1. Biochemistry and Chemical Biology
  2. Immunology and Inflammation

PHLPP1 counter-regulates STAT1-mediated inflammatory signaling

  1. Ksenya Cohen Katsenelson
  2. Joshua D Stender
  3. Agnieszka T Kawashima
  4. Gema Lordén
  5. Satoshi Uchiyama
  6. Victor Nizet
  7. Christopher K Glass
  8. Alexandra C Newton  Is a corresponding author
  1. University of California, San Diego, United States
https://doi.org/10.7554/eLife.48609
Share this article
Cite this article
  1. Ksenya Cohen Katsenelson
  2. Joshua D Stender
  3. Agnieszka T Kawashima
  4. Gema Lordén
  5. Satoshi Uchiyama
  6. Victor Nizet
  7. Christopher K Glass
  8. Alexandra C Newton
(2019)
PHLPP1 counter-regulates STAT1-mediated inflammatory signaling
eLife 8:e48609.
https://doi.org/10.7554/eLife.48609
  2. Download BibTeX
  3. Download .RIS

Abstract

Inflammation is an essential aspect of innate immunity but also contributes to diverse human diseases. Although much is known about the kinases that control inflammatory signaling, less is known about the opposing phosphatases. Here we report that deletion of the gene encoding PH domain Leucine-rich repeat Protein Phosphatase 1 (PHLPP1) protects mice from lethal lipopolysaccharide (LPS) challenge and live Escherichia coli infection. Investigation of PHLPP1 function in macrophages reveals that it controls the magnitude and duration of inflammatory signaling by dephosphorylating the transcription factor STAT1 on Ser727 to inhibit its activity, reduce its promoter residency, and reduce the expression of target genes involved in innate immunity and cytokine signaling. This previously undescribed function of PHLPP1 depends on a bipartite nuclear localization signal in its unique N-terminal extension. Our data support a model in which nuclear PHLPP1 dephosphorylates STAT1 to control the magnitude and duration of inflammatory signaling in macrophages.

https://doi.org/10.7554/eLife.48609.001

Introduction

Gene expression is an exquisitely regulated process that maintains cellular homeostasis and orchestrates appropriate responses to environmental stimuli such as hormones, cytokines, and pathogenic microbes (Dawson and Kouzarides, 2012; Flavahan et al., 2017). Homeostatic control of inflammatory genes is particularly relevant to cancer since chronic inflammation promotes tumorigenesis and influences patient response to cancer therapeutics (Coussens and Werb, 2002; Grivennikov et al., 2010). Dysregulated gene expression, a hallmark of cancer, can arise from mutations in transcription factors (exemplified by p53; see Sabapathy and Lane, 2018), alterations in signaling pathways controlling transcription factor function (for example, hormone-dependent transcription factors in prostate and breast cancers; see Jernberg et al., 2017; Pejerrey et al., 2018), or upregulation of oncogenic transcription factors (notably c-myc, which regulates essential cell-cycle checkpoints; see Kalkat et al., 2017). Aberrant protein phosphorylation underpins all of these mechanisms, via dysregulation of signaling pathways, alterations in transcription factor machinery, and/or effects on the chromatin epigenetic landscape (Rossetto et al., 2012; Whitmarsh and Davis, 2000). Thus, targeting phosphorylation mechanisms is of considerable therapeutic interest.

Macrophages are among the first responders to infection, engaging foreign pathogens via pattern recognition receptors, including the Toll-like receptors (TLRs). TLRs are a conserved family of cell surface or phagosome-associated receptors that discriminate distinct features of microbial and viral pathogens, including lipoproteins (TLR1/2/6), lipopolysaccharide (LPS) (TLR4), flagellin (TLR5), single-stranded RNA (TLR7/8), double-stranded RNA (TLR3), and double-stranded DNA (TLR9) (Karin et al., 2006; O'Neill et al., 2013). Upon pathogen recognition by TLRs, a pro-inflammatory response is initiated that activates the signal-dependent transcription factors nuclear factor-κ B (NFκB), activator protein 1 (AP1), interferon response factors (IRFs), and, through secondary mechanisms, the signal transducer and activator of transcription (STAT) protein family (O'Neill et al., 2013). These activated transcription factors function in a combinatorial manner to drive expression of antimicrobial and inflammatory response genes that aid in elimination of foreign pathogens. However, while inflammation is required for protection against foreign microbes, it can lead to excessive cytokine production, chronic inflammation, and cancer if not properly resolved (Coussens and Werb, 2002; Fullerton and Gilroy, 2016; Grivennikov et al., 2010). Thus, macrophages have evolved regulatory mechanisms to resolve inflammatory responses in a timely manner, including shut down of STAT1 signaling pathways by the suppressor of cytokine signaling (SOCS) family of proteins (O'Shea and Murray, 2008), suppression of nitric oxide production by the enzyme arginase (Wynn and Vannella, 2016), and inhibition of a key subset of NFκB-dependent genes by anti-inflammatory omega-3 fatty acids (Oishi et al., 2017).

STAT1 is the founding member of the STAT transcription factor family and serves as a paradigm for how phosphorylation regulates transcription factor structure, function, and localization (Darnell et al., 1994; Morris et al., 2018; Stark and Darnell, 2012). In the canonical pathway, STATs are recruited from the cytosol to cytokine-bound and Tyr-phosphorylated receptors where they are phosphorylated on a key Tyr residue (Tyr701 for STAT1) by Janus Kinases (JAKs). This phosphorylation event promotes STAT dimerization and nuclear entry, allowing STAT binding to specific promoter sequences and thus initiating gene transcription. Upon promoter binding, STATs become additionally phosphorylated on a regulatory Ser residue at a MAPK consensus sequence (Ser727 for STAT1), a modification that enhances their transcriptional activity (Darnell, 1997; Sadzak et al., 2008; Wen et al., 1995b; Whitmarsh and Davis, 2000). Importantly, STAT1 transduces signals from type I and II interferons (IFNs), resulting in binding to IFN-stimulated response elements (ISREs) and to IFN-gamma (IFNγ)-activated site (GAS) elements in the promoters of IFN-stimulated genes (ISGs), inducing their transcription and stimulating inflammation (Platanias, 2005). While the kinases that phosphorylate Tyr701 and Ser727 on STAT1 have been extensively studied, as have been the phosphatases that dephosphorylate Tyr701, the phosphatases that oppose the Ser727 phosphorylation are unknown.

PH domain Leucine-rich repeat Protein Phosphatase 1 (PHLPP1) is one of the newest members of the phosphatome (Chen et al., 2017; Gao et al., 2005). Originally discovered for its function in suppressing growth factor signaling by dephosphorylating Akt on the hydrophobic motif site, Ser473 (Gao et al., 2005), the repertoire of PHLPP1 substrates is continually expanding (Grzechnik and Newton, 2016). PHLPP1 is a bona fide tumor suppressor: its expression is frequently lost in cancer and its genetic ablation in a mouse model results in prostate neoplasia (Chen et al., 2011; Liu et al., 2009). PHLPP1 is also involved in the immune response, where its dephosphorylation of Akt reduces the capacity of regulatory T cells to transduce T cell receptor signals, a key function in T cell development (Patterson et al., 2011). Additionally, mice lacking PHLPP1 have enhanced chondrocyte proliferation as a result of increased Akt2 activity, diminished FoxO1 levels, and increased Fgf18 expression, suggesting PHLPP1 inhibition could be a strategy to promote cartilage regeneration and repair (Bradley et al., 2015). PHLPP1 also suppresses receptor tyrosine kinase gene expression by a mechanism distinct from its effects on Akt, to influence growth factor signaling, including that mediated by the epidermal growth factor (EGF) receptor (Reyes et al., 2014).

PHLPP1 is unusual among protein phosphatases in that its regulatory modules and catalytic domain are on the same polypeptide. Most notably, it has a PH domain essential for dephosphorylation of protein kinase C (PKC) (Gao et al., 2008), a PDZ ligand necessary for Akt recognition (Gao et al., 2005), and a leucine-rich repeat (LRR) segment required for transcriptional regulation of receptor tyrosine kinases (Reyes et al., 2014). In addition, PHLPP1 possesses an approximately 50 kDa N-terminal extension (NTE) of unknown function. Stoichiometric association with substrates by direct binding to the protein-interaction domains on PHLPP or common scaffolds (e.g. PDZ domain proteins such as Scribble; see Li et al., 2011) allows fidelity and specificity in PHLPP function, and may account for its > 10 fold lower catalytic rate compared to the closely related phosphatase PP2Cα (Sierecki and Newton, 2014). Given its transcriptional regulation of at least one family of genes (Reyes et al., 2014), PHLPP1 is an attractive pharmacological target for modulation of gene expression.

Here we report that nuclear-localized PHLPP1 opposes STAT1 Ser727 phosphorylation to inhibit its transcriptional activity and promote normal resolution of inflammatory signaling. We find that Phlpp1-/- mice have improved survival following infection with Escherichia coli (E. coli), indicating a role of the phosphatase in innate immunity. Since macrophages are key in the initial response to lipopolysaccharide (LPS) from Gram-negative bacteria such as E. coli, we further explored the role of PHLPP1 in controlling LPS-dependent signaling in this cell type. The STAT1 binding motif was identified from the most common promoter sequences of 199 genes that remained elevated following LPS treatment of bone marrow-derived macrophages (BMDMs) from Phlpp1-/- mice compared to those from wild-type (WT) mice. We validated common transcriptional targets of PHLPP1 and STAT1, showing that loss of PHLPP1 upregulates the transcription of several genes including guanylate binding protein 5 (Gbp5), whereas loss of STAT1 downregulates them. Cellular studies revealed that dephosphorylation of STAT1 on Ser727 suppresses its transcriptional activity by a mechanism that depends both on the catalytic activity of PHLPP1 and a previously undescribed nuclear localization signal (NLS) in the NTE of PHLPP1. Taken together, our results identify PHLPP1 as a major player in the resolution of inflammatory signaling.

Results

PHLPP1 regulates the innate immune response

To explore the role of PHLPP1 in acute inflammation, we examined the kinetics and outcome of sepsis-induced death caused by intraperitoneal (i.p.) injection of Gram-negative E. coli bacteria in WT and Phlpp1-/- mice. Surprisingly, absence of PHLPP1 provided a strong protective effect; at a dose where more than 50% of WT mice died within 12 hr of E. coli challenge, 50% of the Phlpp1-/- mice remained alive after 10 days (Figure 1A). Similarly, Phlpp1-/- mice were protected from toxicity induced by the purified Gram-negative bacterial cell wall component LPS, with nearly half of the Phlpp1-/- mice alive after 10 days compared to only 1 out of 16 of the WT mice (Figure 1B). To understand the lower mortality rates in Phlpp1-/- mice, we measured levels of different cytokines in the serum of mice across a time course following LPS injection (Figure 1C–E). Serum levels of pro-inflammatory cytokine interleukin 6 (IL-6) were significantly increased in WT mice within 5 hr of LPS injection, returning to baseline within 12 hr (Figure 1C). In contrast, the Phlpp1-/- mice had 2-fold lower IL-6 levels at 5 hr post-infection, but these levels were sustained for up to 24 hr, suggestive of improper resolution of inflammation. Levels of another pro-inflammatory cytokine, IL-1β, were likewise consistently higher in Phlpp1-/- mice compared with WT mice (Figure 1D). By contrast, levels of the anti-inflammatory cytokine IL-10 did not differ significantly between the WT and Phlpp1-/- mice (Figure 1E). Note that cytokine levels were measured up to 24 hr post LPS injection, when the protective effect of PHLPP1 loss was not yet apparent. These findings indicate an essential role for PHLPP1 in regulation of the innate immune response at the whole organism level.

Figure 1
Download asset Open asset
PHLPP1 knock-out mice are protected against sepsis-induced death.

