Protein Modifications Involved in Cell Signaling

参与细胞信号转导的蛋白质修饰

• 批准号: 10927838
• 负责人: Aleksandra Nita-Lazar
• 金额: $ 99.92万
• 依托单位: NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10927838
• 关键词: ADP ribosylation; Adipose tissue; Antibiotic Therapy; Area; Autoimmune Diseases; Biological; Biological Assay; Biopsy; CD14 gene; CRISPR/Cas technology; Cell Line; Cell physiology; Cells; Clarithromycin; Clustered Regularly Interspaced Short Palindromic Repeats; Collaborations; Complex; Computational Biology; Computer Models; Data; Data Correlations; Data Set; Diet; Disease; Disease Progression; Distal part of ileum; Elements; Endotoxins; Enzyme-Linked Immunosorbent Assay; Eosinophilic Esophagitis; Esophagus; Event; Exposure to; Family; Flow Cytometry; Gastrointestinal tract structure; Gene Expression; Genes; Genus Mycobacterium; Germ-Free; Goals; Homologous Gene; Host Defense; Human; Hydrolase; Immune; Immune signaling; Immune system; Individual; Infection; Inflammasome; Inflammatory; Inflammatory Response; Innate Immune Response; Integral Membrane Protein; Interleukin-1 Receptors; Intervention; Kinetics; Knock-out; Legionella pneumophila; Legionnaires' Disease; Ligands; Literature; Lupus; Lymphocyte Biology; MME gene; Macrophage; Maryland; Mass Spectrum Analysis; Measures; Mediating; Meta-Analysis; Metabolic; Metabolic Diseases; Metabolic Pathway; Methods; Modeling; Modification; Molecular; Molecular Profiling; Mus; Mycobacterium abscessus; Myelogenous; National Institute of Allergy and Infectious Disease; National Institute of Child Health and Human Development; Nuclear; Nucleic Acids; Oregon; Pathogenesis; Pathway interactions; Patients; Pattern; Phosphoproteins; Phosphorylation; Phosphotransferases; Post-Translational Protein Processing; Process; Production; Protein Family; Protein Secretion; Proteins; Proteolysis; Proteome; Proteomics; Protocols documentation; RNA; RNA Interference; Receptor Signaling; Regulation; Resolution; Role; Sampling; Sepsis; Serine; Signal Pathway; Signal Transduction; Site; Site-Directed Mutagenesis; Small Interfering RNA; Staphylococcus aureus; Structure; System; TLR2 gene; TLR4 gene; TLR7 gene; TNF Receptor-Associated Factors; Thailand; Threonine; Time; Tissues; Toll-Like Receptor Pathway; Toll-like receptors; Transcript; Tyrosine; Ubiquitination; United States National Institutes of Health; Universities; Western Blotting; adaptive immune response; analog; cell type; chronic inflammatory disease; cytokine; design; dietary; dosage; exhaustion; experimental study; gut bacteria; host-microbe interactions; ileum; insight; interest; knock-down; microbiota; molecular recognition; mutant; pathogen; pharmacologic; phosphoproteomics; protein complex; protein expression; receptor; response; sensor; therapy development; transcriptome

