Protein neddylation governs the inflammatory status of healthy and malignant myeloid cells in response to TLR stimulation

Protein neddylation governs the inflammatory status of healthy and malignant myeloid cells in response to TLR stimulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Protein neddylation governs the inflammatory status of healthy and malignant myeloid cells in response to TLR stimulation Yumeng Mao, Irineos Papakyriacou, Yonglin Lu, Liam Alford This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9399910/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Acute myeloid leukemia is an aggressive hematological disease, where cancer cells down-regulate antigen presentation and immune-stimulatory molecules. Here, we show that genetic deletion of protein neddylation rewires the human AML proteome towards an immunogenic phenotype, while suppressing cholesterol biosynthesis. As a result, neddylation-deficient AML cells respond more potently to TLR agonists in vitro. Using genome-wide CRISPR/Cas9 screens, we have identified mTOR as an important pathway in deficient cells. Inhibition of lipid metabolism by simvastatin in primary human monocytes amplifies response to LPS, which is governed by mTOR and JAK/STAT pathways. In mice, simvastatin fine-tunes the inflammatory response driven by LPS and polarizes myeloid cells to a stimulatory phenotype. While neither simvastatin nor LPS show efficacy against murine C1498 tumors, a combination of the agents delays tumor growth in mice. Altogether, our results uncover a previously unknown function of protein neddylation in governing the response to TLR stimulation and propose the therapeutic potential of statin drugs in immunotherapy against hematological malignancies. Biological sciences/Cancer/Haematological cancer/Leukaemia/Acute myeloid leukaemia Biological sciences/Immunology/Innate immunity/Pattern recognition receptors/Toll-like receptors Biological sciences/Cell biology Protein neddylation Toll-like receptors Myeloid cells Leukemia Tumor Immunology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Acute myeloid leukemia (AML) is a hematological malignancy caused by a combination of genetic aberrations and dampened immune surveillance [ 1 ]. Patients with AML present heterogenous cell blasts, which contain cells of myeloid lineage at various differentiation and maturation stages [ 2 ]. Treatment of AML includes intensive chemotherapy, radiation therapy, targeted therapy and stem-cell transplantation, and long-term patient survival remains to be improved despite these intensive multi-modal therapies [ 3 ]. With recent progress of immunotherapy in solid cancers, the therapeutic potential of the immune system has been investigated in AML [ 3 ]. However, AML blasts often show low expression of antigen presentation and/or immune-stimulatory molecules, and could also induce multiple immunosuppressive mechanisms to dampen anti-tumor immunity [ 4 ]. In particular, mature myeloid cells including macrophages, monocytes, dendritic cells, and granulocytes, could gain suppressive capacity when in contact with tumor-derived factors [ 5 ]. Moreover, cancer-associated chronic inflammation leads to insufficient activation of certain myeloid cell subsets and promotes the expansion of those with suppressive functions [ 6 ]. Toll-like receptors (TLRs) are broadly expressed by immune cell subsets, which elicit potent activation and differentiation of myeloid cells [ 7 ]. Moreover, TLRs have been detected in patient-derived AML blasts [ 8 ], and could drive cancer cell differentiation [ 9 – 11 ]. However, it is widely reported that the rapid activation of myeloid cells through TLRs could recruit negative regulators of the pathway, which leads to endotoxin tolerance [ 12 ]. Therefore, understanding how these mechanisms influence the immunological functions of healthy and malignant myeloid cells is crucial for developing strategies to enhance immune response against AML cells. Neural precursor cell expressed, developmentally downregulated protein 8 (NEDD8) is a ubiquitin-like protein that covalently attaches to lysine residues of target proteins through the process of neddylation. This post-translational modification regulates protein function in eukaryotic cells and is mediated through a series of enzymatic reactions [ 13 , 14 ]. We previously identified protein neddylation as a breast cancer-intrinsic resistance mechanism against immune checkpoint blockade therapy [ 15 ]. Protein neddylation is a well-regarded target in cancer therapy and a pharmacological inhibitor, pevonedistat, has been developed [ 16 ] and tested in a large clinical trial for patients with AML [ 17 ]. However, we showed that current neddylation inhibitors were equally potent in suppressing the proliferation of neddylation-proficient and -deficient human breast cancer cells, indicating their off-target activities. Therefore, precise deletion of genes in the neddylation pathway using CRISPR/Cas9 genome editing could shed light on the specific contribution of protein neddylation in the innate immune response pathways. In this study, we demonstrate that genetic deletion of NEDD8 substantially skews the proteome of human THP1 cells, which leads to significantly higher response to TLR agonists. Genome-wide CRISPR/Cas9 screening in the control and KO THP1 cell line pair pinpoints a rewired functional network upon NEDD8 loss, which exhibits a higher dependency on the mTOR pathway. Moreover, inhibition of lipid metabolism by simvastatin induces a similar phenotypic change as compared to the KO cells in transmission electron microscopy. In accordance, simvastatin pre-treatment enhances the pro-inflammatory features of primary human monocytes upon LPS stimulation, which relies on the mTOR pathway. In mice, simvastatin fine-tunes the inflammatory landscape induced by LPS and results in delayed growth of AML tumors. Altogether, our study reveals a previously unappreciated role of protein neddylation as a negative regulator for TLR signaling, which is mediated through the lipid metabolism status in healthy and malignant myeloid cells. Results Protein neddylation is a master regulator for the proteome in human AML cells We previously demonstrated that genetic ablation of the NEDD8 gene enhanced the expression of antigen presentation molecules on human breast cancer cells [ 15 ]. Given that impaired antigen presentation capacity is a common feature between immunosuppressive and malignant myeloid cells [ 4 ], we hypothesize that protein neddylation may be a target to enhance the immunostimulatory properties in myeloid cells. Using CRISPR/Cas9, we permanently removed the NEDD8 protein in a human AML cell line, THP1 (Fig. 1 A), which is required for protein neddylation. Control THP1 cells were generated at the same time by transfecting a RNP complex without the gene-targeting crRNA. Label-free mass spectrometry revealed a substantial change in the proteome of KO cells in the principal component analysis ( Figure S1 A ). In total, 249 proteins were up-regulated or became uniquely expressed, and 172 proteins were down-regulated or became undetectable upon NEDD8 deletion, when compared to control THP1 cells (Fig. 1 B). Moreover, the absence of NEDD8 was also confirmed by proteomics analysis. Pathway enrichment analysis of the differentially expressed proteins demonstrated that proteins regulating the innate immunity were significantly enhanced in KO cells (Fig. 1 C). In addition, expression of proteins related to phagosome, lysosome, interferon signaling, as well as oxidative phosphorylation was enriched in upon NEDD8 deletion, while control THP1 cells were enriched for cholesterol biosynthesis, glycolysis and HIF1 signaling (Fig. 1 C). A detailed analysis of individual proteins highlighted the enhanced expression of immune-stimulatory pathways in KO cells, such as HLA class I molecules (HLA-A/B), interferon (JAK1, STING1, STAT1/3), and antioxidants (NQO1, TXNRD1, GPX4, GPX1, PRDX6) (Fig. 1 D). Therefore, we further investigated the inflammatory potential of control or NEDD8 KO THP1 cells upon toll-like receptor stimulation. Neddylation deficiency enhances the response to TLR agonists in human AML cells. Because TLR2 and TLR4 are frequently over-expressed by AML cells [ 18 ], we tested the cytokine release and phenotypic changes in THP1 cells upon stimulation with LPS (TLR4) or Pam3CSK4 (TLR1/2). As expected, TLR agonists induced the secretion of pro-inflammatory cytokines and chemokines, i.e. IL-6, IL-1B, TNFA and CXCL-10, in a dose-dependent manner in both control and knockout cells (Fig. 2 A, 2 B and S1B). However, the levels of these cytokines were significantly enhanced in NEDD8 KO THP1 cells after TLR stimulation, and the increases were more pronounced when cells were treated with LPS, as compared to Pam3CSK4 (Fig. 2 A and 2 B). Of note, the release of soluble CXCL-10 was only increased by Pam3CSK4 in NEDD8 KO THP1 cells ( Figure S1 B ). To determine the expression of cell surface proteins, we quantified HLA class I and II antigen presentation molecules, PD-L1 and interferon gamma receptor alpha (IFNgRa or CD119) upon TLR stimulation. LPS or Pam3CSK4 induced dose-dependent upregulations of surface pan-HLA-II molecules and IFNgRa on THP1 cells, but only Pam3CSK4 drove significant changes of PD-L1 (Fig. 2 C and 2 D). Similar to what has been observed in human breast cancer cells [ 19 ], NEDD8 deficiency caused a significant upregulation of HLA-II molecules on THP1 cells at the baseline (Fig. 2 C and 2 E), and this difference was maintained upon TLR stimulation (Fig. 2 C and 2 D). Pam3CSK4, but not LPS, induced a significant upregulation of surface PD-L1 on KO cells, as compared to stimulated control THP1 cells (Fig. 2 D and 2 F). Pan-HLA-I expression was not changed significantly by TLR stimulation, nor upon the deletion of protein neddylation ( Figure S1 C ). Altogether, we uncovered a previously unknown role of protein neddylation as a negative regulator for the TLR pathway in human AML cells. Genome-wide CRIPSR/Cas9 screening uncovers functional pathways that are essential in neddylation-deficient THP1 cells. The neddylation inhibitor pevonedistat has been widely used to investigate the function of neddylation in immune cell activation. However, we found that pevonedistat was equally efficient in limiting the proliferation of neddylation-proficient and -deficient THP1 cells ( Figure S1 D ) and breast cancer cells [ 19 ], which suggested its neddylation-independent properties. In order to reveal essential mechanisms controlled by protein neddylation in human AML cells, we performed simultaneous genome-wide CRISPR/Cas9 screens in control and NEDD8 KO THP1 cells (Fig. 3 A). In brief, control or KO THP1 cells were transduced with a vector that allowed constitutive expression of recombinant Cas9 protein. Next, the Brunello gRNA library that covers the human genome was transduced at an optimal ratio to allow one gRNA per cell. Cells were harvested on day 4 and day 21 and the gRNA frequencies were compared to identify essential genes for the survival of the cell line pair. After quality control and normalization, we performed PCA analysis based on the gRNA counts and the sample differences on day 4 were comparable between control and KO THP1 cells, but substantially changed after culturing, indicating the technical robustness of the screen ( Figure S2 A ). However, the profiles on day 21 were clearly different between control and KO cells ( Figure S2 A ), suggesting that genetic deletion of NEDD8 rewired the functional network in THP1 cells. Further, we identified essential genes for AML cell survival (743 genes in control and 885 genes in KO), of which 152 genes in control cells and 294 genes in KO cells were unique for their survival (Fig. 3 B). Of note, the NEDD8 gene was more essential in neddylation-proficient THP1 cells, and several genes within the neddylation cascade e.g., GPS1, COPS4, COPS6 , lost essentiality upon NEDD8 deletion, confirming effective pathway disruption ( Figure S2 B ). Moreover, multiple components of the ubiquitination pathway ( UBAs and UBEs ) remained essential in KO cells ( Figure S2 B ), suggesting that ubiquitin-mediated regulation continues to play a critical role in sustaining protein homeostasis, which was consistent with our proteomic data showing comparable numbers of proteins identified in the cell line pair ( Supplemental Table 5 ). Next, we investigated genes that were uniquely essential in KO cells, and revealed the critical functions of metabolism ( HSD17B12, CS) , mitochondrial function ( MRPL57, MRPL44 ATP5F1E ), GTPase regulation ( RAB10 ), translation ( EIF4 ), and transcriptional regulation ( INTS14 ) in sustaining cell survival. Similar to human breast cancer cells [ 19 ], genes of the mTOR pathway, including mTOR and RAPTOR , were also essential for the survival of NEDD8 -deficient THP1 cells compared with controls (Fig. 3 C). Further enrichment analysis revealed pathways with an increased essentiality upon NEDD8 loss, which included metabolic and mTOR-related processes, as well as oxidative phosphorylation/phagosome activity, and cellular stress response pathways (Fig. 3 D). These findings were in line with data from the proteomics analysis, where proteins involve in mTOR, oxidative phosphorylation and lysosomal/phagosome mechanisms became upregulated in the KO cells (Fig. 1 C). Indeed, NEDD8 KO THP1 cells were more sensitive to rapamycin-mediated cell growth inhibition in vitro, suggesting cellular reprogramming through enhanced functional importance of the mTOR pathway (Fig. 3 E). Due to the potential off-target liability of current neddylation inhibitors ( Figure S1 D ), we explored alternative targets that can induce a similar phenotypic change as NEDD8 deletion in AML cells. Because several proteins on the cholesterol biosynthesis pathway were dampened in KO cells (Fig. 1 D), we selected statin drugs as potential candidates. Indeed, NEDD8 deficiency led to reduced sensitivity to simvastatin treatment in THP1 cells (Fig. 3 F). Further, we employed transmission electron microscopy (TEM) to assess the phenotypic changes induced by LPS and/or simvastatin at 1 µM, which did not induce direct inhibition the proliferation of THP1 cells (Fig. 3 F). Genetic deletion of NEDD8 or treatment with simvastatin alone did not substantially change the cellular phenotype in THP1 cells ( Figure S2 C ). In response to LPS treatment, THP1 cells formed phagosome-like structures (Fig. 3 G). Combining LPS with simvastatin cells induced the clear formation of budding structures and enlarged phagosome-like structures in THP1 cells, which were similar to those observed in KO cells treated with LPS (Fig. 3 G). The shift of intracellular organelles could contribute to the enhanced secretion of cytokines and expression of surface proteins in THP1 cells (Fig. 2 A and S3A). Inhibition of lipid metabolism enhances the TLR-induced pro-inflammatory properties of primary monocytes through the mTOR pathway Next, we asked whether similar mechanisms could be induced in non-malignant primary monocytes derived from healthy blood donors. Because monocytes are short-lived in culture and are less susceptible to genome-editing, we used simvastatin in our subsequent experiments. In brief, freshly isolated monocytes were seeded in culture media supplemented with human serum in the presence of 1 µM simvastatin or DMSO. After 48 hours, LPS was added at an increasing concentration and cytokine levels were assessed in the culture media after overnight incubation. Similar to what was observed in NEDD8 KO THP1 cells ( Figure S3 A ), simvastatin significantly enhanced the secretion of IL-1 beta and TNF alpha from primary monocytes in response to LPS (Fig. 4 A). Although soluble IFN gamma was not detected in THP1 cells upon stimulation ( Figure S3 A ), concurrent treatment of LPS and simvastatin significantly enhanced the release of IFN gamma from monocytes (Fig. 4 A). Moreover, GM-CSF showed a trend of increased release from simvastatin-treated, LPS-primed monocytes, while CXCL-10 and IL-10 remained similar, as compared to LPS treatment alone ( Figure S3 B ). We then examined whether immunological proteins were altered by the treatment of LPS and simvastatin on primary monocytes. Simvastatin alone significantly enhanced the expression of IFNgRa at the baseline and showed a trend to induce higher HLA-DR/DP/DQ levels on primary monocytes (Fig. 4 B). Treatment with LPS drove the expression of PD-L1 and HLA-ABC, but caused the down-regulation of IFNgRa on primary monocytes (Fig. 4 B). The expression of PD-L1 and HLA-ABC upon LPS stimulation was further elevated on monocytes by simvastatin treatment (Fig. 4 B). To reveal the underlying mechanisms, we included pharmacological inhibitors together with simvastatin (1 µM) before measuring cytokine release stimulated by LPS (1 µg/ml) (Fig. 4 C). Rapamycin (mTORi) was selected due to the elevated dependency on the mTOR pathway in NEDD8 KO THP1 cells (Fig. 3 D and E ) and Ruxolitinib (JAK1/2i) was chosen because of the significant induction of IFN gamma (Fig. 4 A). Moreover, MCC950, which is an inhibitor against NLRP3 inflammasome [ 20 ], was included to investigate the release of IL-1 beta. Rapamycin abrogated the induction of IL-1 beta and IFN gamma (Fig. 4 D), as well as TNF alpha ( Figure S3 C ) from primary monocytes treated with simvastatin and LPS. Inhibition of the JAK/STAT signaling reduced the production of IFN gamma but had limited impact on IL-1 beta (Fig. 4 E) or TNF alpha ( Figure S3 D ). In contrast, treatment with MCC950 did not result in significant changes in cytokine release (Fig. 4 F and S3E), which suggested that the release of IL-1 beta release was independent of inflammasome activation. Altogether, we showed that mTOR was a master regulator for the enhanced sensitivity to TLR stimulation induced by simvastatin, while JAK1/2 signaling was more specific for the release of IFN gamma from monocytes. Simvastatin fine-tunes the inflammatory landscape upon LPS treatment and delays AML growth in mice Given that simvastatin could boost the pro-inflammatory status in both healthy and malignant human myeloid cells in response to TLR stimulation in vitro, we examined its effects in immune competent C57BL/6NTac mice. As shown in Fig. 5 A, simvastatin was administered at a human-equivalent dose (8.2 mg/kg, Supplementary Table 4 ) daily. One hour after the last dose of simvastatin, mice were infused with 5 µg LPS in the abdominal cavity. Serum and splenocytes were isolated after 3 hours and soluble factors and cell phenotypes were analyzed using Legendplex or flow cytometry, respectively. As expected, LPS treatment induced a rapid release of pro-inflammatory cytokines, including CXCL-1, IL12p40, TNFA, IL-6, G-CSF and IL-1B (Fig. 5 B and S4A). In contrast, IL-18, IL-10, IL-23, TGFB1 and IL12p70 were not induced by LPS in the mouse serum ( Figure S4A ). Simvastatin significantly reduced the baseline production of IL12p40, CCL-22 (Fig. 5 B), as well as IL-18 ( Figure S4A ) in the serum. Although cytokines could still be induced by LPS in mice treated with simvastatin, the levels of CXCL-1, IL12p40, TNFA and CCL22 were significantly lower, when compared to mice treated with only LPS (Fig. 5 B). The induction of IL-6, G-CSF and IL-1B by LPS was not modulated by simvastatin ( Figure S4A ). When assessing the immune cells in the spleens, we observed a significantly higher percentage of CD45 + cells in mice treated with the combination, as compared to LPS alone (Fig. 5 C). Among the innate immune cell types, CD11b+Ly6G+ neutrophils were significantly induced by LPS but reduced when simvastatin was given together (Fig. 5 D). Similar to what was observed in human myeloid cells, the expression of MHC-I was elevated by simvastatin on multiple myeloid cell subsets ( Figure S4B ) and PD-L1 was up-regulated by simvastatin on Ly6C+ monocytes ( Figure S4C ). In macrophages, MHC-II molecules were induced by LPS and were further enhanced by simvastatin (Fig. 5 E). Moreover, arginase-1 (ARG-1), which is an enzyme over-expressed by immunosuppressive myeloid cells [ 21 ], was reduced by simvastatin in macrophages (Fig. 5 F). The expression of MHC-II molecules was enhanced by LPS in dendritic cells (DC) but was not significantly modulated by the addition of simvastatin ( Figure S4D ). In the lymphocyte populations, percentage of T cells remained stable upon LPS challenge, but was significantly elevated by simvastatin (Fig. 5 G). All lymphocyte subsets responded to LPS treatment, indicated by the significant up-regulation of CD69 on the surface, but this activation was not modulated by the addition of simvastatin ( Figure S4E ). Of note, addition of simvastatin significantly reduced the frequency of CD8 + T cells with an exhaustion phenotype (PD1 + LAG3+, Fig. 5 H). Because simvastatin potentiated the immune stimulatory status of myeloid cells in vivo, we asked whether it could enhance the anti-AML efficacy together with LPS. As shown in Fig. 5 I, murine C1498 AML cells were injected subcutaneously in C57BL/6NTac mice, followed by daily treatment of simvastatin at 8.2 mg/kg. LPS (5 µg/mouse) was infused systemically on day 6, 9 and 12 after tumor implantation. Neither simvastatin nor LPS was sufficient in controlling the growth of C1498 tumors as a monotherapy. In contrast, simvastatin significantly reduced the tumor volumes when combined with LPS (Fig. 5 J). Based on these results, we concluded that reducing lipid metabolism by simvastatin could fine-tune the pro-inflammatory landscape driven by LPS, which leads to a more potent anti-AML immunity. Discussion Protein neddylation is a multi-enzymatic post-translational modification machinery process that governs cell homeostasis and function [ 14 ]. In brief, the E1 NEDD8-activating E1 enzyme (NAE) contains two subunits (NEA1 and UBA3), which activates NEDD8 in the presence of ATP, and transfers the activated NEDD8 to E2 enzymes, e.g. UBE2M or UBE2F. NEDD8 is then conjugated to the substrates by one of the E3 ligases, such as RING-box proteins (RBX1/2). Because of its essential role in regulating cell survival, it is challenging to study the function of neddylation using genetically-modified mice. For example, deletion of the rate-limiting E1 enzyme subunits, Uba3 [ 22 ] or Nae1 [ 23 , 24 ], led to embryonic lethality or significantly impaired neuron development in mice. In myeloid cells, transcription of Ube2m [ 25 ] or Rbx2 [ 26 ] mRNAs were elevated upon TLR stimulation and ablation of these genes dampened the pro-inflammatory response in mice. In contrast, E2 or E3 enzymes could negatively regulate cytokine-induced activation of NK cells [ 27 ] or sustain the suppressive functions in regulatory T cells [ 28 ]. Because E2 or E3 enzymes have additional regulatory roles on the ubiquitination system [ 26 ], these observed effects may not be mechanistically exclusive to protein neddylation and may be highly dependent on the cell type. In our study, we took a unique approach to permanently remove the NEDD8 gene from human AML cells using CRISPR/Cas9, which specifically abrogates protein neddylation, with minimal impact on the ubiquitination system [ 19 ]. This cellular model reveals the previously unknown function of neddylation as a negative regulator for the TLR pathway. Genetic deletion of NEDD8 in human AML cells substantially promotes the release of pro-inflammatory cytokines and expression of immune-stimulatory molecules, which indicates cellular differentiation and maturation. Pharmacological inhibitors, e.g. pevonedistat, which target the E1 enzyme of the neddylation system [ 16 ], have been widely used to examine the functional relevance of protein neddylation on the immunological pathways. For example, pevonedistat limits the anti-viral innate immunity because of its negative effects on the cGAS/STING signaling [ 29 ]. However, the off-target and neddylation-independent effects of these small molecule compounds are well-documented [ 30 ] and confirmed by us using control and NEDD8 -deficient human breast cancer [ 19 ] and AML cell line pairs. In order to reveal druggable mechanisms that can resemble the phenotype of neddylation deficiency, we mapped the proteomes and performed genome-wide CRISPR/Cas9 screens in the THP1 control/KO isogenic cell line pair. Our data demonstrate that proteins in the cholesterol biosynthesis pathway are lowly expressed in KO cells and mTOR signaling becomes more essential for cell survival. This is in line with our previous results observed in human breast cancer cells [ 19 ]. Lipid metabolism and peroxidation have known functions in regulating the development and functions in mature and malignant myeloid cells [ 5 , 31 ]. On the one hand, statin drugs directly limit the proliferation of AML blasts [ 32 , 33 ]. On the other hand, hyperactivation of lipid metabolism confers immunosuppressive functions in tumor-infiltrating myeloid cells [ 34 ]. In human THP1 cells, simvastatin at a non-toxic dose mimics the phenotypic changes of NEDD8 deletion at the subcellular level upon TLR stimulation and amplifies the activation of human AML and primary monocytes. This effect is dictated by the mTOR pathway and partially dependent on the JAK/STAT signaling. Because mTOR has a central role in cell metabolism and homeostasis [ 35 ], we speculate that it plays a key role in rewiring myeloid cells to increase their sensitivity to TLR stimulation. Pevonedistat has been tested together with chemotherapy in the PANTHER phase 3 clinical trial in patients with myeloid malignancies [ 17 ]. However, it failed to add clinically meaningful benefits to chemotherapy and the clinical development has been terminated. Given that current neddylation inhibitors carry off-target liabilities and are mostly inhibitory to immune cells [ 14 , 19 ], these results do not de-validate the therapeutic potential of protein neddylation in cancer therapy. Instead, discovery of new and selective approaches, such as anti-sense oligonucleotides, or repurposing existing drugs that induce a similar phenotype to neddylation-deficient cells, such as statin drugs, may offer synergy to chemotherapy or immunotherapy. We show that simvastatin induces a pro-inflammatory phenotype in myeloid cells in mice treated with LPS and synergizes with LPS to limit the growth of murine AML tumors. A large epidemiology study involving more than 10 million people demonstrates that statin drug use significantly reduced the risk of AML development and progression [ 36 ]. Addition of therapeutics that engage with the adaptive immunity, e.g. ICB drugs, may further improve the curative rates in the mouse model. However, it remains to be studied whether deletion of neddylation or statin drugs could prevent the induction of endotoxin tolerance in myeloid cells. Altogether, our study utilizes unique cellular models to reveal the unexpected role of protein neddylation as an immune checkpoint for the TLR pathway in healthy and malignant myeloid cells. Neddylation is a gatekeeper for a wide range of pathways in myeloid cells, which relies on lipid metabolism and mTOR signaling. We propose that specific targeting strategies against neddylation or lipid metabolism inhibitors could be explored to enhance the immune response against AML cells. Materials and Methods Detailed information on the antibodies, reagents and CRISPR-nucleotide sequences used in this study is provided in Supplementary Tables 1, 2 and 3 , respectively. Ethical considerations The study utilized buffy coats from anonymous healthy blood donors provided by the blood center at Uppsala University Hospital. No ethical permission is needed. Six-to-ten weeks old naïve female C57BL/NTac mice were purchased from Taconic and housed at the animal facility at the Rudbeck Laboratories at Uppsala University, under the pathogen-free environment. All studies were approved by the Swedish Board of Agriculture at Jönköping, Sweden and performed by trained staff under ethical permits Dnr: 5.8.18–06394/2020 and 5.2.18-05492.2025. Cellular materials and culture conditions The human acute monocytic leukemia (AML) cell line, THP-1 (TIB-202), and the murine AML cell line, C1498 (TIB-49), were obtained from ATCC. Unless otherwise indicated, all cell lines were grown in Iscove’s Modified Dulbecco’s Medium (IMDM) (Thermo Fisher Scientific) supplemented with 1% penicillin-streptomycin (PenStrep) and heat-inactivated fetal bovine serum (FBS), 20% FBS for THP-1 cells and 10% for C1498 cells, at 37°C in 5% CO₂. Human primary CD14 + monocytes were cultured in IMDM with 1% PenStrep and 10% human AB serum (MP Biomedicals). Cell lines were verified by DNA fingerprinting (Eurofins) and routinely screened for mycoplasma contamination using MycoAlert (Lonza). Isolation of human primary CD14 + monocytes Fresh human primary CD14 + monocytes were isolated from fresh buffy coats provided from the blood center at the Uppsala university hospital, Sweden. Briefly, 10 mL of Lymphoprep (Stem Cell Technologies) was added to the SepMate tubes-50 (Stem Cell Technologies), and whole blood was carefully layered on top. Following centrifugation at 1200 × g for 10 minutes, the PBMC layer was collected and washed twice with phosphate-buffered saline (PBS; Thermo Fisher Scientific). To eliminate residual red blood cells, cells were treated with 5 mL of ACK lysis buffer (Thermo Fisher Scientific) for 10 minutes at room temperature in the dark, and then centrifuged at 500 × g for 5 minutes. Healthy CD14 + primary monocytes were subsequently isolated using the EasySep CD14 + Selection Kit II (Stem Cell Technologies) following the manufacturer’s protocol. CRISPR/Cas9-mediated target gene deletion Using the Neon transfection system (Thermo scientific), ribonucleoprotein (RNP) complexes containing the crRNAs targeting human or mouse genes ( Supplemental Table 3 ), tracrRNAs and an endonuclease Cas9 protein were transfected into cancer cells to delete NEDD8 gene. RNP complexes lacking crRNAs were used as negative reaction (referred as ctrl cells). For each reaction, 1 µL of NEDD8-specific crRNA (100 µM) was mixed with 1 µL tracrRNA (100 µM) in 1.7 µL nuclease free duplex buffer (IDT). The mixture was incubated at 95°C for 5 minutes to enable annealing, followed by cooling to 4°C. Subsequently, 1 µL of Cas9 endonuclease (10 mg/mL, IDT) was added and the solution was left at room temperature for 15 minutes to facilitate the formation of the RNP complex. Next, 0.3 µL carrier DNA (100 µM) was added to enhance gene editing efficiency. Cell pellets (5 × 10⁵ cells) were resuspended in 5 µL of resuspension buffer T and mixed with equal volume of the RNP complex. The Neon transfection system was set up following the manufacturer's instructions, after which the cell mixture was transferred into Neon pipette tips, and electroporation was conducted using specific programs. Transfected cells were then cultured and incubated at 37°C with 5% CO2 until use. Cells were repeatedly transfected with either gene-targeting RNP complexes or control complexes to achieve complete gene deletion. Genome-wide CRISPR/Cas9 screen and data analysis To uncover cellular reprogramming in NEDD8 deficient myeloid cells, genome-wide CRISPR screen was used. The genome-wide Brunello sgRNA library [ 37 ] was synthesized, cloned, packaged into lentivirus, and subsequently transduced into Cas9-expressing target cells, as previously described [ 15 ]. Briefly, the Brunello library-transduced control and NEDD8 KO THP-1 cells were cultured for 4 or 21 days. To maintain complete library representation, cell numbers were kept above 80 million per replicate throughout the experiment. At the end of the culture period, cells were harvested and genomic DNA was extracted using the QIAamp DNA Blood Maxi Kit (Qiagen). sgRNA cassettes were amplified by PCR as previously described[ 38 ], using modified primers PCR2_fw acactctttccctacacgacgctcttccgatctcttgtggaaaggacgaaacac and PCR3_fw aatgatacggcgaccaccgagatctacac[i5]acactctttccctacacgacgctct, resPCR products were subjected to sequencing on an Illumina NovaSeq platform with the following parameters: 20-cycle Read 1 using custom primer CGATCTCTTGTGGAAAGGACGAAACACC, 10-cycle i7 index read for UMI capture, and 6-cycle i5 index read for sample barcoding. For data preprocessing, genes with a total count of less than 100 across all samples were removed. SgRNAs that accounted for less than 10% of the total reads for sgRNAs targeting the same gene were removed from the read table. sgRNA read table and median normalized reads were processed with MAGeCK [ 39 ] test function, genes with log2fc < -0.5 and FDR < 0.01 at day 21 were annotated as essential genes, and gene essentiality was ranked in an ascending order based on the log2FC at day 21, as compared to day 4. Essential pathways were then enriched using Enrichr [ 40 ]. Pathways of two cell lines with p.adj < 0.05 were overlapped to distinguish unique enriched pathways and common enriched pathways. In the volcano plot, Genes with FDR < 0.01 and log2fc < -0.5 at day 21 in both cell lines were labeled as “common essential genes”, otherwise labeled as N8KO/WT essential genes if meet FDR < 0.01 and log2fc < -0.5 in one corresponding group. Proteomics and data analysis For protein extraction, four replicates of THP-1 NEDD8 KO and control cells were lysed in 150 µL of buffer containing 1% β-octyl glucopyranoside and 6 M urea. Cell disruption was carried out with a sonication probe (3 mm, 1 s pulses, 40% amplitude) for 30 s according to the standard protocol. Homogenized samples were incubated at 4°C for 60 min with gentle agitation and then centrifuged at 14,000 × g for 10 min to remove debris. Pellets were compressed to recover residual liquid, and the clarified supernatants containing extracted proteins were collected. Protein concentrations were quantified using the DC Protein Assay (Bio-Rad) with bovine serum albumin (BSA) as the standard. Aliquots containing 35 µg of protein from each sample were used for digestion. Proteins were reduced, alkylated, and digested with trypsin on a 3 kDa centrifugal spin filter (Millipore, Ireland). The resulting peptide filtrates were dried in a SpeedVac concentrator. Peptides were then separated by reverse-phase chromatography on a C18 column using a 150 min gradient and analyzed online by electrospray ionization on a