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Epigenetic dysregulation contributes to the high cardiovascular disease burden in chronic kidney disease (CKD) patients. Although microRNAs (miRNAs) are central epigenetic regulators, which substantially affect the development and progression of cardiovascular disease (CVD), no data on miRNA dysregulation in CKD-associated CVD are available until now. We now performed high-throughput miRNA sequencing of peripheral blood mononuclear cells from ten clinically stable hemodialysis (HD) patients and ten healthy controls, which allowed us to identify 182 differentially expressed miRNAs (e.g., miR-21, miR-26b, miR-146b, miR-155). To test biological relevance, we aimed to connect miRNA dysregulation to differential gene expression. Genome-wide gene expression profiling by MACE (Massive Analysis of cDNA Ends) identified 80 genes to be differentially expressed between HD patients and controls, which could be linked to cardiovascular disease (e.g., KLF6, DUSP6, KLF4), to infection / immune disease (e.g., ZFP36, SOCS3, JUND), and to distinct proatherogenic pathways such as the Toll-like receptor signaling pathway (e.g., IL1B, MYD88, TICAM2), the MAPK signaling pathway (e.g., DUSP1, FOS, HSPA1A), and the chemokine signaling pathway (e.g., RHOA, PAK1, CXCL5). Formal interaction network analysis proved biological relevance of miRNA dysregulation, as 68 differentially expressed miRNAs could be connected to 47 reciprocally expressed target genes. Our study is the first comprehensive miRNA analysis in CKD that links dysregulated miRNA expression with differential expression of genes connected to inflammation and CVD. After recent animal data suggested that targeting miRNAs is beneficial in experimental CVD, our data may now spur further research in the field of CKD-associated human CVD.
High-throughput metabarcoding studies on fungi and other eukaryotic microorganisms are rapidly becoming more frequent and more complex, requiring researchers to handle ever increasing amounts of raw sequence data. Here, we provide a flexible pipeline for pruning and analyzing fungal barcode (ITS rDNA) data generated as paired-end reads on Illumina MiSeq sequencers. The pipeline presented includes specific steps fine-tuned for ITS, that are mostly missing from pipelines developed for prokaryotes. It (1) employs state of the art programs and follows best practices in fungal high-throughput metabarcoding; (2) consists of modules and scripts easily modifiable by the user to ensure maximum flexibility with regard to specific needs of a project or future methodological developments; and (3) is straightforward to use, also in classroom settings. We provide detailed descriptions and revision techniques for each step, thus giving the user maximum control over data treatment and avoiding a black-box approach. Employing this pipeline will improve and speed up the tedious and error-prone process of cleaning fungal Illumina metabarcoding data.