FastEV & Integrations

FastEV highlights different genetic interaction networks from plasma

Integration of miRNA and mRNA data from all FastEV conditions revealed that the conditions were able to pull out activated, neutral and inhibited pathways from the same plasma source.

Figure: FastEV picks up canonical pathways from plasma – pathway activation status differ between conditions. Heatmap of the Canonical Pathway enrichments (p adj <0.05 and abs z-score ≥2) derived from Ingenuity pathway analysis (IPA) of differential miRNA-mRNA networks. FastEV conditions yielded a range of significant pathways compared to plasma. The status of the individual pathways ranged from activated (high z-score indicated with red color) to neutral (z-score 0 indicated with white color) and to inhibited (low z-score indicated with blue color) in different FastEV conditions. Gray color denotes that z-score could not be calculated. Analysis with miRNA data from two different plasmas (fasting and non-fasting) is shown separately. F, fasting plasma, NF, non-fasting plasma.

In focus: interleukin-15 production pathway

We selected interleukin-15 production pathway for a closer inspection. FastEV conditions isolated this pathway in a range of activation states: we picked the conditions showing the pathway in most activated, neutral and inhibited state and show their transcript expression below.

Figure: Expression of IL-15 production pathway genes in three FastEV conditions. Heatmap of the gene expression (p adj <0.05 and abs z-score ≥2) derived from Ingenuity pathway analysis (IPA) of differential miRNA-mRNA networks in three different FastEV conditions compared to plasma. Upregulation of genes is marked with pink color, downregulation with green. Highest pathway activation i.e. high z-score is indicated with orange color, neutral activation level and z-score 0 with white color and inhibition and low z-score with blue color.

Methods: Integrative analysis of miRNA-mRNA networks of FastEV 

Ingenuity pathway analysis (IPA): Input data consisted of differentially expressed (DE) miRNAs between condition vs plasma and their target genes observed in DE mRNA analysis, both with ≥2-fold up- or down-regulation compared to plasma. They were paired by IPA miRNA Target Filter and filtered to include only those with negatively correlated expression changes. IPA Core Analysis was run for this data and the Canonical Pathway results produced. Comparison Analysis of the Canonical Pathway enrichments was run for comparisons of all FastEV conditions to plasma in two batches due to IPA limitations. MiRNA data from two different plasmas (fasting and non-fasting) was run separately to compare the data in the two conditions. To visualize the capability of FastEV conditions to enrich pathways in different activation status, the two batches of IPA results were merged utilizing adjusted p-value tables and z-score tables of all pathways without filtering.

Heatmap of pathways: Pathways with adjusted p-value < 0.05 and absolute z-score ≥ 2 in at least one comparison were extracted for a heatmap. Columns of the heatmap were ordered based on descending z-score value of IL-15 Production pathway and this pathway was placed on the top row of the heatmap. Grey block in the heatmap indicates that z-score could not be calculated for the given pathway for that comparison.

Visualization of IL-15 production network: Gene Heatmap functionality of IPA Comparison Analysis was used for plotting heatmaps of the genes associated with IL-15 Production pathway for three comparisons, namely the one with highest z-score , lowest z-score and one with neutral z-score. For IPA pathway diagram of the IL-15 Production pathway, molecules were colored with the expression changes observed in the comparison with the highest z-score indicating strongest predicted pathway activation. Diagram was extended by displaying the member molecules of the affected molecule complexes (PTK, PKC). Boxes beside the complex molecules show the expression fold changes in three FastEV conditions as bar plots. Complex molecules that did not have any observed expression changes in any of the three conditions were not displayed.

Figure: Integrative analysis of IL-15 production networks enriched by one of the FastEV conditions. ​The network presents differentially expressed integrated miRNA-mRNA analysis of IL-15 production in a FastEV isolated sample relative to pure plasma. This condition showed the highest activation of the monocyte pathway of all the FastEV conditions. Dark color indicates higher absolute z-score or gene expression i.e. up- or down-regulation (pink and green, respectively) than light color. All genes were expressed in all conditions but at different levels: bars show expression level in three conditions (1-activated), (2-neutral) and (3-inhibited) relative to plasma. MiRNAs or non-differential mRNAs are not shown for clarity.

FastEV provides a window to circulating mitochondrial transcriptome

RNAseq data was analyzed for the mitochondrial vs non-mitochondrial transcripts of FastEV conditions and controls. FastEV gave the highest numbers of mitochondrial reads.​
Analysis of mitochondrial miRNA-mRNA pairs showed that different isolation conditions differed vastly in the highlighted differentially expressed networks relative to plasma.

Methods: Integration of mRNAseq and miRNAseq data for visualization of circulating mitochondrial transcriptomic networks

We performed integrative analysis of mitochondrial miRNA-mRNA networks targeting differentially expressed (DE) mRNAs in conditions 1 vs plasma, 2 vs plasma, 3 vs plasma and PEG vs plasma. Data from non-fasting plasma was used for the analysis. DE mRNAs with absolute FC > 2 were filtered to include only mitochondrial genes. Target genes of the DE miRNAs (with absolute FC > 2) relative to plasma were obtained using Ingenuity Pathway (IPA) miRNA Target Filter analysis. These data were paired and filtered to contain only those miRNA with mitochondrial gene targets with negative correlation of expression levels. Results were visualised using network graphs.

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Sequencing mitochondrial mRNAs from FastEV

Figures: High mitochondrial content. Proportions (%) of mitochondrial reads and read counts derived from mitochondrial and non-mitochondrial mRNAs. Data is from one technical replicate per sample, except in case of FastEV conditions 1-3 (n=3) and controls (n=2). PEG, polyethylene glycol, UC, ultracentrifugation.

Different enrichments of mitochondrial mRNAs and miRNAs

Table: Mitochondrial mRNAs and miRNAs. Number of robustly detected mitochondrial miRNAs (out of 106) and mRNAs (out of 37) in FastEV conditions and EV isolation controls (PEG, UC). All mitochondrial mRNAs were always detected, but enrichments relative to plasma differed vastly. Data is from one technical replicate per sample, except in case of three FastEV conditions (n=3) and controls (n=2). MT-RNA, mitochondrial mRNA; PEG, polyethylene glycol; UC, ultracentrifugation.

Integrative analysis of mitochondrial transcripts

Figures: Integrative analysis of circulating mitochondrial miRNA-mRNA networks. Condition 3 highlighted a large differential network relative to plasma. The analysis targeted differentially expressed negatively correlated transcripts in FastEV conditions or EV isolation control (PEG) relative to plain plasma. Data integrates RNAseq data (technical replicates n=3 per FastEV condition and n=2 for PEG) as well as miRNAseq data (non-fasting technical replicates n=1 for all). However, PEG did not yield any differential network relative to plasma. PEG, polyethylene glycol.