1. Quantification of the modification dynamics in TLR signaling pathways. The TLRs are a family of pathogen recognition receptors that alert the host to the presence of pathogens by recognizing molecular signatures, termed pathogen-associated molecular patterns (PAMPs). These sensors act as the first step in the induction of protective innate and adaptive immune responses. There are 11 human TLR homologues and they are each activated by one or more PAMP ligands. TLRs are all transmembrane proteins and their signaling is mediated by association of their internal domains with intracellular components. Classically, the TLR signaling cascade involves the myeloid differentiation primary response gene 88 (MyD88), interleukin-1 receptor-activated kinase (IRAK), and tumor-necrosis factor receptor-associated factor 6 (TRAF6), leading to the activation of Nuclear Factor kappaB (NF-kB). Among the most important genes to be regulated by TLR signaling are those encoding cytokines. Given the key role of cytokines in the orchestration of the inflammatory response, mechanisms of modulating their production garnered substantial interest, in particular in the area of the development of therapies for the treatment of chronic inflammatory diseases. A clearer understanding of the TLR pathway leading to the cytokine production is required for a successful pharmacological intervention. A) We investigated differences in the phosphoprotein signaling cascades triggered by TLR2, TLR4, and TLR7 ligands in a murine macrophage cell line. We performed a global, quantitative, early post-stimulation kinetic analysis of the global mouse macrophage phosphoproteome using high-resolution mass spectrometry (1). We are studying the changes in phosphorylation-dependent signaling in cells where the TLR signaling pathway components (MyD88, TRIF, IRAK family proteins, CD14) are knocked down or knocked out. We currently use the phosphorylation datasets as well as datasets quantifying other PTMs (ADP-ribosylation, ubiquitination, nucleic acid complexing) as constraints for a computational model of the TLR signaling network (project AI001085-07). The candidate proteins whose phosphorylation and ADP-ribosylation (2, 3) changed significantly during the investigated time course are being further examined in biological experiments. We characterized the changes in phosphorylation of specific sites of MARCKS upon LPS stimulation and we are now exploring the biological significance of these sites. We have performed site-directed mutagenesis of the individual sites and the mutant MARCKS has been expressed in the cells where we knocked out the wild-type protein using CRISPR technology and characterized by mass spectrometry. B). We have conducted parallel studies of the proteome and secretome changes using the same cells and ligands as for the phosphoproteome analysis but collecting data after longer periods of time to allow for changes in protein expression and secretion (4). We have validated the data using ELISA-based assays of cytokine production and targeted proteomics. We have performed data correlation with the transcriptome (in collaboration with Dr. Fraser). We identified differences in signaling between individual TLRs and revealed specifics of pathway regulation at the protein level (4, 5). The data will provide more stringent constraints for the TLR signaling model. 2. We are studying the dynamics of the MyD88-associated protein complex (myddosome) following the stimulation of mouse macrophages with pathogen-derived molecules. Our results indicate that MyD88 exists in macrophages in a complex with inhibitory molecules which are released after LPS stimulation, allowing the proteins activating the inflammatory response to interact with MyD88 and initiate the inflammatory signaling cascade. We showed that the dynamics of the myddosome is proteolysis-dependent. We performed quantitative studies of changes in the myddosome and phosphorylation of the myddosome components in cells stimulated with different PAMPS and we have detected compositional and temporal differences in the signaling networks. We are currently exploring the interactomes of macrophages exposed to different pathogens (6, 7). 