Protein Modifications Involved in Cell Signaling

参与细胞信号转导的蛋白质修饰

• 批准号: 10014163
• 负责人: Aleksandra Nita-Lazar
• 金额: $ 71.1万
• 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/10014163
• 关键词: ADP ribosylation; Amino Acids; Area; Autoimmune Diseases; Basal Cell; Bioinformatics; Biological; Biological Assay; Biology; CD14 gene; CRISPR/Cas technology; Cell Line; Cell physiology; Cells; Centromere; Chronic; Clustered Regularly Interspaced Short Palindromic Repeats; Collaborations; Complex; Computational Biology; Computer Simulation; Coupled; Data; Data Correlations; Data Set; Databases; Diet; Disease; Distal part of ileum; Elements; Endotoxins; Enzyme-Linked Immunosorbent Assay; Event; Exposure to; Family; Flow Cytometry; Gastrointestinal tract structure; Gene Expression; Genes; Genomics; Germ-Free; Goals; Histones; Homologous Gene; Host Defense; Human; Immune; Immune signaling; Immune system; Individual; Inflammasome; Inflammatory; Inflammatory Response; Integral Membrane Protein; Interleukin-1 Receptors; Intervention; Kinetics; Knock-out; Label; Lead; Ligands; Literature; Lupus; Lymphocyte Biology; Maryland; Mass Spectrum Analysis; Measures; Mediating; Meta-Analysis; Metabolic; Metabolic Pathway; Methods; Modeling; Modification; Molecular; Molecular Profiling; Mus; Myelogenous; Natural Immunity; Nuclear; Oregon; Pathogenesis; Pathway interactions; Patients; Pattern; Pharmacology; Phosphopeptides; Phosphoproteins; Phosphorylation; Phosphotransferases; Population; Post-Translational Protein Processing; Primary Neoplasm; Process; Production; Protein Family; Protein Secretion; Proteins; Proteolysis; Proteome; Proteomics; RNA Interference; Receptor Signaling; Regulation; Resolution; Serine; Signal Pathway; Signal Transduction; Site; Site-Directed Mutagenesis; Small Interfering RNA; Stable Isotope Labeling; Stimulus; Structure; System; TLR2 gene; TLR4 gene; TLR7 gene; TNF Receptor-Associated Factors; Thailand; The Sun; Threonine; Time; Tissues; Toll-Like Receptor Pathway; Toll-like receptors; Transcript; Tyrosine; Tyrosine Phosphorylation; Ubiquitination; Universities; Variant; Western Blotting; adaptive immune response; base; bioinformatics tool; centromere protein A; cytokine; design; exhaustion; experimental study; extracellular; host microbiota; host-microbe interactions; ileum; interest; knock-down; macrophage; microbiota; monocyte; mutant; neoplastic cell; pathogen; 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 has 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 using as a responding population a well-characterized murine macrophage cell line. We performed a global, quantitative, early post-stimulation kinetic analysis of the global mouse macrophage phosphoproteome using stable isotope labeling with amino acids coupled to phosphopeptide enrichment and high-resolution mass spectrometry. These results advanced our understanding of how macrophages sense and respond to a diverse set of TLR stimuli (1). We are characterizing the changes in tyrosine phosphorylation in the same conditions and obtained quantitative mass spectrometry data showing changes in tyrosine phosphorylation following LPS stimulation. We are studying the changes in phosphorylation-dependent signaling in cells where the TLR signaling pathway components (MyD88, 