Q-Exactive Plus mass spectrometer (Thermo Finnigan). Tandem mass spectrometry was carried out using higher-energy collisional dissociation (HCD). Raw data files were processed and quantified with MaxQuant (version 1.5.1.2). Protein identification was performed against the Homo sapiens proteome database (UniProt, February 2020). Search parameters included: taxonomy set to Homo sapiens , enzyme specificity to trypsin, fixed modification carbamidomethylation (C), and variable modifications phosphorylation (S, T, Y), oxidation (M), and deamidation (N, Q). For data analysis, library-size normalized data was processed and visualized with R. Proteins expressed in more than 3 samples in control group and less than 1 sample in NEDD8 KO group were annotated as “control group unique protein”, similar criteria were applied to annotated “ NEDD8 KO unique protein”. P -values were calculated using t-test. Proteins with p-value 0.3 were annotated as differentially expressed proteins. Differential pathway enrichment was performed with Enrichr [ 40 ], pathways with Adjusted.P.value < 0.05 were kept for visualization. TEM-based analysis of cellular morphology Subcellular changes in THP1 control, NEDD8 KO or simvastatin-treated cells with or without LPS were analyzed using transmission electron microscopy (TEM). Cells were seeded into 6-well flat-bottom plates followed by addition of simvastatin (1 µM) or 0.1% DMSO and incubated for 4 days. Cells were then stimulated with LPS at indicated concentrations for 20 h. For sample preparation, 1 × 10 6 cells per condition were fixed overnight at 4°C in 2.5% glutaraldehyde (Ted Pella) and 1% formaldehyde in 0.1 M phosphate buffer (pH 7.4), followed by washes with the 0.1 M phosphate buffer. Post-fixation was performed with 1% osmium tetroxide (Agar Scientific) for 30 min, after which cells were dehydrated through a graded ethanol series and briefly treated with propylene oxide (Agar Scientific) from 5 min. Samples were embedded in Agar 100 resin (Agar Scientific) overnight and polymerized at 60°C for 48 h. Ultrathin sections (50–60 nm) were cut using a UC7 ultramicrotome (Leica), mounted on formvar-coated grids (Ted Pella), and stained with 5% uranyl acetate followed by 3% lead citrate (Science Services). Sections were imaged on a Tecnai Spirit G2 BioTwin TEM (FEI/Thermo Fisher) at 80 kV, equipped with an Orius SC200 CCD camera (Gatan). Evaluation of immunological profile of myeloid cells To investigate the role of NEDD8 in the immunogenicity of myeloid cells, THP-1 control and NEDD8 KO cell line pair was used. Control or NEDD8 KO cells (2 × 10 4 cells per well) were seeded into 96-well round bottom plate and stimulated with LPS or Pam3CSK4 at the indicated concentrations in a final volume of 200 µl culture medium. The plates were incubated at 37°C for 20 h. For inhibitor treatment experiments, human primary CD14 + monocytes (3 × 10 5 per well) were seeded into 96-well round bottom plates. Simvastatin (1 µM)-treated cells were subsequently incubated with ruxolitinib, rapamycin, or MCC950 at the indicated concentrations, or with 0.1% DMSO as a control. After 2 days of culture for CD14 + monocytes, LPS (1 µg/ml) was added in a final volume of 200 µl culture medium. Following incubation, cells were harvested for analysis of immunological surface markers, while culture supernatants were collected for quantification of secreted pro-inflammatory cytokines. Animal studies Naïve C57BL/6NTac female mice (6–10 weeks old; Taconic) received four daily intraperitoneal (i.p.) injections of simvastatin (8.2 mg/kg, MedChem Express) in 100 µl vehicle (PBS containing 10% DMSO and 20% β-cyclodextrin). On day 3, LPS was administered i.p. one hour after the simvastatin treatment. Mice were carefully monitored and serum and spleens were harvested after 4 hours. To test the anti-tumor efficacy, a murine cancer cell line, C1498, was inoculated into syngeneic C57BL/6NTac female mice (6–10 weeks old; Taconic). In brief, mice were injected subcutaneously (s.c.) with 4 × 10 5 C1498 monocytic cancer cells in 100 µl serum-free IMDM medium (Thermo Fisher Scientific). Starting on day 5, mice received daily intraperitoneal (i.p.) injections of simvastatin (8.2 mg/kg, MedChem Express) in 100 µl vehicle (PBS containing 10% DMSO and 20% β-cyclodextrin) until day 12. LPS was administered i.p. at the indicated dose on days 6, 9, and 12. Tumor volumes and body weights were monitored regularly until tumors reached the humane endpoint of 1500 mm 3 . Tumor volume was calculated using the formula: (length × width 2 ) / 2. Conversion of human doses from mg/kg to mg/m 2 and calculation of animal equivalent doses (AED) were performed using the following formulas: mg/m 2 = Km × mg/kg, and AED (mg/kg) = Human dose (mg/kg) × Km ratio, respectively [ 41 ] ( Supplemental Table 4 ). Flow cytometry analysis For in vitro assays, a multicolor flow cytometer was used to assess the expression of immune-related surface markers on control or NEDD8 KO cells. Briefly, cells were first transferred to a 96-well V bottom plate and centrifuged at 700 × g for 4 min. The pellets were washed twice with PBS and then resuspended in 20 µl PBS containing an aqua fixable live/dead dye (1:200, Thermo Fisher Scientific) and a Fc receptor blocking antibody (1:100, Thermo Fisher Scientific) for 20 min at room temperature. Following two washes with PBS, cells were incubated with 20 µl of a surface marker antibody master mix for 30 min at 4°C. After staining, cells were washed, resuspended in 150 µl PBS, and subjected to flow cytometry analysis. For ex-vivo studies, bone marrow cells were isolated using WT C57BL/6NTac female mice (6–10 weeks old; Taconic) according to a previous study[ 42 ]. Then, erythrocytes lysis was performed using red blood lysis buffer (Thermo Fisher Scientific) for 2 min on ice. Subsequently, cells were centrifuge at 350 x g for 4 min and passed through 40 µm cell strainers for single cell isolation. Single cells were transferred to a 6-well plate at a concentration of 3 million cells/ml. mGM-CSF (100 ng/ml) was then added to the cells followed by incubation at 37°C for 3 days. Bone marrow derived macrophages (BMDM) were harvested and placed into a 96-well round bottom plate at 3 x 10 5 cells per well followed by treatment with 0.1% DMSO or simvastatin (1 µM/well) for 24 or 48 hours. After that, LPS were added to the BMDM-loaded plate at 1, 0.1, 0.01, 0.001 µM for additional 20 hours. Finally, supernatants were collected for cytokine secretion analysis. For quantification of pro-inflammatory cytokines, culture supernatants were collected and centrifuged at 700 × g for 4 min to remove cell debris. Cytokine levels were then measured using the human anti-virus response panel V02; Legendplex, or mouse macrophage/microglia Panel (LEGENDplex, BioLegend) following the manufacturer’s instructions. After that, the plate was washed with PBS and the cytokines-specific beads were resuspended in 150 µl PBS for flow cytometry analysis. For in vivo studies, spleens were harvested from mice and passed through 40 µm cell strainers in order to generate single cells. Red blood cells were lysed using red blood cell lysis buffer (BioLegend) for 2 min on ice, followed by washing with PBS. Single cell suspensions were seeded into 96-well V-bottom plates and incubated with 20 µl PBS containing Aqua Fixable Live/Dead stain (1:200, Thermo Fisher Scientific) and mouse FcR blocker (1:100, Thermo Fisher Scientific) for 30 min at 4°C, then washed with PBS. Surface proteins were stained in 20 µl of a fluorochrome-conjugated antibody mastermix for 30 min at 4°C. For intracellular detection of proteins such as arginase-1, cells were fixed and permeabilized using the True-Nuclear Buffer Set (BioLegend), followed by incubation with fluorochrome-conjugated antibodies (1:50) for 45 min at 4°C. Remaining cells were cryopreserved at − 150°C for later use. To examine serum-specific inflammatory profiles, blood was collected from mice and incubated for 1 h at room temperature to allow clot formation. Samples were centrifuged at 2000 × g for 10 min, and supernatants were transferred into fresh tubes and centrifuged again under the same conditions. The resulting serum was collected into new tubes. Cytokine levels in serum were quantified using the mouse macrophage/microglia Panel (LEGENDplex, BioLegend) according to the manufacturer’s instructions. After washing with PBS, cytokine-specific beads were resuspended in 150 µl PBS for flow cytometric analysis. All samples were acquired on a CytoFLEX LX flow cytometer (Beckman Coulter), and data were analyzed using FlowJo software v10 and plotted with GraphPad Prism. Assessment of cell viability Cell viability was analyzed using the PrestoBlue reagent to evaluate the effects of different inhibitors on monocytic THP-1 cancer cells. Briefly, THP-1 control or NEDD8 KO cells (1 × 10 4 ) were seeded in 96-well flat-bottom plates. Pevonedistat, simvastatin or rapamycin were added at the indicated concentrations, or with DMSO as vehicle control, in a final volume of 200 µl growth medium. Cells were incubated at 37°C with 5% CO₂ for the indicated times. PrestoBlue (Thermofisher scientific) reagent was then added according to the manufacturer’s instructions, and fluorescence was measured at 560 nm (excitation) and 590 nm (emission) using the CLARIOstar Plus instrument (BMG Labtech). Background signal was subtracted, and cell viability was expressed as a percentage relative to DMSO-treated controls. Data were analyzed and plotted using GraphPad Prism software. Western blotting Cell pellets were lysed on ice for 15 min using RIPA buffer (20 mM Tris, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1% NP-40, 1 mM NaF, 1 mM Na₃VO₄, 1 mM sodium phosphate) supplemented with a protease inhibitor cocktail (Thermo Scientific) and free of reducing agents. Lysates were then clarified by centrifugation at 17,000 × g for 12 min at 4°C, and protein concentrations were determined using the Bicinchoninic Acid (BCA) assay (Thermo Scientific). Proteins were mixed with 1× SDS loading buffer, heated at 70°C for 10 min, and separated on 4–12% Bis-Tris precast gels (Invitrogen) by SDS-PAGE. Proteins were then transferred to nitrocellulose membranes using the iBlot Transfer System (Invitrogen) and blocked with 5% nonfat dry milk (OXOID) for 1 h at room temperature. Membranes were incubated overnight at 4°C with primary antibodies against target or loading control proteins, followed by 1 h incubation with HRP-conjugated secondary antibodies at room temperature. Protein bands were visualized using SuperSignal West Pico Plus or West Femto chemiluminescent substrates (Thermo Scientific) on an Amersham Imaging System (GE Healthcare). All washes were performed with TBST (1× TBS, 0.05% Tween 20). Statistical analysis Flow cytometry data were processed using FlowJo V10, and all results were summarized and analyzed in GraphPad Prism 10. Statistical analyses included unpaired two tailed T -tests with a significance threshold of 0.05, as detailed in the figure legends. Declarations Conflict of interest statements Y.M. is a recipient of research grants from the Novo Nordisk Foundation on an unrelated project. Y.M. receives consulting fees from Quotient Therapeutics and holds company shares from AstraZeneca. Other authors declare no conflict of interest. Acknowledgements We appreciate the contributions of Ms. Marta Rubies Bedos, Ms. Antrea-Anna Gkatzioura and the members of the research group in the experimental procedures. This project is supported by a research grant from the Swedish Cancer Society (232656Pj). Y.M.’s research group is generously supported by grants from SciLifeLab (SLL2019/9, SciLifeLab Fellowship), the Swedish Foundation for Strategic Research (FFL-0043), the Swedish Research Council (2022 − 01461) and the Swedish Cancer Society (220474JIA). I.P. received additional support from the Royal Swedish Academy of Sciences (ME2024-0042). We further acknowledge the BioVis platform at Uppsala University, with special thanks to Dirk Pacholsky, Monika Hodik, and Karin Staxang for their assistance with flow cytometry and transmission electron microscopy. We also extend our gratitude to Dr. Anna Widgren and Prof. Jonas Bergquist for conducting the proteomics quantifications at the Mass Spectrometry-Based Proteomics Facility at Uppsala University. Part of this work was performed by CRISPR Functional Genomics (CFG), a SciLifeLab-funded infrastructure at Karolinska Institutet, Stockholm, Sweden. We also acknowledge support from the National Genomics Infrastructure, SNIC (project 2017-7-265), and from the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX). We sincerely thank the staff at the animal facility at Rudbecklaboratoriet, Uppsala University, for their invaluable support with the animal experiments. References Dohner, H., et al., Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN . Blood, 2022. 140(12): p. 1345–1377. DiNardo, C.D., et al., Acute myeloid leukaemia . Lancet, 2023. 401(10393): p. 2073–2086. Kantarjian, H., et al., Current status and research directions in acute myeloid leukemia . Blood Cancer J, 2024. 14(1): p. 163. Tettamanti, S., et al., Catch me if you can: how AML and its niche escape immunotherapy . Leukemia, 2022. 36(1): p. 13–22. van Vlerken-Ysla, L., et al., Functional states of myeloid cells in cancer . Cancer Cell, 2023. 41(3): p. 490–504. Gabrilovich, D.I., S. Ostrand-Rosenberg, and V. Bronte, Coordinated regulation of myeloid cells by tumours . Nat Rev Immunol, 2012. 12(4): p. 253–68. Fitzgerald, K.A. and J.C. Kagan, Toll-like Receptors and the Control of Immunity . Cell, 2020. 180(6): p. 1044–1066. Rybka, J., et al., The expression of Toll-like receptors in patients with acute myeloid leukemia treated with induction chemotherapy . Leuk Res, 2015. 39(3): p. 318–22. Lawler, M.E., et al., TLR2 agonism suppresses myeloid leukemogenesis by reprogramming leukemia stem cells . Blood Adv, 2025. 9(22): p. 5888–5901. Ignatz-Hoover, J.J., et al., The role of TLR8 signaling in acute myeloid leukemia differentiation . Leukemia, 2015. 29(4): p. 918–926. Nourizadeh, M., et al., Synergistic effect of Toll-like receptor 4 and 7/8 agonists is necessary to generate potent blast-derived dendritic cells in Acute Myeloid Leukemia . Leuk Res, 2012. 36(9): p. 1193–9. Netea, M.G., et al., Defining trained immunity and its role in health and disease . Nat Rev Immunol, 2020. 20(6): p. 375–388. Enchev, R.I., B.A. Schulman, and M. Peter, Protein neddylation: beyond cullin-RING ligases . Nat Rev Mol Cell Biol, 2015. 16(1): p. 30–44. Zhang, S., et al., Protein neddylation and its role in health and diseases . Signal Transduct Target Ther, 2024. 9(1): p. 85. Papakyriacou, I., et al., Loss of NEDD8 in cancer cells causes vulnerability to immune checkpoint blockade in triple-negative breast cancer . Nat Commun, 2024. 15(1): p. 3581. Soucy, T.A., et al., An inhibitor of NEDD8-activating enzyme as a new approach to treat cancer . Nature, 2009. 458(7239): p. 732–6. Ades, L., et al., Pevonedistat plus azacitidine vs azacitidine alone in higher-risk MDS/chronic myelomonocytic leukemia or low-blast-percentage AML . Blood Adv, 2022. 6(17): p. 5132–5145. Guzman, M.L., et al., Nuclear factor-kappaB is constitutively activated in primitive human acute myelogenous leukemia cells . Blood, 2001. 98(8): p. 2301–7. Papakyriacou, I., et al., Loss of NEDD8 in cancer cells causes vulnerability to immune checkpoint blockade in triple-negative breast cancer . Nat Commun, 2024. 15(1): p. 3581. Coll, R.C., et al., A small-molecule inhibitor of the NLRP3 inflammasome for the treatment of inflammatory diseases . Nat Med, 2015. 21(3): p. 248–55. Chang, C.I., J.C. Liao, and L. Kuo, Macrophage arginase promotes tumor cell growth and suppresses nitric oxide-mediated tumor cytotoxicity . Cancer Res, 2001. 61(3): p. 1100–6. Tateishi, K., et al., The NEDD8 system is essential for cell cycle progression and morphogenetic pathway in mice . J Cell Biol, 2001. 155(4): p. 571–9. Vogl, A.M., et al., Neddylation inhibition impairs spine development, destabilizes synapses and deteriorates cognition . Nat Neurosci, 2015. 18(2): p. 239–51. Xu, C., et al., Hepatic neddylation deficiency triggers fatal liver injury via inducing NF-kappaB-inducing kinase in mice . Nat Commun, 2022. 13(1): p. 7782. Wen, X., et al., Inhibition of the neddylation E2 enzyme UBE2M in macrophages protects against E. coli-induced sepsis . J Biol Chem, 2025. 301(1): p. 108085. Chang, S.C. and J.L. Ding, Ubiquitination by SAG regulates macrophage survival/death and immune response during infection . Cell Death Differ, 2014. 21(9): p. 1388–98. Nikolic, I., et al., Enhancing anti-tumor immunity of natural killer cells through targeting IL-15R signaling . Cancer Cell, 2025. 43(11): p. 2034–2050 e11. Wu, D., et al., The Ube2m-Rbx1 neddylation-Cullin-RING-Ligase proteins are essential for the maintenance of Regulatory T cell fitness . Nat Commun, 2022. 13(1): p. 3021. Li, C., et al., RNF111-facilitated neddylation potentiates cGAS-mediated antiviral innate immune response . PLoS Pathog, 2021. 17(3): p. e1009401. Mao, H. and Y. Sun, Neddylation-Independent Activities of MLN4924 . Adv Exp Med Biol, 2020. 