Guizelini, D. | Search bioRxiv

bioRxiv2025

Cytokine expression profile in the human brain of older adults

Alzheimers disease (AD) is a complex neurodegenerative condition linked to chronic neuroinflammation. This study investigates the cytokine gene expression profile in cortical tissue samples from elderly individuals with and without AD to identify potential biomarkers and enhance our understanding of disease pathogenesis. Utilizing high-depth RNA sequencing data, we identified a set of cytokines whose expression significantly associated with different aspects of the AD phenotype, including measures of neurofibrillary tangles, amyloid-{beta} deposition, and a person-specific rate of cognitive decline. Single-nucleus transcriptomics data facilitated the identification of specific cell types, such as microglia and oligodendrocytes, that significantly contribute to the inflammatory response in AD. Additionally, we observed a strong correlation between the expression of certain cytokines and genetic risk for the disease. Our findings indicate that cytokine-mediated neuroinflammation plays a vital role in AD progression and that modulating the immune response may offer a promising strategy for developing new therapies.

molecular biology↗

bioRxiv2024

a2iHelper: a Python toolkit for a differential editing site analysis of RNA-Seq data

BackgroundA-to-I RNA editing, mediated by ADAR enzymes, plays a crucial role in cancer and autoimmune disorders but lacks standardized tools for differential analysis. After reviewing 55 studies, it highlights significant methodological heterogeneity, hindering result comparability and reproducibility. To address this, we developed a2iHelper, a Python package that streamlines RNA editing analysis by filtering noise, performing statistical analyses, and generating visualizations. a2iHelper integrates seamlessly with Python machine learning tools, aiming to standardize and simplify RNA editing research. ResultsWe applied our methods to analyze A-to-I editing in a public dataset comparing wild-type and ADAR knockout. The source code is open, freely available on GitHub, and organized in a well-documented Python package. Using Snakemake for preprocessing, we conducted differential editing analysis on the top 104 most edited genes. The results include p-values from statistical tests, Odds ratios for Manhattan plots, and correlations between editing frequencies and gene expression, visualized in various plots. ConclusionsWe developed a2iHelper, a Python-based package for analyzing and visualizing A-to-I RNA editing data. It allows researchers with minimal programming experience to perform organized, reproducible editing analyses. Novice users can easily detect editing sites, filter noise, and generate figures, while advanced users can integrate functionalities into their workflows. a2iHelper runs on personal computers without needing High-Performance Computing resources.

bioinformatics↗