Frontiers in Endocrinology最新文献

Ovarian aging is a major health concern for women. Ovarian aging is associated with reduced health span and longevity. Mitochondrial dysfunction is one of the hallmarks of ovarian aging. In addition to providing oocytes with optimal energy, the mitochondria provide a co-substrate that drives epigenetic processes. Studies show epigenetic alterations, both nuclear and mitochondrial contribute to ovarian aging. Both, nuclear and mitochondrial genomes cross-talk with each other, resulting in two ways orchestrated anterograde and retrograde response that involves epigenetic changes in nuclear and mitochondrial compartments. Epigenetic alterations causing changes in metabolism impact ovarian function. Key mitochondrial co-substrate includes acetyl CoA, NAD+, ATP, and α-KG. Thus, enhancing mitochondrial function in aging ovaries may preserve ovarian function and can lead to ovarian longevity and reproductive and better health outcomes in women. This article describes the role of mitochondria-led epigenetics involved in ovarian aging and discusses strategies to restore epigenetic reprogramming in oocytes by preserving, protecting, or promoting mitochondrial function.

Introduction: Adamantinomatous craniopharyngioma (ACP) is difficult to cure completely and prone to recurrence after surgery. Ferroptosis as an iron-dependent programmed cell death, may be a critical process in ACP. The study aimed to screen diagnostic markers related to ferroptosis in ACP to improve diagnostic accuracy.

Methods: Gene expression profiles of ACP were obtained from the gene expression omnibus (GEO) database. Limma package was used to analyze the differently expressed genes (DEGs). The intersection of DEGs and ferroptosis-related factors was obtained as differently expressed ferroptosis-related genes (DEFRGs). Enrichment analysis was processed, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), disease ontology (DO), gene set enrichment analysis (GSEA), and Gene Set Variation Analysis (GSVA) analysis. Machine learning algorithms were undertaken for screening diagnostic markers associated with ferroptosis in ACP. The levels of DEFRGs were verified in ACP patients. A nomogram was drawn to predict the relationship between key DEFRG expression and risk of disease. The disease groups were then clustered by consensus clustering analysis.

Results: DEGs were screened between ACP and normal samples. Ferroptosis-related factors were obtained from the FerrDb V2 and GeneCard databases. The correlation between DEFRGs and ferroptosis markers was also confirmed. A total of 6 overlapped DEFRGs were obtained. Based on the results of the nomogram, CASP8, KRT16, KRT19, and TP63 were the protective factors of the risk of disease, while GOT1 and TFAP2C were the risk factors. According to screened DEFRGs, the consensus clustering matrix was differentiated, and the number of clusters was stable. CASP8, KRT16, KRT19, and TP63, were upregulated in ACP patients, while GOT1 was downregulated. CASP8, KRT16, KRT19, TP63, CASP8, and GOT1 affect multiple ferroptosis marker genes. The combination of these genes might be the biomarker for ACP diagnosis via participating ferroptosis process.

Discussion: Ferroptosis-related genes, including CASP8, KRT16, KRT19, TP63, and GOT1 were the potential markers for ACP, which lays the theoretical foundation for ACP diagnosis.

Introduction: Type 1 diabetes (T1D) is a chronic condition marked by insulin deficiency and hyperglycemia, with an increasing global incidence, particularly among children. Despite improvements in diabetes management, individuals with T1D continue to experience higher rates of cardiovascular disease (CVD), the leading cause of mortality in this population. Traditional CVD risk factors such as dyslipidemia and poor glycemic control are insufficient to fully explain the elevated risk in T1D, prompting further investigation into additional factors. Emerging evidence suggests that metabolic dysfunction-associated steatotic liver disease (MASLD) plays a critical role in this heightened CVD risk.

Objective: This narrative review aims to explore the relationship between MASLD and CVD in individuals with T1D. The review focuses on the prevalence of MASLD, its contributing risk factors, and the potential impact of liver dysfunction on cardiovascular outcomes in this population.

Methods: A review of existing literature was conducted, focusing on observational studies, cohort studies, and meta-analyses that investigate the prevalence of MASLD in T1D populations and its association with CVD. The review also examines the physiological mechanisms linking MASLD and CVD, including insulin resistance, systemic inflammation, and hepatic dyslipidemia. Key studies were evaluated to identify patterns in MASLD prevalence based on diagnostic modalities and to assess the independent contribution of MASLD to cardiovascular risk in T1D patients.

Conclusion: MASLD is increasingly recognized as a significant contributor to CVD in individuals with T1D, particularly in those with shared risk factors like obesity and insulin resistance. Evidence suggests that MASLD exacerbates hepatic and systemic metabolic dysfunction, increasing CVD risk through mechanisms such as chronic inflammation and atherogenic lipid profiles. Routine liver health assessments and tailored management strategies targeting MASLD should be incorporated into clinical care for individuals with T1D to mitigate long-term cardiovascular complications.