(A) Survival curve of WT and Phlpp1-/- mice i.p. infected with 1 × 107 cfu of E. coli. Values are expressed as percent survival of 15 mice for each genotype. **p<0.01 by log-rank test. (B) Survival curve of WT and Phlpp1-/- mice i.p. injected with 15 mg/kg LPS. Values are expressed as percent survival of 16 mice for each genotype. *p<0.05 by log-rank test. (C–E) ELISA showing IL-6 (C), IL-1β (D) and IL-10 (E) levels in serum at the indicated times after i.p. injection of 10 mg/kg LPS. Data represent mean ± SEM. Statistics analyzed by Student’s t-test *p<0.05, **p<0.01.

https://doi.org/10.7554/eLife.48609.002

Loss of PHLPP1 results in increased STAT1-dependent transcription in macrophages

Since macrophages are a key cell type involved in the initial response to E. coli infection and LPS challenge, we analyzed the transcriptome of BMDMs isolated from WT or Phlpp1-/- mice before and after stimulation by the major LPS component, Kdo2-Lipid A (KLA), for 1, 6, or 24 hr (Figure 2A). RNA-Seq analysis identified 1,654 mRNA transcripts induced more than two-fold by KLA treatment, with a false discovery rate (FDR) less than 0.05 at any of the time points. Expression of approximately 12% of these genes (199 genes; Supplementary file 1) was increased in macrophages from Phlpp1-/- mice compared to those from littermate control WT mice 6 hr following KLA treatment; transcript levels of these genes remained significantly elevated (>two fold) 24 hr later. Another set of genes exhibited reduced expression 24 hr following KLA treatment (144 genes; Supplementary file 2). Gene ontology analysis revealed that many of the genes whose expression was elevated in the Phlpp1-/- macrophages are associated with inflammatory signaling: these included genes annotated for their involvement in the innate immune response, cytokine-cytokine receptor interactions, LPS signaling, interferon-β response, and tumor necrosis factor (TNF) signaling-dependent pathways (Figure 2B). Genes significantly decreased in Phlpp1-/- compared to WT macrophages were enriched most significantly in nodes related to central carbon metabolism, and to a lesser extent, chronic inflammatory responses and LPS signaling (Figure 2B).

Figure 2
Download asset Open asset
Loss of PHLPP1 modulates the expression of inflammatory genes in macrophages.

(A) Heat map for mRNA-Seq expression of the 1,654 mRNA transcripts regulated greater than two-fold with a FDR < 0.05 in BMDMs isolated from WT or Phlpp1-/- animals treated with 100 ng/ml KLA for 1, 6 or 24 hr. Data represent the log2 difference between the mRNA expression in Phlpp1-/- macrophages compared to wild-type macrophages. (B) Gene ontology analysis for the 199 elevated (red arrow) or 144 decreased (blue arrow) transcripts in Phlpp1-/- macrophages compared to wild-type macrophages. (C) De novo motif analysis using HOMER (Hypergeometric Optimization of Motif EnRichment) for the 199 promoters corresponding to the genes elevated in the Phlpp1-/- macrophages. (D) Pie graph showing the percentage of promoters of elevated genes that contain STAT or IRF binding motifs. (E–G) Normalized mRNA-Seq values for (E) Cd69 (F) Ifit2 and (G) Gbp5 mRNA in BMDMs isolated from WT or Phlpp1-/- animals treated with 100 ng/ml KLA for 0, 1, 6, or 24 hr. RPKM – Reads Per Kilobase Million. Values are expressed as mean ± SEM. *p<0.05 (Student’s t-test) compared to WT cells. See also Supplementary file 1 and Supplementary file 2.

https://doi.org/10.7554/eLife.48609.003

To gain insight into gene regulatory mechanisms affected by loss of PHLPP1, we performed de novo motif analysis of the promoters of upregulated genes in Phlpp1-/- macrophages using Hypergeometric Optimization of Motif EnRichment (HOMER), a suite of tools for motif discovery and Next Generation Sequencing (NGS) analysis (Heinz et al., 2010). This algorithm defines motifs that are statistically enriched in a targeted promoter list compared to random promoter sequences with comparable GC content. The analysis revealed significant enrichment of STAT (p<10−18) and IRF (p<10−9) motifs (Figure 2C) in the promoters of genes whose expression was statistically increased in Phlpp1-/- macrophages compared to WT macrophages. Of the 199 genes with elevated expression, 46% of the genes had promoters with a consensus STAT binding motif, 51% had promoters with a potential binding site for IRF family of transcription factors, and 26% had promoters with predicted binding sites for both STAT and IRF (Figure 2D). We selected for further analysis three genes whose expression was elevated in the Phlpp1-/- compared to WT macrophages and which had proximal STAT1 binding motifs in their promoters: Cd69, Ifit2, and Gbp5. Normalized mRNA-Seq data for each of these three genes confirmed elevated mRNA levels in Phlpp1-/- macrophages compared to WT macrophages (Figure 2E–G). Thus, loss of PHLPP1 leads to sustained KLA-induced expression of genes involved in inflammation, of which 46% have predicted STAT motifs in their proximal regulatory regions.

If PHLPP1 suppresses STAT-regulated gene transcription, we reasoned that 1] knockdown of a STAT family member should reduce transcription of the same genes affected by loss of PHLPP1 and 2] knockdown of PHLPP1 should enhance STAT binding to its promoters. STAT1 is required for LPS-induced gene expression in macrophages (Ohmori and Hamilton, 2001) and implicated as a PHLPP1 target in iNOS regulation (Alamuru et al., 2014). STAT1 knockdown by siRNA in thioglycollate-elicited peritoneal macrophages resulted in a 2-fold reduction in KLA-induced transcription of Cd69, Ifit2, and Gbp5 at 6 hr compared to a control siRNA transfection, with transcript levels dropping to near baseline by 24 hr (Figure 3A–C). The effect of PHLPP1 knockdown on STAT1 promoter occupancy was examined by chromatin immunoprecipitation (ChIP) using STAT1-specific antibodies. KLA induced STAT1 binding to the promoters of Cd69, Ifit2, and Gbp5, with maximal binding observed 1 hr post stimulation, followed by a decay in binding to near baseline after 24 hr (Figure 3D–F). In contrast, binding to these promoters was enhanced and sustained in Phlpp1-/- macrophages relative to WT cells. The degree of enhancement and the kinetics of activation/resolution varied depending on the gene examined: PHLPP1 loss had the most robust early effect (1 hr) on the Ifit2 promoter and at a later time (24 hr) on the Cd69 promoter. Thus, PHLPP1 suppresses KLA-stimulated binding of STAT1 to its promoters and thereby reduces transcription of its target genes.

Figure 3
Download asset Open asset
PHLPP1 controls STAT1 genomic recruitment and STAT1-dependent gene expression.

(A–C) Quantitative PCR analysis for (A) Cd69 (B) Ifit2 and (C) Gbp5 mRNA isolated from thioglycollate-elicited peritoneal macrophages treated with control siRNA (siCtl) or siRNA specifically targeting Stat1 (siStat1) and subsequently treated with vehicle or 100 ng/ml KLA for 6 or 24 hr. Values are expressed as mean ± SEM from replicate experiments. *p<0.05 (Student’s t-test) compared to siCtl treated cells. (D–F) Quantitative PCR analysis of ChIPs for STAT1 at the (D) Cd69 (E) Ifit2 and (F) Gbp5 promoter in BMDMs isolated from WT or Phlpp1-/- animals and treated with 100 ng/ml KLA for 0, 1, 6 or 24 hr. Values are expressed as mean ± SEM. *p<0.05 (Student’s t-test) compared to WT cells.

https://doi.org/10.7554/eLife.48609.004

PHLPP1 binds to STAT1 and dephosphorylates Ser727

We next examined whether PHLPP1 affects the phosphorylation state of the two regulatory STAT1 phosphorylation sites, Ser727 and Tyr701. Primary BMDMs were isolated from WT and Phlpp1 -/- mice and the kinetics and magnitude of KLA-triggered phosphorylation at each of the two STAT1 sites were compared. Loss of PHLPP1 in BMDMs led to a robust increase in STAT1 phosphorylation on the regulatory site Ser727 but did not affect Tyr701 phosphorylation (Figure 4A–B). PHLPP1 loss also resulted in an increase in Erk phosphorylation at its activation loop sites, as previously reported (Reyes et al., 2014). Incubation of in vitro phosphorylated STAT1 with immunoprecipitated FLAG-tagged PHLPP1 resulted in dephosphorylation at Ser727, suggesting that PHLPP1 directly dephosphorylates STAT1 (Figure 4C). Furthermore, overexpression of PHLPP1 in HEK-293T cells reduced IFNγ-dependent phosphorylation of STAT1 on Ser727 but not on Tyr701 (Figure 4D–E). Thus, PHLPP1 selectively dephosphorylates the Ser727 regulatory phosphorylation on STAT1 in vitro and in cells.

Figure 4
Download asset Open asset
PHLPP1 regulates STAT1 phosphorylation on Ser727.

(A) Western blot analysis of primary BMDM from WT or Phlpp1-/- mice treated with 100 ng/ml KLA for the indicated times and probed with the indicated antibodies. (B) Ratio of pSTAT1 (S727):total STAT1, pSTAT1 (Y701):total STAT1 or phosphoERK (T202/Y204):total ERK normalized to the highest value; data represent the mean ± SEM of five independent experiments as in (A). **p<0.01 (Student’s t-test) compared to WT cells. (C) Western blot analysis of an in vitro phosphatase assay of purified and phosphorylated STAT1 and immunoprecipitated FLAG-PHLPP1, incubated for 0 or 120 min at 30°C (on the left). On the right, quantification of pSTAT1 (S727) divided by total STAT1 and normalized to 0 time point. Values are expressed as mean ± SEM of three independent experiments. **p<0.01 (Student’s t-test). (D) Western blot analysis of HEK-293T cells over-expressing vector control (Vector) or HA-tagged PHLPP1 and treated with 10 ng/ml IFNγ for 0, 1, 6, or 24 hr. (E) Graphs represent the quantification of three independent experiments as presented in (D). Values are expressed as mean relative units of pSTAT1 (S727) or (Y701) divided by β-Actin and normalized to vector 0 hr ± SEM. *p<0.05 (Student’s t-test) compared to vector control expressing cells. See also Figure 6—figure supplement 1.

https://doi.org/10.7554/eLife.48609.005

Because the abundance of PHLPP1 in the cell is much lower than other phosphatases such as PP2A (Hein et al., 2015), we next sought to determine whether regulation of STAT1 promoter activity was solely due to PHLPP1 phosphatase activity or occurred in combination with other phosphatases. Taking advantage of the insensitivity of PHLPP phosphatases to the PP1/PP2A inhibitor okadaic acid (OA) (Gao et al., 2005), we examined whether OA treatment affected KLA-dependent changes on Ser727 phosphorylation in primary BMDMs from WT mice. Figure 5A–B shows that the KLA-induced increase in Ser727 phosphorylation was relatively insensitive to OA, under conditions where the phosphorylation of Erk (at Thr202/Tyr204) and Akt (at Thr308) was significantly increased upon OA addition. These data are consistent with PHLPP1, a PP2C family member, being the primary regulator of phosphorylation on the activity-tuning Ser727 site of STAT1.

Figure 5
Download asset Open asset
STAT1 Ser727 phosphorylation and transcriptional activity are insensitive to okadaic acid.

(A) Western blot analysis of primary BMDMs from WT or Phlpp1-/- mice treated with 100 ng/ml KLA for 0 or 30 min followed by treatment with 1 µM OA or DMSO control for an additional 15 min and probed with the indicated antibodies; pAkt antibody recognizes phosphorylated Thr308. (B) Graphs represent the quantification of three independent experiments as presented in (A). Values are expressed as the mean ± SEM of the ratio of pSTAT1 (S727) to total STAT1 normalized to the highest value; *p<0.05, **p<0.01 and n.s.- non-significant (Student’s t-test).

https://doi.org/10.7554/eLife.48609.006

We next addressed whether enhanced promoter binding of STAT1 upon loss of PHLPP1 resulted in enhanced transcriptional activation using a luciferase reporter assay. WT or Phlpp1-/- mouse embryonic fibroblasts (MEFs) were co-transfected with a firefly luciferase reporter construct containing GAS promoter elements, as well as a renilla luciferase controlled by a constitutive CMV promoter as an internal control. STAT1 promoter activity was assessed by monitoring luminescence following IFNγ stimulation. Figure 6A shows that STAT1 promoter activity was significantly higher in Phlpp1-/- MEFs compared to WT MEFs at both 6 hr and 24 hr. Pre-treatment of cells with okadaic acid, under conditions that increased the phosphorylation of PP2A-sensitive substrates (see Figure 5A and Figure 6—figure supplement 1), had no effect on STAT1 promoter activity (Figure 6A); note that treatment with a PKC inhibitor also had no effect on STAT1 promoter activity (Figure 6—figure supplement 2). Because STAT1 functions in the nucleus, we next asked whether PHLPP1 regulation of STAT1 occurs in the cytoplasm or nucleus. To this end, we assessed the effect of expressing either the PP2C domain of PHLPP1 or a nuclear-targeted (NLS) PP2C domain of PHLPP1 (Figure 6B) on IFNγ-induced STAT1 promoter activity via the GAS luciferase assay. The overexpressed PP2C domain of PHLPP1 (Figure 6C, blue) was considerably less effective in inhibiting STAT1 promoter activity compared to full-length PHLPP1 (Figure 6C, red). However, forcing the PP2C domain into the nucleus by attaching an NLS to its N-terminus inhibited STAT1 promoter activity as effectively as overexpression of full-length PHLPP1 (Figure 6C, orange). Analysis of the subcellular localization of the constructs used in this experiment revealed that full-length PHLPP1 was primarily cytosolic, the isolated PP2C domain had increased nuclear localization, and the NLS-PP2C was enriched in the nucleus (Figure 6—figure supplement 3). To address whether PHLPP1 catalytic activity is required for STAT1 regulation, we utilized a phosphatase-dead PP2C domain in which two active site residues, Asp1210 and Asp1413 (Sierecki and Newton, 2014) were mutated to Ala (DDAA). The catalytically-inactive NLS-PP2C was no longer able to suppress STAT1 activity (Figure 6C, purple); immunofluorescence confirmed its nuclear localization (Figure 6—figure supplement 3). Thus, both the catalytic activity and nuclear localization of PHLPP1 are necessary for it to regulate STAT1 transcriptional activity.

