprehended. S100A16, a newly identified calcium-binding protein, is linked to lipid metabolism. Our research has discovered elevated levels of the S100A16 protein in both serum and liver tissue of ALD patients. A similar surge in hepatic S100A16 expression was noted in a Gao-binge alcohol feeding mouse model. S100a16 knockdown alleviated ethanol-induced liver injury, steatosis and inflammation. Conversely, S100a16 transgenic mice showed aggravating phenomenon. Mechanistically, we identify mesencephalic astrocyte-derived neurotrophic factor (MANF) as a regulated entity downstream of S100a16 deletion. MANF inhibited ER-stress signal transduction induced by alcohol stimulation. Meanwhile, MANF silencing suppressed the inhibition effect of S100a16 knockout on ethanol-induced lipid droplets accumulation in primary hepatocytes. Our data suggested that S100a16 deletion protects mice against alcoholic liver lipid accumulation and inflammation dependent on upregulating MANF and inhibiting ER stress. This offers a potential therapeutic avenue for ALD treatment.

Deep-learning and conventional radiomics to predict IDH genotyping status based on magnetic resonance imaging data in adult diffuse glioma.

Objectives: In adult diffuse glioma, preoperative detection of isocitrate dehydrogenase (IDH) status helps clinicians develop surgical strategies and evaluate patient prognosis. Here, we aim to identify an optimal machine-learning model for prediction of IDH genotyping by combining deep-learning (DL) signatures and conventional radiomics (CR) features as model predictors. Methods: In this study, a total of 486 patients with adult diffuse gliomas were retrospectively collected from our medical center (n=268) and the public database (TCGA, n=218). All included patients were randomly divided into the training and validation sets by using nested 10-fold cross-validation. A total of 6,736 CR features were extracted from four MRI modalities in each patient, namely T1WI, T1CE, T2WI, and FLAIR. The LASSO algorithm was performed for CR feature selection. In each MRI modality, we applied a CNN+LSTM-based neural network to extract DL features and integrate these features into a DL signature after the fully connected layer with sigmoid activation. Eight classic machine-learning models were analyzed and compared in terms of their prediction performance and stability in IDH genotyping by combining the LASSO-selected CR features and integrated DL signatures as model predictors. In the validation sets, the prediction performance was evaluated by using accuracy and the area under the curve (AUC) of the receiver operating characteristics, while the model stability was analyzed by using the relative standard deviation of the AUC (RSDAUC). Subgroup analyses of DL signatures and CR features were also individually conducted to explore their independent prediction values. Results: Logistic regression (LR) achieved favorable prediction performance (AUC: 0.920 ± 0.043, accuracy: 0.843 ± 0.044), whereas support vector machine with the linear kernel (l-SVM) displayed low prediction performance (AUC: 0.812 ± 0.052, accuracy: 0.821 ± 0.050). With regard to stability, LR also showed high robustness against data perturbation (RSDAUC: 4.7%). Subgroup analyses showed that DL signatures outperformed CR features (DL, AUC: 0.915 ± 0.054, accuracy: 0.835 ± 0.061, RSDAUC: 5.9%; CR, AUC: 0.830 ± 0.066, accuracy: 0.771 ± 0.051, RSDAUC: 8.0%), while DL and DL+CR achieved similar prediction results. Conclusion: In IDH genotyping, LR is a promising machine-learning classification model. Compared with CR features, DL signatures exhibit markedly superior prediction values and discriminative capability.

Landscape of PCOS co-expression gene and its role in predicting prognosis and assisting immunotherapy in endometrial cancer.

