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. 2016 Mar 31;7:10846.
doi: 10.1038/ncomms10846.

Quaking Promotes Monocyte Differentiation Into Pro-Atherogenic Macrophages by Controlling pre-mRNA Splicing and Gene Expression

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Quaking Promotes Monocyte Differentiation Into Pro-Atherogenic Macrophages by Controlling pre-mRNA Splicing and Gene Expression

Ruben G de Bruin et al. Nat Commun. .
Free PMC article

Abstract

A hallmark of inflammatory diseases is the excessive recruitment and influx of monocytes to sites of tissue damage and their ensuing differentiation into macrophages. Numerous stimuli are known to induce transcriptional changes associated with macrophage phenotype, but posttranscriptional control of human macrophage differentiation is less well understood. Here we show that expression levels of the RNA-binding protein Quaking (QKI) are low in monocytes and early human atherosclerotic lesions, but are abundant in macrophages of advanced plaques. Depletion of QKI protein impairs monocyte adhesion, migration, differentiation into macrophages and foam cell formation in vitro and in vivo. RNA-seq and microarray analysis of human monocyte and macrophage transcriptomes, including those of a unique QKI haploinsufficient patient, reveal striking changes in QKI-dependent messenger RNA levels and splicing of RNA transcripts. The biological importance of these transcripts and requirement for QKI during differentiation illustrates a central role for QKI in posttranscriptionally guiding macrophage identity and function.

