PARP9 and PARP14 cross-regulate macrophage activation via STAT1 ADP-ribosylation

Hiroshi Iwata, Claudia Goettsch, Amitabh Sharma, Piero Ricchiuto, Wilson Wen Bin Goh, Arda Halu, Iwao Yamada, Hideo Yoshida, Takuya Hara, Mei Wei, Noriyuki Inoue, Daiju Fukuda, Alexander Mojcher, Peter C Mattson, Albert-László Barabási, Mark Boothby, Elena Aikawa, Sasha A Singh, Masanori Aikawa, Hiroshi Iwata, Claudia Goettsch, Amitabh Sharma, Piero Ricchiuto, Wilson Wen Bin Goh, Arda Halu, Iwao Yamada, Hideo Yoshida, Takuya Hara, Mei Wei, Noriyuki Inoue, Daiju Fukuda, Alexander Mojcher, Peter C Mattson, Albert-László Barabási, Mark Boothby, Elena Aikawa, Sasha A Singh, Masanori Aikawa

Abstract

Despite the global impact of macrophage activation in vascular disease, the underlying mechanisms remain obscure. Here we show, with global proteomic analysis of macrophage cell lines treated with either IFNγ or IL-4, that PARP9 and PARP14 regulate macrophage activation. In primary macrophages, PARP9 and PARP14 have opposing roles in macrophage activation. PARP14 silencing induces pro-inflammatory genes and STAT1 phosphorylation in M(IFNγ) cells, whereas it suppresses anti-inflammatory gene expression and STAT6 phosphorylation in M(IL-4) cells. PARP9 silencing suppresses pro-inflammatory genes and STAT1 phosphorylation in M(IFNγ) cells. PARP14 induces ADP-ribosylation of STAT1, which is suppressed by PARP9. Mutations at these ADP-ribosylation sites lead to increased phosphorylation. Network analysis links PARP9-PARP14 with human coronary artery disease. PARP14 deficiency in haematopoietic cells accelerates the development and inflammatory burden of acute and chronic arterial lesions in mice. These findings suggest that PARP9 and PARP14 cross-regulate macrophage activation.