1217: p. 363–372. Dembitz, V., S.C. James, and P. Gallipoli, Targeting lipid metabolism in acute myeloid leukemia: biological insights and therapeutic opportunities . Leukemia, 2025. 39(8): p. 1814–1823. Stirewalt, D.L., et al., Mevastatin can increase toxicity in primary AMLs exposed to standard therapeutic agents, but statin efficacy is not simply associated with ras hotspot mutations or overexpression . Leuk Res, 2003. 27(2): p. 133–45. Dimitroulakos, J., et al., Increased sensitivity of acute myeloid leukemias to lovastatin-induced apoptosis: A potential therapeutic approach . Blood, 1999. 93(4): p. 1308–18. Jin, R., L. Neufeld, and T.L. McGaha, Linking macrophage metabolism to function in the tumor microenvironment . Nat Cancer, 2025. 6(2): p. 239–252. Goul, C., R. Peruzzo, and R. Zoncu, The molecular basis of nutrient sensing and signalling by mTORC1 in metabolism regulation and disease . Nat Rev Mol Cell Biol, 2023. 24(12): p. 857–875. Jowan Al-Nusair, M.E., Bayan Khasawneh, Mohammed Al-Nusair, Adolfo Torres, Mohanad Elchouemi, Arsalan Asad, Thomas Wright, Ahmad Mahdi, Statin use and the risk of Acute Myeloid Leukemia: A retrospective propensity-matched cohort study . Blood, 2025. Volume 146. Doench, J.G., et al., Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9 . Nat Biotechnol, 2016. 34(2): p. 184–191. Schmierer, B., et al., CRISPR/Cas9 screening using unique molecular identifiers . Mol Syst Biol, 2017. 13(10): p. 945. Li, W., et al., MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens . Genome Biol, 2014. 15(12): p. 554. Kuleshov, M.V., et al., Enrichr: a comprehensive gene set enrichment analysis web server 2016 update . Nucleic Acids Res, 2016. 44(W1): p. W90-7. Nair, A.B. and S. Jacob, A simple practice guide for dose conversion between animals and human . J Basic Clin Pharm, 2016. 7(2): p. 27–31. Toda, G., et al., Preparation and culture of bone marrow-derived macrophages from mice for functional analysis . STAR Protoc, 2021. 2(1): p. 100246. Additional Declarations Yes there is potential conflict of interest. Supplementary Files Papakyriacousupplementalinformation.docx Supplementary information UncutmembraneFigure1A.pptx Uncut membrane for western blot in Figure 1A PapkyriacouSupplFiguers.pptx Supplementary figures Cite Share Download PDF Status: Under Review Version 1 posted Review # 1 received at journal 04 May, 2026 Reviewer # 1 agreed at journal 20 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 13 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9399910","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":622466472,"identity":"62564290-85c8-4e22-acd4-bfc33d26ad13","order_by":0,"name":"Yumeng Mao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYHAD5gMHSNXClkCyFh4D4tQZ3MhOfFzwiyGav73n48GvOxgSNxxgf/gAv5bczcYz+xhyZ5w5u+Gw7BmQFh5jvPYBtWyT5u1hyG24kbvhsGQbWAubBAEt23+DtMy/kfMAqoX9+Q9CtjDz/GDI3XAjh+HgR7AWBjN8Ohgkz7zdLM3bIJG78cwxg8OMZySMZx7mMcbrML7juRs/8/yxyZ13vPnxx587bGT7jrc//IBPi8IBIMHYBjGWGWgdkMTrLAYG+QYQ+QfCYfzZQED5KBgFo2AUjEgAAMq8Vi2xYPKVAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6142-6628","institution":"Uppsala University","correspondingAuthor":true,"prefix":"","firstName":"Yumeng","middleName":"","lastName":"Mao","suffix":""},{"id":622466473,"identity":"44ae96ca-a0b9-4370-923a-9c08868af928","order_by":1,"name":"Irineos Papakyriacou","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Irineos","middleName":"","lastName":"Papakyriacou","suffix":""},{"id":622466474,"identity":"3803cc0f-825d-463c-aeff-8dcc6329dd3f","order_by":2,"name":"Yonglin Lu","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Yonglin","middleName":"","lastName":"Lu","suffix":""},{"id":622466475,"identity":"8bbdd113-a892-40b8-aacb-374bb1d91b77","order_by":3,"name":"Liam Alford","email":"","orcid":"","institution":"Uppsala University","correspondingAuthor":false,"prefix":"","firstName":"Liam","middleName":"","lastName":"Alford","suffix":""}],"badges":[],"createdAt":"2026-04-13 07:07:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9399910/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9399910/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107488682,"identity":"2343c3b6-d4c6-41d6-b3ae-33cb85e9bd04","added_by":"auto","created_at":"2026-04-22 02:45:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNEDD8 regulates the proteome in human THP1 cells. \u003c/strong\u003eCRISPR/Cas9 gene editing was used to knock-out the \u003cem\u003eNEDD8\u003c/em\u003egene in the human AML cell line, THP-1. \u003cstrong\u003eA)\u003c/strong\u003eNEDD8 protein expression levels were analyzed by Western blot. Representative image of three independent replicates was shown. Label-free quantitative proteomics was performed by mass spectrometry in THP-1 control (Ctrl) and \u003cem\u003eNEDD8\u003c/em\u003eknock-out (KO) cells using four biological replicates per condition. \u003cstrong\u003eB)\u003c/strong\u003e Significantly up-regulated (red) or down-regulated (blue) upon \u003cem\u003eNEDD8\u003c/em\u003e deletion were shown in the volcano plot. Examples of uniquely expressed protein in either ctrl or KO THP1 cells were indicated. Differential expression was assessed using Welch’s unequal variances t-test. \u003cstrong\u003eC)\u003c/strong\u003e Pathway enrichment analysis was performed using significantly up- or down-regulated proteins together with uniquely expressed protein in KO or ctrl THP1 cells. A default Fisher test was used to identify enriched pathways from Reactome and Gene Ontology Biological Process collections, with Benjamini-Hochberg correction applied to \u003cem\u003ep \u003c/em\u003evalues. \u003cstrong\u003eD)\u003c/strong\u003eDifferentially expressed and unique proteins in the ctrl/KO pair were grouped by biological process and visualized in a heat map. Data were shown from four replicates per cell line.\u003c/p\u003e","description":"","filename":"Slide1.png","url":"https://assets-eu.researchsquare.com/files/rs-9399910/v1/7d81e7709ad03243d4e54c03.png"},{"id":107488708,"identity":"5b62ad8d-f026-4ccc-8330-a26e0a4a91e4","added_by":"auto","created_at":"2026-04-22 02:45:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":196571,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNEDD8\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e-deficient THP1 cells exhibit increased pro-inflammatory response upon TLR stimulation.\u003c/strong\u003e Control or \u003cem\u003eNEDD8 \u003c/em\u003eKO THP1 cells (2 × 10\u003csup\u003e4 \u003c/sup\u003ecells per well) were treated with \u003cstrong\u003eA)\u003c/strong\u003e LPS (TLR4 agonist) or \u003cstrong\u003eB)\u003c/strong\u003e Pam3CSK4 (TLR1/2 agonist) at indicated doses for 20 h. \u003cstrong\u003eS\u003c/strong\u003eecretion of soluble pro-inflammatory cytokines, IL-6 (n=5) IL-1 beta (n=4) and TNF alpha (n=3), was measured using a multiplex flow cytometry panel (Legendplex). \u003cstrong\u003eTHP1 control or \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eNEDD8\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e KO cells were treated with C)\u003c/strong\u003e LPS or \u003cstrong\u003eD)\u003c/strong\u003e Pam3CSK4 at indicated doses for 20 h. Surface expression of HLA-DR/DP/DQ, PD-L1 and IFNgRa on live cells was quantified by flow cytometry (at least 5 independent experiments). Each dot represented an independent experiment. Representative histograms of \u003cstrong\u003eE)\u003c/strong\u003e HLA-DR-DP-DQ or \u003cstrong\u003eF)\u003c/strong\u003e PD-L1 after Pam3CSK4 treatment were shown. Values are presented as mean ± SD, and statistical significance was assessed using unpaired \u003cem\u003eT \u003c/em\u003etest.\u003c/p\u003e","description":"","filename":"Slide2.png","url":"https://assets-eu.researchsquare.com/files/rs-9399910/v1/0ee54bcf84b1a91acd6856c7.png"},{"id":107487311,"identity":"838f1878-407f-4134-85b1-bf705531dd28","added_by":"auto","created_at":"2026-04-22 02:40:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":327926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNEDD8\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e deficiency rewires functional gene essentiality in THP1 cells. A) \u003c/strong\u003eControl or \u003cem\u003eNEDD8\u003c/em\u003e KO THP1 cells were transduced with recombinant Cas9 and a genome-wide Brunello gRNA library and gRNA abundances were compared between day 21 and day 4. Data processing and statistical analysis were performed using the MAGeCK pipeline, with \u003cem\u003ep \u003c/em\u003evalues calculated using a negative binomial model. One genome-wide screen was performed.\u003cstrong\u003e B) \u003c/strong\u003eVenn diagram showing the number of genes uniquely or commonly depleted in ctrl and \u003cem\u003eNEDD8\u003c/em\u003e KO THP1 cells.\u003cstrong\u003e C) \u003c/strong\u003eUniquely essential gene hits (p.adj \u0026lt; 0.01, negative log2fc \u0026lt;-0.5 on day 21 in only one group) in THP1 control and \u003cem\u003eNEDD8\u003c/em\u003e KO cells were shown in the volcano plot, in blue or red color, while the common hits (essential in both cell lines) were plotted in grey color. X axis shows the rank difference (rank in KO - rank in Ctrl) and Y axis shows FDR difference (absolute log10(FDR in KO / FDR in Ctrl)), selected unique hits in the groups are labelled. \u003cstrong\u003eD) \u003c/strong\u003ePathway enrichment analysis of uniquely significant essential genes in \u003cem\u003eNEDD8\u003c/em\u003e KO THP1 cells.\u003cstrong\u003e \u003c/strong\u003eEnrichment was assessed by the default Fisher test, and Benjamini-Hochberg–adjusted\u003cem\u003e p\u003c/em\u003e values are reported. The THP1 Ctrl/\u003cem\u003eNEDD8\u003c/em\u003e KO cell line pair (1 × 10\u003csup\u003e4\u003c/sup\u003e cells per well) were treated with \u003cstrong\u003eE)\u003c/strong\u003e rapamycin (mTORi, 72 h) or \u003cstrong\u003eF)\u003c/strong\u003e simvastatin (96 h) or with DMSO as a control. Dose-dependent effects on cell viability were quantified using PrestoBlue reagent, and dose response curves were calculated. Data represented three independent experiments. \u003cstrong\u003eG)\u003c/strong\u003e THP1 control or \u003cem\u003eNEDD8\u003c/em\u003e KO cells were treated with 0.1% DMSO or simvastatin (1 μM) for 4 days, followed by PBS or LPS (1 μM) treatment for an additional 20 hours. Morphological features of 1 × 10\u003csup\u003e3\u003c/sup\u003e cells per condition were examined by transmission electron microscopy. Blue arrows indicate phagosomes; red squares highlight budding structures. Representative images from three technical replicates are shown. Scale bar: 2 μm.\u003c/p\u003e","description":"","filename":"Slide3.png","url":"https://assets-eu.researchsquare.com/files/rs-9399910/v1/50d067fbfc3f27442cfc4aba.png"},{"id":107434751,"identity":"e56d5b19-e41c-45f5-a515-bf6c02544248","added_by":"auto","created_at":"2026-04-21 13:04:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":204887,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSimvastatin enhances cytokine release from freshly isolated primary human monocytes following LPS stimulation.\u0026nbsp; A) \u003c/strong\u003ePrimary human CD14+ monocytes (3 x 10\u003csup\u003e5 \u003c/sup\u003ecells/well) were treated with simvastatin (1 μM) or 0.1% DMSO for 2 days, followed by stimulation with LPS at the indicated doses for an additional 20 h. Soluble cytokines including IL-1B (n=4), TNFA (n=3), and IFNG (n=4) were quantified using a human multiplex flow-cytometry-based Legendplex assay.\u003cstrong\u003e B\u003c/strong\u003e) Surface expression levels of PD-L1, IFNgRa, HLA-ABC and HLA-DR/DP/DQ were measured by flow cytometry and reported as mean fluorescent intensity (at least 5 independent donors). Data was presented as mean ± SD, and statistical significance was determined by unpaired two tail \u003cem\u003eT\u003c/em\u003e test.\u003cstrong\u003e C) \u003c/strong\u003eHuman CD14+ monocytes (3 x 10\u003csup\u003e5 \u003c/sup\u003ecells/well) were cultured with simvastatin (1 μM) together with ruxolitinib (Jak1/2i), MCC950 (NLRP3i), or rapamycin (mTORi) at the indicated concentrations (0.1% DMSO) for 2 days, followed by LPS stimulation (1 μg/ml, 20 h). Soluble cytokines, e.g., IL-1B and IFNG, were quantified by Legendplex after treatment with\u003cstrong\u003e D)\u003c/strong\u003e rapamycin\u003cstrong\u003e (\u003c/strong\u003en=4),\u003cstrong\u003e E) \u003c/strong\u003eruxolitinib (n=5) or\u003cstrong\u003e F)\u003c/strong\u003e MCC950 at least six independent donors were included. Data was presented as mean. Statistical significance was determined by unpaired two tail \u003cem\u003eT\u003c/em\u003e test. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ns, not significant.\u003c/p\u003e","description":"","filename":"Slide4.png","url":"https://assets-eu.researchsquare.com/files/rs-9399910/v1/5007cb0b387e89fd2932d827.png"},{"id":107487052,"identity":"50260b47-4ca4-447f-a712-2eefdc12ac97","added_by":"auto","created_at":"2026-04-22 02:39:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":212890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInhibition of lipid metabolism in mice polarizes LPS-induced myeloid cell functions against AML\u003c/strong\u003e.\u003cstrong\u003e \u0026nbsp;A)\u003c/strong\u003e Treatment schedule of C57BL/6NTac mice receiving intraperitoneally (i.p.) simvastatin (8.2 mg/kg) in combination with LPS (5 μg per mouse). At the study end point, serum and splenocytes were harvested. \u003cstrong\u003eB)\u003c/strong\u003e Serum cytokine levels, including CXCL1, TNFA, IL-12p40, and CCL22, were measured using a multiplex flow cytometry panel (Legendplex) (8 mice per group). Splenocytes were analyzed by flow cytometry. \u003cstrong\u003eC) \u003c/strong\u003eCD45+ immune cells and \u003cstrong\u003eD\u003c/strong\u003e) CD11b+Ly6G+ neutrophils were compared across treatment groups. \u003cstrong\u003eE)\u003c/strong\u003e Mean fluorescence intensity (MFI) of MHC-II surface expression or \u003cstrong\u003eF)\u003c/strong\u003e intracellular arginase 1 (ARG1) levels in macrophages were quantified. \u003cstrong\u003eG)\u003c/strong\u003e The proportion of CD3+NK1.1neg T cells, \u003cstrong\u003eH)\u003c/strong\u003eCD8+ T cells gated into LAG3+PD-1+ subsets were compared among treatment groups. Each dot represented values from an individual spleen (n=8). Average values were shown and unpaired two tailed \u003cem\u003eT \u003c/em\u003etest was used for statistical analysis. \u003cstrong\u003eI)\u003c/strong\u003e Four hundred thousand acute myelogenous leukemia (AML) cells, C1498, were injected subcutaneously (s.c.) in 100 μl medium into 6-10 week-old female C57BL/6NTac mice. Vehicle control or simvastatin (8.2 mg/kg) was administered intraperitoneally (i.p.) daily from days 5 to 12, with LPS given on days 6, 9, and 12 after tumor inoculation. \u003cstrong\u003eJ)\u003c/strong\u003eTumor growth was monitored in all mice until the study endpoint (n = 8 per group). Data were shown as mean ± SEM. Statistical analysis was performed using unpaired two tailed \u003cem\u003eT \u003c/em\u003etest.\u003c/p\u003e","description":"","filename":"Slide5.png","url":"https://assets-eu.researchsquare.com/files/rs-9399910/v1/c59baad51f004c522e13b1de.png"},{"id":107489792,"identity":"07e8c8aa-4d2b-4a52-bcfd-d566e3940fe6","added_by":"auto","created_at":"2026-04-22 02:48:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1778352,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9399910/v1/6eef00bd-4753-4886-b764-c0e91c73d181.pdf"},{"id":107434747,"identity":"037d76b4-a038-4fc2-96f4-0cd1dbc2d4bd","added_by":"auto","created_at":"2026-04-21 13:04:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27109,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"Papakyriacousupplementalinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9399910/v1/f88971bc76cf6eec3c871ad4.docx"},{"id":107488519,"identity":"93a6fc94-90ee-4727-98e4-d2e935288caa","added_by":"auto","created_at":"2026-04-22 02:44:56","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":218442,"visible":true,"origin":"","legend":"Uncut membrane for western blot in Figure 1A","description":"","filename":"UncutmembraneFigure1A.pptx","url":"https://assets-eu.researchsquare.com/files/rs-9399910/v1/ee701ce0b7580749271d2fe5.pptx"},{"id":107434753,"identity":"28cc2da8-f87b-4d80-a0d6-1d336d107f9e","added_by":"auto","created_at":"2026-04-21 13:04:04","extension":"pptx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":29287380,"visible":true,"origin":"","legend":"Supplementary figures","description":"","filename":"PapkyriacouSupplFiguers.pptx","url":"https://assets-eu.researchsquare.com/files/rs-9399910/v1/c161878d9c1fac03fe95b366.pptx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential conflict of interest.","formattedTitle":"Protein neddylation governs the inflammatory status of healthy and malignant myeloid cells in response to TLR stimulation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute myeloid leukemia (AML) is a hematological malignancy caused by a combination of genetic aberrations and dampened immune surveillance [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Patients with AML present heterogenous cell blasts, which contain cells of myeloid lineage at various differentiation and maturation stages [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Treatment of AML includes intensive chemotherapy, radiation therapy, targeted therapy and stem-cell transplantation, and long-term patient survival remains to be improved despite these intensive multi-modal therapies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith recent progress of immunotherapy in solid cancers, the therapeutic potential of the immune system has been investigated in AML [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, AML blasts often show low expression of antigen presentation and/or immune-stimulatory molecules, and could also induce multiple immunosuppressive mechanisms to dampen anti-tumor immunity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In particular, mature myeloid cells including macrophages, monocytes, dendritic cells, and granulocytes, could gain suppressive capacity when in contact with tumor-derived factors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, cancer-associated chronic inflammation leads to insufficient activation of certain myeloid cell subsets and promotes the expansion of those with suppressive functions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eToll-like receptors (TLRs) are broadly expressed by immune cell subsets, which elicit potent activation and differentiation of myeloid cells [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover, TLRs have been detected in patient-derived AML blasts [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and could drive cancer cell differentiation [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, it is widely reported that the rapid activation of myeloid cells through TLRs could recruit negative regulators of the pathway, which leads to endotoxin tolerance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, understanding how these mechanisms influence the immunological functions of healthy and malignant myeloid cells is crucial for developing strategies to enhance immune response against AML cells.