BACKGROUND: Endometrial carcinoma (EC) is the sixth most frequent malignancy in women and is often linked to high estrogen exposure. Polycystic ovarian syndrome (PCOS) is a known risk factor for EC, but the underlying mechanisms remain unclear. METHODS: We investigated shared gene signals and potential biological pathways to identify effective therapy options for PCOS- and EC-related malignancies. Weighted gene expression network analysis (WGCNA) was used to identify genes associated with PCOS and EC using gene expression data from the Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) datasets. Enrichment analysis using Cluego software revealed that the steroid hormone biosynthetic process was a critical feature in both PCOS and EC. A predictive signature encompassing genes involved in steroid hormone production was developed using multivariate and least absolute shrinkage and selection operator (LASSO) regression analysis to predict the prognosis of EC. Then, we conducted further experimental verification. RESULTS: Patients in the TCGA cohort with high predictive scores had poorer outcomes than those with low scores. We also investigated the relationship between tumor microenvironment (TME) features and predictive risk rating and found that patients with low-risk scores had higher levels of inflammatory and inhibitory immune cells. Also, we found that immunotherapy against anti-CTLA4 and anti-PD-1/PD-L1 was successful in treating individuals with low risk. Low-risk individuals were more responsive to crizotinib therapy, according to further research performed using the "pRRophetic" R package. We further confirmed that IGF2 expression was associated with tumor cell migration, proliferation, and invasion in EC cells. CONCLUTIONS: By uncovering the pathways and genes linking PCOS and EC, our findings may provide new therapeutic strategies for patients with PCOS-related EC.

Genetic analysis of potential biomarkers and therapeutic targets associated with ferroptosis from bronchopulmonary dysplasia.

Ferroptosis is a recently identified form of cell death that is distinct from the conventional modes such as necrosis, apoptosis, and autophagy. Its role in bronchopulmonary dysplasia (BPD) remains inadequately understood. To address this gap, we obtained BPD-related RNA-seq data and ferroptosis-related genes (FRGs) from the GEO database and FerrDb, respectively. A total of 171 BPD-related differentially expressed ferroptosis-related genes (DE-FRGs) linked to the regulation of autophagy and immune response were identified. Least absolute shrinkage and selection operator and SVM-RFE algorithms identified 23 and 14 genes, respectively, as marker genes. The intersection of these 2 sets yielded 9 genes (ALOX12B, NR1D1, LGMN, IFNA21, MEG3, AKR1C1, CA9, ABCC5, and GALNT14) with acceptable diagnostic capacity. The results of the functional enrichment analysis indicated that these identified marker genes may be involved in the pathogenesis of BPD through the regulation of immune response, cell cycle, and BPD-related pathways. Additionally, we identified 29 drugs that target 5 of the marker genes, which could have potential therapeutic implications. The ceRNA network we constructed revealed a complex regulatory network based on the marker genes, further highlighting their potential roles in BPD. Our findings offer diagnostic potential and insight into the mechanism underlying BPD. Further research is needed to assess its clinical utility.

S100 Calcium Binding Protein A16 Promotes Cell Proliferation by triggering LATS1 ubiquitin degradation mediated by CUL4A ligase to inhibit Hippo pathway in Glioma development.

Background: S100 Calcium Binding Protein A16 (S100A16), a novel member of S100 protein family, is linked to tumorigenic processes and abundantly expressed in CNS tissues. Our study aimed to explore the biological function and possible mechanism of S100A16 in the progression of glioma. Methods: Sequence data of S100A16 and survival prognosis of glioma patients were initially analyzed using public databases. Glioma tissues were collected to examine S100A16 expression levels. Glioma cell lines and nude mice were subjected to in vitro and in vivo functional experiments. Western blot, immunofluorescence (IF), immunoprecipitation (IP) and ubiquitination assays were done to further elucidate the underlying mechanism. Results: This study firstly revealed that S100A16 was markedly up-regulated in glioma, and patients with higher S100A16 levels have a shorter survival time. S100A16 overexpression promoted the proliferation, invasion and migration of glioma cells, and the tumor formation of nude mice. Importantly, we identified S100A16 as a negative regulator of the Hippo pathway which could decrease LATS1 expression levels, promote the YAP nuclear import and initiate the downstream target genes CYR61 and CTGF. Moreover, our data showed that S100A16 destabilized LATS1 protein by inducing the CUL4A-mediated LATS1 ubiquitination degradation. Conclusions: This study demonstrated a vital biological role of S100A16 in glioma progression mechanism by promoting CUL4A-mediated LATS1 ubiquitination to inhibit Hippo signaling pathway. S100A16 could be a novel biomarker and treatment option for glioma patients.

Roles of Pyroptosis-Related Genes in the Diagnosis and Subtype Classification of Periodontitis.