3. We performed a quantitative analysis of the proteome of the cells from the terminal ileum (chosen as a site of intense host-microbe interactions) of germ-free and normal mice (collaboration with Drs. Shulzhenko and Morgun). The data showed changes in the immune processes-related protein expression and in specific metabolic pathways. The correlation of the proteome and transcriptome data revealed several differentially regulated pathways and significant transcriptome-proteome discordance in the adaptation of the host to the microbiota. This discovery leads to a conclusion that transcript level analysis is not sufficient to predict protein levels and their influence on the function of many specific cellular pathways, so only the combination of the quantitative data at different levels will lead to the complete understanding of the complex relationships between the host and the microbiota (8). Our further studies of the differences between the germ-free and normal mice dependent on the dietary changes led to the discovery of the role of Mmp12+ macrophages in white adipose tissue (9). 4. In collaboration with Dr. Ernst (University of Maryland) we are examining the effects of different LPS structures on bacterial pathogenesis, focusing on macrophage signaling and the proteome, phosphoproteome and cytokine secretion changes. We have identified LPS-structure-dependent differences in the TLR signaling and inflammasome signaling (10, 11, 12, 13) and now study the effects of synthetic LPS-mimics on the TLR-signaling network components. 5. In collaboration with Dr. Leelahavanichkul from the Chulalongkorn University in Thailand we have been performing proteome, secretome and phosphoproteome studies of the endotoxin-induced exhaustion and sepsis (14, 15, 16, 17, 18). We are following up with studies of LPS-tolerance in the context of acyloxacyl hydrolase (AOAH) deficiency (collaboration with Dr. Robert Munford, NIAID). 6. Our new collaborative efforts focus on infection and disease. With Dr. Zelazny (NIH CC) we study the effects of clarithromycin on Mycobacterium abscessus. With Dr. Machner (NICHD) we uncovered the role of N-Ras in the Legionella pneumophila infection and its effect on the macrophage phosphoproteome. With Dr. Rochman (Cincinnati Childrens) we have uncovered the role or the minichromosome complex in eosinophilic esophagitis in the proteomic screen of esophageal biopsies (19). References: 1. Sjoelund V, Smelkinson M, and Nita-Lazar A. (2014) J Proteome Res. 2014 Nov 7;13(11):5185-97. 2. Daniels CM, et al. (2020) J Proteome Res. 19 (9). 3. Daniels CM, Nuccio A, Kaplan PR, Nita-Lazar A (2020). Methods Mol Biol 2184, 145-160. 4. Koppenol-Raab M, et al. (2017) Mol Cell Proteomics 16(4 suppl 1):S172-S186. 5. Koppenol-Raab M., and Nita-Lazar A. (2017) Methods Mol Biol. 1636:301-312 6. Gillen J, Nita-Lazar A (2019). Front Physiol 10, 425. 7. Gillen J, et al. (2020). Expert Rev Proteomics 17, 341-354. 8. Manes, N.P., et al. (2017). mSystems. 2(5). pii: e00107-17. 9. Li, Z. et al (2022) J. Exp. Med. 219 (7). 10. Khan MM, et al. (2019). Pathog Dis 77. 11. Khan MM, et al. (2019). ACS Infect Dis 5, 493-505. 12. Khan MM. et al. (2018). J Mol Biol 430, 2641-2660. 13. Ernst O. et al. (2021). mSystems 6 (4). 14. Ondee T, et al. (2019). Int J Mol Sci 20. 15. Ondee T. et al. (2019) Cells. Sep 11;8(9):1064. 16. Gillen J, Ondee T, et al (2021). Biomolecules 11. 17. Phuengmaung et al. (2023) Int J Mol Sci 24 (10). 18. Saisorn et al. (2023) Int J Mol Sci 24 (12). 19. Rochman et al. (2023) JCI Insight e172143.