\u003c/p\u003e \u003cp\u003eNeural precursor cell expressed, developmentally downregulated protein 8 (NEDD8) is a ubiquitin-like protein that covalently attaches to lysine residues of target proteins through the process of neddylation. This post-translational modification regulates protein function in eukaryotic cells and is mediated through a series of enzymatic reactions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We previously identified protein neddylation as a breast cancer-intrinsic resistance mechanism against immune checkpoint blockade therapy [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Protein neddylation is a well-regarded target in cancer therapy and a pharmacological inhibitor, pevonedistat, has been developed [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and tested in a large clinical trial for patients with AML [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, we showed that current neddylation inhibitors were equally potent in suppressing the proliferation of neddylation-proficient and -deficient human breast cancer cells, indicating their off-target activities. Therefore, precise deletion of genes in the neddylation pathway using CRISPR/Cas9 genome editing could shed light on the specific contribution of protein neddylation in the innate immune response pathways.\u003c/p\u003e \u003cp\u003eIn this study, we demonstrate that genetic deletion of \u003cem\u003eNEDD8\u003c/em\u003e substantially skews the proteome of human THP1 cells, which leads to significantly higher response to TLR agonists. Genome-wide CRISPR/Cas9 screening in the control and KO THP1 cell line pair pinpoints a rewired functional network upon \u003cem\u003eNEDD8\u003c/em\u003e loss, which exhibits a higher dependency on the mTOR pathway. Moreover, inhibition of lipid metabolism by simvastatin induces a similar phenotypic change as compared to the KO cells in transmission electron microscopy. In accordance, simvastatin pre-treatment enhances the pro-inflammatory features of primary human monocytes upon LPS stimulation, which relies on the mTOR pathway. In mice, simvastatin fine-tunes the inflammatory landscape induced by LPS and results in delayed growth of AML tumors. Altogether, our study reveals a previously unappreciated role of protein neddylation as a negative regulator for TLR signaling, which is mediated through the lipid metabolism status in healthy and malignant myeloid cells.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProtein neddylation is a master regulator for the proteome in human AML cells\u003c/h2\u003e \u003cp\u003eWe previously demonstrated that genetic ablation of the \u003cem\u003eNEDD8\u003c/em\u003e gene enhanced the expression of antigen presentation molecules on human breast cancer cells [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Given that impaired antigen presentation capacity is a common feature between immunosuppressive and malignant myeloid cells [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], we hypothesize that protein neddylation may be a target to enhance the immunostimulatory properties in myeloid cells. Using CRISPR/Cas9, we permanently removed the NEDD8 protein in a human AML cell line, THP1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), which is required for protein neddylation. Control THP1 cells were generated at the same time by transfecting a RNP complex without the gene-targeting crRNA. Label-free mass spectrometry revealed a substantial change in the proteome of KO cells in the principal component analysis (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u003c/b\u003e). In total, 249 proteins were up-regulated or became uniquely expressed, and 172 proteins were down-regulated or became undetectable upon \u003cem\u003eNEDD8\u003c/em\u003e deletion, when compared to control THP1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Moreover, the absence of NEDD8 was also confirmed by proteomics analysis. Pathway enrichment analysis of the differentially expressed proteins demonstrated that proteins regulating the innate immunity were significantly enhanced in KO cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). In addition, expression of proteins related to phagosome, lysosome, interferon signaling, as well as oxidative phosphorylation was enriched in upon \u003cem\u003eNEDD8\u003c/em\u003e deletion, while control THP1 cells were enriched for cholesterol biosynthesis, glycolysis and HIF1 signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). A detailed analysis of individual proteins highlighted the enhanced expression of immune-stimulatory pathways in KO cells, such as HLA class I molecules (HLA-A/B), interferon (JAK1, STING1, STAT1/3), and antioxidants (NQO1, TXNRD1, GPX4, GPX1, PRDX6) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Therefore, we further investigated the inflammatory potential of control or \u003cem\u003eNEDD8\u003c/em\u003e KO THP1 cells upon toll-like receptor stimulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eNeddylation deficiency enhances the response to TLR agonists in human AML cells.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBecause TLR2 and TLR4 are frequently over-expressed by AML cells [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], we tested the cytokine release and phenotypic changes in THP1 cells upon stimulation with LPS (TLR4) or Pam3CSK4 (TLR1/2). As expected, TLR agonists induced the secretion of pro-inflammatory cytokines and chemokines, i.e. IL-6, IL-1B, TNFA and CXCL-10, in a dose-dependent manner in both control and knockout cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and S1B). However, the levels of these cytokines were significantly enhanced in \u003cem\u003eNEDD8\u003c/em\u003e KO THP1 cells after TLR stimulation, and the increases were more pronounced when cells were treated with LPS, as compared to Pam3CSK4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Of note, the release of soluble CXCL-10 was only increased by Pam3CSK4 in \u003cem\u003eNEDD8\u003c/em\u003e KO THP1 cells (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo determine the expression of cell surface proteins, we quantified HLA class I and II antigen presentation molecules, PD-L1 and interferon gamma receptor alpha (IFNgRa or CD119) upon TLR stimulation. LPS or Pam3CSK4 induced dose-dependent upregulations of surface pan-HLA-II molecules and IFNgRa on THP1 cells, but only Pam3CSK4 drove significant changes of PD-L1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Similar to what has been observed in human breast cancer cells [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], \u003cem\u003eNEDD8\u003c/em\u003e deficiency caused a significant upregulation of HLA-II molecules on THP1 cells at the baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), and this difference was maintained upon TLR stimulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Pam3CSK4, but not LPS, induced a significant upregulation of surface PD-L1 on KO cells, as compared to stimulated control THP1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Pan-HLA-I expression was not changed significantly by TLR stimulation, nor upon the deletion of protein neddylation (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC\u003c/b\u003e). Altogether, we uncovered a previously unknown role of protein neddylation as a negative regulator for the TLR pathway in human AML cells.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenome-wide CRIPSR/Cas9 screening uncovers functional pathways that are essential in neddylation-deficient THP1 cells.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe neddylation inhibitor pevonedistat has been widely used to investigate the function of neddylation in immune cell activation. However, we found that pevonedistat was equally efficient in limiting the proliferation of neddylation-proficient and -deficient THP1 cells (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD\u003c/b\u003e) and breast cancer cells [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which suggested its neddylation-independent properties. In order to reveal essential mechanisms controlled by protein neddylation in human AML cells, we performed simultaneous genome-wide CRISPR/Cas9 screens in control and \u003cem\u003eNEDD8\u003c/em\u003e KO THP1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In brief, control or KO THP1 cells were transduced with a vector that allowed constitutive expression of recombinant Cas9 protein. Next, the Brunello gRNA library that covers the human genome was transduced at an optimal ratio to allow one gRNA per cell. Cells were harvested on day 4 and day 21 and the gRNA frequencies were compared to identify essential genes for the survival of the cell line pair.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter quality control and normalization, we performed PCA analysis based on the gRNA counts and the sample differences on day 4 were comparable between control and KO THP1 cells, but substantially changed after culturing, indicating the technical robustness of the screen (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA\u003c/b\u003e). However, the profiles on day 21 were clearly different between control and KO cells (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA\u003c/b\u003e), suggesting that genetic deletion of \u003cem\u003eNEDD8\u003c/em\u003e rewired the functional network in THP1 cells. Further, we identified essential genes for AML cell survival (743 genes in control and 885 genes in KO), of which 152 genes in control cells and 294 genes in KO cells were unique for their survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Of note, the \u003cem\u003eNEDD8\u003c/em\u003e gene was more essential in neddylation-proficient THP1 cells, and several genes within the neddylation cascade e.g., \u003cem\u003eGPS1, COPS4, COPS6\u003c/em\u003e, lost essentiality upon \u003cem\u003eNEDD8\u003c/em\u003e deletion, confirming effective pathway disruption (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB\u003c/b\u003e). Moreover, multiple components of the ubiquitination pathway (\u003cem\u003eUBAs\u003c/em\u003e and \u003cem\u003eUBEs\u003c/em\u003e) remained essential in KO cells (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB\u003c/b\u003e), suggesting that ubiquitin-mediated regulation continues to play a critical role in sustaining protein homeostasis, which was consistent with our proteomic data showing comparable numbers of proteins identified in the cell line pair (\u003cb\u003eSupplemental Table\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eNext, we investigated genes that were uniquely essential in KO cells, and revealed the critical functions of metabolism (\u003cem\u003eHSD17B12, CS)\u003c/em\u003e, mitochondrial function (\u003cem\u003eMRPL57, MRPL44 ATP5F1E\u003c/em\u003e), GTPase regulation (\u003cem\u003eRAB10\u003c/em\u003e), translation (\u003cem\u003eEIF4\u003c/em\u003e), and transcriptional regulation (\u003cem\u003eINTS14\u003c/em\u003e) in sustaining cell survival. Similar to human breast cancer cells [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], genes of the mTOR pathway, including \u003cem\u003emTOR\u003c/em\u003e and \u003cem\u003eRAPTOR\u003c/em\u003e, were also essential for the survival of \u003cem\u003eNEDD8\u003c/em\u003e-deficient THP1 cells compared with controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Further enrichment analysis revealed pathways with an increased essentiality upon \u003cem\u003eNEDD8\u003c/em\u003e loss, which included metabolic and mTOR-related processes, as well as oxidative phosphorylation/phagosome activity, and cellular stress response pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). These findings were in line with data from the proteomics analysis, where proteins involve in mTOR, oxidative phosphorylation and lysosomal/phagosome mechanisms became upregulated in the KO cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Indeed, \u003cem\u003eNEDD8\u003c/em\u003e KO THP1 cells were more sensitive to rapamycin-mediated cell growth inhibition in vitro, suggesting cellular reprogramming through enhanced functional importance of the mTOR pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eDue to the potential off-target liability of current neddylation inhibitors (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD\u003c/b\u003e), we explored alternative targets that can induce a similar phenotypic change as \u003cem\u003eNEDD8\u003c/em\u003e deletion in AML cells. Because several proteins on the cholesterol biosynthesis pathway were dampened in KO cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), we selected statin drugs as potential candidates. Indeed, \u003cem\u003eNEDD8\u003c/em\u003e deficiency led to reduced sensitivity to simvastatin treatment in THP1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Further, we employed transmission electron microscopy (TEM) to assess the phenotypic changes induced by LPS and/or simvastatin at 1 \u0026micro;M, which did not induce direct inhibition the proliferation of THP1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Genetic deletion of \u003cem\u003eNEDD8\u003c/em\u003e or treatment with simvastatin alone did not substantially change the cellular phenotype in THP1 cells (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC\u003c/b\u003e). In response to LPS treatment, THP1 cells formed phagosome-like structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Combining LPS with simvastatin cells induced the clear formation of budding structures and enlarged phagosome-like structures in THP1 cells, which were similar to those observed in KO cells treated with LPS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). The shift of intracellular organelles could contribute to the enhanced secretion of cytokines and expression of surface proteins in THP1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and S3A).