Pyroptosis is widely involved in many diseases, including periodontitis. Nonetheless, the functions of pyroptosis-related genes (PRGs) in periodontitis are still not fully elucidated. Therefore, we aimed to investigate the role of PRGs in periodontitis. Three datasets (GSE10334, GSE16134, and GSE173078) from the Gene Expression Omnibus (GEO) were selected to analyze the differences in expression values of the PRGs between nonperiodontitis and periodontitis tissue samples using difference analysis. Following this, five hub PRGs (charged multivesicular body protein 2B, granzyme B, Z-DNA-binding protein 1, interleukin-1ß, and interferon regulatory factor 1) predicting periodontitis susceptibility were screened by establishing a random forest model, and a predictive nomogram model was constructed on the basis of these genes. Decision curve analysis suggested that the PRG-based predictive nomogram model could provide clinical benefits to patients. Three distinct PRG patterns (cluster A, cluster B, and cluster C) in the periodontitis samples were revealed according to the 48 significant PRGs, and the difference in the immune cell infiltration among the three patterns was explored. We observed that all infiltrating immune cells, except type 2 T helper cells, differ significantly among the three patterns. To quantify the PRG patterns, the PRG score was calculated by principal component analysis. According to the results, cluster B had the highest PRG score, followed by cluster A and cluster C. In conclusion, PRGs significantly contribute to the development of periodontitis. Our study of PRG patterns might open up a new avenue to guide individualized treatment plans for patients with periodontitis.

Association between daily eating frequency and mortality in people with diabetes: Findings from NHANES 1999-2014.

Background: Previous studies have shown that increasing the frequency of eating is beneficial in terms of cardiovascular metabolic risk factors; however, limited evidence is available for the association between daily eating frequency and mortality, especially in people with diabetes. Therefore, we aimed to explore the association between eating frequency and long-term mortality in populations with diabetes. Methods: We selected 4,924 individuals suffering from diabetes (mean age: 57.77 years; 51.3% men) from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2014. Daily eating frequency was used as the exposure factor in this study. We extracted the mortality data from the National Death Index records and matched them with the population of NHANES. All participants were followed up from the date of getting enrolled in NHANES to 31 December 2015. Multivariate Cox proportional hazards regression, Kaplan-Meier survival curves, and restricted cubic spline were used to assess the associations between eating frequency and all-cause and cause-specific mortality among people with diabetes. Results: During 34,950 person-years of follow-up, 1,121 deaths were documented, including 272 cardiovascular disease (CVD)-related deaths and 156 cancer-related deaths. After adjusting for confounding factors, the daily eating frequency was linearly inversely associated with all-cause and CVD-related mortality, and the HR (95% CIs) for per one-time increment of eating frequency was 0.88 (0.80-0.98) and 0.77 (0.63-0.93), respectively. Sensitivity analyses showed that the main results and statistical significance were still stable. Conclusion: Higher eating frequency was independently related to lower all-cause and CVD-related mortality in people with diabetes, which can be used as a potential strategy for daily-diet management among populations suffering from diabetes.

Stem cell plasticity, acetylation of H3K14, and de novo gene activation rely on KAT7.

In the conventional model of transcriptional activation, transcription factors bind to response elements and recruit co-factors, including histone acetyltransferases. Contrary to this model, we show that the histone acetyltransferase KAT7 (HBO1/MYST2) is required genome wide for histone H3 lysine 14 acetylation (H3K14ac). Examining neural stem cells, we find that KAT7 and H3K14ac are present not only at transcribed genes but also at inactive genes, intergenic regions, and in heterochromatin. KAT7 and H3K14ac were not required for the continued transcription of genes that were actively transcribed at the time of loss of KAT7 but indispensable for the activation of repressed genes. The absence of KAT7 abrogates neural stem cell plasticity, diverse differentiation pathways, and cerebral cortex development. Re-expression of KAT7 restored stem cell developmental potential. Overexpression of KAT7 enhanced neuron and oligodendrocyte differentiation. Our data suggest that KAT7 prepares chromatin for transcriptional activation and is a prerequisite for gene activation.

Efficacy and safety of mycophenolate mofetil in the treatment of moderate to severe Graves' orbitopathy: a meta-analysis.