PHLPP1 has a bipartite nuclear localization signal in its N-Terminal extension

Bioinformatics analysis of the sequence of PHLPP1 using SeqNLS (Lin and Hu, 2013) revealed a potential Arg-rich bipartite NLS (92RRRRR-X-122RRGRLKR) in the N-terminal extension unique to the PHLPP1 isozyme (Figure 6D). To test whether these basic segments function as an NLS, we examined the subcellular localization in HeLa cells of the NTE alone or NTE in which the basic residues in each or both halves of the potential bipartite NLS were mutated to Ala (Figure 6E). Immunofluorescence revealed that the NTE localizes to the nucleus. Mutation of the first NLS or the second NLS increased the distribution of the NTE to the cytosol, and mutation of both decreased the nuclear:cytoplasmic ratio to be comparable to that of a construct of the NTE with a strong Nuclear Export Signal (NES) (LALKLAGLDI from PKI; see Wen et al., 1995a) (Figure 6F). Full-length PHLPP1 was primarily cytosolic, leading us to ask whether there may also be an NES. Bioinformatics analysis of the primary sequence identified a potential Leu-rich NES (Xu et al., 2015) immediately following the last LRR and preceding the phosphatase domain (Figure 6—figure supplement 4). Attachment of this 14-residue sequence to the N-terminus of the NTE resulted in distribution of the NTE to the cytosol (Figure 6—figure supplement 4). Thus, PHLPP1 nuclear localization is controlled by a bipartite NLS in the NTE and is opposed by an NES following the LRR. Lastly, we examined the effect of mutating the NLS on the ability of full-length PHLPP1 to reduce STAT1 transcriptional activity as assessed using the GAS promoter assay. The reduction in IFNγ-induced STAT1 activity resulting from PHLPP1 overexpression (Figure 6G, red) was abolished upon mutation of NLS2 (Figure 6G, brown) or both halves of the NLS (NLS1/2) (Figure 6G, purple). Mutation of NLS1 had an intermediate effect (Figure 6G, blue). These data reveal that a bipartite NLS in the NTE of PHLPP1 localizes PHLPP1 to the nucleus, where it suppresses the transcriptional activity of STAT1.

Figure 6 with 4 supplements see all
Download asset Open asset
PHLPP1 suppresses STAT1 transcriptional activity by a mechanism that depends on its catalytic activity and an NLS in its N-Terminal Extension.

(A) Luciferase reporter assay in WT (+/+) and Phlpp1-/- (-/-) MEFs over-expressing GAS luciferase reporter and treated with 10 ng/ml IFNγ for 0, 1, 6, or 24 hr in combination with 1 µM OA or DMSO control treatment for 15 min. Values are expressed as mean of relative light units (RLU) ± SEM of three independent experiments. *p<0.05 (Student’s t-test). See also Figure 6—figure supplement 1. (B) Schematic of HA-tagged PHLPP1 constructs used in this study: the PP2C domain of PHLPP1 (PP2C), nuclear targeted PP2C with NLS (NLS- PP2C), NLS-PP2C with active site residues Asp1210 and Asp1413 mutated to Ala (NLS-PP2C DDAA), and full-length PHLPP1 (PHLPP1). (C) Luciferase reporter assay in HEK-293T cells over-expressing GAS luciferase reporter in combination with either vector control (vector, black) or the constructs described in (B) and treated with 10 ng/ml IFNγ for 0, 1, 6, or 24 hr. Values are expressed as mean RLU ± SEM of four independent experiments. All data points at 24 hr were significant against each other except for vector to PP2C, vector to NLS-PP2C DDAA, P1 to NLS-PP2C, and PP2C to NLS-PP2C DDAA. *p<0.05, **p<0.01 (Student’s t-test). (D) Schematic showing position and sequence of bipartite NLS in the NTE, and NLS mutants used in this study. (E) HeLa cells over-expressing the constructs used in Figure 6D were stained for HA (green), α-Tubulin (red), and DAPI (blue). Scale bar indicates 15 µm. (F) The Nuclear to Cytoplasmic ratio was calculated for each construct (300 cells per construct) and values are expressed as mean ± SEM. All data points were significant against each other except for NLS1 to NLS2, and NLS2 to NLS1/2. **p<0.01, n.s. – non-significant (Student’s t-test). (G) Luciferase reporter assay in HEK-293T cells over-expressing a GAS luciferase reporter in combination with either vector control (vector, black) or the constructs described in (D) however in the context of a full-length PHLPP1 and treated with 10 ng/ml IFNγ for 0, 1, 6, or 24 hr. Values are expressed as mean RLU ± SEM of eight independent experiments. *p<0.05, **p<0.01, n.s. - non-significant (Student’s t-test).

https://doi.org/10.7554/eLife.48609.007

We next assessed which domain of PHLPP1 contributes to the observed regulation of STAT1 activity on the GAS promoter. Overexpression of full-length PHLPP1 in HEK-293T cells markedly reduced GAS promoter activity (Figure 7A, red) compared to the vector only control (Figure 7A, black). A construct of PHLPP1 lacking the NTE (deletion of first 512 amino acids of its N-terminus; PHLPP1ΔNTE, blue) was less effective than full-length PHLPP1 in reducing STAT1 activity, whereas a construct comprised of just the NTE (amino acids 1–512, green) caused a significant increase in GAS promoter activity, suggesting a dominant-negative function of this segment. Co-immunoprecipitation assays revealed a robust interaction of STAT1 with the immunoprecipitated NTE of PHLPP1, in contrast to barely detectable binding of STAT1 to PHLPP1 lacking the NTE (Figure 7B). Intermediate binding was observed between STAT1 and full-length PHLPP1. Quantification of three independent experiments revealed that the isolated NTE of PHLPP1 binds STAT1 approximately five times more strongly than full-length PHLPP1 and 26 times more strongly than PHLPP1 lacking the NTE (Figure 7C). These data reveal that the NTE of PHLPP1 interacts with STAT1 and reduces its promoter activity.

Figure 7
Download asset Open asset
STAT1 associates with the N-Terminal Extension of PHLPP1.

(A) Luciferase reporter assay in HEK-293T cells over-expressing GAS luciferase reporter in combination with either vector control (Vector, black), PHLPP1 NTE (NTE, green), PHLPP1ΔNTE (blue), or PHLPP1 (red) and treated with 10 ng/ml IFNγ for 0, 1, 6, or 24 hr. Values are expressed as mean of RLU ± SEM of five independent experiments. All data points at 24 hr were significant against each other except for vector to PHLPP1ΔNTE, and PHLPP1ΔNTE to PHLPP1. *p<0.05, **p<0.01, ***p<0.001 (Student’s t-test). (B) Western blot analysis of detergent-solubilized lysate of HEK-293T cells transfected with vector control (Vector), HA-tagged NTE of PHLPP1 (HA-NTE), PHLPP1 with the NTE deleted (HA-P1ΔNTE) or full-length PHLPP1 (HA-P1) and immunoprecipitated (IP) using HA antibody; blots were probed for co-IP of STAT1 tag using GFP antibody. (C) Quantification of GFP-STAT1 IP divided by HA IP and normalized to HA-NTE IP. Values are expressed as mean ± SEM of three independent experiments. ***p<0.001, ****p<0.0001 (Student’s t-test).

https://doi.org/10.7554/eLife.48609.012

Discussion

The finding that Phlpp1-/- mice are protected from LPS-induced death allowed us to identify PHLPP1 as a physiologically relevant phosphatase in the overall innate immune response. It is likely that this immunoregulatory phenotype reflects roles of PHLPP1 in several immune cell types, and future studies of mice with cell-specific deletions of Phlpp1 will be of great interest. Investigation of Phlpp1-/- macrophages indicates a significant role in counter-regulation of STAT1-dependent transcription that emerges as a secondary response to TLR4 ligation. Our mechanistic analyses show that PHLPP1 dephosphorylates STAT1 on a key regulatory site to suppress its transcriptional activity towards an array of genes involved in mounting an inflammatory response to IFNγ. Specifically, PHLPP1 directly dephosphorylates Ser727 on STAT1 in vitro and specifically suppresses phosphorylation of Ser727, but not Tyr701, on STAT1 in cells, correlating to decreased transcriptional activity of STAT1 at one of its major binding sites, the GAS promoter. The intrinsic catalytic activity and nuclear localization of PHLPP1 is required for this transcriptional regulation; while the isolated PP2C domain is not efficient in suppressing GAS promoter activity, forcing the PP2C domain into the nucleus is as effective as the full-length phosphatase in controlling transcriptional activity. Nuclear localization of the full-length enzyme is driven by a bipartite NLS we identify in the NTE. Elimination of PHLPP1 results in global changes in KLA-dependent transcriptional regulation, with 20% of the approximately 2000 genes whose expression changes upon KLA stimulation differing by more than two-fold in BMDMs from Phlpp1-/- mice compared to WT mice.

Phosphorylation of STAT1 on Ser727 has been proposed to occur following the binding of the Tyr-phosphorylated STAT1 dimer to chromatin (Sadzak et al., 2008). Ser727 phosphorylation on the C-terminal transactivation domain of STAT1 is necessary for maximal transcriptional activity. Identification of PHLPP1 as a phosphatase that opposes this phosphorylation provides a mechanism to counter-regulate the activity of this key transcription factor. Several lines of evidence suggest that PHLPP1 may be the major phosphatase that controls this regulatory site. First, genetic depletion of PHLPP1 increases both STAT1 Ser727 phosphorylation and transcriptional activity at the GAS promoter, whereas PHLPP1 overexpression decreases both STAT1 Ser727 phosphorylation and transcriptional activity at the promoter. Second, both the IFNγ-induced phosphorylation of Ser727 and resulting increase in transcriptional activity at the GAS promoter are insensitive to OA, a phosphatase inhibitor that is ineffective towards PP2C family members but highly effective towards the abundant PP2A in cells. The insensitivity of STAT1 Ser727 phosphorylation to OA is consistent with PHLPP1 directly dephosphorylating this site in cells, a reaction it catalyzes in vitro. Furthermore, although PHLPP1 does suppress the signaling output of Akt (by dephosphorylating Ser473; see Gao et al., 2005) and Erk (by reducing the steady-state levels of RTKs; see Reyes et al., 2014), its effect on STAT1 is unlikely to involve either of these targets because the activities of both kinases are sensitive to OA. Nor are the effects on Ser727 a result of PHLPP1 reducing PKC steady-state levels (Baffi et al., 2019; Gao et al., 2008), as the general PKC inhibitor Gö6983 did not alter GAS promoter activity (Figure 6—figure supplement 2). Third, genetic depletion of either PHLPP1 or STAT1 has opposing effects on transcriptional targets of STAT1: whereas KLA causes a larger increase in mRNA of Cd69, Ifit2, and Gbp5 in BMDMs from Phlpp1-/- mice compared to WT mice, a reduction in these transcripts is observed upon STAT1 knockdown. Lastly, we have previously shown that PHLPP1 regulates transcription of genes and binds chromatin (Reyes et al., 2014). Cumulatively, these data are consistent with PHLPP1 being the major phosphatase to oppose the activating phosphorylation of STAT1 on Ser727, thereby limiting its transcriptional activity.

The interaction of PHLPP1 with STAT1, mediated by its NTE, affords fidelity and specificity in its dephosphorylation of the transcription factor. PHLPP1 binding to STAT1 is consistent with this multi-valent protein utilizing its protein-interaction domains to position it near its substrates, either via direct interaction or by binding protein scaffolds, such as PDZ domain proteins that coordinate Akt signaling (Li et al., 2011). Such coordination is essential for its dephosphorylation of relevant substrates, in part due to the low catalytic activity of the phosphatase (approximately one reaction per sec towards peptide substrates, over an order of magnitude lower than that of the related phosphatase PP2Cα; see Sierecki and Newton, 2014). The importance of enzyme proximity to its substrate is best illustrated with Akt, where deletion of the last three amino acids of PHLPP1 to remove the PDZ ligand abolishes the ability of PHLPP1 to dephosphorylate Akt in cells (Gao et al., 2005). Thus, binding of PHLPP1 via its NTE to STAT1 affords an efficient mechanism to restrict its activity by directly opposing its phosphorylation in the nucleus (see Figure 8).

