Potential shared pathogenic mechanisms between endometriosis and inflammatory bowel disease indicate a strong initial effect of immune factors.

Introduction: Over 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. Methods: Based 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. Results: 113 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. Discussion: Our 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.

Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.

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.

Osteoprotective Effects in Postmenopausal Osteoporosis Rat Model: Oral Tocotrienol vs. Intraosseous Injection of Tocotrienol-Poly Lactic-Co-Glycolic Acid Combination.

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.

Association between Viral Infections and Risk of Autistic Disorder: An Overview.

Autism spectrum disorder (ASD) is a neurodevelopmental condition of the central nervous system (CNS) that presents with severe communication problems, impairment of social interactions, and stereotypic behaviours. Emerging studies indicate possible associations between viral infections and neurodegenerative and neurobehavioural conditions including autism. Viral infection during critical periods of early in utero neurodevelopment may lead to increased risk of autism in the offspring. This review is aimed at highlighting the association between viral infections, including viruses similar to COVID-19, and the aetiology of autism. A literature search was conducted using Pubmed, Ovid/Medline, and Google Scholar database. Relevant search terms included "rubella and autism", "cytomegalovirus and autism", "influenza virus and autism", "Zika virus and autism", "COVID-19 and autism". Based on the search terms, a total of 141 articles were obtained and studies on infants or children with congenital or perinatal viral infection and autistic behaviour were evaluated. The possible mechanisms by which viral infections could lead to autism include direct teratogenic effects and indirect effects of inflammation or maternal immune activation on the developing brain. Brain imaging studies have shown that the ensuing immune response from these viral infections could lead to disruption of the development of brain regions and structures. Hence, long-term follow up is necessary for infants whose mothers report an inflammatory event due to viral infection at any time during pregnancy to monitor for signs of autism. Research into the role of viral infection in the development of ASD may be one avenue of improving ASD outcomes in the future. Early screening and diagnosis to detect, and maybe even prevent ASD are essential to reduce the burden of this condition.

Update on Atypicalities of Central Nervous System in Autism Spectrum Disorder.

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.

Apoptosis transcriptional mechanism of feline infectious peritonitis virus infected cells.

Apoptosis has been postulated to play an important role during feline infectious peritonitis virus (FIPV) infection; however, its mechanism is not well characterized. This study is focused on apoptosis and transcriptional profiling of FIPV-infected cells following in vitro infection of CRFK cells with FIPV 79-1146 WSU. Flow cytometry was used to determine mode of cell death in first 42 h post infection (hpi). FIPV infected cells underwent early apoptosis at 9 hpi (p < 0.05) followed by late apoptosis at 12 hpi (p < 0.05) and necrosis from 24 hpi (p < 0.05). Then, next generation sequencing was performed on 9 hpi and control uninfected cells by Illumina analyzer. An aggregate of 4546 genes (2229 down-regulated and 2317 up-regulated) from 17 cellular process, 11 molecular functions and 130 possible biological pathways were affected by FIPV. 131 genes from apoptosis cluster (80 down-regulated and 51 up-regulated) along with increase of apoptosis, p53, p38 MAPK, VEGF and chemokines/cytokines signaling pathways were probably involved in apoptosis process. Six of the de-regulated genes expression (RASSF1, BATF2, MAGEB16, PDCD5, TNFα and TRAF2) and TNFα protein concentration were analyzed by RT-qPCR and ELISA, respectively, at different time-points. Up-regulations of both pro-apoptotic (i.e. PDCD5) and anti-apoptotic (i.e. TRAF2) were detected from first hpi and continuing to deregulate during apoptosis process in the infected cells.

Transcriptome analysis of feline infectious peritonitis virus infection.

Feline infectious peritonitis (FIP) is a lethal systemic disease caused by FIP virus (FIPV). There are no effective vaccines or treatment available, and the virus virulence determinants and pathogenesis are not fully understood. Here, we describe the sequencing of RNA extracted from Crandell Rees Feline Kidney (CRFK) cells infected with FIPV using the Illumina next-generation sequencing approach. Bioinformatics analysis, based on Felis catus 2X annotated shotgun reference genome, using CLC bio Genome Workbench is used to map both control and infected cells. Kal's Z test statistical analysis is used to analyze the differentially expressed genes from the infected CRFK cells. In addition, RT-qPCR analysis is used for further transcriptional profiling of selected genes in infected CRFK cells and Peripheral Blood Mononuclear Cells (PBMCs) from healthy and FIP-diagnosed cats.