Search

IntroductionOver the past decades, immune dysregulation has been consistently demonstrated being common charactoristics of endometriosis (EM) and Inflammatory Bowel Disease (IBD) in numerous studies. However, the underlying pathological mechanisms remain unknown. In this study, bioinformatics techniques were used to screen large-scale gene expression data for plausible correlations at the molecular level in order to identify common pathogenic pathways between EM and IBD.MethodsBased on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects.Results113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD.DiscussionOur research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.

Background: Endometriosis (EM) is a common gynecological condition in women of reproductive age, with diverse causes and a not yet fully understood pathogenesis. Traditional diagnostics rely on single diagnostic biomarkers and does not integrate a variety of different biomarkers. This study introduces multiple machine learning techniques, enhancing the accuracy of predictive models. A novel diagnostic approach that combines various biomarkers provides a new clinical perspective for improving the diagnostic efficiency of endometriosis, holding significant potential for clinical application.Methods: In this study, GSE51981 was used as a test set, and 11 machine learning algorithms (Lasso, Stepglm, glmBoost, Support Vector Machine, Ridge, Enet, plsRglm, Random Forest, LDA, XGBoost, and NaiveBayes) were employed to construct 113 predictive models for endometriosis. The optimal model was determined based on the AUC values derived from various algorithms. These genes were then evaluated using nine machine learning algorithms (Random Forest, SVM, Gradient Boosting Machine, LASSO, XGB, NNET, Generalized Linear Model, KNN, and Decision Tree) to assess significance scores and identify diagnostic genes for each algorithm. The diagnostic value of these genes was further validated in external datasets from GSE7305, GSE11691, and GSE120103.Results: Analysis of the GSE51981 dataset revealed 62 DEGs. The Stepglm [Both] and plsRglm algorithms identified 30 genes with the most potential using the AUC evaluation. Subsequently, nine machine learning algorithms were applied to select diagnostic genes, leading to the identification of five key diagnostic genes using the LASSO algorithm. The ADAT1 gene exhibited the best single-gene predictive performance, with an AUC of 0.785. A combination of genes (FOS, EPHX1, DLGAP5, PCSK5, and ADAT1) achieves an AUC of 0.836 in the test dataset. Moreover, these genes consistently exhibited an AUC exceeding 0.78 in all validation datasets, demonstrating superior predictive performance. Furthermore, correlation analysis with immune infiltration strengthened their predictive value by demonstrating the close relationship of the diagnostic genes with immune infiltrating cells.Conclusion: A combination of biomarkers consisting of FOS, EPHX1, DLGAP5, PCSK5, and ADAT1 can serve as a diagnostic tool for endometriosis, enhancing diagnostic efficiency. The association of these genes with immune infiltrating cells reveals their potential role in the pathogenesis of endometriosis, providing new insights for early detection and treatment.

In response to Pi deprivation, phosphate 1 (PHO1) is a significant regulator at trans-eQTL hotspots in Brassica rapa. Brassica rapa short-read sequencing data analysis revealed four PHO1 paralog genes, PHO1_A, PHO1_B, PHO1_C, and PHO1_D, placed in tandem with very high sequence similarity. However, based on short-read genomic sequence data, only three transcripts are accessible. Five bacterial artificial chromosomes (BACs) can be sequenced using a long-read sequencer, which improves de novo assembly and identifies structural variants. The PHO1 gene’s quadruplicating tandem positions in the genomic sequence were confirmed by an analysis of long-read data. Transcript analysis identified only three groups of PHO1 paralogs (ortholog AT1G14040 in Arabidopsis), i.e., PHO1_A, PHO1_B, and PHO1_D, expressed in B. rapa leaf tissues under Pi deficiency. PHO1_A, with transcript ID XM_009150437.2, has five different splice variants found. These splice variants’ truncated proteins demonstrated PHO1_A’s function in P control as opposed to protein encoding.

Osteoporosis, the most common bone disease, is associated with compromised bone strength and increased risk of fracture. Previous studies have shown that oxidative stress contributes to the progression of osteoporosis. Specifically, for postmenopausal osteoporosis, the reduction in estrogen levels leads to increased oxidative stress in bone remodeling. Tocotrienol, a member of vitamin E that exhibits antioxidant activities, has shown potential as an agent for the treatment of osteoporosis. Most studies on the osteoprotective effects of tocotrienols had used the oral form of tocotrienols, despite their low bioavailability due the lack of transfer proteins and high metabolism in the liver. Several bone studies have utilized tocotrienol combined with a nanocarrier to produce a controlled release of tocotrienol particles into the system. However, the potential of delivering tocotrienol–nanocarrier combination through the intraosseous route has never been explored. In this study, tocotrienol was combined with a nanocarrier, poly lactic-co-glycolic acid (PLGA), and injected intraosseously into the bones of ovariectomized rats to produce targeted and controlled delivery of tocotrienol into the bone microenvironment. This new form of tocotrienol delivery was compared with the conventional oral delivery in terms of their effects on bone parameters. Forty Sprague–Dawley rats were divided into five groups. The first group was sham operated, while other groups were ovariectomized (OVX). Following 2 months, the right tibiae of all the rats were drilled at the metaphysis region to provide access for intraosseous injection. The estrogen group (OVX + ESTO) and tocotrienol group (OVX + TTO) were given daily oral gavages of Premarin (64.5 mg/kg) and annatto-tocotrienol (60 mg/kg), respectively. The locally administered tocotrienol group (OVX + TTL) was given a single intraosseous injection of tocotrienol–PLGA combination. After 8 weeks of treatment, both OVX + TTO and OVX + TTL groups have significantly lower bone markers and higher bone mineral content than the OVX group. In terms of bone microarchitecture, both groups demonstrated significantly higher trabecular separation and connectivity density than the OVX group (p < 0.05). Both groups also showed improvement in bone strength by the significantly higher stress, strain, stiffness, and Young’s modulus parameters. In conclusion, daily oral tocotrienol and one-time intraosseous injection of tocotrienol–PLGA combination were equally effective in offering protection against ovariectomy-induced bone changes.

Autism spectrum disorder (ASD) is a heterogeneous, behaviorally defined, neurodevelopmental disorder that has been modeled as a brain-based disease. The behavioral and cognitive features of ASD are associated with pervasive atypicalities in the central nervous system (CNS). To date, the exact mechanisms underlying the pathophysiology of ASD still remain unknown and there is currently no cure or effective treatment for this disorder. Many publications implicated the association of ASD with inflammation, immune dysregulation, neurotransmission dysfunction, mitochondrial impairment and cell signaling dysregulation. This review attempts to highlight evidence of the major pathophysiology of ASD including abnormalities in the brain structure and function, neuroglial activation and neuroinflammation, glutamatergic neurotransmission, mitochondrial dysfunction and mechanistic target of rapamycin (mTOR) signaling pathway dysregulation. Molecular and cellular factors that contributed to the pathogenesis of ASD and how they may affect the development and function of CNS are compiled in this review. However, findings of published studies have been complicated by the fact that autism is a very heterogeneous disorder; hence, we addressed the limitations that led to discrepancies in the reported findings. This review emphasizes the need for future studies to control study variables such as sample size, gender, age range and intelligence quotient (IQ), all of which that could affect the study measurements. Neuroinflammation or immune dysregulation, microglial activation, genetically linked neurotransmission, mitochondrial dysfunctions and mTOR signaling pathway could be the primary targets for treating and preventing ASD. Further research is required to better understand the molecular causes and how they may contribute to the pathophysiology of ASD.