Figure 8
Download asset Open asset
Proposed model for PHLPP1-dependent suppression of STAT1 activity.

Binding of IFNγ to IFN receptors results in their dimerization and phosphorylation, promoting the recruitment of JAK, which phosphorylates STAT1 on Tyr701. This promotes the dimerization of STAT1 and its translocation into the nucleus where it binds the GAS promoter to allow the transcription of inflammatory response genes. Activity of STAT1 is enhanced by phosphorylation on Ser727. However, nuclear-localized PHLPP1, which binds STAT1 via its N-terminal extension, tunes the activity of STAT1 by directly dephosphorylating this site to keep activity finely controlled. Loss of PHLPP1 results in poor resolution of inflammatory response. The nuclear localization of PHLPP1 is controlled by a bipartite NLS (pink rectangles) in the NTE (green line) and an NES (purple rectangle) in the segment between the LRR (gray oval) and the PP2C domain (red oval).

https://doi.org/10.7554/eLife.48609.013

The regulation of STAT1 by PHLPP1 occurs in the nucleus, and we identify motifs in the phosphatase that control both the entry into (NLS) and exit from (NES) the nucleus. First, we identify a bipartite NLS in the NTE of PHLPP1 whose integrity is necessary for the phosphatase to regulate the transcriptional activity of STAT1. Second, we identify an NES in the segment between the LRR and PP2C domain that drives export out of the nucleus. Under the ‘unstimulated’ conditions of our immunofluorescence, PHLPP1 localized primarily to the cytosol, suggesting masking of the NLS and exposure of the NES. Inputs that regulate the exposure of the NLS and NES are likely important regulators of PHLPP1 function.

Our transcriptomic data support a key role for PHLPP1 in the resolution of the inflammatory response specific to genes downstream of type II IFN signaling pathways. This suggests the possibility that PHLPP1 can selectively discriminate between inflammatory promoters that are differentially regulated by distinct transcription factor families. Surprisingly, over 50% of the inflammatory genes that fail to properly resolve in the macrophages from Phlpp1-/- mice contain a consensus STAT-binding motif in their proximal promoters. Our studies have demonstrated a direct interaction between PHLPP1 and STAT1, thus it is highly likely that PHLPP1 is recruited to gene promoters through its association with STAT1. Elevated STAT1 occupancy and delayed dismissal kinetics of STAT1 from its target promoters in Phlpp1-/- macrophages indicate a major function of PHLPP1-dependent dephosphorylation in termination of STAT1 signaling and its dismissal from chromatin.

Germline mutations that impair STAT1 function, by reducing either Tyr701 phosphorylation (L706S) or DNA binding (Q463H and E320Q), increase the susceptibility of otherwise healthy patients to mycobacterial and viral infection (Chapgier et al., 2006; Dupuis et al., 2001). This increased susceptibility was proposed to arise because of reduced transcription of genes involved in bacterial and viral immunity from the GAS and ISRE promoters, respectively. Similarly, genetic ablation of Stat1 on the background of a mouse that has enhanced TLR4 signaling (because of deletion of Il6st, a key regulator of systemic inflammatory responses during LPS-mediated endotoxemia) provides protection against LPS-induced toxemic death compared to mice with normal STAT1 levels (Luu et al., 2014). Although the current study does not provide direct evidence that enhanced phosphorylation of STAT1 causes the protective effect of PHLPP1 loss on both E. coli-induced sepsis and LPS-induced endotoxemia in mice, our data indicate that PHLPP1 inhibitors could be explored as adjunctive therapies to antibiotics and supportive care of patients with Gram-negative sepsis, a leading cause of mortality in intensive care units.

Materials and methods

Key resources table
Reagent type
(species) or resource		DesignationSource or referenceIdentifiersAdditional
information
Cell line (Mus musculus)Primary Bone Marrow Derived Macrophage Cells from Phlpp1+/+ miceThis paperWT BMDMmale, 6–8 week old C57BL/6 mice, Charles River Laboratories
Cell line (Mus musculus)Primary Bone Marrow Derived Macrophage Cells from Phlpp1-/- miceThis paperPhlpp1-/- BMDMmale, 6–8 week old C57BL/6 mice, Charles River Laboratories
Cell line (Mus musculus)Immortalized
MEFs from Phlpp1+/+ mice		Lloyd Trotman Lab, CSHL, PMID: 21840483stably expressing shp53 and GFP
Cell line (Mus musculus)Immortalized MEFs from Phlpp1-/- miceLloyd Trotman Lab, CSHL, PMID: 21840483stably expressing shp53 and GFP
Cell line (Homo sapiens)HeLaATCCCCL-2
Cell line (Homo sapiens)HEK-293TATCCCRL-11268
Cell line (Mus musculus)NCTC clone L929ATCCCCL-1L cell, L-929, derivative of Strain L
Strain, strain background (Escherichia coli)E. coli K1 strain RS218PMID: 6995336Victor Nizet Lab, University of California San Diego
Strain, strain background (Mus musculus)WT and littermate control Phlpp1-/- micePMID: 20080691female, 10 to 14 week old, C57BL/6, for mouse infection studies
Antibodyanti-HA (rat, monoclonal)Roche11867425001Western Blot (1:1000)
Antibodyanti-GFP (rabbit, polyclonal)Cell Signaling2555Western Blot (1:1000)
Antibodyanti-STAT1 (rabbit, polyclonal)Cell Signaling9172Western Blot (1:1000)
Antibodyanti-STAT1 pSer727 (rabbit, polyclonal)Cell Signaling9177Western Blot (1:1000)
Antibodyanti-STAT1 pTyr701
(rabbit, polyclonal)		Cell Signaling7649Western Blot (1:1000)
Antibodyanti-Erk1/2 pThr202/pTyr204 (rabbit, polyclonal)Cell Signaling9101Western Blot (1:1000)
Antibodyanti-Erk1/2 (rabbit, polyclonal)Cell Signaling9102Western Blot (1:1000)
Antibodyanti-Akt pThr308 (rabbit, polyclonal)Cell Signaling9275Western Blot (1:1000)
Antibodyanti-Akt
(rabbit, polyclonal)		AbCam126811Western Blot (1:1000)
Antibodyanti-PHLPP1 (rabbit, polyclonal)Proteintech22789–1-APWestern Blot (1:1000)
Antibodyanti-FLAG (mouse, monoclonal)Sigma-AldrichF3165Western Blot (1:1000)
Antibodyanti-β-Actin (mouse, monoclonal)Sigma-AldrichA2228Western Blot (1:2000)
Antibodyanti-α-tubulin (mouse, monoclonal)Sigma-AldrichT6074Western Blot (1:1000)
Antibodyanti-STAT1 (rabbit, polyclonal)Santa Cruz Biotechnologysc-345Chromatin Immunoprecipitation
Antibodyanti-HA (mouse, monoclonal)BioLegend901503Immunoprecipitation
Antibodyanti-α-tubulin (rabbit, monoclonal)Cell Signaling2125Immunofluorescence (1:200)
Antibodyanti-Alexa647 (goat anti-Mouse IgG)Life TechnologiesA21235Immunofluorescence (1:500)
Antibodyanti-Alexa477 (goat anti-Rabbit IgG)Life TechnologiesA11034Immunofluorescence (1:500)
Recombinant DNA reagentpCMV 3XFLAG-PHLPP1 WTThis paperresidues 1–1717 of PHLPP1
Recombinant DNA reagentpCDNA3 HA-NLS-PHLPP1 PP2CThis paperresidues 653–906 of PHLPP1
Recombinant DNA reagentpCDNA3 HA-NLS-PHLPP1 PP2C D1210/1413AThis paperDDAAAspartate at residues 1210 and 1413 mutated to Alanine
Recombinant DNA reagentpCDNA3 HA PHLPP1 NTE-WTThis paperresidues 1–512 of PHLPP1
Recombinant DNA reagentpCDNA3 HA-PHLPP1 NTE-NLS1 mut.This paperresidues 1–512 of PHLPP1
Recombinant DNA reagentpCDNA3 HA-PHLPP1 NTE-NLS2 mut.This paperresidues 1–512 of PHLPP1
Recombinant DNA reagentpCDNA3 HA-PHLPP1 NTE-NLS1/2 mut.This paperresidues 1–512 of PHLPP1
Recombinant DNA reagentpCDNA3 HA-NES-PHLPP1 NTEThis paperresidues 1–512 of PHLPP1
Recombinant DNA reagentpCDNA3 HA-PHLPP1 ΔNTEPMID: 15808505Addgene: 22404residues 513–1717 of PHLPP1
Recombinant DNA reagentpCDNA3 HA-PHLPP1NES-PHLPP1 NTEThis paperresidues 1–512 of PHLPP1
Sequence-based reagentStat1 SMART siRNA poolsDharmaconL-058881
Sequence-based reagentControl SMART siRNA poolsDharmaconD-001810-10-05
Peptide, recombinant proteinmurine IFNγPeproTech315–0510 ng/mL
Peptide, recombinant proteinhuman IFNγPeproTech300–0210 ng/mL
Peptide, recombinant proteinrecombinant STAT1BiosourcePHF00110.3 uM
Peptide, recombinant proteinrecombinant human Cdk1/CyclinBMillipore14–4500.2 uM
Peptide, recombinant proteinE. coli O111:B4 LPSSigma-AldrichL4391
Chemical compound, drugOkadaic Acid (OA)Millipore4596161 µM
Chemical compound, drugGö6983Calbiochem365251250 nM
Chemical compound, drugStaurosporineCalbiochem5693971 µM
Chemical compound, drugKLAAvanti Polar Lipids699500100 ng/mL
Chemical compound, drugRO-3306EnzoALX-270–463144 uM
Commercial assay or kitDual-Glo Luciferase
Assay System		PromegaE2940
Commercial assay or kitIL-6 ELISA KitR and D SystemsDY406
Commercial assay or kitIL-1β ELISA KitR and D SystemsDY401
Commercial assay or kitIL-10 ELISA KitR and D SystemsDY417

Materials and antibodies

OA (459616) was purchased from Millipore. Gö6983 (365251) and staurosporine (569397) were purchased from Calbiochem. Antibody against HA (11867425001) was purchased from Roche; antibodies against GFP (2555), STAT1 (9172), phosphorylated Ser727 on STAT1 (9177), phosphorylated Tyr701 on STAT1 (7649), phosphorylated Thr202/Tyr204 on Erk1/2 (9101), total Erk1/2 (9102), and phosphorylated Thr308 on Akt (9275) were purchased from Cell Signaling. Antibody against total Akt (126811) was obtained from AbCam. Antibodies against PHLPP1 were purchased from Cosmo (KIAA0606) and Proteintech (22789–1-AP); antibodies against FLAG (F3165), β-Actin (A2228), and α-tubulin (T6074) were purchased from Sigma-Aldrich. The pcDNA3 HA-tagged PHLPP1 and PHLPP2 constructs for mammalian cell expression were described previously (Brognard et al., 2007; Gao et al., 2008; Gao et al., 2005). Full-length PHLPP1 was cloned into pCMV 3xFLAG vector (Sigma-Aldrich, E4401). An NLS was cloned to the N-terminus of the PP2C domain of PHLPP1. A double mutant of NLS-PP2C at residues D1210A and D1413A was cloned by site-directed mutagenesis. The HA-tagged PHLPP1 N-terminal extension (PHLPP1 NTE), residues 1–512, was cloned into pcDNA3 vector (Invitrogen). The NLS1 and NLS2 mutations were cloned by site-directed mutagenesis into HA-PHLPP1 NTE. The NES from PKI (LALKLALDI) was cloned into the N-terminus of HA-PHLPP1 NTE. The PHLPP1 NES (residues 1125–1134, LPPKLQELDL) was subcloned directly downstream of the HA-tag in HA-PHLPP1 NTE, to generate HA-PHLPP1NES-NTE.

Isolation and treatment of macrophages

Primary BMDM cells were isolated from male 6- to 8-week-old C57BL/6 mice (Charles River Laboratories). BMDMs were obtained by PBS flush of femurs and tibias (Weischenfeldt and Porse, 2008), red blood cells lysed, and remaining cells plated in RPMI 1640 supplemented with 20% fetal bovine serum (FBS, Gibco, cat. 12657–029), 30% L-cell conditioned medium, 100 U/ml penicillin, 100 µg/ml streptomycin, and 2 mM L-glutamine. Cells were seeded in non-tissue culture treated Optilux Petri dishes (BD Biosciences), incubated at 37°C in a 5% CO2 atmosphere for 7 days, then treated with 100 ng/ml KLA (699500, Avanti Polar Lipids) for noted times. Peritoneal macrophages were collected by flushing mouse peritoneal cavities with PBS following 48 hr post peritoneal injection with 3 ml of thioglycolate (Ray and Dittel, 2010).