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) as additional constraints for a computational model of the TLR signaling network (project AI001085-07). The candidate proteins whose phosphorylation changed significantly during the investigated time course are being further examined in biological experiments. We have 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. We are applying dimethyl labeling to studies of the PTMs in the TLR pathway in primary murine macrophages and human elutriated monocytes. 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 (2). 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 Iain Fraser). We identified differences in signaling between individual TLRs and revealed specifics of pathway regulation at the protein level (2,3 ). The data will provide more stringent constraints for the TLR signaling model. C). We are studying the dynamics of the MyD88-associated protein complex (myddosome) following the stimulation of mouse macrophages with pathogen-derived molecules. Our initial 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 have shown that the dynamics of the myddosome is proteolysis-dependent. We have performed quantitative studies of changes in the myddosome in cells stimulated with different PAMPS and we are analyzing the results. We are exploring the interactomes of macrophages exposed to different pathogens (4). 3. We have 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. Natalia Shulzhenko and Andrey Morgun). The data showed large changes in the immune processes-related protein expression and in certain 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 definite 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 (6). We continue the studies of the differences between the germ-free and normal mice dependent on the dietary changes. 4. In collaboration with Drs. Davis Goodlett and Robert Ernst from the University of Maryland we are examining the effects of different LPS structures on bacterial pathogenesis, focusing on the signaling in macrophages, looking at the proteome, phosphoproteome and cytokine secretion changes in the macrophages. We have identified changes in the TLR signaling and inflammasome signaling (7, 8, 9). 5. In collaboration with Dr. Asada Leelahavanichkul from the Chulangkorn University in Thailand we are performing proteome, secretome and phosphoproteome studies of the endotoxin-induced exhaustion on the FcGIIb deficient macrophages (10). References: 1. Sjoelund V, Smelkinson M, and Nita-Lazar A. (2014) J Proteome Res. 2014 Nov 7;13(11):5185-97. 2. Koppenol-Raab M, Sjoelund V, Manes NP, Gottschalk RA, Dutta B, Benet ZL, Fraser ID, Nita-Lazar A. (2017) Mol Cell Proteomics 16(4 suppl 1):S172-S186. 3. Koppenol-Raab M., and Nita-Lazar A. (2017) Methods Mol Biol. 1636:301-312 4. Gillen J, Nita-Lazar A (2019). Front Physiol 10, 425. https://doi.org/10.3389/fphys.2019.00425 5. Khan, M. M., Koppenol-Raab, M., Kuriakose, M., Manes, N. P., Goodlett, D. R., and Nita-Lazar, A. (2018) J. Proteomics pii: S1874-3919(18)30111-8. 6. Manes, N.P., Shulzhenko, N., Nuccio, A.G., Azeem, S., Morgun, A,. and Nita-Lazar, A. (2017). mSystems. 2(5). pii: e00107-17. 