Background: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have been widely used for type 2 diabetes (T2D) and weight management. However, the causal relationship of GLP-1RAs with erectile dysfunction (ED) was still unclear.

Methods: Mendelian randomization (MR) analysis was conducted to reveal the association of genetically proxied GLP-1RAs with ED. The proportion of potential mediators mediating GLP-1RAs to ED was also assessed by two-step MR. Finally, a series of sensitivity analyses and Two-Sep cis-MR (TSCMR) were performed to evaluate the robustness of the results.

Results: MR evidence suggested that genetically proxied GLP-1RAs reduced the risk of ED [odds ratio (OR): 0.493; 95% confidence interval (95% CI): 0.430 to 0.565; P<0.001]. Further mediation analysis via two-step MR showed that this effect was partly mediated through reduced T2D, obesity, hypertension and cardiovascular disease (CVD), with mediated proportions of 2.89% (95% CI: 1.28% to 4.49%), 6.83% (95% CI: 2.25% to 11.41%), 3.22% (95% CI: 1.21% to 5.23%), and 3.06% (95% CI: 0.51% to 5.62%), respectively.

Conclusions: GLP-1RAs were associated with a reduced risk of ED, and to a lesser extent, T2D, obesity, hypertension and CVD mediated this effect. Nevertheless, the potential implications of our results for ED prevention policies required validation in further clinical randomized controlled trials.

Three-dimensional cultures are widely used to study bone and cartilage. These models often focus on the interaction between osteoblasts and osteoclasts or osteoblasts and chondrocytes. A culture of osteoblasts, osteoclasts and chondrocytes would represent the cells that interact in the joint and a model with these cells could be used to study many diseases that affect the joints. The goal of this study was to develop 3D bone-cartilage interface (3D-BCI) that included osteoblasts, osteocytes, osteoclasts, and cartilage. Fluorescently tagged cell lines were developed to assess the interactions as cells differentiate to form bone and cartilage. Mouse cell line, MC3T3, was labeled with a nuclear GFP tag and differentiated into osteoblasts and osteocytes in Matrigel. Raw264.7 cells transfected with a red cytoplasmic tag were added to the system and differentiated with the MC3T3 cells to form osteoclasts. A new method was developed to differentiate chondrocyte cell line ATDC5 in a cartilage spheroid, and the ATDC5 spheroid was added to the MC3T3 and Raw264.7 cell model. We used an Incucyte and functional analysis to assess the cells throughout the differentiation process. The 3D-BCI model was found to be positive for TRAP, ALP, Alizarin red and Alcian blue staining to confirm osteoblastogenesis, osteoclastogenesis, and cartilage formation. Gene expression confirmed differentiation of cells based on increased expression of osteoblast markers: Alpl, Bglap, Col1A2, and Runx2, cartilage markers: Acan, Col2A1, Plod2, and osteoclast markers: Acp5, Rank and Ctsk. Based on staining, protein expression and gene expression results, we conclude that we successfully developed a mouse model with a 3D bone-cartilage interface.

Background: Recently, serum metabolites have shown potential in predicting survival outcomes and may be related to the pathogenesis of prostate cancer. Nevertheless, the precise impact concerning the genetic effect of metabolites on prostate cancer risk remains obscure. In this context, we conducted a Mendelian randomization (MR) study aiming to explore the causality between genetically determined metabolites and the risk of prostate cancer.

Methods: We conducted a two-sample MR analysis aiming to identify the underlying metabolites associated with prostate cancer. Exposure information was obtained from the largest metabolome-based genome-wide association (GWAS) data containing 7,824 Europeans. Genome-wide association analysis was utilized to detect instrumental variables (IVs) for metabolites. We applied the inverse-variance weighted (IVW) approach as the primary method, and to augment the reliability and robustness of our findings, additional analysis methods encompassing weighted median, MR-Egger, and leave-one-out analysis were utilized. MR-Egger intercept test was implemented to explore the pleiotropy. Cochran's Q test was utilized to quantify the degree of heterogeneity. Additionally, we performed metabolic pathway analysis and single-cell RNA sequencing analysis.

Results: We found that three serum metabolites were causally associated with prostate cancer after utilizing rigorous screening standards. Utilizing single nucleotide polymorphisms as IVs, a 1-SD increase in fructose was associated with 77% higher risk of prostate cancer (OR:1.77, 95%CI: 1.05-2.97, P_IVW=0.031), a 1-SD increase in N1-methyl-3-pyridone-4-carboxamide was associated with 29% higher risk of prostate cancer (OR:1.29, 95%CI: 1.05-1.58, P_IVW=0.017), and a 1-SD increase in 12-hydroxyeicosatetraenoate (12-HETE) was associated with 18% higher risk of prostate cancer (OR:1.18, 95%CI: 1.07-1.31, P_IVW=0.0008). Metabolites that were causally linked to the risk of prostate cancer were mainly enriched in the valine, leucine and isoleucine biosynthesis pathway (P=0.026) and the nicotinate and nicotinamide metabolism pathway (P=0.048).