Cell culture

MEFs from WT or Phlpp1-/- mice stably expressing shp53 were a kind gift from Lloyd Trotman (CSHL) and have been described previously (Chen et al., 2011); MEFs, HEK-293T, and HeLa (ATCC) cells were grown in Dulbecco’s modified Eagle medium (DMEM, 10–013-CV, Corning) supplemented with 10% fetal bovine serum (S11150, Atlanta biologicals) and 1% penicillin/streptomycin (15140–122, Gibco) at 37°C in 5% (vol/vol) CO2. Cells used were periodically tested for Mycoplasma contamination using a PCR-based protocol (Uphoff and Drexler, 2011) and showed no evidence of contamination.

mRNA isolation and qPCR analysis

RNA was purified using Direct-zol RNA Miniprep Kits (Zymo Research) from triplicate experiments and quantified using a NanoDrop Spectrophotometer (ThermoFisher Scientific). RNA was either reverse transcribed into cDNA for quantitative real-time PCR using gene-specific primers or used for next-generation library preparation. For cDNA generation, one μg of total mRNA was reverse transcribed using the SuperScript III Reverse Transcriptase (ThermoFisher Scientific). The resulting cDNA (25 ng) was used to perform real-time PCR using SYBR Green Master Mix (ThermoFisher Scientific) and 50 nM mix of forward and reverse primers. The real-time PCR values for individual genes were normalized to the house keeping gene, 36B4, using the ΔΔCT method (Livak and Schmittgen, 2001). The primer sequences used in this study are:

  • 36B4_qPCR_F AATCTCCAGAGGCACCATTG

  • 36B4_qPCR_R CCGATCTGCAGACACACACT

  • Cd69_qPCR_F CTATCCCTTGGGCTGTGTTAAT

  • Cd69_qPCR_R ACATGGTGGTCAGATGATTCC

  • Ifit2_qPCR_F GAGTTTGAGGACAGGGTGTTTA

  • Ifit2_qPCR_R AGACCTCTGCAGTGCTTTAC

  • Gbp5_qPCR_F GGAAGTGCTGCAGACCTATT

  • Gbp5_qPCR_R GCTCTTTCTTGTTCCGCTTTAC

Next-generation sequence library preparation and analysis

Libraries were prepared from two biological replicates per condition. RNA-Seq libraries were prepared as previously described (Kaikkonen et al., 2013). Sequencing libraries were prepared using magnetic beads similar to described previously using barcoded adapters (NextFlex, Bioo Scientific) (Garber et al., 2012). Libraries were sequenced for 36 or 50 cycles on an Illumina Genome Analyzer II or HiSeq 2000, respectively, according to the manufacturer’s instructions. mRNA-Seq results were trimmed to remove A-stretches originating from the library preparation. Each sequence tag returned by the Illumina Pipeline was aligned to the mm10 assembly using ELAND allowing up to two mismatches. Only tags that mapped uniquely to the genome were considered for further analysis. Peak finding, MOTIF discovery, and downstream analysis was performed using HOMER, a software suite created for analysis of high-throughput sequencing data (Heinz et al., 2010). Detailed instructions for analysis can be found at http://homer.ucsd.edu/homer/. Data visualization was performed using Microsoft Excel, JavaTreeGraph and software packages available in R.

RNA interference experiments

SMART siRNA pools for examined genes were purchased from Dharmacon (Control: D-001810-10-05, Stat1: L-058881). Thioglycollate-elicited peritoneal macrophages were transfected with 30 nM siRNA for 48 hr using Deliver X (Affymetrix) according to the manufacturer’s instructions prior to being stimulated with KLA for designated times.

Chromatin immunoprecipitation

ChIP assays were performed as described before (Stender et al., 2017). Cells were crosslinked with 2 mM disuccinimidyl glutarate for 30 min prior to 10 min treatment with 1% formaldehyde. The antibodies used in these studies were: STAT1 (sc-345, Santa Cruz Biotechnology). For the precipitations protein A Dynabeads (10003D, Invitrogen) were coated with antibody prior to pulldown and excess antibody was washed away. Pulldowns occurred while rotating for 16 hr at 4°C. Beads were then washed with TSE I (20 mMTris/HCl pH 7.4 at 20°C, 150 mM NaCl, 0.1% SDS, 1% Triton X-100, 2 mM EDTA), twice with TSE III (10 mM Tris/HCl pH 7.4 at 20°C, 250 mM LiCl, 1% IGEPAL CA-630, 0.7% Deoxycholate, 1 mM EDTA), and twice with TE followed by elution from the beads using elution buffer (0.1 M NaHCO3, 1% SDS). Elutions were subsequently de-crosslinked overnight at 65°C and DNA was purified using ChIP DNA Clean and Concentrator (Zymo Research) and DNA was used for qPCR. The primer sequences used in this study are:

  • Cd69_ChIP_F TCCCTGCTGTCTGAAATGTG

  • Cd69_ChIP_R GTGGAAGGATGTCTTCGATTCT

  • Ifit2_ChIP_F GCATTGTGCAAGGAGAATTCTATG

  • Ifit2_ChIP_R TTCCGGAATTGGGAGAGAGA

  • Gbp5_ChIP_F TAAACAGCGCTTGAAACAATGA

  • Gbp5_ChIP_R AGGCTTGAATGTCACTGAACTA

Luciferase assay

Cells were plated in a 96-well plate and transfected when approximately 80% confluent. Transfections of pRL-CMV encoding Renilla luciferase (Heinz et al., 2010), together with a firefly luciferase promoter-reporter construct containing eight GAS consensus sequences (Horvai et al., 1997), control vector, or the indicated PHLPP constructs, were performed using Lipofectamine 3000 reagent (Invitrogen, L3000) for MEFs or Fugene six reagent (Promega, E269A) for HEK-293T cells. Cells were treated with murine or human IFNγ (PeproTech, 315–05, 300–02, respectively) for the indicated times at 37°C and activity was measured using the Dual-Glo Luciferase Assay System (Promega, E2940) in a Tecan Infinite M200 Pro multi-well plate reader. Promoter activity was corrected for the luciferase activity of the internal control plasmid, pRL-CMV, and Relative Response Ratios (RRR) were calculated.

Immunoprecipitation and western blot

DNA was transfected into HEK-293T cells using FuGene 6. Cells were collected 24 hr post-transfection and then lysed in a buffer containing 50 mM Na2HPO4 (pH 7.5), 1 mM sodium pyrophosphate, 20 mM NaF, 2 mM EDTA, 2 mM EGTA, 1% SDS, 1 mM DTT, 1 µM microcystin, 20 µM benzamidine, 40 µg/ml leupeptin, and 1 mM PMSF and then were sonicated briefly. For co-immunoprecipitation, cells were lysed and the detergent-solubilized cell lysates were incubated with an anti-HA antibody (BioLegend, 901503) at 4°C overnight. Samples were incubated with protein A/G PLUS-Agarose (Santa Cruz Cat sc-2003) for 1 hr at 4°C and washed three times in lysis buffer containing 0.3 M NaCl and 0.1% Triton X 100. Bound proteins and lysates were separated by SDS/PAGE gel and analyzed by western blot.

Immunofluorescence

HeLa cells were plated on glass coverslips and transfected using FuGene 6. 24 hr after transfection, cells were fixed with 4% paraformaldehyde for 20 min at room temperature, followed by fixation with 100% methanol for 3 min at −20°C. Cells were permeabilized and blocked in 0.3% Triton X 100% and 3% BSA for 30 min at room temperature, followed by three 5 min washes in PBS-T. Primary antibodies were diluted at the following dilutions: mouse anti-HA, 1:500; rabbit anti-α-tubulin (Cell Signaling, 2125), 1:200. Secondary antibodies were diluted at the following dilutions: Alexa647 anti-mouse (Life Technologies, A21235), 1:500; Alexa488 anti-rabbit (Life Technologies, A11034), 1:500. Coverslips were mounted onto slides with ProLong Diamond Antifade Mountant with DAPI (ThermoFisher, P36966). Images were acquired on a Zeiss Axiovert 200M microscope (Carl Zeiss Microimaging Inc) using an iXon Ultra 888 EMCCD camera (ANDOR) controlled by MetaFluor software (Molecular Devices) and analyzed on ImageJ (NIH). The Nuclear to Cytoplasmic ratio was calculated as follows: the mean signal intensity was measured for a region of the nucleus and cytoplasm for each cell, and the mean signal intensity of the background was subtracted from these values. Then the Nuclear to Cytoplasmic ratio was calculated by dividing the background subtracted mean signal intensity for the nuclear signal by the background subtracted value for the cytoplasmic signal.

In vitro phosphatase assay

pCMV 3xFLAG PHLPP1 was transfected into HEK-293T cells plated in four 10 cm plates (approximately 9 × 106 cells per plate, 80% transfection efficiency) using Fugene 6. Cells were collected 48 hr post-transfection and lysed in a buffer containing 20 mM Tris (pH 7.5), 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1% Triton X 100, 2.5 mM sodium pyrophosphate, 1 mM Na3VO4, 1 mM DTT, 1 mM PMSF, 1 µM microcystin, 20 µM benzamidine, and 40 µg/ml leupeptin. The detergent-solubilized cell lysates were incubated with anti-FLAG M2 affinity gel (30 µl per plate, Sigma-Aldrich, A2220) for 1 hr at 4°C, washed four times in lysis buffer and the beads were resuspended in 40 µl 200 mM Tris, 4 mM DTT, 20 mM MnCl2 for use in in vitro phosphatase assay. STAT1 (0.3 µM) (Biosource, PHF0011) was phosphorylated in vitro by incubation with recombinant human cdk1/cyclinB (0.2 µM) (Millipore, 14–450) at 30°C for 90 min in the presence of 1 mM ATP, and 1 X PK buffer (NEB, B6022) containing 50 mM Tris, 10 mM MgCl2, 0.1 mM EDTA, 2 mM DTT, 0.01% Brij, pH 7.5, and the reaction was quenched by addition of 144 µM CDK1 inhibitor RO3306 (Enzo, ALX-270–463). Phosphorylated STAT1 substrate was added to 1/4 vol of beads with bound PHLPP1 (or to lysis buffer control) and reactions were allowed to proceed for an additional 120 min at 30°C. For the zero minute time point, beads were added after the 120 min incubation and all reactions were immediately quenched with 4xSB (sample buffer). Samples were analyzed by western blot.

Mouse infection and endotoxin challenge

Bacterial sepsis in mice was induced by injection of E. coli K1 strain RS218 and LPS endotoxemia was induced by injection of purified E. coli O111:B4 LPS (Sigma-Aldrich). The E. coli culture was grown overnight in Luria broth (LB) medium (Hardy Diagnostics) at 37°C with shaking. The bacterial culture was diluted 1:50 in fresh LB, grown to mid-log phase, washed twice with PBS and reconstituted in PBS to yield the appropriate inoculum. For survival experiments, 10 to 14 week-old female C57BL/6 WT and littermate control Phlpp1-/- mice were injected i.p. with 5 × 107 colony forming units (cfu) E. coli or 15 mg/kg LPS and mouse survival recorded for 10 days following injection. For measurement of serum IL-6, IL-10 and IL-1β levels, mice were injected with 10 mg/kg LPS, and at 4, 8, 12 and 24 hr after injection, 80 µl of blood was collected by submandibular bleeding using a lancet into a serum separating blood collection tubes (BD) that were spun at 1500 × g for 10 min to separate serum. Serum cytokines were quantified by specific ELISA (R and D) following the manufacturer’s protocol. All protocols for mouse experiments were conducted in accordance with the institutional guidelines and were approved by the Institutional Animal Care and Usage Committee (IACUC) at the University of California, San Diego.

Data availability

Sequencing data have been deposited in GEO under accession code GSE116314. All data generated or analysed during this study are included in the manuscript and supporting files.

The following data sets were generated
    1. Cohen Katsenelson K
    2. Stender JD
    3. Glass CK
    4. Newton AC
    (2018) NCBI Gene Expression Omnibus
    ID GSE116314. Transcriptomic changes in wild-type and Phlpp1-/- mice following KLA stimulation.
    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE116314

References

Decision letter

  1. Kim Orth
    Reviewing Editor; HHMI/University of Texas Southwestern Medical Center, United States
  2. Tadatsugu Taniguchi
    Senior Editor; Institute of Industrial Science, The University of Tokyo, Japan
  3. Kim Orth
    Reviewer; HHMI/University of Texas Southwestern Medical Center, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

[Editors’ note: a previous version of this study was rejected after peer review, but the authors submitted for reconsideration. The first decision letter after peer review is shown below.]