The role of mycophenolate mofetil (MMF) in the treatment of Graves' orbitopathy (GO) has attracted much attention. This study is to evaluate the benefit and safety of MMF in moderate-to-severe GO. A meta-analysis of clinical control trials comparing MMF (with or without glucocorticoid (GC)) for the treatment of GO with GC was conducted. We searched the databases, including PubMed, EMBASE, the Cochrane Library, Web of Science, Wanfang, and China National Knowledge Infrastructure (CNKI), for articles published up to 15 June 2022. The primary outcome is referred to the improvement in overall response, and secondary outcomes included the change in clinical activity score (CAS) and adverse events (AEs). Of the 289 articles initially searched, 6 studies were finally eligible for inclusion. The results showed that MMF (with or without GC) was superior to GC in the treatment of GO (OR 3.34, 95% CI 2.17-5.14; p < 0.00001). Subgroup analyses also showed that MMF monotherapy was more effective than GC (OR 4.46, 95% CI 2.52-7.87; p < 0.00001). Compared to methylprednisolone (MP) monotherapy, a combination of MP and MMF was more effective. CAS decreased even more significantly (WMD 0.29, 95% CI 0.10-0.48; p = 0.002) and fewer AEs occurred (OR 0.2, 95% CI 0.06-0.72; p = 0.01) in patients receiving MMF. The pooled data suggested that MMF treatment in GO might be promising. Compared with GC therapy, MMF is safer and more effective. However, more large-sample and high-quality studies targeting MMF use in GO patients and long-term surveillance of prognosis are urgently needed.

S100a16 deficiency prevents hepatic stellate cells activation and liver fibrosis via inhibiting CXCR4 expression.

INTRODUCTION: Liver fibrosis caused by hepatic stellate cells (HSCs) activation is implicated in the pathogenesis of liver diseases. To date, there has been no effective intervention means for this process. S100 proteins are calcium-binding proteins that regulate cell growth and differentiation. This study aimed to investigate whether S100A16 induces HSCs activation and participates in liver fibrosis progression. METHODS: HSCs were isolated, and the relationship between S100A16 expression and HSCs activation was studied. S100a16 knockdown and transgenic mice were generated and subjected to HSCs activation and liver fibrosis stimulated by different models. Clinical samples were collected for further confirmation. Alterations in gene expression in HSCs were investigated, using transcriptome sequencing to determine the underlying mechanisms. RESULTS: We observed increased S100A16 levels during HSCs activation. Genetic silencing of S100a16 prevented HSCs activation in vitro. Furthermore, S100a16 silencing exhibited obvious protective effects against HSCs activation and fibrosis progression in mice. In contrast, S100a16 transgenic mice exhibited spontaneous liver fibrosis. S100A16 was also upregulated in the HSCs of patients with fibrotic liver diseases. RNA sequencing revealed that C-X-C motif chemokine receptor 4 (Cxcr4) gene was a crucial regulator of S100A16 induction during HSCs activation. Mechanistically, S100A16 bound to P53 to induce its degradation; this augmented CXCR4 expression to activate ERK 1/2 and AKT signaling, which then promoted HSCs activation and liver fibrosis. CONCLUSIONS: These data indicate that S100a16 deficiency prevents liver fibrosis by inhibiting Cxcr4 expression. Targeting S100A16 may provide insight into the pathogenesis of liver fibrosis and pave way for the design of novel clinical therapeutic strategies.

Characterization of Fatty Acid Metabolism-Related Genes Landscape for Predicting Prognosis and Aiding Immunotherapy in Glioma Patients.

Glioma is a highly malignant brain tumor with a poor survival rate. The involvement of fatty acid metabolism in glioma was examined to find viable treatment options. The information was gathered from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. A prognostic signature containing fatty acid metabolism-dependent genes (FAMDs) was developed to predict glioma outcome by multivariate and most minor absolute shrinkage and selection operator (LASSO) regression analyses. In the TCGA cohort, individuals with a good score had a worse prognosis than those with a poor score, validated in the CGGA cohort. According to further research by "pRRophetic" R package, higher-risk individuals were more susceptible to crizotinib. According to a complete study of the connection between the predictive risk rating model and tumor microenvironment (TME) features, high-risk individuals were eligible for activating the immune cell-associated receptor pathway. We also discovered that anti-PD-1/PD-L1 and anti-CTLA4 immunotherapy are more effective in high-risk individuals. Furthermore, we demonstrated that CCNA2 promotes glioma proliferation, migration, and invasion and regulates macrophage polarization. Therefore, examining the fatty acid metabolism pathway aids our understanding of TME invasion properties, allowing us to develop more effective immunotherapies for glioma.