The Inflammatory Gene PYCARD of the Entorhinal Cortex as an Early Diagnostic Target for Alzheimer's Disease

A novel paracetamol derivative alleviates lipopolysaccharide-induced neuroinflammation

Neuroinflammation has been implicated as a pathological contributor to several neurodegenerative disorders. Increasing evidence suggests that paracetamol (PCM, acetaminophen) has unappreciated anti-neuroinflammatory properties. However, PCM possesses hepatotoxicity in higher dosages, which are needed for achieving therapeutic concentrations in the brain. To lessen this effect and improve drug efficacy, PCM was in this study converted into an L-type amino acid transporter 1 (LAT1)-utilizing derivative and tested whether this LAT1-mediated delivery approach could enhance the relief of neuroinflammation, using both in vitro and in vivo lipopolysaccharide (LPS)-stimulated models. The gained results confirmed the derivative's improved transport into mouse primary astrocytes, immortalized microglia (BV2), and human immortalized microglia (SV40) via LAT1. In the LPS-stimulated BV2 model, the derivative effectively reduced the prostaglandin E2 (PGE2) level by 57% compared to the LPS treatment. Moreover, a more profound reduction of brain PGE2 production was confirmed in the LPS-stimulated mouse model. Finally, the global proteome of the whole mouse brain revealed that the derivative was able to reverse the altered expression of several inflammatory biomarkers, including ras-related C3 botulinum toxin substrate 1 (Rac1), cytochrome c oxidase subunit 2 (COX2), phospholipid phosphatase-related protein type 2 (Plppr2), ubiquitin-conjugating enzyme E2 variant 1 (Ube2v1) and A-kinase anchor protein 1, mitochondrial (Akap1).

Machine learning-based prediction of disease progression in primary progressive multiple sclerosis

Primary progressive multiple sclerosis (PPMS) affects 10-15% of multiple sclerosis patients and presents significant variability in the rate of disability progression. Identifying key biological features and patients at higher risk for fast progression is crucial to develop and optimize treatment strategies. Peripheral blood cell transcriptome has the potential to provide valuable information to predict patients' outcomes. In this study, we utilized a machine learning framework applied to the baseline blood transcriptional profiles and brain MRI radiological enumerations to develop prognostic models. These models aim to identify PPMS patients likely to experience significant disease progression and who could benefit from early treatment intervention. RNA-sequence analysis was performed on total RNA extracted from peripheral blood mononuclear cells of PPMS patients in the placebo arm of the ORATORIO clinical trial (NCT01412333), using Illumina NovaSeq S2. Cross-validation algorithms from Partek Genome Suite (www.partek.com) were applied to predict disability progression and brain volume loss over 120 weeks. For disability progression prediction, we analysed blood RNA samples from 135 PPMS patients (61 females and 74 males) with a mean ± standard error age of 44.0 ± 0.7 years, disease duration of 5.9 ± 0.32 years and a median baseline Expanded Disability Status Scale (EDSS) score of 4.3 (range 3.5-6.5). Over the 120-week study, 39.3% (53/135) of patients reached the disability progression end-point, with an average EDSS score increase of 1.3 ± 0.16. For brain volume loss prediction, blood RNA samples from 94 PPMS patients (41 females and 53 males), mean ± standard error age of 43.7 ± 0.7 years and a median baseline EDSS of 4.0 (range 3.0-6.5) were used. Sixty-seven per cent (63/94) experienced significant brain volume loss. For the prediction of disability progression, we developed a two-level procedure. In the first level, a 10-gene predictor achieved a classification accuracy of 70.9 ± 4.5% in identifying patients reaching the disability end-point within 120 weeks. In the second level, a four-gene classifier distinguished between fast and slow disability progression with a 506-day cut-off, achieving 74.1 ± 5.2% accuracy. For brain volume loss prediction, a 12-gene classifier reached an accuracy of 70.2 ± 6.7%, which improved to 74.1 ± 5.2% when combined with baseline brain MRI measurements. In conclusion, our study demonstrates that blood transcriptome data, alone or combined with baseline brain MRI metrics, can effectively predict disability progression and brain volume loss in PPMS patients.

Exploring the Role of Chemokine-Related Gene Deregulation and Immune Infiltration in Ischemic Stroke: Insights into CXCL16 and SEMA3E as Potential Biomarkers