Thank you for submitting your work entitled "PHLPP1 counter-regulates STAT1-mediated inflammatory signaling" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Kim Orth as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor.

Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife. Although the work is seen as important, it is not a complete story and would require experiments that will take longer than the allowed time. If the author chooses to address the issues brought up by these reviewers, we encourage them to submit a new manuscript to eLife with this data and we will do our best to use the same editors and reviewers for the new submission.

Reviewer #1:

The authors report that the PHLPP1 phosphatase specifically targets phosphorylated Ser727 on STAT1 and thereby inhibits its activity as a transcription factor. This is an important finding because STAT1 is a transcriptional activator that mediates the cellular response to interferons, cytokines and growth factors. The authors begin by showing that Phlpp1-/- mice have improved survival during E. coli infection and LPS-induced toxicity, indicating an important role for the PHLPP1 phosphatase in innate immune signaling. The authors then performed RNA-seq BMDMs derived from WT and Phlpp1-/- mice to determine how loss of Phlpp1 altered gene expression and found a significant enrichment in STAT binding motifs in the Phlpp1-null elevated transcripts. Importantly, the authors validated the Phlpp1's effect on STAT1 promoter occupancy through ChIP experiments. Finally, the authors show that dephosphorylation of Ser727 on STAT1 does indeed suppress its activity as a transcription factor and that PHLPP1 specifically dephosphorylates Ser727 in vitro and that loss of Phlpp1 increases basal STAT1 Ser727 phosphorylation in vivo. Although this manuscript elucidates an important level of control in STAT1 signaling the following major and minor concerns must be addressed before this manuscript can be accepted for publication.

Essential revisions:

Figure 2E-G:

The text states that these are RT-PCR data (subsection “Loss of PHLPP1 results in a prolonged STAT1-dependent transcription in macrophages”), however the figure legend states that they are RNA-seq values. Which are they? If they are qRT-PCR then are they relative expression values or absolute expression? There is no detailed information in the Materials and methods section as to how the qPCR was performed and no sequences for primers to normalization genes if they are indeed relative values. The reference given is behind a paywall so the common reader would not be able to access any detailed information about the qRT-PCR methods used. The methods used to obtain these data must be further elucidated to be able to determine if the proper controls and normalization were performed.

Figure 5:

The analysis of the subcellular localization of PHLPP1 would be greatly improved by the addition of an experiment in which nuclear export was inhibited by Leptomcyin B. The authors reported that full-length PHLPP1 is cytosolic, but they failed to take into account that nuclear export/import is an equilibrium process and the only way to tell if a protein is indeed primarily localized in the cytosol is to block nuclear export.

It would also strengthen this line of inquiry if the authors tried (even if just with bioinformatics) to identify the native NLS of PHLPP1. The authors add a foreign NLS (from what protein? what type of NLS?) to show that driving the protein to the nucleus inhibits STAT1 promoter activity suggesting nuclear localization of PHLPP1 is important for its activity. They do not elucidate PHLPP1's native NLS, which must exist in order for the effect of nuclear localization of PHLPP1 on STAT1 promoter activity to actually be significant in vivo.

Figure 5D shows that PHLPP1 dephosphorylates STAT1 Ser727 in vitro and is out of place in Figure 5 which is all about nuclear localization. Figure 5D should either be incorporated into Figure 6 or moved to the supplement. This would leave room in Figure 5 for the suggested Leptomycin B experiment and NLS bioinformatics in this figure.

Figure 7:

Can you comment on the fact that pAkt T308 is elevated in all conditions in the PHLPP1 KO in 7A? PHLPP1 dephosphorylation of Ser473 was mentioned in the Discussion section, but not T308.

As it is presented, Figure 7C is confusing. It seems like the luciferase assay graph and the pAkt blot should be separated into Figure 7C and 7D or at least the blot needs to be mentioned in the figure legend which currently only explains the luciferase assay for Figure 7C. I understand that the blot is only for validation purposes, but it still needs to be mentioned in the figure legend if it is be included.

Subsection “PHLPP1 binds to STAT1 and dephosphorylates Ser727”: Please revise to "…interacts with STAT1 and reduces its promoter activity". You have shown that the PHLPP1 NTE can pull down STAT1 and that the PHLPP1 NTE reduces STAT1 promoter activity, but not exactly that one causes the other.

And as these were IPed out of lysate and not shown to directly interact with purified recombinant proteins, a direct interaction cannot be claimed.

Reviewer #2:

The presented paper identifies a potential phosphatase for STAT1. Deletion of the phosphatase-encoding gene Phlpp1 increases survival of mice upon LPS or E. coli challenge. Molecular analysis in macrophages revealed that PHLPP1 dephosphorylates Ser727 in STAT1 and thus suppresses inflammatory signaling.

Strong points of the paper are its detail in the characterization of PHLPP1 and its effect on STAT1. Weak points of the paper are the apparent presence of PHLPP1 even in knockouts, minimal in vivo analysis and exclusive focus on macrophages.

1) The manuscript is lacking controls and/or appropriate quantification for their knockdown and knockouts. The authors should show the efficiency of Phlpp1 knockdown by WB or PCR and that siCtl does not affect STAT1. Most importantly, neither the CRISPR knockout (Figure 6A) in BMDM nor BMDM from PHLPP1 KO (Figure S1) are convincingly lacking PHLPP1. The band intensity of PHLPP1 is almost the same between knockout and WT. Why is there a band at all for PHLPP1 in PHLPP1 KO? In Figure 6 and Figure S1, there seems to be only one band, whereas in Figure 7C the higher molecular band is marked with the arrow for PHLPP1. The authors have to show convincingly that PHLPP1 is absent in their KO cells.

2) The focus of the manuscript is the characterization of molecular interactions of PHLPP1. Nevertheless, the in vivo experiments should be analyzed in more detail. Is there increased dissemination in peripheral organs and blood in WT mice compared to KO mice? The increase in inflammatory cytokines could be either due to a higher inflammatory profile of immune cells or an increased recruitment/number. Thus, ideally the authors would measure recruitment of neutrophils, macrophages and DCs to the peritoneum and lymph nodes.

3) The authors show in vivo levels of only 3 cytokines and conclude from 2 cytokines that Phlpp1-/- mice had a diminished initial pro-inflammatory cytokine burst. Only IL-1β levels were consistently higher in KO mice. The authors should analyze more cytokines to draw this conclusion. For in vitro experiments, e.g. in vivo levels of IFNγ would be highly relevant and should be measured.

4) Figure 5C. Is the transfection efficiency the same? From the images, it seems that different constructs have different transfection efficacy. Confocal images lack scale bars. Bar graphs lack statistical analysis.

5) The rationale for choosing GBP5, IFIT2 and CD69 as representative genes should be provided.

Reviewer #3:

This is an interesting study that seeks to identify roles of the PHLPP1 phosphatase. The authors conclude that PHLPP1 dephosphorylates STAT1 on Ser727 and serves to promote resolution of inflammation by suppressing STAT1 activity. This is an important finding because while the kinases that cause STAT1 Ser727 phosphorylation have been characterized, little is known concerning the mechanism of dephosphorylation.

The authors' conclusions are supported by the data presented, although several aspects of the analysis appear incomplete.

1) The biological focus of the study is the protection against endotoxin and bacterial sepsis while the mechanistic studies focus on the resolution of inflammation. There is a logical problem here. Indeed, this is acknowledged by the authors who briefly state in subsection “PHLPP1 regulates the innate immune response” that the reduced mortality is most likely associated with a diminished initial cytokine response. The mechanism of the reduced initial cytokine response is not defined. Instead, the manuscript focusses on a second phenotype – reduced resolution – that may have little to do with the reduced mortality associated with endotoxin and bacterial sepsis. This lack of coherence between biology and mechanism needs to be fixed.

2) The conclusion that PHLPP1 deficiency promotes sustained STAT1 signaling is not strongly supported by the gene expression data (Figure 2E-G) that show increased expression at 6 hours and a similar level of decline in expression at 24 hours. Since the heat map is presented as the ratio of KO:WT, it is not clear that this expression pattern is not shared by the other genes in this group. A similar argument could be made for some of the genes in the ChIP analysis that show increased binding at all time points (Figure 3D-F). Please clarify. Moreover, this increase in gene expression at all time points correlates poorly with the decrease in the initial cytokine response that may contribute to the protection against sepsis and LPS (see #1 above).

3) The authors conclude (Abstract) that PHLPP1 catalytic activity is required for suppression of STAT1. The data to support this conclusion is incomplete. While the authors do show – by mutational analysis – that PHLPP1 catalytic activity is required for PHLPP1 Ser727 dephosphorylation in vitro (Figure 5D), this is not demonstrated in vivo. For example, knockout macrophages complemented with WT and catalytically inactive PHLPP1 should be compared to investigate the regulation of STAT1 Ser727 phosphorylation.

4) The CRISPR studies are unclear. First, the KO cells still express PHLPP1 (Figure 6A and Figure S1). Second, no controls for the CRISPR KO are presented. Third, the conclusion that KLA-induced phosphorylation is sustained at 24 hours in the PHLPP1 KO cells is not convincing (Figure S1). Fourth, it is unclear why Phlpp1-/- BMDM were not used for these studies – these cells prepared from Phlpp1-/- mice would not express PHLPP1 protein.

5) No information is provided concerning methods and statistical analysis of the promoter motif analysis (Subsection “Loss of PHLPP1 results in a prolonged STAT1-dependent transcription in macrophages” and Figure 2C,D legends).

6) No controls for the siRNA SMARTpool study (Figure 3C) are presented. Minimally, this requires the use of two independent single oligonucleotides. Preferably also complementation analysis.

[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]

Thank you for submitting your article "PHLPP1 Counter-regulates STAT1-mediated Inflammatory Signaling" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Kim Orth as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Reviewing Editor and Tadatsugu Taniguchi as the Senior Editor.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

The authors added significant amount of data for their molecular analysis of the role of PHLPP1 in suppressing STAT1 signaling.

Essential revisions:

Reviewer #1:

The authors have satisfied this reviewer with their revisions to the first submission.

Reviewer #2:

The authors added significant amount of data for their molecular analysis of the role of PHLPP1 in suppressing STAT1 signaling. Comments addressing the concerns of reviewer 1 and the use of BMDMs from Phlpp1-/- mice as suggested by reviewer 3 significantly improved the quality of the manuscript and support the claims made.

In their rebuttal, the authors wrote that they replaced all knockdown experiments with BMDMs. However, siRNA knockdowns of STAT1 are still used in Figure 3. Is there a reason for why the authors did not show that STAT1 is actually knocked down, that siCrtl does not affect STAT1, and the efficiency of the knockdown?

The in vivo role of PHLPP1 remains still largely unexplored. The authors acknowledge this and state that this is not in their area of expertise. The cytokine profiles are only measured until 24 hours post injection of LPS though, at which time the mortality rate between the two groups is identical. Difference are observed 48 hours post infection. As authors are unable to repeat the experiment and/or measure more cytokines, this should at least be clearly mentioned. The molecular analysis that the authors focus on seems sound, but the evidence that the observed in vivo phenotype is due to the lack of dephosphorylation of STAT1 is minimal. As the authors suggest exploration of PHLPP1 inhibitors for treatment of sepsis in the Discussion section, this lack of direct evidence should be mentioned.

Reviewer #3:

The manuscript has been improved. While the authors have not addressed all the points raised in my critique, I believe that the revised manuscript does address the primary criticisms and that the paper is now acceptable for publication.

https://doi.org/10.7554/eLife.48609.020

Author response

[Editors’ note: the author responses to the first round of peer review follow.]

Reviewer #1:

The authors report that the PHLPP1 phosphatase specifically targets phosphorylated Ser727 on STAT1 and thereby inhibits its activity as a transcription factor. This is an important finding because STAT1 is a transcriptional activator that mediates the cellular response to interferons, cytokines and growth factors. The authors begin by showing that Phlpp1-/- mice have improved survival during E. coli infection and LPS-induced toxicity, indicating an important role for the PHLPP1 phosphatase in innate immune signaling. The authors then performed RNA-seq BMDMs derived from WT and Phlpp1-/- mice to determine how loss of Phlpp1 altered gene expression and found a significant enrichment in STAT binding motifs in the Phlpp1-null elevated transcripts. Importantly, the authors validated the Phlpp1's effect on STAT1 promoter occupancy through ChIP experiments. Finally, the authors show that dephosphorylation of Ser727 on STAT1 does indeed suppress its activity as a transcription factor and that PHLPP1 specifically dephosphorylates Ser727 in vitro and that loss of Phlpp1 increases basal STAT1 Ser727 phosphorylation in vivo. Although this manuscript elucidates an important level of control in STAT1 signaling the following major and minor concerns must be addressed before this manuscript can be accepted for publication. Essential revisions: Figure 2E-G: The text states that these are RT-PCR data (subsection “Loss of PHLPP1 results in a prolonged STAT1-dependent transcription in macrophages”), however the figure legend states that they are RNA-seq values. Which are they? If they are qRT-PCR then are they relative expression values or absolute expression? There is no detailed information in the Materials and methods section as to how the qPCR was performed and no sequences for primers to normalization genes if they are indeed relative values. The reference given is behind a paywall so the common reader would not be able to access any detailed information about the qRT-PCR methods used. The methods used to obtain these data must be further elucidated to be able to determine if the proper controls and normalization were performed.