\u003c/p\u003e \u003cp\u003e \u003cb\u003eInhibition of lipid metabolism enhances the TLR-induced pro-inflammatory properties of primary monocytes through the mTOR pathway\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNext, we asked whether similar mechanisms could be induced in non-malignant primary monocytes derived from healthy blood donors. Because monocytes are short-lived in culture and are less susceptible to genome-editing, we used simvastatin in our subsequent experiments. In brief, freshly isolated monocytes were seeded in culture media supplemented with human serum in the presence of 1 \u0026micro;M simvastatin or DMSO. After 48 hours, LPS was added at an increasing concentration and cytokine levels were assessed in the culture media after overnight incubation. Similar to what was observed in \u003cem\u003eNEDD8\u003c/em\u003e KO THP1 cells (\u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA\u003c/b\u003e), simvastatin significantly enhanced the secretion of IL-1 beta and TNF alpha from primary monocytes in response to LPS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Although soluble IFN gamma was not detected in THP1 cells upon stimulation (\u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA\u003c/b\u003e), concurrent treatment of LPS and simvastatin significantly enhanced the release of IFN gamma from monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Moreover, GM-CSF showed a trend of increased release from simvastatin-treated, LPS-primed monocytes, while CXCL-10 and IL-10 remained similar, as compared to LPS treatment alone (\u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then examined whether immunological proteins were altered by the treatment of LPS and simvastatin on primary monocytes. Simvastatin alone significantly enhanced the expression of IFNgRa at the baseline and showed a trend to induce higher HLA-DR/DP/DQ levels on primary monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Treatment with LPS drove the expression of PD-L1 and HLA-ABC, but caused the down-regulation of IFNgRa on primary monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The expression of PD-L1 and HLA-ABC upon LPS stimulation was further elevated on monocytes by simvastatin treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eTo reveal the underlying mechanisms, we included pharmacological inhibitors together with simvastatin (1 \u0026micro;M) before measuring cytokine release stimulated by LPS (1 \u0026micro;g/ml) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Rapamycin (mTORi) was selected due to the elevated dependency on the mTOR pathway in \u003cem\u003eNEDD8\u003c/em\u003e KO THP1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and \u003cb\u003eE\u003c/b\u003e) and Ruxolitinib (JAK1/2i) was chosen because of the significant induction of IFN gamma (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Moreover, MCC950, which is an inhibitor against NLRP3 inflammasome [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], was included to investigate the release of IL-1 beta. Rapamycin abrogated the induction of IL-1 beta and IFN gamma (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), as well as TNF alpha (\u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eC\u003c/b\u003e) from primary monocytes treated with simvastatin and LPS. Inhibition of the JAK/STAT signaling reduced the production of IFN gamma but had limited impact on IL-1 beta (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE) or TNF alpha (\u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eD\u003c/b\u003e). In contrast, treatment with MCC950 did not result in significant changes in cytokine release (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF and S3E), which suggested that the release of IL-1 beta release was independent of inflammasome activation. Altogether, we showed that mTOR was a master regulator for the enhanced sensitivity to TLR stimulation induced by simvastatin, while JAK1/2 signaling was more specific for the release of IFN gamma from monocytes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSimvastatin fine-tunes the inflammatory landscape upon LPS treatment and delays AML growth in mice\u003c/h3\u003e\n\u003cp\u003eGiven that simvastatin could boost the pro-inflammatory status in both healthy and malignant human myeloid cells in response to TLR stimulation in vitro, we examined its effects in immune competent C57BL/6NTac mice. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, simvastatin was administered at a human-equivalent dose (8.2 mg/kg, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e) daily. One hour after the last dose of simvastatin, mice were infused with 5 \u0026micro;g LPS in the abdominal cavity. Serum and splenocytes were isolated after 3 hours and soluble factors and cell phenotypes were analyzed using Legendplex or flow cytometry, respectively. As expected, LPS treatment induced a rapid release of pro-inflammatory cytokines, including CXCL-1, IL12p40, TNFA, IL-6, G-CSF and IL-1B (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and S4A). In contrast, IL-18, IL-10, IL-23, TGFB1 and IL12p70 were not induced by LPS in the mouse serum (\u003cb\u003eFigure S4A\u003c/b\u003e). Simvastatin significantly reduced the baseline production of IL12p40, CCL-22 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), as well as IL-18 (\u003cb\u003eFigure S4A\u003c/b\u003e) in the serum. Although cytokines could still be induced by LPS in mice treated with simvastatin, the levels of CXCL-1, IL12p40, TNFA and CCL22 were significantly lower, when compared to mice treated with only LPS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The induction of IL-6, G-CSF and IL-1B by LPS was not modulated by simvastatin (\u003cb\u003eFigure S4A\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen assessing the immune cells in the spleens, we observed a significantly higher percentage of CD45\u0026thinsp;+\u0026thinsp;cells in mice treated with the combination, as compared to LPS alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Among the innate immune cell types, CD11b+Ly6G+ neutrophils were significantly induced by LPS but reduced when simvastatin was given together (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Similar to what was observed in human myeloid cells, the expression of MHC-I was elevated by simvastatin on multiple myeloid cell subsets (\u003cb\u003eFigure S4B\u003c/b\u003e) and PD-L1 was up-regulated by simvastatin on Ly6C+ monocytes (\u003cb\u003eFigure S4C\u003c/b\u003e). In macrophages, MHC-II molecules were induced by LPS and were further enhanced by simvastatin (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Moreover, arginase-1 (ARG-1), which is an enzyme over-expressed by immunosuppressive myeloid cells [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], was reduced by simvastatin in macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). The expression of MHC-II molecules was enhanced by LPS in dendritic cells (DC) but was not significantly modulated by the addition of simvastatin (\u003cb\u003eFigure S4D\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eIn the lymphocyte populations, percentage of T cells remained stable upon LPS challenge, but was significantly elevated by simvastatin (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). All lymphocyte subsets responded to LPS treatment, indicated by the significant up-regulation of CD69 on the surface, but this activation was not modulated by the addition of simvastatin (\u003cb\u003eFigure S4E\u003c/b\u003e). Of note, addition of simvastatin significantly reduced the frequency of CD8\u0026thinsp;+\u0026thinsp;T cells with an exhaustion phenotype (PD1\u0026thinsp;+\u0026thinsp;LAG3+, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003eBecause simvastatin potentiated the immune stimulatory status of myeloid cells in vivo, we asked whether it could enhance the anti-AML efficacy together with LPS. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI, murine C1498 AML cells were injected subcutaneously in C57BL/6NTac mice, followed by daily treatment of simvastatin at 8.2 mg/kg. LPS (5 \u0026micro;g/mouse) was infused systemically on day 6, 9 and 12 after tumor implantation. Neither simvastatin nor LPS was sufficient in controlling the growth of C1498 tumors as a monotherapy. In contrast, simvastatin significantly reduced the tumor volumes when combined with LPS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ). Based on these results, we concluded that reducing lipid metabolism by simvastatin could fine-tune the pro-inflammatory landscape driven by LPS, which leads to a more potent anti-AML immunity.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eProtein neddylation is a multi-enzymatic post-translational modification machinery process that governs cell homeostasis and function [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In brief, the E1 NEDD8-activating E1 enzyme (NAE) contains two subunits (NEA1 and UBA3), which activates NEDD8 in the presence of ATP, and transfers the activated NEDD8 to E2 enzymes, e.g. UBE2M or UBE2F. NEDD8 is then conjugated to the substrates by one of the E3 ligases, such as RING-box proteins (RBX1/2). Because of its essential role in regulating cell survival, it is challenging to study the function of neddylation using genetically-modified mice. For example, deletion of the rate-limiting E1 enzyme subunits, \u003cem\u003eUba3\u003c/em\u003e [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] or \u003cem\u003eNae1\u003c/em\u003e [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], led to embryonic lethality or significantly impaired neuron development in mice. In myeloid cells, transcription of \u003cem\u003eUbe2m\u003c/em\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] or \u003cem\u003eRbx2\u003c/em\u003e [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] mRNAs were elevated upon TLR stimulation and ablation of these genes dampened the pro-inflammatory response in mice. In contrast, E2 or E3 enzymes could negatively regulate cytokine-induced activation of NK cells [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] or sustain the suppressive functions in regulatory T cells [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Because E2 or E3 enzymes have additional regulatory roles on the ubiquitination system [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], these observed effects may not be mechanistically exclusive to protein neddylation and may be highly dependent on the cell type. In our study, we took a unique approach to permanently remove the \u003cem\u003eNEDD8\u003c/em\u003e gene from human AML cells using CRISPR/Cas9, which specifically abrogates protein neddylation, with minimal impact on the ubiquitination system [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This cellular model reveals the previously unknown function of neddylation as a negative regulator for the TLR pathway. Genetic deletion of \u003cem\u003eNEDD8\u003c/em\u003e in human AML cells substantially promotes the release of pro-inflammatory cytokines and expression of immune-stimulatory molecules, which indicates cellular differentiation and maturation.\u003c/p\u003e \u003cp\u003ePharmacological inhibitors, e.g. pevonedistat, which target the E1 enzyme of the neddylation system [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], have been widely used to examine the functional relevance of protein neddylation on the immunological pathways. For example, pevonedistat limits the anti-viral innate immunity because of its negative effects on the cGAS/STING signaling [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, the off-target and neddylation-independent effects of these small molecule compounds are well-documented [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and confirmed by us using control and \u003cem\u003eNEDD8\u003c/em\u003e-deficient human breast cancer [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and AML cell line pairs. In order to reveal druggable mechanisms that can resemble the phenotype of neddylation deficiency, we mapped the proteomes and performed genome-wide CRISPR/Cas9 screens in the THP1 control/KO isogenic cell line pair. Our data demonstrate that proteins in the cholesterol biosynthesis pathway are lowly expressed in KO cells and mTOR signaling becomes more essential for cell survival. This is in line with our previous results observed in human breast cancer cells [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Lipid metabolism and peroxidation have known functions in regulating the development and functions in mature and malignant myeloid cells [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. On the one hand, statin drugs directly limit the proliferation of AML blasts [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. On the other hand, hyperactivation of lipid metabolism confers immunosuppressive functions in tumor-infiltrating myeloid cells [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In human THP1 cells, simvastatin at a non-toxic dose mimics the phenotypic changes of \u003cem\u003eNEDD8\u003c/em\u003e deletion at the subcellular level upon TLR stimulation and amplifies the activation of human AML and primary monocytes. This effect is dictated by the mTOR pathway and partially dependent on the JAK/STAT signaling. Because mTOR has a central role in cell metabolism and homeostasis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], we speculate that it plays a key role in rewiring myeloid cells to increase their sensitivity to TLR stimulation.\u003c/p\u003e \u003cp\u003ePevonedistat has been tested together with chemotherapy in the PANTHER phase 3 clinical trial in patients with myeloid malignancies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, it failed to add clinically meaningful benefits to chemotherapy and the clinical development has been terminated. Given that current neddylation inhibitors carry off-target liabilities and are mostly inhibitory to immune cells [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], these results do not de-validate the therapeutic potential of protein neddylation in cancer therapy. Instead, discovery of new and selective approaches, such as anti-sense oligonucleotides, or repurposing existing drugs that induce a similar phenotype to neddylation-deficient cells, such as statin drugs, may offer synergy to chemotherapy or immunotherapy. We show that simvastatin induces a pro-inflammatory phenotype in myeloid cells in mice treated with LPS and synergizes with LPS to limit the growth of murine AML tumors. A large epidemiology study involving more than 10\u0026nbsp;million people demonstrates that statin drug use significantly reduced the risk of AML development and progression [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Addition of therapeutics that engage with the adaptive immunity, e.g. ICB drugs, may further improve the curative rates in the mouse model. However, it remains to be studied whether deletion of neddylation or statin drugs could prevent the induction of endotoxin tolerance in myeloid cells.\u003c/p\u003e \u003cp\u003eAltogether, our study utilizes unique cellular models to reveal the unexpected role of protein neddylation as an immune checkpoint for the TLR pathway in healthy and malignant myeloid cells. Neddylation is a gatekeeper for a wide range of pathways in myeloid cells, which relies on lipid metabolism and mTOR signaling. We propose that specific targeting strategies against neddylation or lipid metabolism inhibitors could be explored to enhance the immune response against AML cells.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eDetailed information on the antibodies, reagents and CRISPR-nucleotide sequences used in this study is provided in \u003cb\u003eSupplementary Tables\u0026nbsp;1, 2 and 3\u003c/b\u003e, respectively.\u003c/p\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003eThe study utilized buffy coats from anonymous healthy blood donors provided by the blood center at Uppsala University Hospital. No ethical permission is needed. Six-to-ten weeks old na\u0026iuml;ve female C57BL/NTac mice were purchased from Taconic and housed at the animal facility at the Rudbeck Laboratories at Uppsala University, under the pathogen-free environment. All studies were approved by the Swedish Board of Agriculture at J\u0026ouml;nk\u0026ouml;ping, Sweden and performed by trained staff under ethical permits Dnr: 5.8.18\u0026ndash;06394/2020 and 5.2.18-05492.2025.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCellular materials and culture conditions\u003c/h2\u003e \u003cp\u003eThe human acute monocytic leukemia (AML) cell line, THP-1 (TIB-202), and the murine AML cell line, C1498 (TIB-49), were obtained from ATCC. Unless otherwise indicated, all cell lines were grown in Iscove\u0026rsquo;s Modified Dulbecco\u0026rsquo;s Medium (IMDM) (Thermo Fisher Scientific) supplemented with 1% penicillin-streptomycin (PenStrep) and heat-inactivated fetal bovine serum (FBS), 20% FBS for THP-1 cells and 10% for C1498 cells, at 37\u0026deg;C in 5% CO₂. Human primary CD14\u0026thinsp;+\u0026thinsp;monocytes were cultured in IMDM with 1% PenStrep and 10% human AB serum (MP Biomedicals). Cell lines were verified by DNA fingerprinting (Eurofins) and routinely screened for mycoplasma contamination using MycoAlert (Lonza).