Transfer RNA fragments replace microRNA regulators of the cholinergic poststroke immune blockade

Abstract

Stroke is a leading cause of death and disability. Recovery depends on a delicate balance between inflammatory responses and immune suppression, tipping the scale between brain protection and susceptibility to infection. Peripheral cholinergic blockade of immune reactions fine-tunes this immune response, but its molecular regulators are unknown. Here, we report a regulatory shift in small RNA types in patient blood sequenced 2 d after ischemic stroke, comprising massive decreases of microRNA levels and concomitant increases of transfer RNA fragments (tRFs) targeting cholinergic transcripts. Electrophoresis-based size-selection followed by qRT-PCR validated the top six up-regulated tRFs in a separate cohort of stroke patients, and independent datasets of small and long RNA sequencing pinpointed immune cell subsets pivotal to these responses, implicating CD14+ monocytes in the cholinergic inflammatory reflex. In-depth small RNA targeting analyses revealed the most-perturbed pathways following stroke and implied a structural dichotomy between microRNA and tRF target sets. Furthermore, lipopolysaccharide stimulation of murine RAW 264.7 cells and human CD14+ monocytes up-regulated the top six stroke-perturbed tRFs, and overexpression of stroke-inducible tRF-22-WE8SPOX52 using a single-stranded RNA mimic induced down-regulation of immune regulator Z-DNA binding protein 1. In summary, we identified a "changing of the guards" between small RNA types that may systemically affect homeostasis in poststroke immune responses, and pinpointed multiple affected pathways, which opens new venues for establishing therapeutics and biomarkers at the protein and RNA level.

Keywords: acetylcholine; immunology; ischemic stroke; microRNA; transfer RNA fragment.

Conflict of interest statement

The authors declare no competing interest.

Copyright © 2020 the Author(s). Published by PNAS.

Figures

Fig. 1.

Poststroke differential expression of small RNA species and tRF homology clustering. (A) Whole-blood total RNA samples were collected on day 2 poststroke from patients of the PREDICT cohort (NCT01079728) (16) and age-matched controls. (B) PCA of DE tRFs/miRs in patients’ blood separated stroke and control samples. (C) Volcano plot of DE tRFs from stroke patients and controls (horizontal line at adjusted P = 0.05) showing up-regulation of most DE tRFs. (D) Volcano plot of DE miRs shows predominant down-regulation in stroke patients compared with controls (horizontal line at adjusted P = 0.05). Red dots in C and D reflect Cholino-tRFs and Cholino-miRs, respectively. (E) t-SNE visualization of tRF homology based on pairwise alignment scores of sequences of all detected tRFs shows grouped tRFs of several specific amino acid origins.

Fig. 2.

qRT-PCR validation of the top six up-regulated tRFs in PREDICT stroke patients following size selection for small RNA. (A) RNA-seq counts normalized to the size of the library [using DESeq2 (23)] of the top six up-regulated tRFs (from left to right). Asterisks indicate adjusted P values of Wald test via DESeq2, **P < 0.01, ***P < 0.001; shown are box-plots with whiskers minimum to maximum. (B) Size-selection workflow for validations in a separate subgroup of PREDICT stroke patients (n = 32) using the same control group (n = 10). (C) qRT-PCR validations using normalized expression (hsa-miR-30d-5p, hsa-let7d-5p, hsa-miR-106b-3p, and hsa-miR-3615 served as housekeeping transcripts) (SI Appendix, Expanded Methods), relative to the control group (line at mean normalized expression for the control group = 1) confirmed up-regulation of top six DE tRFs identified in RNA-seq, one-way ANOVA, **P < 0.01, ***P < 0.001, box-plots with whiskers minimum to maximum.

Fig. 3.

Immune cell tRF expression clustering and cell type-specific analysis. (A) Analysis of RNA-seq datasets from T lymphocytes (CD4+ T helper cells and CD8+ T cytotoxic cells), B lymphocytes (CD19+), NK cells (CD56+), monocytes (CD14+), neutrophils (CD15+), erythrocytes (CD235a+), serum, exosomes, and whole blood (24) yielded a blood tRF profile. (B) Definition of presence/absence of small RNAs in these blood compartments via statistical assertion of log-normal count distribution (values between 0 and 1, closer to 1: present). (C) Detailed analysis of identified tRFs found eight subclusters based on cell types expressing specific molecules. (D) t-SNE of all found tRFs represented by gray dots, DE tRFs identified in the PREDICT study are marked with cluster-specific color. (E) t-SNE of all tRFs found, Cholino-tRFs identified in the PREDICT study are marked with cluster-specific color.

Fig. 4.