1。定量TLR信号通路中的修饰动力学。 TLR是一个病原体识别受体的家族，通过识别分子特征，称为病原体相关的分子模式（PAMPS）来提醒宿主存在病原体的存在。这些传感器是保护性先天和适应性免疫反应的第一步。有11种人类TLR同源物，每个人都被一个或多个弹药配体激活。 TLR都是跨膜蛋白，其信号传导是由其内部结构域与细胞内成分的关联介导的。从经典上讲，TLR信号传导级联反应涉及髓样分化的一级反应基因88（MYD88），白介素-1受体激活激活激酶（IRAK）和肿瘤 - 不良因子受体相关因子6（TRAF6），导致核因子Kappab（NF-KBB）的激活。在由TLR信号调节的最重要的基因中，是那些编码细胞因子的基因。鉴于细胞因子在炎症反应的编排中的关键作用，调节其生产的机制引起了极大的兴趣，尤其是在慢性炎症性疾病治疗的疗法开发领域。成功的药理学干预需要更清楚地了解导致细胞因子产生的TLR途径。 a）我们研究了由鼠巨噬细胞系中TLR2，TLR4和TLR7配体触发的磷酸蛋白信号传导级联反应的差异。我们使用高分辨率质谱法对全球小鼠巨噬细胞磷蛋白组进行了全局，定量的早期刺激动力学分析（1）。我们正在研究TLR信号通路成分（MyD88，Trif，Irak家族蛋白，CD14）的细胞中磷酸化依赖性信号传导的变化。我们目前使用磷酸化数据集以及量化其他PTM（ADP-核糖化，泛素化，核酸络合）的数据集作为TLR信号网络的计算模型（Project AI001085-07）的约束。在研究时间过程中，在生物学实验中进一步研究了磷酸化和ADP-核糖基化（2，3）的候选蛋白（2，3）。我们表征了LPS刺激时MARCKS特定位点磷酸化的变化，现在我们正在探索这些位点的生物学意义。我们已经对各个位点进行了定向的诱变，突变的马克克人已经在细胞中表达，在该细胞中，我们使用CRISPR技术敲除野生型蛋白质，并以质谱为特征。 b）。我们已经使用相同的细胞和配体进行了与磷蛋白组分析相同的细胞和配体进行蛋白质组和分泌变化的平行研究，但是在较长时间后收集数据以允许蛋白质表达和分泌的变化（4）。我们已经使用基于ELISA的细胞因子产生和靶向蛋白质组学的测定法验证了数据。我们已经与转录组（与Fraser博士合作）进行了数据相关性。我们确定了单个TLR之间的信号传导差异，并在蛋白质水平上揭示了途径调节的细节（4，5）。数据将为TLR信号模型提供更严格的约束。 2。我们正在研究用病原体衍生的分子刺激小鼠巨噬细胞后与MyD88相关蛋白复合物（myddosom）的动力学。我们的结果表明，MyD88存在于巨噬细胞中，具有抑制性分子的复合物，这些分子在LPS刺激后释放，从而使蛋白质激活炎症反应以与MyD88相互作用并启动炎症信号级联。我们证明了myddosome的动力学是依赖蛋白水解的。我们对用不同PAMP刺激的细胞中myddosome成分的myddosome组的变化和磷酸化进行了定量研究，我们检测到信号网络中的组成和时间差异。我们目前正在探索暴露于不同病原体的巨噬细胞的相互作用（6，7）。 3。我们对无菌和正常小鼠（与Shulzhenko和Morgun博士的协作）（选择为强烈的宿主 - 微生物相互作用的位置）对细胞的蛋白质组进行了定量分析。数据显示免疫过程相关蛋白表达和特定代谢途径的变化。蛋白质组和转录组数据的相关性揭示了几种差异调节的途径以及宿主适应微生物群的明显转录组 - 蛋白质不一致。这一发现得出的结论是，转录水平分析不足以预测蛋白质水平及其对许多特定细胞途径功能的影响，因此，只有在不同水平上定量数据的组合才能使宿主与微生物群之间的复杂关系完全理解（8）。我们对无菌和正常小鼠之间差异的进一步研究取决于饮食变化，从而发现了MMP12+巨噬细胞在白色脂肪组织中的作用（9）。 4。与恩斯特博士（马里兰大学）合作，我们正在研究不同LPS结构对细菌发病机理的影响，重点是巨噬细胞信号传导以及蛋白质组，磷酸蛋白质组和细胞因子分泌变化。我们已经确定了LPS结构依赖性的TLR信号传导和炎性体信号传导的差异（10、11、12、13），现在研究了合成LPS仿真对TLR signaling网络组件的影响。 5。与泰国Chulalongkorn大学的Leelahavanichkul博士合作，我们一直在对内毒素诱导的疲惫和败血症进行蛋白质组，秘密和磷蛋白组研究（14、15、16、17、18）。我们正在跟进在acyloxacyl Hydrolase（AOAH）缺乏症（与Niaid的Robert Munford博士合作）的背景下对LPS耐受性的研究。 6。我们的新合作努力集中在感染和疾病上。使用Zelazny博士（NIH CC），我们研究了克拉霉素对亚麻分枝杆菌的影响。使用Machner博士（NICHD），我们发现了N-RAS在肺炎军团菌感染中的作用及其对巨噬细胞磷酸蛋白酶的影响。在罗奇曼（Rochman）博士（辛辛那提儿童）中，我们发现了食管活检的蛋白质组学筛查中的嗜酸性食管炎中的作用或微小浓度小体复合物（19）。 参考： 1。SjoelundV，Smelkinson M和Nita-Lazar A.（2014）J Proteome Res。 2014年11月7日； 13（11）：5185-97。 2。DanielsCM等。 （2020）J蛋白质组res。 19（9）。 3。DanielsCM，Nuccio A，Kaplan PR，Nita-Lazar A（2020）。方法摩尔生物2184，145-160。 4。Koppenol-Raab M等。 （2017）mol细胞蛋白质组学16（4供应1）：S172-S186。 5。Koppenol-Raab M.和Nita-Lazar A.（2017）方法mol Biol。 1636：301-312 6。GillenJ，Nita-Lazar A（2019）。前物理学10，425。 7。GillenJ等。 （2020）。专家Rev Proteomics 17，341-354。 8. Manes，N.P。等。 （2017）。 MSYSTEMS。 2（5）。 PII：E00107-17。 9。Li，Z。等（2022）J。Exp。医学219（7）。 10。KhanMM等。 （2019）。病原体77。 11。KhanMM等。 （2019）。 ACS感染DIS 5，493-505。 12。KhanMM。等。 （2018）。 J Mol Biol 430，2641-2660。 13。ErnstO.等。 （2021）。 MSYSTEMS 6（4）。 14。OndeeT等。 （2019）。 Int J Mol Sci 20。 15。OndeeT.等。 （2019）细胞。 9月11日; 8（9）：1064。 16。GillenJ，Ondee T等（2021）。生物分子11。 17。Phuengmaung等。 （2023）Int J Mol Sci 24（10）。 18。Saisorn等。 （2023）Int J Mol Sci 24（12）。 19。Rochman等。 （2023）JCI Insight E172143。