The data in Figure 2E-G are RNA-Seq, whereas those in Figure 3A-C are qPCR. We have updated the text to indicate “normalized mRNA-Seq data” where appropriate. In addition, we have updated the Materials and methods section to include the primer sequences for 36B4 that were used for normalization. We have also included additional references for the Materials and methods section.

Figure 5:

The analysis of the subcellular localization of PHLPP1 would be greatly improved by the addition of an experiment in which nuclear export was inhibited by Leptomcyin B. The authors reported that full-length PHLPP1 is cytosolic, but they failed to take into account that nuclear export/import is an equilibrium process and the only way to tell if a protein is indeed primarily localized in the cytosol is to block nuclear export.

It would also strengthen this line of inquiry if the authors tried (even if just with bioinformatics) to identify the native NLS of PHLPP1.

We have now identified a bipartite NLS in the unstructured N-Terminal Extension (NTE) of PHLPP1 (new Figure 6D). We show that a construct encoding the NTE localizes to the nucleus, that mutation of either half of the bipartite mutation significantly increases localization in the cytosol, and that mutation of both halves of the bipartite motif results in cytosolic localization that is almost as complete as the addition of a strong NES (new Figure 6E and 6F). Additionally, we show that the decrease in STAT1 promoter activity observed upon overexpression of PHLPP1 in HEK cells depends on a functional NLS: mutation of either or both halves of the NLS reduces or abolishes, respectively, the PHLPP1-dependent effects on STAT1 promoter activity (new Figure 6G). Additionally, we identify a nuclear exclusion signal (NES) preceding the phosphatase domain (Figure 6—figure supplement 2). We appreciate the reviewer’s suggestion we address the mechanism for nuclear localization as identification of the NLS provides the first functional role of the NTE, which is unique to PHLPP1. We have also revised the model in Figure 8 to indicate the NLS and NES.

The authors add a foreign NLS (from what protein? what type of NLS?) to show that driving the protein to the nucleus inhibits STAT1 promoter activity suggesting nuclear localization of PHLPP1 is important for its activity.

We have added the information on the NLS, which is from PKI.

They do not elucidate PHLPP1's native NLS, which must exist in order for the effect of nuclear localization of PHLPP1 on STAT1 promoter activity to actually be significant in vivo.

See above.

Figure 5D shows that PHLPP1 dephosphorylates STAT1 Ser727 in vitro and is out of place in Figure 5 which is all about nuclear localization. Figure 5D should either be incorporated into Figure 6 or moved to the supplement. This would leave room in Figure 5 for the suggested Leptomycin B experiment and NLS bioinformatics in this figure.

We appreciate these helpful recommendations and have reorganized all the biochemical figures as follows: the in vitro dephosphorylation assay (previously Figure 5D) is now presented in the new Figure 4, which focusses exclusively on phosphorylation of Ser727 of STAT1 by PHLPP1. Specifically, the figure shows that (1) STAT1 phosphorylation on Ser727 is enhanced in primary bone marrow derived macrophages from Phlpp1-/- mice compared to WT mice (new Figure 6 A,B), (2) pSer727 is dephosphorylated in vitro by PHLPP1 (Figure 4 C,D), and (3) STAT1 has reduced phosphorylation on Ser727 in cells overexpressing PHLPP1 (Figure 4 D,E).

Figure 7:

Can you comment on the fact that pAkt T308 is elevated in all conditions in the PHLPP1 KO in 7A? PHLPP1 dephosphorylation of Ser473 was mentioned in the Discussion section, but not T308.

Phosphate at the hydrophobic motif (Ser473) decreases the phosphatase sensitivity at the activation loop (Thr308), that is why the PHLPP1 KO cells have elevated phosphorylation at both sites, even though only Ser473 is a direct PHLPP. In the revision, we replaced the original Figure 7A,B with a better analysis, which now is the new Figure 5A,B. Here, we make the point that the Ser727 phosphorylation is okadaic-acid insensitive in the primary bone marrow-derived macrophages, under conditions where known PPA sites are okadaic-acid sensitive (the phosphorylation of T308 on Akt and T202/Y204 on Erk increases with okadaic acid yet there is no detectable change on Ser727 phosphorylation).

As it is presented, Figure 7C is confusing. It seems like the luciferase assay graph and the pAkt blot should be separated into Figure 7C and 7D or at least the blot needs to be mentioned in the figure legend which currently only explains the luciferase assay for Figure 7C. I understand that the blot is only for validation purposes, but it still needs to be mentioned in the figure legend if it is be included.

The luciferase assay is now Figure 6A; we removed the blot and replaced it with the new data in the new Figure 5A,B.

Subsection “PHLPP1 binds to STAT1 and dephosphorylates Ser727”: Please revise to "…interacts with STAT1 and reduces its promoter activity". You have shown that the PHLPP1 NTE can pull down STAT1 and that the PHLPP1 NTE reduces STAT1 promoter activity, but not exactly that one causes the other.

And as these were IPed out of lysate and not shown to directly interact with purified recombinant proteins, a direct interaction cannot be claimed.

Point well taken; text modified as suggested.

Reviewer #2:

The presented paper identifies a potential phosphatase for STAT1. Deletion of the phosphatase-encoding gene Phlpp1 increases survival of mice upon LPS or E. coli challenge. Molecular analysis in macrophages revealed that PHLPP1 dephosphorylates Ser727 in STAT1 and thus suppresses inflammatory signaling.

Strong points of the paper are its detail in the characterization of PHLPP1 and its effect on STAT1. Weak points of the paper are the apparent presence of PHLPP1 even in knockouts, minimal in vivo analysis and exclusive focus on macrophages.

1) The manuscript is lacking controls and/or appropriate quantification for their knockdown and knockouts. The authors should show the efficiency of Phlpp1 knockdown by WB or PCR and that siCtl does not affect STAT1. Most importantly, neither the CRISPR knockout (Figure 6A) in BMDM nor BMDM from PHLPP1 KO (Figure S1) are convincingly lacking PHLPP1. The band intensity of PHLPP1 is almost the same between knockout and WT. Why is there a band at all for PHLPP1 in PHLPP1 KO?

We have replaced all the experiments using CRISPR knockdown with ones using primary BMDM isolated directly from the WT vs. Phlpp1-/- mice. The new Figure 4A compares the KLA-induced phosphorylation of STAT1 at Ser727 and Tyr701 in these macrophages, with the data from 5 independent experiments quantified in the new Figure 5B.

In Figure 6 and Figure S1, there seems to be only one band, whereas in Figure 7C the higher molecular band is marked with the arrow for PHLPP1. The authors have to show convincingly that PHLPP1 is absent in their KO cells.

As noted above, Figure 6 (with the CRIPSR knockdown) has been replaced with experiments using BMDM from the Phlpp1-/- mice. The immunoreactive band in the PHLPP1 blot in original Figure 7C (now Figure 4C) that is present in all lanes is β-catenin; all commercially available PHLPP1 antibodies raised to the C-term of PHLPP1 seem to label it (Lobert et al., 2013).

2) The focus of the manuscript is the characterization of molecular interactions of PHLPP1. Nevertheless, the in vivo experiments should be analyzed in more detail. Is there increased dissemination in peripheral organs and blood in WT mice compared to KO mice? The increase in inflammatory cytokines could be either due to a higher inflammatory profile of immune cells or an increased recruitment/number. Thus, ideally the authors would measure recruitment of neutrophils, macrophages and DCs to the peritoneum and lymph nodes.

These are very interesting suggestions but beyond the scope of our expertise. Additionally, the two co-first authors are no longer in our labs, and the third author is on maternity leave. While we recruited another postdoc to address all the biochemical revisions, we are not equipped to do whole animal studies. Thus, although very interesting experiments, our current contribution focuses on the molecular mechanisms of how PHLPP1 suppresses STAT1 signaling.

3) The authors show in vivo levels of only 3 cytokines and conclude from 2 cytokines that Phlpp1-/- mice had a diminished initial pro-inflammatory cytokine burst. Only IL-1β levels were consistently higher in KO mice. The authors should analyze more cytokines to draw this conclusion. For in vitro experiments, e.g. in vivo levels of IFNγ would be highly relevant and should be measured.

We have focused on the mechanistic aspects given our lack of personnel (please see previous comment).

4) Figure 5C. Is the transfection efficiency the same? From the images, it seems that different constructs have different transfection efficacy. Confocal images lack scale bars. Bar graphs lack statistical analysis.

The transfection efficiency was similar for all constructs and ranged from 50-75%. We have now added a scale bar (15 µm) and statistical analysis.

5) The rationale for choosing GBP5, IFIT2 and CD69 as representative genes should be provided.

We chose these genes because they were significantly elevated in the Phlpp1-/- comparted to WT MEFs. Additionally, they have proximal STAT1 binding motifs in their promoter regions. We have clarified this in the text:

“We next selected three genes whose expression was elevated in the Phlpp1-/- compared to WT macrophages and which had proximal STAT1 binding motifs in their promoters to further analyze: Cd69, Ifit2, and Gbp5.”

Reviewer #3:

This is an interesting study that seeks to identify roles of the PHLPP1 phosphatase. The authors conclude that PHLPP1 dephosphorylates STAT1 on Ser727 and serves to promote resolution of inflammation by suppressing STAT1 activity. This is an important finding because while the kinases that cause STAT1 Ser727 phosphorylation have been characterized, little is known concerning the mechanism of dephosphorylation.

The authors' conclusions are supported by the data presented, although several aspects of the analysis appear incomplete.

1) The biological focus of the study is the protection against endotoxin and bacterial sepsis while the mechanistic studies focus on the resolution of inflammation. There is a logical problem here. Indeed, this is acknowledged by the authors who briefly state in subsection “PHLPP1 regulates the innate immune response” that the reduced mortality is most likely associated with a diminished initial cytokine response. The mechanism of the reduced initial cytokine response is not defined. Instead, the manuscript focusses on a second phenotype – reduced resolution – that may have little to do with the reduced mortality associated with endotoxin and bacterial sepsis. This lack of coherence between biology and mechanism needs to be fixed.

The biological phenotype of the Phlpp1-/- mice likely arises from an abundance of perturbations to the normal physiology of the mouse. We begin with the mouse experiment because it clearly points to a defect in innate immunity, however the primary focus of our manuscript is to identify a previously undescribed mechanism for the suppression of STAT1 signaling by PHLPP1. This mechanism is likely one of many that contribute to the whole animal phenotype, and we have tried to express this in the manuscript.

2) The conclusion that PHLPP1 deficiency promotes sustained STAT1 signaling is not strongly supported by the gene expression data (Figure 2E-G) that show increased expression at 6 hours and a similar level of decline in expression at 24 hours. Since the heat map is presented as the ratio of KO:WT, it is not clear that this expression pattern is not shared by the other genes in this group. A similar argument could be made for some of the genes in the ChIP analysis that show increased binding at all time points (Figure 3D-F). Please clarify. Moreover, this increase in gene expression at all time points correlates poorly with the decrease in the initial cytokine response that may contribute to the protection against sepsis and LPS (see #1 above).

We acknowledge that the genes evaluated show a decline in expression at 24 hours, however they are expressed at significantly higher levels in the Phlpp1-/- macrophages compared with WT. We conclude that STAT1 signaling is sustained (or elevated) in the Phlpp1-/- macrophages relative to the WT macrophages based on the following observations:

1) There are elevated genes in the Phlpp1-/- relative to WT macrophages.

2) The promoter regions of these genes have an enrichment of STAT1 binding motifs relative to genome background.

3) The ChIP signal for STAT1 is elevated on representative promoters in Phlpp1-/- macrophages relative to WT cells.

4) STAT1 phosphorylation is enhanced and sustained in Phlpp1 KO macrophages relative to WT.

3) The authors conclude (Abstract) that PHLPP1 catalytic activity is required for suppression of STAT1. The data to support this conclusion is incomplete. While the authors do show – by mutational analysis – that PHLPP1 catalytic activity is required for PHLPP1 Ser727 dephosphorylation in vitro (Figure 5D), this is not demonstrated in vivo. For example, knockout macrophages complemented with WT and catalytically inactive PHLPP1 should be compared to investigate the regulation of STAT1 Ser727 phosphorylation.