Immune cell gene-expression clustering and long RNA pathways perturbed in stroke blood. (A) Published cell type-specific long RNA profiles (25) were used to visualize transcriptomes of T lymphocytes (CD4+ T helper cells and CD8+ T cytotoxic cells), B lymphocytes (CD19+), NK cells (CD56+), monocytes (CD14+), and neutrophils (CD15+). (B) t-SNE visualization of 15,032 genes on the basis of their expression in blood-borne immune cells extrapolated from transcriptional activities in regulatory circuits (25). Genes are colored by the cell type in which their expression was highest. Cholinergic core and receptor genes were mainly found in the CD14+ monocytic compartment. (C) Enrichment of poststroke DE genes (log2FC > 1.4) in circulation- and immunity-related pathways, presented as t-SNE of GO terms by their shared genes (SI Appendix, Expanded Methods); color denotes t-SNE cluster, size denotes number of significant genes in term; deeper color indicates lower enrichment P value (all P < 0.05). Distance between terms indicates the number of shared genes between the GO terms, closer meaning more shared genes.

Fig. 5.

GO enrichment of miR targets and perturbed pathways; divergent influence of miRs and tRFs in CD14+ TF regulatory circuits. (A) t-SNE visualization of GO terms enriched in the targets of miRs perturbed by stroke, performed separately for positively (green) and negatively (red) perturbed miRs, segregated into 13 functional clusters. Size of circles represents the number of genes in the respective GO term; depth of color represents enrichment P value (all P < 0.05). (B) Bar graph of clusters identified in A ordered by the number of enriched genes (Fisher’s exact test, Benjamini–Hochberg adjusted P < 0.05) shows most pertinent processes with miR involvement. (C) The top 18 DE TFs in stroke patients’ blood present a gradient of targeting by miRs and/or tRFs (left = 100% miR targeting, right = 100% tRF targeting; value shown as “tRF fraction – 0.5” to center on 50/50 regulation by miRs and tRFs). Size of points and color denote absolute count-change and direction of differential regulation, respectively. “C” marks TFs targeting cholinergic core or receptor genes. (D) Small RNA targeting of TFs active in CD14+ cells was analyzed using miRNeo (19). (E) Force-directed network of all TFs active in CD14+ monocytes self-segregates to form largely distinct TF clusters targeted by DE tRFs and miRs in stroke patients’ blood. Yellow = TF, red = TF DE in stroke patients’ blood, green = miR, purple = tRF. Size of node denotes activity toward targets.

Fig. 6.

tRF changes upon LPS stimulation of murine RAW 264.7 macrophages and human CD14+ monocytes and tRF-mimic transfection. (A) LPS stimulation of RAW 264.7 murine macrophage cells induced clear morphologic changes within 18 h. Extracted RNA was subjected to size selection and cDNA synthesized from the ≤50-nt fraction alone. (Scale bar, 100 μm, magnification in the upper panel 2.5×) (B) LPS-stimulated RAW 264.7 cells show dexamethasone-suppressible elevated levels of poststroke induced tRFs. Normalized qRT-PCR values (using mmu-miR-30d-5p, mmu-let7d-5p as housekeeping transcripts) (SI Appendix, Expanded Methods), compared to unstimulated controls. Each dot represents two to four technical replicates, ANOVA with Tukey post hoc, *P < 0.05, **P < 0.01, bar graphs ±SD(lg). (C) Murine Zbp1 sequence carries an 8-nt-long fragment in the 3′UTR complementary to tRF-22-WE8SPOX52. (D) To test the miR-like mechanism of action, RAW 264.7 cells were transfected with ssRNA tRF-22-WE8SPOX mimics or negative control ssRNA, and RNA was extracted 24 h after transfection and subjected to polyA-selected RNA-seq (E) and qRT-PCR (F). (E) Long RNA-seq of cells transfected with ssRNA tRF-22-WE8SPOX52 mimics revealed significantly down-regulated expression of Z-DNA binding protein (Zbp1) as compared to negative control (NC). *P < 0.05, shown is adjusted P value of Wald test via DESeq2, bar graph ±SD. (F) qRT-PCR from an independent cell culture experiment confirmed the down-regulation of Zbp1 expression after ssRNA tRF-22-WE8SPOX52 mimic transfection (relative normalized expression using Gapdh as a housekeeping gene), *P < 0.05 one-way ANOVA, each dot represents a technical cell culture replicate, bar graph ±SD(lg). (G) MACS-sorted CD14+ cells from healthy human donors were stimulated with LPS with or without addition of nicotine and collected 6, 12, and 18 h thereafter. Extracted RNA was subjected to size selection and cDNA synthesized from the ≤50-nt fraction. Timepoints 6 h and 18 h are shown in SI Appendix, Fig. S8. (H) At 12 h after LPS stimulation, human monocytes exhibited up-regulation of poststroke induced tRFs as compared to unstimulated controls or cells treated with nicotine alone. This reaction was boosted by the addition of nicotine. Shown is relative expression (hsa-miR-30d and hsa-let7d-5p were used as housekeeping transcripts) (SI Appendix, Expanded Methods) normalized to the nonstimulated group. Each dot represents one donor. ANOVA with Tukey post hoc, *P < 0.05, **P < 0.01, ***P < 0.001 vs. nonstimulated cells; #P < 0.05, ##P < 0.01, ###P < 0.001 vs. cells upon addition of nicotine, bar graphs ±SD(lg).