项目成果

Quantitative analysis of phosphorylation-based protein signaling networks in the immune system by mass spectrometry.

• DOI: 10.1002/wsbm.123
• 发表时间: 2011-05
• 期刊: Wiley interdisciplinary reviews. Systems biology and medicine
• 作者: Nita-Lazar A

Mass Spectrometry-Based Methodology for Identification of Native Histone Variant Modifications From Mammalian Tissues and Solid Tumors.

基于质谱法鉴定哺乳动物组织和实体瘤天然组蛋白变体修饰的方法。

• DOI: 10.1016/bs.mie.2016.09.035
• 发表时间: 2017
• 期刊: Methods in enzymology
• 作者: Nuccio,AG;Bui,M;Dalal,Y;Nita-Lazar,A
• 通讯作者: Nita-Lazar,A

LPS Tolerance Inhibits Cellular Respiration and Induces Global Changes in the Macrophage Secretome.

• DOI: 10.3390/biom11020164
• 发表时间: 2021-01-27
• 期刊: Biomolecules
• 影响因子: 5.5
• 作者: Gillen J;Ondee T;Gurusamy D;Issara-Amphorn J;Manes NP;Yoon SH;Leelahavanichkul A;Nita-Lazar A
• 通讯作者: Nita-Lazar A

Cell-cycle-dependent structural transitions in the human CENP-A nucleosome in vivo.

• DOI: 10.1016/j.cell.2012.05.035
• 发表时间: 2012-07-20
• 期刊: Cell
• 影响因子: 64.5
• 作者: Bui M;Dimitriadis EK;Hoischen C;An E;Quénet D;Giebe S;Nita-Lazar A;Diekmann S;Dalal Y
• 通讯作者: Dalal Y

Less Severe Sepsis in Cecal Ligation and Puncture Models with and without Lipopolysaccharide in Mice with Conditional Ezh2-Deleted Macrophages (LysM-Cre System).

• DOI: 10.3390/ijms24108517
• 发表时间: 2023-05-10
• 期刊: INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
• 影响因子: 5.6
• 作者: Phuengmaung, Pornpimol;Khiewkamrop, Phuriwat;Makjaroen, Jiradej;Issara-Amphorn, Jiraphorn;Boonmee, Atsadang;Benjaskulluecha, Salisa;Ritprajak, Patcharee;Nita-Lazar, Aleksandra;Palaga, Tanapat;Hirankarn, Nattiya;Leelahavanichkul, Asada
• 通讯作者: Leelahavanichkul, Asada