Our new experiments focus on primary BMDM which are have exceptionally low transfection efficiency and not amenable to the rescue experiment noted. When we overexpress a catalytically-dead PHLPP1 PP2C domain, it no longer regulates STAT1 transcriptional activity whereas an active PP2C does. These cell-based assays are consistent with PHLPP1 regulating STAT1 via a catalytic mechanism. Additionally, PHLPP1 dephosphorylates Ser727 in vitro.

4) The CRISPR studies are unclear. First, the KO cells still express PHLPP1 (Figure 6A and Figure S1). Second, no controls for the CRISPR KO are presented. Third, the conclusion that KLA-induced phosphorylation is sustained at 24 hours in the PHLPP1 KO cells is not convincing (Figure S1). Fourth, it is unclear why Phlpp1-/- BMDM were not used for these studies – these cells prepared from Phlpp1-/- mice would not express PHLPP1 protein.

The reviewer brings up an excellent point, to use the Phlpp1-/- BMDM. We have replaced all the CRISPR KO with experiments using the BMDM; see new Figure 4A,B and new Figure 5A,B.

5) No information is provided concerning methods and statistical analysis of the promoter motif analysis (Subsection “Loss of PHLPP1 results in a prolonged STAT1-dependent transcription in macrophages” and Figure 2C,D legends).

We have updated the text to include more details on the promoter analysis; we have also updated the figure legends.

6) No controls for the siRNA SMARTpool study (Figure 3C) are presented. Minimally, this requires the use of two independent single oligonucleotides. Preferably also complementation analysis.

These studies were internally controlled for with a control siRNA and a SMARTpool of four oligos synthesized by Dharmacon. The purpose of this experiment was to demonstrate that these are targets of STAT1, and the presented data support this conclusion.

[Editors' note: the author responses to the re-review follow.]

Essential revisions:

Reviewer #2:

The authors added significant amount of data for their molecular analysis of the role of PHLPP1 in suppressing STAT1 signaling. Comments addressing the concerns of reviewer 1 and the use of BMDMs from Phlpp1-/- mice as suggested by reviewer 3 significantly improved the quality of the manuscript and support the claims made.

In their rebuttal, the authors wrote that they replaced all knockdown experiments with BMDMs. However, siRNA knockdowns of STAT1 are still used in Figure 3. Is there a reason for why the authors did not show that STAT1 is actually knocked down, that siCrtl does not affect STAT1, and the efficiency of the knockdown?

Apologies for the misleading statement – we replaced all the biochemical/mechanistic experiments addressing PHLPP1 loss with ones using BMDMS, but the reviewer is correct in noting that the validation experiments in Figure 3 were with siRNA knockdown of STAT1. The knockdown of STAT1 was confirmed by the RNA-Seq. Also, given the clear effect of the STAT1 knockdown, but not siCtrl, on genes with STAT1 promoters, we did not feel that addressing efficiency for these experiments was necessary.

The in vivo role of PHLPP1 remains still largely unexplored, which you acknowledge, stating that this is not in their area of expertise. The cytokine profiles are only measured until 24 hours post injection of LPS though, at which time the mortality rate between the two groups is identical. Differences are observed at 48 hours post infection. As you cannot repeat the experiment and/or measure more cytokines, this should at least be clearly mentioned. The molecular analysis that you focus on appears sound, but the evidence that the observed in vivo phenotype is due to the lack of dephosphorylation of STAT1 is minimal. As you suggest exploration of PHLPP1 inhibitors for treatment of sepsis in the Discussion section, this lack of direct evidence must be mentioned.

These are all good points and we have adjusted the text as in the Results section:

“Note that cytokine levels were measured up to 24 h post LPS injection, when the protective effect of PHLPP1 loss was not yet apparent.”

and in the Discussion section:

"Although the current study does not provide direct evidence that enhanced phosphorylation of STAT1 causes the protective effect of PHLPP1 loss on both E. coli-induced sepsis and LPS-induced endotoxemia in mice, our data indicate that PHLPP1 inhibitors could be explored as adjunctive therapies to antibiotics and supportive care of patients with Gram-negative sepsis, a leading cause of mortality in intensive care units.”

https://doi.org/10.7554/eLife.48609.021

Article and author information

Author details

  1. Ksenya Cohen Katsenelson

    Department of Pharmacology, University of California, San Diego, San Diego, United States
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing, Performed the experiments, Conceived the project
    Contributed equally with
    Joshua D Stender and Agnieszka T Kawashima
    Competing interests
    No competing interests declared

  2. Joshua D Stender

    Department of Cellular and Molecular Medicine, University of California, San Diego, San Diego, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing, Performed the experiments, Conceived the project
    Contributed equally with
    Ksenya Cohen Katsenelson and Agnieszka T Kawashima
    Competing interests
    No competing interests declared

  3. Agnieszka T Kawashima

    1. Department of Pharmacology, University of California, San Diego, San Diego, United States
    2. Department of Pharmacology and Biomedical Sciences Graduate Program, University of California, San Diego, San Diego, United States
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Investigation, Methodology, Writing—review and editing, Performed the experiments, Conceived the project
    Contributed equally with
    Ksenya Cohen Katsenelson and Joshua D Stender
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0176-1656

  4. Gema Lordén

    Department of Pharmacology, University of California, San Diego, San Diego, United States
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Investigation, Methodology, Writing—review and editing, Performed the experiments, Conceived the project
    Competing interests
    No competing interests declared

  5. Satoshi Uchiyama

    Department of Pediatrics, University of California, San Diego, San Diego, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing, Performed the experiments, Conceived the project
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0979-7998

  6. Victor Nizet

    1. Department of Pediatrics, University of California, San Diego, San Diego, United States
    2. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, United States
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Investigation, Methodology, Writing—review and editing, Conceived the project
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3847-0422

  7. Christopher K Glass

    Department of Cellular and Molecular Medicine, University of California, San Diego, San Diego, United States
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Investigation, Methodology, Writing—review and editing, Conceived the project
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4344-3592

  8. Alexandra C Newton

    Department of Pharmacology, University of California, San Diego, San Diego, United States
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing—original draft, Project administration, Writing—review and editing, Conceived the project
    For correspondence
    anewton@ucsd.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2906-3705

Funding

National Institutes of Health (R35 GM122523)

  • Alexandra C Newton

National Institutes of Health (DK091183)

  • Christopher K Glass

National Institutes of Health (GM067946)

  • Alexandra C Newton

National Institutes of Health (HL125352)

  • Victor Nizet

National Institutes of Health (DK063491)

  • Christopher K Glass

National Institutes of Health (T32 CA009523)

  • Ksenya Cohen Katsenelson

National Institutes of Health (T32 GM007752)

  • Agnieszka T Kawashima

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Maya Kunkel for advice and Mira Sastri, Gregory Fonseca, and Ali Syed for assistance with reagents and methodologies. This work was supported by NIH R35 GM122523 (ACN), NIH GM067946 (ACN), NIH HL125352 (VN), NIH DK091183 (CKG) and DK063491 (CKG). KC-K. was supported in part by NIH/NCI T32 CA009523 and ATK was supported in part by the University of California San Diego Graduate Training Grant in Cellular and Molecular Pharmacology through the National Institutes of Health Institutional Training Grant T32 GM007752 from the NIGMS.

Ethics

Animal experimentation: All protocols for mouse experiments were conducted in accordance with the institutional guidelines and were approved by the Institutional Animal Care and Usage Committee (IACUC) at the University of California, San Diego. IACUC protocol numbers S06288, S00227M.

Senior Editor

  1. Tadatsugu Taniguchi, Institute of Industrial Science, The University of Tokyo, Japan

Reviewing Editor

  1. Kim Orth, HHMI/University of Texas Southwestern Medical Center, United States

Reviewer

  1. Kim Orth, HHMI/University of Texas Southwestern Medical Center, United States

Publication history

  1. Received: May 20, 2019
  2. Accepted: July 30, 2019
  3. Version of Record published: August 13, 2019 (version 1)

Copyright

© 2019, Cohen Katsenelson et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,681
    Page views
  • 230
    Downloads
  • 16
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Ksenya Cohen Katsenelson
  2. Joshua D Stender
  3. Agnieszka T Kawashima
  4. Gema Lordén
  5. Satoshi Uchiyama
  6. Victor Nizet
  7. Christopher K Glass
  8. Alexandra C Newton
(2019)
PHLPP1 counter-regulates STAT1-mediated inflammatory signaling
eLife 8:e48609.
https://doi.org/10.7554/eLife.48609

Categories and tags

Research organism

Further reading

    1. Biochemistry and Chemical Biology
    2. Cancer Biology

    Pancreatic tumors exhibit myeloid-driven amino acid stress and upregulate arginine biosynthesis

    Juan J Apiz Saab, Lindsey N Dzierozynski ... Alexander Muir
    Research Advance

    Nutrient stress in the tumor microenvironment requires cancer cells to adopt adaptive metabolic programs for survival and proliferation. Therefore, knowledge of microenvironmental nutrient levels and how cancer cells cope with such nutrition is critical to understand the metabolism underpinning cancer cell biology. Previously, we performed quantitative metabolomics of the interstitial fluid (the local perfusate) of murine pancreatic ductal adenocarcinoma (PDAC) tumors to comprehensively characterize nutrient availability in the microenvironment of these tumors (M. R. Sullivan, Danai, et al., 2019). Here, we develop Tumor Interstitial Fluid Medium (TIFM), a cell culture medium that contains nutrient levels representative of the PDAC microenvironment, enabling us to study PDAC metabolism ex vivo under physiological nutrient conditions. We show that PDAC cells cultured in TIFM adopt a cellular state closer to that of PDAC cells present in tumors compared to standard culture models. Further, using the TIFM model, we found arginine biosynthesis is active in PDAC and allows PDAC cells to maintain levels of this amino acid despite microenvironmental arginine depletion. We also show that myeloid derived arginase activity is largely responsible for the low levels of arginine in PDAC tumors. Altogether, these data indicate that nutrient availability in tumors is an important determinant of cancer cell metabolism and behavior, and cell culture models that incorporate physiological nutrient availability have improved fidelity to in vivo systems and enable the discovery of novel cancer metabolic phenotypes.

    1. Biochemistry and Chemical Biology
    2. Chromosomes and Gene Expression

    An acetylation-mediated chromatin switch governs H3K4 methylation read-write capability

    Kanishk Jain, Matthew R Marunde ... Brian D Strahl
    Short Report Updated

    In nucleosomes, histone N-terminal tails exist in dynamic equilibrium between free/accessible and collapsed/DNA-bound states. The latter state is expected to impact histone N-termini availability to the epigenetic machinery. Notably, H3 tail acetylation (e.g. K9ac, K14ac, K18ac) is linked to increased H3K4me3 engagement by the BPTF PHD finger, but it is unknown if this mechanism has a broader extension. Here, we show that H3 tail acetylation promotes nucleosomal accessibility to other H3K4 methyl readers, and importantly, extends to H3K4 writers, notably methyltransferase MLL1. This regulation is not observed on peptide substrates yet occurs on the cis H3 tail, as determined with fully-defined heterotypic nucleosomes. In vivo, H3 tail acetylation is directly and dynamically coupled with cis H3K4 methylation levels. Together, these observations reveal an acetylation ‘chromatin switch’ on the H3 tail that modulates read-write accessibility in nucleosomes and resolves the long-standing question of why H3K4me3 levels are coupled with H3 acetylation.

    1. Biochemistry and Chemical Biology
    2. Medicine

    Improved isolation of extracellular vesicles by removal of both free proteins and lipoproteins

    Dmitry Ter-Ovanesyan, Tal Gilboa ... David R Walt
    Research Advance

    Extracellular vesicles (EVs) are released by all cells into biofluids such as plasma. The separation of EVs from highly abundant free proteins and similarly sized lipoproteins remains technically challenging. We developed a digital ELISA assay based on Single Molecule Array (Simoa) technology for ApoB-100, the protein component of several lipoproteins. Combining this ApoB-100 assay with previously developed Simoa assays for albumin and three tetraspanin proteins found on EVs (Ter-Ovanesyan, Norman et al., 2021), we were able to measure the separation of EVs from both lipoproteins and free proteins. We used these five assays to compare EV separation from lipoproteins using size exclusion chromatography with resins containing different pore sizes. We also developed improved methods for EV isolation based on combining several types of chromatography resins in the same column. We present a simple approach to quantitatively measure the main impurities of EV isolation in plasma and apply this approach to develop novel methods for enriching EVs from human plasma. These methods will enable applications where high-purity EVs are required to both understand EV biology and profile EVs for biomarker discovery.