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIsolation of human primary CD14 + monocytes\u003c/h3\u003e\n\u003cp\u003eFresh human primary CD14\u0026thinsp;+\u0026thinsp;monocytes were isolated from fresh buffy coats provided from the blood center at the Uppsala university hospital, Sweden. Briefly, 10 mL of Lymphoprep (Stem Cell Technologies) was added to the SepMate tubes-50 (Stem Cell Technologies), and whole blood was carefully layered on top. Following centrifugation at 1200 \u0026times; g for 10 minutes, the PBMC layer was collected and washed twice with phosphate-buffered saline (PBS; Thermo Fisher Scientific). To eliminate residual red blood cells, cells were treated with 5 mL of ACK lysis buffer (Thermo Fisher Scientific) for 10 minutes at room temperature in the dark, and then centrifuged at 500 \u0026times; g for 5 minutes. Healthy CD14\u0026thinsp;+\u0026thinsp;primary monocytes were subsequently isolated using the EasySep CD14\u0026thinsp;+\u0026thinsp;Selection Kit II (Stem Cell Technologies) following the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e\n\u003ch3\u003eCRISPR/Cas9-mediated target gene deletion\u003c/h3\u003e\n\u003cp\u003eUsing the Neon transfection system (Thermo scientific), ribonucleoprotein (RNP) complexes containing the crRNAs targeting human or mouse genes (\u003cb\u003eSupplemental Table\u0026nbsp;3\u003c/b\u003e), tracrRNAs and an endonuclease Cas9 protein were transfected into cancer cells to delete NEDD8 gene. RNP complexes lacking crRNAs were used as negative reaction (referred as ctrl cells). For each reaction, 1 \u0026micro;L of NEDD8-specific crRNA (100 \u0026micro;M) was mixed with 1 \u0026micro;L tracrRNA (100 \u0026micro;M) in 1.7 \u0026micro;L nuclease free duplex buffer (IDT). The mixture was incubated at 95\u0026deg;C for 5 minutes to enable annealing, followed by cooling to 4\u0026deg;C. Subsequently, 1 \u0026micro;L of Cas9 endonuclease (10 mg/mL, IDT) was added and the solution was left at room temperature for 15 minutes to facilitate the formation of the RNP complex. Next, 0.3 \u0026micro;L carrier DNA (100 \u0026micro;M) was added to enhance gene editing efficiency. Cell pellets (5 \u0026times; 10⁵ cells) were resuspended in 5 \u0026micro;L of resuspension buffer T and mixed with equal volume of the RNP complex. The Neon transfection system was set up following the manufacturer's instructions, after which the cell mixture was transferred into Neon pipette tips, and electroporation was conducted using specific programs. Transfected cells were then cultured and incubated at 37\u0026deg;C with 5% CO2 until use. Cells were repeatedly transfected with either gene-targeting RNP complexes or control complexes to achieve complete gene deletion.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide CRISPR/Cas9 screen and data analysis\u003c/h2\u003e \u003cp\u003eTo uncover cellular reprogramming in \u003cem\u003eNEDD8\u003c/em\u003e deficient myeloid cells, genome-wide CRISPR screen was used. The genome-wide Brunello sgRNA library [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] was synthesized, cloned, packaged into lentivirus, and subsequently transduced into Cas9-expressing target cells, as previously described [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Briefly, the Brunello library-transduced control and \u003cem\u003eNEDD8\u003c/em\u003e KO THP-1 cells were cultured for 4 or 21 days. To maintain complete library representation, cell numbers were kept above 80\u0026nbsp;million per replicate throughout the experiment. At the end of the culture period, cells were harvested and genomic DNA was extracted using the QIAamp DNA Blood Maxi Kit (Qiagen). sgRNA cassettes were amplified by PCR as previously described[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], using modified primers PCR2_fw acactctttccctacacgacgctcttccgatctcttgtggaaaggacgaaacac\u003c/p\u003e \u003cp\u003eand PCR3_fw aatgatacggcgaccaccgagatctacac[i5]acactctttccctacacgacgctct, resPCR products were subjected to sequencing on an Illumina NovaSeq platform with the following parameters: 20-cycle Read 1 using custom primer CGATCTCTTGTGGAAAGGACGAAACACC, 10-cycle i7 index read for UMI capture, and 6-cycle i5 index read for sample barcoding. For data preprocessing, genes with a total count of less than 100 across all samples were removed. SgRNAs that accounted for less than 10% of the total reads for sgRNAs targeting the same gene were removed from the read table. sgRNA read table and median normalized reads were processed with MAGeCK [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] test function, genes with log2fc \u0026lt; -0.5 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01 at day 21 were annotated as essential genes, and gene essentiality was ranked in an ascending order based on the log2FC at day 21, as compared to day 4. Essential pathways were then enriched using Enrichr [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Pathways of two cell lines with p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were overlapped to distinguish unique enriched pathways and common enriched pathways. In the volcano plot, Genes with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and log2fc \u0026lt; -0.5 at day 21 in both cell lines were labeled as \u0026ldquo;common essential genes\u0026rdquo;, otherwise labeled as N8KO/WT essential genes if meet FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and log2fc \u0026lt; -0.5 in one corresponding group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eProteomics and data analysis\u003c/h2\u003e \u003cp\u003eFor protein extraction, four replicates of THP-1 \u003cem\u003eNEDD8\u003c/em\u003e KO and control cells were lysed in 150 \u0026micro;L of buffer containing 1% β-octyl glucopyranoside and 6 M urea. Cell disruption was carried out with a sonication probe (3 mm, 1 s pulses, 40% amplitude) for 30 s according to the standard protocol. Homogenized samples were incubated at 4\u0026deg;C for 60 min with gentle agitation and then centrifuged at 14,000 \u0026times; g for 10 min to remove debris. Pellets were compressed to recover residual liquid, and the clarified supernatants containing extracted proteins were collected. Protein concentrations were quantified using the DC Protein Assay (Bio-Rad) with bovine serum albumin (BSA) as the standard. Aliquots containing 35 \u0026micro;g of protein from each sample were used for digestion. Proteins were reduced, alkylated, and digested with trypsin on a 3 kDa centrifugal spin filter (Millipore, Ireland). The resulting peptide filtrates were dried in a SpeedVac concentrator. Peptides were then separated by reverse-phase chromatography on a C18 column using a 150 min gradient and analyzed online by electrospray ionization on a Q-Exactive Plus mass spectrometer (Thermo Finnigan). Tandem mass spectrometry was carried out using higher-energy collisional dissociation (HCD). Raw data files were processed and quantified with MaxQuant (version 1.5.1.2). Protein identification was performed against the \u003cem\u003eHomo sapiens\u003c/em\u003e proteome database (UniProt, February 2020). Search parameters included: taxonomy set to \u003cem\u003eHomo sapiens\u003c/em\u003e, enzyme specificity to trypsin, fixed modification carbamidomethylation (C), and variable modifications phosphorylation (S, T, Y), oxidation (M), and deamidation (N, Q). For data analysis, library-size normalized data was processed and visualized with R. Proteins expressed in more than 3 samples in control group and less than 1 sample in \u003cem\u003eNEDD8\u003c/em\u003e KO group were annotated as \u0026ldquo;control group unique protein\u0026rdquo;, similar criteria were applied to annotated \u0026ldquo;\u003cem\u003eNEDD8\u003c/em\u003e KO unique protein\u0026rdquo;. \u003cem\u003eP\u003c/em\u003e-values were calculated using t-test. Proteins with p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and absolute average log2 fold change\u0026thinsp;\u0026gt;\u0026thinsp;0.3 were annotated as differentially expressed proteins. Differential pathway enrichment was performed with \u003cem\u003eEnrichr\u003c/em\u003e [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], pathways with Adjusted.P.value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were kept for visualization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTEM-based analysis of cellular morphology\u003c/h2\u003e \u003cp\u003eSubcellular changes in THP1 control, \u003cem\u003eNEDD8\u003c/em\u003e KO or simvastatin-treated cells with or without LPS were analyzed using transmission electron microscopy (TEM). Cells were seeded into 6-well flat-bottom plates followed by addition of simvastatin (1 \u0026micro;M) or 0.1% DMSO and incubated for 4 days. Cells were then stimulated with LPS at indicated concentrations for 20 h. For sample preparation, 1 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e cells per condition were fixed overnight at 4\u0026deg;C in 2.5% glutaraldehyde (Ted Pella) and 1% formaldehyde in 0.1 M phosphate buffer (pH 7.4), followed by washes with the 0.1 M phosphate buffer. Post-fixation was performed with 1% osmium tetroxide (Agar Scientific) for 30 min, after which cells were dehydrated through a graded ethanol series and briefly treated with propylene oxide (Agar Scientific) from 5 min. Samples were embedded in Agar 100 resin (Agar Scientific) overnight and polymerized at 60\u0026deg;C for 48 h. Ultrathin sections (50\u0026ndash;60 nm) were cut using a UC7 ultramicrotome (Leica), mounted on formvar-coated grids (Ted Pella), and stained with 5% uranyl acetate followed by 3% lead citrate (Science Services). Sections were imaged on a Tecnai Spirit G2 BioTwin TEM (FEI/Thermo Fisher) at 80 kV, equipped with an Orius SC200 CCD camera (Gatan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of immunological profile of myeloid cells\u003c/h2\u003e \u003cp\u003eTo investigate the role of NEDD8 in the immunogenicity of myeloid cells, THP-1 control and \u003cem\u003eNEDD8\u003c/em\u003e KO cell line pair was used. Control or \u003cem\u003eNEDD8\u003c/em\u003e KO cells (2 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells per well) were seeded into 96-well round bottom plate and stimulated with LPS or Pam3CSK4 at the indicated concentrations in a final volume of 200 \u0026micro;l culture medium. The plates were incubated at 37\u0026deg;C for 20 h.\u003c/p\u003e \u003cp\u003eFor inhibitor treatment experiments, human primary CD14\u0026thinsp;+\u0026thinsp;monocytes (3 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e per well) were seeded into 96-well round bottom plates. Simvastatin (1 \u0026micro;M)-treated cells were subsequently incubated with ruxolitinib, rapamycin, or MCC950 at the indicated concentrations, or with 0.1% DMSO as a control. After 2 days of culture for CD14\u0026thinsp;+\u0026thinsp;monocytes, LPS (1 \u0026micro;g/ml) was added in a final volume of 200 \u0026micro;l culture medium. Following incubation, cells were harvested for analysis of immunological surface markers, while culture supernatants were collected for quantification of secreted pro-inflammatory cytokines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAnimal studies\u003c/h2\u003e \u003cp\u003eNa\u0026iuml;ve C57BL/6NTac female mice (6\u0026ndash;10 weeks old; Taconic) received four daily intraperitoneal (i.p.) injections of simvastatin (8.2 mg/kg, MedChem Express) in 100 \u0026micro;l vehicle (PBS containing 10% DMSO and 20% β-cyclodextrin). On day 3, LPS was administered i.p. one hour after the simvastatin treatment. Mice were carefully monitored and serum and spleens were harvested after 4 hours. To test the anti-tumor efficacy, a murine cancer cell line, C1498, was inoculated into syngeneic C57BL/6NTac female mice (6\u0026ndash;10 weeks old; Taconic). In brief, mice were injected subcutaneously (s.c.) with 4 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e C1498 monocytic cancer cells in 100 \u0026micro;l serum-free IMDM medium (Thermo Fisher Scientific). Starting on day 5, mice received daily intraperitoneal (i.p.) injections of simvastatin (8.2 mg/kg, MedChem Express) in 100 \u0026micro;l vehicle (PBS containing 10% DMSO and 20% β-cyclodextrin) until day 12. LPS was administered i.p. at the indicated dose on days 6, 9, and 12. Tumor volumes and body weights were monitored regularly until tumors reached the humane endpoint of 1500 mm\u003csup\u003e3\u003c/sup\u003e. Tumor volume was calculated using the formula: (length \u0026times; width\u003csup\u003e2\u003c/sup\u003e) / 2. Conversion of human doses from mg/kg to mg/m\u003csup\u003e2\u003c/sup\u003e and calculation of animal equivalent doses (AED) were performed using the following formulas: mg/m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;Km \u0026times; mg/kg, and AED (mg/kg) = Human dose (mg/kg) \u0026times; Km ratio, respectively [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] (\u003cb\u003eSupplemental Table\u0026nbsp;4\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFlow cytometry analysis\u003c/h2\u003e \u003cp\u003eFor in vitro assays, a multicolor flow cytometer was used to assess the expression of immune-related surface markers on control or \u003cem\u003eNEDD8\u003c/em\u003e KO cells. Briefly, cells were first transferred to a 96-well V bottom plate and centrifuged at 700 \u0026times; g for 4 min. The pellets were washed twice with PBS and then resuspended in 20 \u0026micro;l PBS containing an aqua fixable live/dead dye (1:200, Thermo Fisher Scientific) and a Fc receptor blocking antibody (1:100, Thermo Fisher Scientific) for 20 min at room temperature. Following two washes with PBS, cells were incubated with 20 \u0026micro;l of a surface marker antibody master mix for 30 min at 4\u0026deg;C. After staining, cells were washed, resuspended in 150 \u0026micro;l PBS, and subjected to flow cytometry analysis. For ex-vivo studies, bone marrow cells were isolated using WT C57BL/6NTac female mice (6\u0026ndash;10 weeks old; Taconic) according to a previous study[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Then, erythrocytes lysis was performed using red blood lysis buffer (Thermo Fisher Scientific) for 2 min on ice. Subsequently, cells were centrifuge at 350 x g for 4 min and passed through 40 \u0026micro;m cell strainers for single cell isolation. Single cells were transferred to a 6-well plate at a concentration of 3\u0026nbsp;million cells/ml. mGM-CSF (100 ng/ml) was then added to the cells followed by incubation at 37\u0026deg;C for 3 days. Bone marrow derived macrophages (BMDM) were harvested and placed into a 96-well round bottom plate at 3 x 10\u003csup\u003e5\u003c/sup\u003e cells per well followed by treatment with 0.1% DMSO or simvastatin (1 \u0026micro;M/well) for 24 or 48 hours. After that, LPS were added to the BMDM-loaded plate at 1, 0.1, 0.01, 0.001 \u0026micro;M for additional 20 hours. Finally, supernatants were collected for cytokine secretion analysis. For quantification of pro-inflammatory cytokines, culture supernatants were collected and centrifuged at 700 \u0026times; g for 4 min to remove cell debris. Cytokine levels were then measured using the human anti-virus response panel V02; Legendplex, or mouse macrophage/microglia Panel (LEGENDplex, BioLegend) following the manufacturer\u0026rsquo;s instructions. After that, the plate was washed with PBS and the cytokines-specific beads were resuspended in 150 \u0026micro;l PBS for flow cytometry analysis.\u003c/p\u003e \u003cp\u003eFor in vivo studies, spleens were harvested from mice and passed through 40 \u0026micro;m cell strainers in order to generate single cells. Red blood cells were lysed using red blood cell lysis buffer (BioLegend) for 2 min on ice, followed by washing with PBS. Single cell suspensions were seeded into 96-well V-bottom plates and incubated with 20 \u0026micro;l PBS containing Aqua Fixable Live/Dead stain (1:200, Thermo Fisher Scientific) and mouse FcR blocker (1:100, Thermo Fisher Scientific) for 30 min at 4\u0026deg;C, then washed with PBS. Surface proteins were stained in 20 \u0026micro;l of a fluorochrome-conjugated antibody mastermix for 30 min at 4\u0026deg;C. For intracellular detection of proteins such as arginase-1, cells were fixed and permeabilized using the True-Nuclear Buffer Set (BioLegend), followed by incubation with fluorochrome-conjugated antibodies (1:50) for 45 min at 4\u0026deg;C. Remaining cells were cryopreserved at \u0026minus;\u0026thinsp;150\u0026deg;C for later use.\u003c/p\u003e \u003cp\u003eTo examine serum-specific inflammatory profiles, blood was collected from mice and incubated for 1 h at room temperature to allow clot formation. Samples were centrifuged at 2000 \u0026times; g for 10 min, and supernatants were transferred into fresh tubes and centrifuged again under the same conditions. The resulting serum was collected into new tubes. Cytokine levels in serum were quantified using the mouse macrophage/microglia Panel (LEGENDplex, BioLegend) according to the manufacturer\u0026rsquo;s instructions. After washing with PBS, cytokine-specific beads were resuspended in 150 \u0026micro;l PBS for flow cytometric analysis.