Syncytiotrophoblast Membrane Extracellular Vesicles (STB-EVs) Derived MicroRNA Biomarkers of Preeclampsia-Supplemental Data
Description
This folder contains the differentially expressed microRNA as well as the outcomes of functional enrichment analysis involving Gene Ontology terms for Biological Processes (GO:BP), Molecular Functions (GO:MF), Cellular Components (GO:CC), SPIA, and KEGG Pathways for placental samples and medium/large syncytiotrophoblast membrane extracellular vesicles (m/lSTB-EVs). Additionally, it contains the results of the analysis of correlations between microRNAs and messenger RNAs.
Files
Steps to reproduce
The bioinformatics analysis for microRNA was conducted using Oasis 2.0 (https://oasis.ims.bio/small_rna_classification.php), a bioinformatic web tool known for its speed, reliability, and accessibility. Initially, the fastq files underwent compression using the Oasis compressor to facilitate subsequent analysis. The compressed files were then uploaded to the Oasis 2.0 web tool. Quality control (QC) metrics were obtained using FastQC (v.0.11.2) both before and after the trimming of adapters/barcodes [trimmomatic (v.0.32)], along with length filtering set at a minimum of 15 and a maximum of 50 nucleotides. Acceptable mismatches were defined as 5% of the read length. The reads were aligned against the human reference genome, microRNAome, and sRNAome (piwi-interacting RNA, small interfering RNA, small nuclear RNA, and small nucleolar RNA) stored in the Oasis database using STAR (2.4.1d). Unmapped reads from this step underwent realignment to the human reference genome (Homo Sapiens hg38) for the prediction and archival of novel microRNAs in the Oasis-Db, utilizing Bowtie (v1.0.0). Subsequently, unmapped reads from this second step were mapped to bacterial, archaeal, and viral genomes using Kraken (v.0.15.5-beta) to identify and characterize infectious contaminants. For the detection of potentially orthologous or cross-species microRNA reads that could not be classified as human or infectious contaminants in any of the three prior steps, realignment was carried out against all non-human microRNAs in miRbase 208 using STAR (v.2.4.1d). Any remaining unmapped reads after these four simultaneous steps were subsequently discarded. Gene expression values were quantified using STAR quant mode, and exploratory analysis of the results, including visualization, was conducted on Oasis. A gene count table was generated from each gene count. The DESeq2 package (v.1.32.0 in R v.4.0.5) generated a list of differentially expressed small RNAs between normal and preeclampsia samples, following the standard protocol and accompanying DESeq’s tutorial. This analysis was conducted separately for the top 100 upregulated and downregulated microRNAs identified in the placenta and m/lSTB-EVs. Restricting the examination to up to 100 microRNAs (miRNAs) with a fold change (LFC ≥ 0.75) and an adjusted p-value (< 0.05), the imposed limit was imposed by the default settings of the tool and the computational demands associated with processing extensive lists of microRNAs. The prediction of mRNA targets for the loaded microRNAs was accomplished using the microT-CDS algorithm with a microT-threshold of 0.8. To refine the results further, the outcomes of this analysis were fused at the level of categories or pathways. Additionally, removing, summarizing, and visualizing redundant Gene Ontology (GO) terms were undertaken using REVIGO (http://revigo.irb.hr/) to address the computational intensity and streamline the data.