Figures

Figure 1. Bioinformatics to identify candidate regulators…
Figure 1. Bioinformatics to identify candidate regulators of macrophage activation.
(a) Venn diagrams showing the distribution of quantified proteins from mouse RAW264.7 and human THP-1 cells in unstimulated control, IFNγ-stimulated and IL-4-stimulated macrophages: M(-), M(IFNγ) and M(IL-4), respectively. (b) Data set-filtering strategy. Upper panels: superimposition of the 0-h-normalized protein abundance profiles for M(-) (grey traces) versus M(IFNγ) (red traces) or M(IL-4) (blue traces) data sets in RAW264.7 and THP-1 cells. Lower panels: extracted protein profiles of interest generated by data filtering. Red traces only graphs: extracted profiles of proteins whose abundances exceed the M(IFNγ) threshold (+0.13, maximum protein abundance in unstimulated control at 8 h, dashed line). From these M(IFNγ)-filtered traces, those that decreased in IL-4 stimulation when compared with their unstimulated control are plotted to the right for RAW264.7 and THP-1 cells, respectively. (c) Hierarchical clustering of 38 proteins from that were identified in both RAW264.7 and THP-1 data sets. Each row corresponds to a protein gene ID.
Figure 2. Network analysis links PARP9–PARP14 with…
Figure 2. Network analysis links PARP9–PARP14 with coronary artery disease.
The PARP14 (blue)–PARP9 (purple) module consists of the first neighbours of each protein (light blue and orange nodes, respectively). The significance of closeness of the PARP9–PARP14 first neighbours in the interactome (PARP9–PARP14 module) and disease modules compared with random expectation is indicated by P values. The random expectation was same size-connected components of PARP9–PARP14 module and a disease module drawn randomly from the network. Closeness between PARP9–PARP14 modules and other diseases such as cardiovascular, metabolic and IFNγ-related diseases was evaluated in the network. The inner circle contains significantly close disease modules.
Figure 3. PARP9 and PARP14 expression in…
Figure 3. PARP9 and PARP14 expression in vitro and in vivo.
(a) TMT-derived 0-h-normalized protein abundance profiles for PARP9 and PARP14 from mouse RAW264.7 and human THP-1 M(IFNγ) and M(IL-4) data sets. (b) PARP9 and PARP14 gene expression at 24 h after stimulation (n=3). (c) PARP9 and PARP14 protein expression visualized by western blot. The time course in the relative protein abundances of PARP9 and PARP14 normalized to β-actin were quantified (graph, n=3). *P<0.05 and **P<0.01, respectively, by Student's t-test. Error bars indicate s.d. (d) Representative images of PARP9 and PARP14 expression in atherosclerotic plaques from the aorta of an Apoe−/− mouse (n=3) fed a high-fat diet and from the carotid artery of a human (n=5). Scale bars, 100 μm.
Figure 4. The molecular functions of PARP9…
Figure 4. The molecular functions of PARP9 and PARP14 in macrophages in vitro.
(a) The consequences of PARP9 and PARP14 silencing on IFNγ stimulated (TNFα, IL-1β and CCL2/MCP-1) and IL-4 stimulated (MRC1) gene expression in human primary macrophages (n=8). (b) The consequences of PARP9 and PARP14 silencing on IFNγ stimulation (TNFα and iNOS) and IL-4 stimulation (Arg1 and MRC1) gene expression in mouse bone marrow-derived macrophages (n=3). (c) The ratio of phosphorylated STAT1 and STAT6 protein levels to total STAT1 and STAT6 (pSTAT1/tSTAT1 ratio and pSTAT6/tSTAT6 ratio) in human primary macrophages (n=6 and n=5, respectively) of the PARP9 and PARP14 silencing experiments. * P<0.05 and **P<0.01, respectively, by Student's t-test. Error bars indicate s.d.
Figure 5. Potential interaction of PARP9 and…
Figure 5. Potential interaction of PARP9 and PARP14.
(a) PARP14 silencing and enforced expression significantly affected PARP9 gene expression in IFNγ-stimulated THP-1 cells (n=3). PARP9 silencing increased PARP14 gene expression (n=3). (b) Co-IP assay revealed a complex between PARP9 and PARP14. (c) Intracellular colocalization of PARP9 and PARP14 in the cytosol in M(-) and M(IFNγ). (d) PARP9 inhibits ADP-ribosylation of STAT1α and STAT6 by PARP14 (protein ribosylation assay). PARP14 auto-ribosylation is also indicated. *P<0.05 and **P<0.01, respectively, by Student's t-test. Error bars indicate s.d.
Figure 6. Identification of PARP14-induced ribosylation sites…
Figure 6. Identification of PARP14-induced ribosylation sites in STAT1.
(a) The amino-acid sequence of human STAT1α C terminus. Green amino acids indicate ribosylated peptides; confirmed ribosylation sites are underlined. STAT1 is phosphorylated at indicated tyrosine (red). (b; Left panels) MS/MS spectra for the mono-ADP-ribosylated peptides and corresponding unmodified forms. ADP-ribose fragments are annotated in green. *, ribosylation site; m, oxidized methionine. The grey circles indicate background or undetermined ions. (Right panels) MS1-based quantification of PARP9 inhibition of PARP14-mediated STAT1α ribosylation at E657 (upper panel) and E705 (lower panel), respectively. (c) Effects of mutated amino acids at E657 and E705 in STAT1 (ribosylation sites for PARP14) on its Tyr701 phosphorylation and pro-inflammatory gene expression in mouse bone marrow-derived macrophages (n=4). *P<0.05 and **P<0.01, respectively, by Student's t-test. Error bars indicate s.d.
Figure 7. Role of haematopoietic PARP14 in…
Figure 7. Role of haematopoietic PARP14 in acute arterial lesion formation in mice.
(a–c) Cultured peritoneal macrophages derived from PARP14−/− and PARP14+/+ mice. (a) IFNγ and IL-4 pathway gene expression profiles (n=3). (b) Secretion of inflammatory factors into culture media (n=3). (c) Western blot and corresponding densitometry quantification of phosphorylated STAT1 and STAT6. Each data point is the average of triplicate samples per donor (n=3). (d) Left: representative images of haematoxylin and eosin (H&E; top) and Mac3 (bottom) staining. Scale bars, 100 μm. Right: quantification of lesion formation in mechanically injured femoral arteries of PARP14−/− and PARP+/+ mice. Mac3 staining represents macrophage accumulation (n=4–5). (e) LCM of the neointima followed by gene expression analysis (n=4). (f) Flow cytometry analysis of splenic CD11b+Ly6G− monocytes after induction of mechanically injured femoral arteries of PARP14+/+ and PARP14−/− mice (n=3). (g) Representative H&E staining images and quantification of neointima formation in mechanically injured femoral arteries after bone marrow transplantation (BMT) PARP14+/+→+/+ and PARP14−/−→+/+ mice (n=6). Scale bars, 100 μm. * P<0.05 and **P<0.01, respectively, by Student's t-test. Error bars indicate s.d.
Figure 8. Haematopoietic PARP14 in mouse atheromata…
Figure 8. Haematopoietic PARP14 in mouse atheromata and PARP9–PARP14 expression in human plaques.
(a) Representative image and quantification of aortic root lesion formation and CD68+ macrophage accumulation (green, Alexa 488) in high-fat and high-cholesterol diet-fed LDLR−/− mice whose bone marrow was reconstituted by PARP14−/− mice (BMTPARP14−/−→LDLR−/− mice, n=5), compared with LDLR−/− mice with PARP14+/+ bone marrow (BMTPARP14+/+→LDLR−/− mice, n=6–7). Scale bars, 100 μm. (b) mRNA expression of the aorta from a. n=6–8. (c) Immunofluorescence staining of PARP14 and PARP9 proteins (green, Alexa 488) in human carotid plaques. CD68 (red, Alexa 594). Nuclei (blue, 4,6-diamidino-2-phenylindole, DAPI). Scale bars, 100 μm; insets, 10 μm (n=5). Prevalence of PARP14+ or PARP9+ macrophages in macrophage-poor versus macrophage-rich plaques. *P<0.05 and **P<0.01, respectively, by Student's t-test. Error bars indicate s.d.
Figure 9. Single-cell gene expression analysis of…
Figure 9. Single-cell gene expression analysis of CD14+ macrophages.
(a) Heterogeneity in IFNγ-stimulated compared with unstimulated cells in combined all donor cells (n=520). (b) Similarity map of cells from all donors/conditions reveals three subpopulations; IFNγ-stimulated cells (Cluster 1), unstimulated cells (Cluster 2) and mixed populations (Cluster 3). Cluster 1 inset—there are two further subpopulations within IFNγ-stimulated cells, such as Groups 1 and 2. (c) The relative expression data for genes related to macrophage function are compared between Groups 1 and 2 (n=112 and n=63, respectively). (d) Similarity maps of 91 genes examined. (e) Gene similarity map of PARP9/14, STAT, JAK and IRF genes based on analysis with all cells from all donors and conditions. *P<0.05 and **P<0.01, respectively, by Student's t-test. Error bars indicate s.d.

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