Single-Molecule Fluorescent In Situ Hybridization (smFISH) for RNA Detection in the Fungal Pathogen Candida albicans

Candida albicans is the most prevalent human fungal pathogen. Its pathogenicity is linked to the ability of C. albicans to reversibly change morphology and to grow as yeast, pseudohyphae, or hyphal cells in response to environmental stimuli. Understanding the molecular regulation controlling those morphological switches remains a challenge that, if solved, could help eradicate C. albicans infections.While numerous studies investigated gene expression changes occurring during C. albicans morphological switches using bulk approaches (e.g., RNA sequencing), here we describe a single-cell and single-molecule RNA imaging and analysis protocol to measure absolute mRNA counts in morphologically intact cells. To detect endogenous mRNAs in single fixed cells, we optimized a single-molecule fluorescent in situ hybridization (smFISH) protocol for C. albicans, which allows one to quantify the differential expression of mRNAs in yeast, pseudohyphae, or hyphal cells. We quantified the expression of two mRNAs, a cell cycle-controlled mRNA (CLB2) and a transcription factor (EFG1), which show expression changes in the different morphological cell types and nutrient conditions. In this protocol, we described in detail the major steps of this approach: growth and fixation, hybridization, imaging, cell segmentation, and mRNA spot analysis. Raw data is provided with the protocol to favor reproducibility. This approach could benefit the molecular characterization of C. albicans and other filamentous fungi, pathogenic or nonpathogenic.

A meta-analysis of microarray datasets to identify biological regulatory networks in Alzheimer's disease

Background: Alzheimer's Disease (AD) is an age-related progressive neurodegenerative disorder characterized by mental deterioration, memory deficit, and multiple cognitive abnormalities, with an overall prevalence of ∼2% among industrialized countries. Although a proper diagnosis is not yet available, identification of miRNAs and mRNAs could offer valuable insights into the molecular pathways underlying AD's prognosis. Method: This study aims to utilize microarray bioinformatic analysis to identify potential biomarkers of AD, by analyzing six microarray datasets (GSE4757, GSE5281, GSE16759, GSE28146, GSE12685, and GSE1297) of AD patients, and control groups. Furthermore, this study conducted gene ontology, pathways analysis, and protein-protein interaction network to reveal major pathways linked to probable biological events. The datasets were meta-analyzed using bioinformatics tools, to identify significant differentially expressed genes (DEGs) and hub genes and their targeted miRNAs'. Results: According to the findings, CXCR4, TGFB1, ITGB1, MYH11, and SELE genes were identified as hub genes in this study. The analysis of DEGs using GO (gene ontology) revealed that these genes were significantly enriched in actin cytoskeleton regulation, ECM-receptor interaction, and hypertrophic cardiomyopathy. Eventually, hsa-mir-122-5p, hsa-mir-106a-5p, hsa-mir-27a-3p, hsa-mir16-5p, hsa-mir-145-5p, hsa-mir-12-5p, hsa-mir-128-3p, hsa-mir 3200-3p, hsa-mir-103a-3p, and hsa-mir-9-3p exhibited significant interactions with most of the hub genes. Conclusion: Overall, these genes can be considered as pivotal biomarkers for diagnosing the pathogenesis and molecular functions of AD.