Figures

Figure 1
Figure 1. Quaking is expressed in macrophages within atherosclerotic lesions.
(a) Pan-QKI mRNA expression levels in CD68+ macrophages of early and advanced atherosclerotic lesions isolated by laser-capture microdissection (n=4). Data expressed as mean±s.e.m.; Student's t-test, *P<0.05. Scale bar, 50 μm. (b) Immunohistochemical analysis of co-localization of pan-QKI and CD68 expression in preliminary intimal thickening (PIT), FCA and intraplaque haemorrage (PIH). Dashed line denotes intimal/adventitial border. Scale bar, 50 μm. (c) Immunohistochemical analysis of QKI-5, -6 and -7 expression in PIT, FCA and IPH (top), and quantification of QKI-positive cells mm2 per tissue sample (n=5). Data expressed as mean±s.e.m.; one-way analysis of variance (ANOVA), Bonferroni's post-hoc test; *P<0.05, **P<0.01. (d) Quantitative RT–PCR (qRT–PCR) analysis of QKI mRNA expression in naive BM-derived CD115+ mouse monocytes and 7 days M-CSF stimulated macrophages of either wt-littermates (LM) or quaking viable (qkv) mice (n=at least 3 mice per condition). Data expressed as mean±s.e.m.; one-way ANOVA, Bonferroni's post-hoc test; *P<0.05 and **P<0.01. (e) Western blot analysis of QKI-5, -6 and -7 expression levels in 7 days M-CSF stimulated macrophages derived from BM of wt and qkv mice. Each lane represents an individual mouse lysate (biological n=3). (f) Immunohistochemical analysis for atherosclerotic plaque-resident macrophages (% MoMa-positive area) in aortic root sections of γ-irradiated (8 Gy) LDLR/ mice that subsequently were transplanted with BM from either qkv mice (qkv-BM) or littermates (LM)(LM-BM) and fed a high-fat diet for 8 weeks to develop atherosclerotic lesions (n=12 per group). Scale bar, 200 μm. Data expressed as mean±s.e.m.; Student's t-test, with *P<0.05.
Figure 2
Figure 2. QKI is highly expressed in macrophages derived from PB monocytes.
(a) mRNA expression levels of distinct QKI isoforms following negative selection and FACS sorting for blood-derived human monocyte subsets, namely classical (CD14++, CD16), intermediate (CD14++,CD16+) and non-classical (CD14+,CD16+). Expression is depicted relative to copies per glyceraldehyde 3-phosphate dehydrogenase (GAPDH). Data expressed as mean±s.e.m.; one-way analysis of variance (ANOVA), Bonferroni's post-hoc test; *P<0.05 and **P<0.01. (b) Phase-contrast photomicrographs of human PB monocytes cultured for 7 days in the presence of either GM-CSF or M-CSF. Scale bar, 50 μm. (c) Quantitative RT–PCR (qRT–PCR) analysis for QKI mRNA isoforms in naive PB monocytes isolated using CD14+ microbeads, 7 days GM-CSF and 7 days M-CSF differentiated macrophages (n=3). Expression is depicted relative to copies per GAPDH. Data expressed as mean±s.e.m.; one-way ANOVA, Bonferroni's post-hoc test; *P<0.05 and **P<0.01. (d) Western blot analysis of QKI protein isoforms in naive monocytes, 7 days GM-CSF and 7 days M-CSF differentiated macrophages (pan-QKI and CD14: n=5, QKI-5, 6 and 7: n=1) with quantification of pan-QKI (n=5). Data expressed as mean±s.e.m.; Student's t-test, with **P<0.01. Equivalent concentrations of whole-cell lysates were loaded per lane as determined using a BCA protein assay.
Figure 3
Figure 3. Characterization of monocyte and macrophage biology in a unique QKI haploinsufficient patient.
(a) Schematic of chromosomal translocation event in the qkI haploinsufficient patient (Pat-QKI+/−), reducing QKI expression to ∼50% that of her sister control (Sib-QKI+/+). (b) Top: UCSC Genome Browser display of reference genome QKI locus with standard and chimeric reads for the patient and sibling. The reduced expression levels and altered 3′-untranslated region (UTR) composition in the patient RNA as compared with a sibling control is noteworthy. Patient shows increased intronic RNA extending to the point where chimeric reads map at the breakpoint to chr5. Middle: chromosome diagrams showing normal chromosomes 5 and 6 with the red line, indicating the location of the breakage fusion point. Bottom: sequence across the fusion point. The chromosomal origin of the AG dinucleotide is ambiguous. (c) Photomicrographs of sibling and patient macrophages, cultured in GM-CSF or M-CSF for 7 days, respectively. (d,e) Assessment of QKI isoform mRNA and protein expression in 4-day GM-CSF-stimulated macrophages in the sibling and patient. (f) Hierarchical clustering (Euclidean algorithm) of key monocyte differentiation genes depicting changes in RNA-seq-derived mRNA abundance where dark blue=low expression, whereas light blue=high expression (* and/or # indicates ≥1.5-fold expression change in monocytes or macrophages, respectively). (g) Venn diagrams with numbers of differentially expressed genes (minimally ±1.5-fold; patient/sibling expression) for unstimulated (top) and GM-CSF stimulated macrophages (bottom). An expression cutoff (Pat+Sib expression≥1CPM) was applied. (h) The most differentially expressed genes, harbouring a QRE are depicted. (i) Genome-wide scatterplot of mRNA abundance (y axis: Log10 CPM) versus the log2FC (x axis: Patient/sibling CPM) after an expression cutoff (Pat+Sib expression ≥1 CPM) in monocytes (left) and GM-CSF-stimulated macrophages (right). Blue dots indicate QRE-containing transcripts minimally ±1.5-fold differentially expressed. Grey dots do not fulfill these criteria. (j) CDF (y axis) for QKI target (QRE containing: blue line) and non-target (non-QRE containing: cyan line) mRNAs (x axis: log2FC) in monocytes (left) and macrophages (right). Left shift indicates lower expression of QKI target genes, whereas a right shift indicates higher expression of QKI targets in the patient samples. Distributions were compared using a Wilcoxon rank-sum test.