\u003c/p\u003e \u003cp\u003eAll samples were acquired on a CytoFLEX LX flow cytometer (Beckman Coulter), and data were analyzed using FlowJo software v10 and plotted with GraphPad Prism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of cell viability\u003c/h2\u003e \u003cp\u003eCell viability was analyzed using the PrestoBlue reagent to evaluate the effects of different inhibitors on monocytic THP-1 cancer cells. Briefly, THP-1 control or \u003cem\u003eNEDD8\u003c/em\u003e KO cells (1 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e) were seeded in 96-well flat-bottom plates. Pevonedistat, simvastatin or rapamycin were added at the indicated concentrations, or with DMSO as vehicle control, in a final volume of 200 \u0026micro;l growth medium. Cells were incubated at 37\u0026deg;C with 5% CO₂ for the indicated times. PrestoBlue (Thermofisher scientific) reagent was then added according to the manufacturer\u0026rsquo;s instructions, and fluorescence was measured at 560 nm (excitation) and 590 nm (emission) using the CLARIOstar Plus instrument (BMG Labtech). Background signal was subtracted, and cell viability was expressed as a percentage relative to DMSO-treated controls. Data were analyzed and plotted using GraphPad Prism software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting\u003c/h2\u003e \u003cp\u003eCell pellets were lysed on ice for 15 min using RIPA buffer (20 mM Tris, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1% NP-40, 1 mM NaF, 1 mM Na₃VO₄, 1 mM sodium phosphate) supplemented with a protease inhibitor cocktail (Thermo Scientific) and free of reducing agents. Lysates were then clarified by centrifugation at 17,000 \u0026times; g for 12 min at 4\u0026deg;C, and protein concentrations were determined using the Bicinchoninic Acid (BCA) assay (Thermo Scientific). Proteins were mixed with 1\u0026times; SDS loading buffer, heated at 70\u0026deg;C for 10 min, and separated on 4\u0026ndash;12% Bis-Tris precast gels (Invitrogen) by SDS-PAGE. Proteins were then transferred to nitrocellulose membranes using the iBlot Transfer System (Invitrogen) and blocked with 5% nonfat dry milk (OXOID) for 1 h at room temperature. Membranes were incubated overnight at 4\u0026deg;C with primary antibodies against target or loading control proteins, followed by 1 h incubation with HRP-conjugated secondary antibodies at room temperature. Protein bands were visualized using SuperSignal West Pico Plus or West Femto chemiluminescent substrates (Thermo Scientific) on an Amersham Imaging System (GE Healthcare). All washes were performed with TBST (1\u0026times; TBS, 0.05% Tween 20).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFlow cytometry data were processed using FlowJo V10, and all results were summarized and analyzed in GraphPad Prism 10. Statistical analyses included unpaired two tailed \u003cem\u003eT\u003c/em\u003e-tests with a significance threshold of 0.05, as detailed in the figure legends.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest statements\u003c/h2\u003e \u003cp\u003eY.M. is a recipient of research grants from the Novo Nordisk Foundation on an unrelated project. Y.M. receives consulting fees from Quotient Therapeutics and holds company shares from AstraZeneca. Other authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe appreciate the contributions of Ms. Marta Rubies Bedos, Ms. Antrea-Anna Gkatzioura and the members of the research group in the experimental procedures. This project is supported by a research grant from the Swedish Cancer Society (232656Pj). Y.M.\u0026rsquo;s research group is generously supported by grants from SciLifeLab (SLL2019/9, SciLifeLab Fellowship), the Swedish Foundation for Strategic Research (FFL-0043), the Swedish Research Council (2022\u0026thinsp;\u0026minus;\u0026thinsp;01461) and the Swedish Cancer Society (220474JIA). I.P. received additional support from the Royal Swedish Academy of Sciences (ME2024-0042). We further acknowledge the BioVis platform at Uppsala University, with special thanks to Dirk Pacholsky, Monika Hodik, and Karin Staxang for their assistance with flow cytometry and transmission electron microscopy. We also extend our gratitude to Dr. Anna Widgren and Prof. Jonas Bergquist for conducting the proteomics quantifications at the Mass Spectrometry-Based Proteomics Facility at Uppsala University. Part of this work was performed by CRISPR Functional Genomics (CFG), a SciLifeLab-funded infrastructure at Karolinska Institutet, Stockholm, Sweden. We also acknowledge support from the National Genomics Infrastructure, SNIC (project 2017-7-265), and from the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX). We sincerely thank the staff at the animal facility at Rudbecklaboratoriet, Uppsala University, for their invaluable support with the animal experiments.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDohner, H., et al., \u003cem\u003eDiagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN\u003c/em\u003e. Blood, 2022. 140(12): p. 1345\u0026ndash;1377.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiNardo, C.D., et al., \u003cem\u003eAcute myeloid leukaemia\u003c/em\u003e. Lancet, 2023. 401(10393): p. 2073\u0026ndash;2086.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKantarjian, H., et al., \u003cem\u003eCurrent status and research directions in acute myeloid leukemia\u003c/em\u003e. Blood Cancer J, 2024. 14(1): p. 163.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTettamanti, S., et al., \u003cem\u003eCatch me if you can: how AML and its niche escape immunotherapy\u003c/em\u003e. Leukemia, 2022. 36(1): p. 13\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Vlerken-Ysla, L., et al., \u003cem\u003eFunctional states of myeloid cells in cancer\u003c/em\u003e. Cancer Cell, 2023. 41(3): p. 490\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGabrilovich, D.I., S. Ostrand-Rosenberg, and V. Bronte, \u003cem\u003eCoordinated regulation of myeloid cells by tumours\u003c/em\u003e. Nat Rev Immunol, 2012. 12(4): p. 253\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFitzgerald, K.A. and J.C. Kagan, \u003cem\u003eToll-like Receptors and the Control of Immunity\u003c/em\u003e. Cell, 2020. 180(6): p. 1044\u0026ndash;1066.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRybka, J., et al., \u003cem\u003eThe expression of Toll-like receptors in patients with acute myeloid leukemia treated with induction chemotherapy\u003c/em\u003e. Leuk Res, 2015. 39(3): p. 318\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawler, M.E., et al., \u003cem\u003eTLR2 agonism suppresses myeloid leukemogenesis by reprogramming leukemia stem cells\u003c/em\u003e. Blood Adv, 2025. 9(22): p. 5888\u0026ndash;5901.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIgnatz-Hoover, J.J., et al., \u003cem\u003eThe role of TLR8 signaling in acute myeloid leukemia differentiation\u003c/em\u003e. Leukemia, 2015. 29(4): p. 918\u0026ndash;926.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNourizadeh, M., et al., \u003cem\u003eSynergistic effect of Toll-like receptor 4 and 7/8 agonists is necessary to generate potent blast-derived dendritic cells in Acute Myeloid Leukemia\u003c/em\u003e. Leuk Res, 2012. 36(9): p. 1193\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNetea, M.G., et al., \u003cem\u003eDefining trained immunity and its role in health and disease\u003c/em\u003e. Nat Rev Immunol, 2020. 20(6): p. 375\u0026ndash;388.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEnchev, R.I., B.A. Schulman, and M. Peter, \u003cem\u003eProtein neddylation: beyond cullin-RING ligases\u003c/em\u003e. Nat Rev Mol Cell Biol, 2015. 16(1): p. 30\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, S., et al., \u003cem\u003eProtein neddylation and its role in health and diseases\u003c/em\u003e. Signal Transduct Target Ther, 2024. 9(1): p. 85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapakyriacou, I., et al., \u003cem\u003eLoss of NEDD8 in cancer cells causes vulnerability to immune checkpoint blockade in triple-negative breast cancer\u003c/em\u003e. Nat Commun, 2024. 15(1): p. 3581.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoucy, T.A., et al., \u003cem\u003eAn inhibitor of NEDD8-activating enzyme as a new approach to treat cancer\u003c/em\u003e. Nature, 2009. 458(7239): p. 732\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdes, L., et al., \u003cem\u003ePevonedistat plus azacitidine vs azacitidine alone in higher-risk MDS/chronic myelomonocytic leukemia or low-blast-percentage AML\u003c/em\u003e. Blood Adv, 2022. 6(17): p. 5132\u0026ndash;5145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuzman, M.L., et al., \u003cem\u003eNuclear factor-kappaB is constitutively activated in primitive human acute myelogenous leukemia cells\u003c/em\u003e. Blood, 2001. 98(8): p. 2301\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapakyriacou, I., et al., \u003cem\u003eLoss of NEDD8 in cancer cells causes vulnerability to immune checkpoint blockade in triple-negative breast cancer\u003c/em\u003e. Nat Commun, 2024. 15(1): p. 3581.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColl, R.C., et al., \u003cem\u003eA small-molecule inhibitor of the NLRP3 inflammasome for the treatment of inflammatory diseases\u003c/em\u003e. Nat Med, 2015. 21(3): p. 248\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang, C.I., J.C. Liao, and L. Kuo, \u003cem\u003eMacrophage arginase promotes tumor cell growth and suppresses nitric oxide-mediated tumor cytotoxicity\u003c/em\u003e. Cancer Res, 2001. 61(3): p. 1100\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTateishi, K., et al., \u003cem\u003eThe NEDD8 system is essential for cell cycle progression and morphogenetic pathway in mice\u003c/em\u003e. J Cell Biol, 2001. 155(4): p. 571\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVogl, A.M., et al., \u003cem\u003eNeddylation inhibition impairs spine development, destabilizes synapses and deteriorates cognition\u003c/em\u003e. Nat Neurosci, 2015. 18(2): p. 239\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, C., et al., \u003cem\u003eHepatic neddylation deficiency triggers fatal liver injury via inducing NF-kappaB-inducing kinase in mice\u003c/em\u003e. Nat Commun, 2022. 13(1): p. 7782.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen, X., et al., \u003cem\u003eInhibition of the neddylation E2 enzyme UBE2M in macrophages protects against E. coli-induced sepsis\u003c/em\u003e. J Biol Chem, 2025. 301(1): p. 108085.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang, S.C. and J.L. Ding, \u003cem\u003eUbiquitination by SAG regulates macrophage survival/death and immune response during infection\u003c/em\u003e. Cell Death Differ, 2014. 21(9): p. 1388\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNikolic, I., et al., \u003cem\u003eEnhancing anti-tumor immunity of natural killer cells through targeting IL-15R signaling\u003c/em\u003e. Cancer Cell, 2025. 43(11): p. 2034\u0026ndash;2050 e11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, D., et al., \u003cem\u003eThe Ube2m-Rbx1 neddylation-Cullin-RING-Ligase proteins are essential for the maintenance of Regulatory T cell fitness\u003c/em\u003e. Nat Commun, 2022. 13(1): p. 3021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, C., et al., \u003cem\u003eRNF111-facilitated neddylation potentiates cGAS-mediated antiviral innate immune response\u003c/em\u003e. PLoS Pathog, 2021. 17(3): p. e1009401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMao, H. and Y. Sun, \u003cem\u003eNeddylation-Independent Activities of MLN4924\u003c/em\u003e. Adv Exp Med Biol, 2020. 1217: p. 363\u0026ndash;372.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDembitz, V., S.C. James, and P. Gallipoli, \u003cem\u003eTargeting lipid metabolism in acute myeloid leukemia: biological insights and therapeutic opportunities\u003c/em\u003e. Leukemia, 2025. 39(8): p. 1814\u0026ndash;1823.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStirewalt, D.L., et al., \u003cem\u003eMevastatin can increase toxicity in primary AMLs exposed to standard therapeutic agents, but statin efficacy is not simply associated with ras hotspot mutations or overexpression\u003c/em\u003e. Leuk Res, 2003. 27(2): p. 133\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDimitroulakos, J., et al., \u003cem\u003eIncreased sensitivity of acute myeloid leukemias to lovastatin-induced apoptosis: A potential therapeutic approach\u003c/em\u003e. Blood, 1999. 93(4): p. 1308\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin, R., L. Neufeld, and T.L. McGaha, \u003cem\u003eLinking macrophage metabolism to function in the tumor microenvironment\u003c/em\u003e. Nat Cancer, 2025. 6(2): p. 239\u0026ndash;252.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoul, C., R. Peruzzo, and R. Zoncu, \u003cem\u003eThe molecular basis of nutrient sensing and signalling by mTORC1 in metabolism regulation and disease\u003c/em\u003e. Nat Rev Mol Cell Biol, 2023. 24(12): p. 857\u0026ndash;875.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJowan Al-Nusair, M.E., Bayan Khasawneh, Mohammed Al-Nusair, Adolfo Torres, Mohanad Elchouemi, Arsalan Asad, Thomas Wright, Ahmad Mahdi, \u003cem\u003eStatin use and the risk of Acute Myeloid Leukemia: A retrospective propensity-matched cohort study\u003c/em\u003e. Blood, 2025. Volume 146.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoench, J.G., et al., \u003cem\u003eOptimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9\u003c/em\u003e. Nat Biotechnol, 2016. 34(2): p. 184\u0026ndash;191.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmierer, B., et al., \u003cem\u003eCRISPR/Cas9 screening using unique molecular identifiers\u003c/em\u003e. Mol Syst Biol, 2017. 13(10): p. 945.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, W., et al., \u003cem\u003eMAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens\u003c/em\u003e. Genome Biol, 2014. 15(12): p. 554.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuleshov, M.V., et al., \u003cem\u003eEnrichr: a comprehensive gene set enrichment analysis web server 2016 update\u003c/em\u003e. Nucleic Acids Res, 2016. 44(W1): p. W90-7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNair, A.B. and S. Jacob, \u003cem\u003eA simple practice guide for dose conversion between animals and human\u003c/em\u003e. J Basic Clin Pharm, 2016. 7(2): p. 27\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToda, G., et al., \u003cem\u003ePreparation and culture of bone marrow-derived macrophages from mice for functional analysis\u003c/em\u003e. STAR Protoc, 2021. 2(1): p. 100246.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"leukemia","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"leu","sideBox":"Learn more about [Leukemia](http://www.nature.com/leu/)","snPcode":"41375","submissionUrl":"https://mts-leu.nature.com/cgi-bin/main.plex","title":"Leukemia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Protein neddylation, Toll-like receptors, Myeloid cells, Leukemia, Tumor Immunology","lastPublishedDoi":"10.21203/rs.3.rs-9399910/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9399910/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAcute myeloid leukemia is an aggressive hematological disease, where cancer cells down-regulate antigen presentation and immune-stimulatory molecules. Here, we show that genetic deletion of protein neddylation rewires the human AML proteome towards an immunogenic phenotype, while suppressing cholesterol biosynthesis. As a result, neddylation-deficient AML cells respond more potently to TLR agonists in vitro. Using genome-wide CRISPR/Cas9 screens, we have identified mTOR as an important pathway in deficient cells. Inhibition of lipid metabolism by simvastatin in primary human monocytes amplifies response to LPS, which is governed by mTOR and JAK/STAT pathways. In mice, simvastatin fine-tunes the inflammatory response driven by LPS and polarizes myeloid cells to a stimulatory phenotype. While neither simvastatin nor LPS show efficacy against murine C1498 tumors, a combination of the agents delays tumor growth in mice. Altogether, our results uncover a previously unknown function of protein neddylation in governing the response to TLR stimulation and propose the therapeutic potential of statin drugs in immunotherapy against hematological malignancies.\u003c/p\u003e","manuscriptTitle":"Protein neddylation governs the inflammatory status of healthy and malignant myeloid cells in response to TLR stimulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 13:03:59","doi":"10.21203/rs.3.rs-9399910/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-05-04T11:49:28+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-04-20T06:44:08+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-04-13T16:46:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T15:05:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-13T15:00:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Leukemia","date":"2026-04-13T07:02:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"leukemia","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"leu","sideBox":"Learn more about [Leukemia](http://www.nature.com/leu/)","snPcode":"41375","submissionUrl":"https://mts-leu.nature.com/cgi-bin/main.plex","title":"Leukemia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3edc5f6e-9cb8-4dd3-8d59-681c424ccff7","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-05-04T11:49:28+00:00","index":1,"fulltext":"This content is not available."}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66240679,"name":"Biological sciences/Cancer/Haematological cancer/Leukaemia/Acute myeloid leukaemia"},{"id":66240680,"name":"Biological sciences/Immunology/Innate immunity/Pattern recognition receptors/Toll-like receptors"},{"id":66240681,"name":"Biological sciences/Cell biology"}],"tags":[],"updatedAt":"2026-04-21T13:03:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 13:03:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9399910","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9399910","identity":"rs-9399910","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}