Ischemic stroke is a leading cause of mortality and disability globally. Understanding the role of chemokine-related differently expressed genes (CDGs) in ischemic stroke pathophysiology is essential for advancing diagnostic and therapeutic strategies. We conducted comprehensive analyses using the GSE16561 dataset: chemokine pathway enrichment via GSVA, differential expression of 12 CDGs, Pearson correlation, and functional enrichment analyses (GO and KEGG). Machine learning algorithms were employed to develop diagnostic models, evaluated using ROC curve analysis. A nomogram was constructed and validated with independent datasets (GSE58294). Gene set enrichment analysis (GSEA) and immuno-infiltration analysis were also performed. Chemokine pathway scores were significantly elevated in ischemic stroke, indicating their potential involvement. Logistic regression emerged as the most effective diagnostic model, with CXCL16 and SEMA3E as significant biomarkers. The nomogram exhibited high discriminatory ability (AUC = 0.964), well-calibrated predictions, and clinical utility across datasets. GSEA highlighted key biological pathways associated with CXCL16 and SEMA3E. Immuno-infiltration analysis revealed significant differences in immune cell infiltration between control and ischemic stroke groups, with distinct correlations between CXCL16 and SEMA3E expression and immune cell populations. This study highlights the deregulation of CDGs in ischemic stroke and their implications in critical biological processes. CXCL16 and SEMA3E are identified as key biomarkers with potential diagnostic utility. Insights from gene set enrichment and immuno-infiltration analyses provide mechanistic understanding, suggesting novel therapeutic targets and enhancing clinical decision-making in ischemic stroke management.

The Inflammasome Adaptor Protein ASC in Plasma as a Biomarker of Early Cognitive Changes

Dementia is a group of symptoms including memory loss, language difficulties, and other types of cognitive and functional impairments that affects 57 million people worldwide, with the incidence expected to double by 2040. Therefore, there is an unmet need to develop reliable biomarkers to diagnose early brain impairments so that emerging interventions can be applied before brain degeneration. Here, we performed biomarker analyses for apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC), neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and amyloid-β 42/40 (Aβ42/40) ratio in the plasma of older adults. Participants had blood drawn at baseline and underwent two annual clinical and cognitive evaluations. The groups tested either cognitively normal on both evaluations (NN), cognitively normal year 1 but cognitively impaired year 2 (NI), or cognitively impaired on both evaluations (II). ASC was elevated in the plasma of the NI group compared to the NN and II groups. Additionally, Aβ42 was increased in the plasma in the NI and II groups compared to the NN group. Importantly, the area under the curve (AUC) for ASC in participants older than 70 years old in NN vs. NI groups was 0.81, indicating that ASC is a promising plasma biomarker for early detection of cognitive decline.

Why U matters: detection and functions of pseudouridine modifications in mRNAs

The uridine modifications pseudouridine (Ψ), dihydrouridine, and 5-methyluridine are present in eukaryotic mRNAs. Many uridine-modifying enzymes are associated with human disease, underscoring the importance of uncovering the functions of uridine modifications in mRNAs. These modified uridines have chemical properties distinct from those of canonical uridines, which impact RNA structure and RNA-protein interactions. Ψ, the most abundant of these uridine modifications, is present across (pre-)mRNAs. Recent work has shown that many Ψs are present at intermediate to high stoichiometries that are likely conducive to function and at locations that are poised to influence pre-/mRNA processing. Technological innovations and mechanistic investigations are unveiling the functions of uridine modifications in pre-mRNA splicing, translation, and mRNA stability, which are discussed in this review.

A Study on Autophagy Related Biomarkers in Alzheimer's Disease Based on Bioinformatics

Alzheimer's disease (AD) is a neurodegenerative disease with an annual incidence increase that poses significant health risks to people. However, the pathogenesis of AD is still unclear. Autophagy, as an intracellular mechanism can degrade damaged cellular components and abnormal proteins, which is closely related to AD pathology. The goal of this work is to uncover the intimate association between autophagy and AD, and to mine potential autophagy-related AD biomarkers by identifying key differentially expressed autophagy genes (DEAGs) and exploring the potential functions of these genes. GSE63061 and GSE140831 gene expression profiles of AD were downloaded from the Gene Expression Omnibus (GEO) database. R language was used to standardize and differentially expressed genes (DEGs) of AD expression profiles. A total of 259 autophagy-related genes were discovered through the autophagy gene databases ATD and HADb. The differential genes of AD and autophagy genes were integrated and analyzed to screen out DEAGs. Then the potential biological functions of DEAGs were predicted, and Cytoscape software was used to detect the key DEAGs. There were ten DEAGs associated with the AD development, including nine up-regulated genes (CAPNS1, GAPDH, IKBKB, LAMP1, LAMP2, MAPK1, PRKCD, RAB24, RAF1) and one down-regulated gene (CASP1). The correlation analysis reveals the potential correlation among 10 core DEAGs. Finally, the significance of the detected DEAGs expression was verified, and the value of DEAGs in AD pathology was detected by the receiver operating characteristic curve. The area under the curve values indicated that ten DEAGs are potentially valuable for the study of the pathological mechanism and may become biomarkers of AD. This pathway analysis and DEAG screening in this study found a strong association between autophagy-related genes and AD, providing new insights into the pathological progression of AD. Exploring the relationship between autophagy and AD: analysis of genes associated with autophagy in pathological mechanisms of AD using bioinformatics. 10 autophagy-related genes play an important role in the pathological mechanisms of AD.