Figure 4
Figure 4. QKI influences pre-mRNA splicing in naive PB monocytes and macrophages.
(a) SpliceTrap assessment of the proximal ACUAA RNA motif enrichment in 50-bp windows upstream and downstream of alternatively spliced cassette exons (as compared with a background set of exons; grey circles). The relationship between the frequency of exon exclusion (blue triangles) or exon inclusion (red squares) and ACUAA RNA motif enrichment over the genomic locus are depicted. (b) Sashimi plots illustrate RNA-seq read coverage for selected alternative splicing events in Pat-QKI+/− versus Sib-QKI+/+ PB monocytes (orange) and macrophages (blue). Splicing events (se) are highlighted by inverted brackets. The location of ACUAA motifs and QKI PAR-CLIPs are provided below. Splicing events were defined based on the genomic organization of RefSeq transcripts (bottom tracks). Full event details are provided in Supplementary Data 3. (c) PCR validation of alternatively spliced cassette exons in Sib-QKI+/+ and Pat-QKI+/ PB-derived monocytes and macrophages. Primers were designed to target constitutive flanking exons. PCR product size for exon inclusion (top) and exclusion (bottom) variants are provided (left). (d) Phase-contrast and fluorescence-microscopy photographs (scale bar, 50 μm) of primary human, PB macrophages of healthy controls that have been treated with FAM-labelled GapmeRs, to reduce QKI expression. (e) Quantitative RT–PCR (qRT–PCR) of QKI mRNA isoform expression in GapmeR-treated macrophages (n=3). Data expressed as mean±s.e.m.; Student's t-test, with **P<0.01. (f) PCR validation of alternatively spliced cassette exons in GapmeR-treated PB-derived macrophages. Primers were designed to target constitutive flanking exons. PCR product size for exon inclusion (top) and exclusion (bottom) variants are provided (left). A representative illustration is shown of an n=3 donors. Data expressed as mean±s.e.m.; Student's t-test, with **P<0.01 and #P=0.08.
Figure 5
Figure 5. QKI influences mRNA transcript abundance during differentiation of THP-1 monocyte-like cells to THP-1 macrophage-like cells.
(a) mRNA expression of the QKI isoforms as compared with glyceraldehyde 3-phosphate dehydrogenase (GAPDH) in THP-1 ‘monocytes' and 8 days differentiated THP-1 ‘macrophages' (biological n=3). Data expressed as mean±s.e.m.; Student's t-test; *P<0.05 and **P<0.01. (b) Western blot analysis of whole-cell lysates of THP-1 ‘monocytes' and THP-1 ‘macrophages'. (c) Western blot quantification of QKI protein isoforms, normalized to β-actin in THP-1 ‘monocytes' and THP-1 ‘macrophages' (n=3). Data expressed as mean±s.e.m.; Student's t-test; **P<0.01. (d) Hierarchical clustering (Euclidean algorithm) of key monocyte differentiation genes depicting changes in microarray-derived mRNA abundance THP-1 ‘monocytes' (left two lanes) and THP-1 ‘macrophages' (right two lanes), where dark blue=low expression, whereas light blue=high expression (* and/or # beside gene name is indicative of a significant ≥1.5-fold change in expression in monocytes or macrophages, respectively). (e) Venn diagrams depicting the number of microarray-derived differentially expressed genes (minimally ±1.5-fold; sh-QKI/sh-Cont expression, q-value≤0.05) for unstimulated THP-1 ‘monocytes' (left Venn diagram) and THP-1 ‘macrophages' (right Venn diagram). (f) The most significantly differentially expressed genes harbouring a QRE are shown. (g) Genome-wide scatterplot of mRNA abundance in THP-1 ‘monocytes' (left scatterplot) and THP-1 ‘macrophages' (right scatterplot); y axis: Log10 probe intensity versus the x axis: log2FC: sh-QKI average probe intensity/sh-Cont average probe intensity. Blue dots indicate QRE-containing transcripts that are minimally ±1.5 fold differentially expressed (q≤0.05). Grey dots do not fulfill these criteria. (h) CDF (y axis) for QKI target (QRE containing: blue line) and non-target (non-QRE containing: cyan line) mRNAs (x axis: log2FC) in THP-1 ‘monocytes' (left plot) and THP-1 ‘macrophages' (right plot). Left shift indicates lower expression of QKI target genes in the sh-QKI samples, whereas a right shift is indicative of higher expression of QKI targets in the sh-QKI samples. Distributions were compared using a Wilcoxon rank-sum test.
Figure 6
Figure 6. QKI expression levels influence pre-mRNA splicing during THP-1-based monocyte-like to macrophage-like cell differentiation.