Conclusions: Our MR analysis provided suggestive evidence supporting the causal relationships between three identified serum metabolites and prostate cancer, necessitating further investigation to elucidate the underlying mechanisms through which these blood metabolites and metabolic pathways may impact the initiation and progression of prostate cancer.

Introduction: Capping actin protein, gelsolin-like (CAPG) is a potential therapeutic target in various cancers. However, the potential immunotherapeutic effects and prognostic value of CAPG in uterine corpus endometrial carcinoma (UCEC) remain unclear.

Methods: The characterization, methylation effects, prognostic value, targeted miRNAs of CAPG, and the correlation of CAPG with immune cell infiltration and ferroptosis in UCEC were investigated using multiple public databases and online tools. Furtherly, we explored the potential physiological function of CAPG using EdU and Transwell migration assays, identified the cell localization and expression of CAPG and GPX4 by immunofluorescence, and detected the intracellular Fe²⁺ levels using a FerroOrange fluorescent probe in Ishikawa cells. Additionally, the OncoPredict package was used to analyze the potential chemotherapeutic drugs for UCEC.

Results: CAPG showed generally high expression in tumor group. The overall survival rate of the high-risk group was significantly lower than that of the low-risk group. Enrichment analysis indicated that CAPG is involved in immune-related pathways and is closely associated with the tumor microenvironment. CAPG expression levels were affected by abnormal DNA methylation and/or targeted miRNAs, infiltration levels and marker genes of various immune cells, thereby impacting immune response, ferroptosis, and patient prognosis. Ferroptosis analysis indicated that ALOX5 and VLDLR were the top CAPG-related ferroptosis markers; glutathione metabolism levels in tumor group were generally high, and decitabine was a ferroptosis inducer. CAPG-siRNA suppressed the cell proliferation and invasion, and markedly elevated the expression levels of immune-related genes IL8, TNF, TLR4 and the intracellular Fe²⁺ levels. CAPG co-located with GPX4 in nucleus and co-regulated ferroptosis and metabolism in Ishikawa cells. Moreover, four chemotherapy drugs showed better sensitivity to UCEC patients in the low-risk cohort.

Conclusions: CAPG may serve as a potential biomarker of UCEC owing to its role in modulating the immune response and ferroptosis, providing novel perspectives for combined immunotherapy of UCEC.

Background: The pathological and physiological characteristics between HBsAg-positive HBV infection and occult hepatitis B infection (OBI) are currently unclear. This study aimed to explore the immune microenvironment in the peripheral circulation of OBI patients through integration of proteomic and metabolomic sequencing, and to identify molecular biomarkers for clinical diagnosis of HBsAg-positive HBV and OBI.

Methods: This research involved collection of plasma from 20 patients with OBI (negative for HBsAg but positive for HBV DNA, with HBV DNA levels < 200 IU/mL), 20 patients with HBsAg-positive HBV infection, and 10 healthy individuals. Mass spectrometry-based detection was used to analyze the proteome, while nuclear magnetic resonance spectroscopy was employed to study the metabolomic phenotypes. Differential molecule analysis, pathway enrichment and functional annotation, as well as weighted correlation network analysis (WGCNA), were conducted to uncover the characteristics of HBV-related liver disease. Prognostic biomarkers were identified using machine learning algorithms, and their validity was confirmed in a larger cohort using enzyme linked immunosorbent assay (ELISA).

Results: HBsAg-positive HBV individuals showed higher ALT levels (p=0.010) when compared to OBI patients. The influence of HBV infection on metabolic functions and inflammation was evident through the analysis of distinct metabolic pathways in HBsAg-positive HBV and OBI groups. Tissue tracing demonstrated a connection between Kupffer cells and HBsAg-positive HBV infection, as well as between hepatocytes and OBI. Immune profiling revealed the correlation between CD4 Tem cells, memory B cells and OBI, enabling a rapid response to infection reactivation through cytokine secretion and antibody production. A machine learning-constructed and significantly expressed molecule-based diagnostic model effectively differentiated HBsAg-positive and OBI groups (AUC values > 0.8). ELISA assay confirmed the elevation of FGB and FGG in OBI samples, suggesting their potential as biomarkers for distinguishing OBI from HBsAg-positive infection.

Conclusions: The immune microenvironment and metabolic status of HBsAg-positive HBV patients and OBI patients vary significantly. The machine learning-based diagnostic model described herein displayed impressive classification accuracy, presenting a non-invasive means of differentiating between OBI and HBsAg-positive HBV infections.