(a) Schematic depicting detectable alternative splicing events with the splicing-sensitive microarray platform and number of inclusion (incl.; top lines) or exclusion (excl.; bottom lines) events observed in unstimulated THP-1 ‘monocytes' (left) and 3-day PMA-stimulated THP-1 ‘macrophages' (n=3, q≤0.05). (b) Scatterplots of skip (y axis) and include (x axis) probe set intensity for selected alternative splicing events in sh-Cont (blue boxes) versus sh-QKI (orange circles) in unstimulated and 3 days PMA-stimulated THP-1 ‘monocytes' and ‘macrophages', respectively. Regression coefficients (constrained to pass the origin) are depicted as solid lines. The log2 difference in the slopes (termed separation score; ss) are provided to the right of the plots for each event, with for example, an ss of −1.72, indicating a 3.3-fold more inclusion of ADD3 exon 13 in sh-QKI versus sh-Cont THP-1 ‘monocytes'. Full event details are provided in Supplementary Data 6. CE, cassette exon; Alt 5′ or 3′, alternative 5′ or 3′ splice site; RI, retained intron. (c) SpliceTrap assessment of average proximal ACUAA RNA motif enrichment in 50 bp windows upstream and downstream of alternatively spliced cassette exons as compared with a background set of exons (grey circles). The relationship between the frequency of exon exclusion (blue triangles) or exon inclusion (red squares) and ACUAA RNA motif enrichment are depicted. (d) PCR validation of alternatively spliced cassette exons in sh-Cont and sh-QKI THP-1 ‘monocytes' and ‘macrophages'. Primers were designed to target constitutive flanking exons. PCR product size for exon inclusion (top) and exclusion (bottom) variants are provided (left). All experiments depict biological n=3. (e) PCR validation of three splicing events in wt and qkv mouse-derived primary monocytes and 7 days M-CSF-stimulated macrophages. PCR product size for exon inclusion (top) and exclusion (bottom) variants are provided (left). Depicted is a representative PCR for at least a biological n≥3.
Figure 7
Figure 7. QKI expression levels have an impact on monocyte adhesion as well as migration and differentiation.
(a) Cumulative population doublings (y axis: CPDs) were counted to assess the effect of QKI reduction on cellular proliferation over time (x axis: days). Population growth curves were compared using linear regression analysis.(b) Quantification of cellular apoptosis, where annexin V+ and propidium iodide+ cells were categorized as apoptotic, as determined by FACS analysis. (c) Quantification of sh-Cont and sh-QKI THP-1 ‘monocyte' adhesion to collagen matrix pretreated with platelet-rich plasma under flow, mimicking in-vivo endothelial denudation. Direction of flow is indicated below the photomicrographs (n=3). Data expressed as mean±s.e.m.; Student's t-test; *P<0.05. Scale bar, 100 μm. (Also see Supplementary Movies 1 and 2). (d) Assessment of integrin-mediated adhesion. Quantification of adhesion to collagen for untreated, PMA- or TS2/16-treated sh-Cont and sh-QKI THP-1 ‘monocytes' are plotted. TS2/16 is an antibody that turns all β1-integrins in the high-affinity conformation, inducing cellular adhesion. (e) Quantification of cellular transwell migration towards either fMLP (for THP-1 ‘monocytes') or macrophage chemoattractant protein 1 (MCP-1; for PB monocytes from either sibling or patient (n=4 technical replicates). Data expressed as mean±s.e.m.; Student's t-test; *P<0.05 and **P<0.01.
Figure 8
Figure 8. QKI regulates the expression of atherosclerosis-related mRNAs and impairs foam cell formation.
(a) Quantitative RT–PCR (qRT–PCR) analysis of established atherosclerosis-related genes in wt of qkv-derived monocytes and macrophages. Gene expression in qkv samples are relative to either WT monocytes or WT macrophages (n≥3). Data expressed as mean±s.e.m.; Student's t-test; *P<0.05, **P<0.01. (b) RNA-seq-derived expression values to illustrate the expression of established atherosclerosis-related genes in sibling or patient monocytes and macrophages. (c) qRT–PCR analysis of QKI isoform mRNA expression in unstimulated THP-1 ‘macrophages', or treated with β-VLDL or acLDL for 24 h. Data expressed as mean±s.e.m.; one-way analysis of variance (ANOVA), Bonferroni's post-hoc test; *P<0.05. (d) qRT–PCR analysis of well-known atherosclerosis-related genes in sh-cont or sh-QKI THP-1 ‘macrophages' that were either left untreated or treated with acLDL or β-VLDL to induce foam cell formation (n=3). Data expressed as mean±s.e.m.; Student's t-test; *P<0.05 and **P<0.01. (e) Photomicrographs of an Oil-red-O staining to assess β-VLDL, acLDL uptake in either sh-Cont or sh-QKI THP-1 ‘macrophages'. Scale bar, 100 μm. Inset is a high-magnification image of lipid-droplet accumulation. Data expressed as mean±s.e.m.; Student's t-test; **P<0.01. (f) Photomicrographs of an Oil-red-O staining to assess β-VLDL, acLDL or oxidized LDL (oxLDL) uptake in either Sib-QKI+/+ (upper panels) or Pat-QKI+/− (lower panels) macrophages that were first differentiated for 7 days with GM-CSF. Scale bar, 100 μm. Inset is a high-magnification image of lipid-droplet accumulation. Data expressed as mean±s.e.m.; Student's t-test; *P<0.05 and **P<0.01.
Figure 9
Figure 9. Schematic depicting how QKI posttranscriptionally regulates monocyte to macrophage differentiation and atherosclerosis development.
QKI mRNA expression increases in intermediate monocytes, reaching a peak in the non-classical monocyte (middle). Monocytes adhere to the endothelium at sites of tissue injury, leading to their activation and migration into the subendothelial space. This process requires QKI, as the targeted ablation of QKI impaired monocyte adhesion and migration, and the evident transition in cellular phenotype requires extensive reprogramming of the posttranscriptional landscape. On tissue entrance, the monocyte differentiates into a macrophage, a conversion that was associated with a potent increase in QKI protein. This increase potentiates the interaction of QKI with (pre-)mRNA targets, enhancing splicing and target mRNA repression. The loss of QKI in macrophages results in an inability to adopt the macrophage phenotype and a perturbation of lipid uptake and foam cell formation.

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