7. Khan MM, Chattagul S, Tran BQ, Freiberg JA, Nita-Lazar A, Shirtliff ME, Sermswan RW, Ernst RK, Goodlett DR (2019). Pathog Dis 77. https://doi.org/10.1093/femspd/ftz005 8. Khan MM, Ernst O, Manes NP, Oyler BL, Fraser IDC, Goodlett DR, Nita-Lazar A (2019).. ACS Infect Dis 5, 493-505. https://doi.org/10.1021/acsinfecdis.9b00080 9.Khan MM, Ernst O, Sun J, Fraser IDC, Ernst RK, Goodlett DR, Nita-Lazar A (2018). J Mol Biol 430, 2641-2660. https://doi.org/10.1016/j.jmb.2018.06.032 10. Ondee T, Jaroonwitchawan T, Pisitkun T, Gillen J, Nita-Lazar A, Leelahavanichkul A, Somparn P (2019). Int J Mol Sci 20. https://doi.org/10.3390/ijms20061354

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信号传导级联涉及髓样分化的主要反应基因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），导致核因子激活核因子Kappab（NF-KB）。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，尤其是在治疗慢性炎症性疾病治疗的疗法方面。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TLR2，TLR4和TLR7配体触发的磷酸蛋白信号传导级联反应的差异，它是响应群体的特征良好的鼠巨噬细胞系。我们使用稳定的同位素标记与与磷酸肽富集和高分辨率质谱法相关的氨基酸标记，对全球小鼠巨噬细胞磷酸蛋白酶进行了全局，定量的早期刺激动力学分析。这些结果提出了我们对巨噬细胞如何感知和对各种TLR刺激的反应的理解（1）。我们正在表征在相同条件下酪氨酸磷酸化的变化，并获得了定量质谱数据，显示了LPS刺激后酪氨酸磷酸化的变化。我们正在研究细胞中磷酸化依赖性信号传导的变化，其中TLR信号传导途径成分（MYD88，IRAK家族蛋白，CD14）被击倒或撞倒。目前，我们使用磷酸化数据集以及量化其他PTM（ADP-核糖基化，泛素化）的数据集作为TLR信号网络的计算模型（Project AI001085-07）的其他约束。在研究时间过程中，在生物学实验中进一步检查了磷酸化的候选蛋白质发生了显着变化。我们已经表征了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。我们将二甲基标记应用于原代鼠巨噬细胞和人类洗脱单核细胞中TLR途径中的PTM的研究。 b）。我们使用与磷蛋白组分析相同的细胞和配体进行了对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（2）。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Iain Fraser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（2,3）。数据将为TLR信号模型提供更严格的约束。 c）。在用病原体衍生的分子刺激小鼠巨噬细胞后，我们正在研究与MyD88相关蛋白复合物（myddosome）的动力学。我们的最初结果表明，Myd88存在于巨噬细胞中，具有抑制性分子的巨噬细胞，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。我们已经表明，myddosome的动力学取决于蛋白水解。我们已经对用不同的PAMP刺激的细胞中的myddosom体的变化进行了定量研究，我们正在分析结果。我们正在探索暴露于不同病原体的巨噬细胞的相互作用（4）。 3。我们已经对无菌和正常小鼠的末端回肠的细胞（作为强烈宿主 - 微生物相互作用的位置）进行了定量分析（与Natalia shulzhenko和Andrey Morgun合作）。数据显示免疫过程相关蛋白表达和某些代谢途径的变化很大。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对微生物群的明显转录组蛋白质不一致。这一发现得出了一个明确的结论，即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对复合物的完全理解宿主与微生物群之间的关系（6）。我们继续研究取决于饮食变化的无菌和正常小鼠之间的差异。 4。与Drs合作。马里兰州大学的戴维斯·古德利特（Davis Goodlett）和罗伯特·恩斯特（Robert Ernst）我们正在研究不同LPS结构对细菌发病机理的影响，重点是巨噬细胞的信号传导，研究巨噬细胞中蛋白质组，磷蛋白组和细胞因子分泌变化。我们已经确定了TLR信号传导和炎性体信号传导的变化（7、8、9）。 5。与泰国Chulangkorn大学的Asada Leelahavanichkul博士合作，我们正在对内毒素诱导的FCGIIB缺乏的巨噬细胞进行蛋白质组，分泌组和磷酸蛋白酶研究（10）。 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。Koppenol-Raab M，Sjoelund V，Manes NP，Gottschalk RA，Dutta B，Benet ZL，Fraser ID，Nita-Lazar A.（2017）Mol Cell蛋白质组学16（4 Suppl）16（4 Suppl 1）：S172-S186。 3。Koppenol-Raab M.和Nita-Lazar A.（2017）方法mol Biol。 1636：301-312 4。GillenJ，Nita-Lazar A（2019）。 Front Physiol 10，425。https：//doi.org/10.3389/fphys.2019.00425 5。Khan，M.M.，Koppenol-Raab，M.，Kuriakose，M.，Manes，N.P.，Goodlett，D.R。和Nita-Lazar，A。 。 6. Manes，N.P。，Shulzhenko，N.，Nuccio，A.G.，Azeem，S.，Morgun，A，。和Nita-Lazar，A。（2017）。 MSYSTEMS。 2（5）。 PII：E00107-17。 7. Khan MM，Chattagul S，Tran BQ，Freiberg JA，Nita-Lazar A，Shirtliff ME，Sermswan RW，Ernst RK，Goodlett DR（2019）。病原DIS77。https：//doi.org/10.1093/femspd/ftz005 8。KhanMM，Ernst O，Manes NP，Oyler BL，Fraser IDC，Goodlett DR，Nita-Lazar A（2019）.. ACS INFECT DIS 5，493-505。 https://doi.org/10.1021/acsinfecdis.9b00080 9.Khan MM，Ernst O，Sun J，Fraser IDC，Ernst RK，Goodlett DR，Nita-Lazar A（2018）。 J Mol Biol 430，2641-2660。 https://doi.org/10.1016/j.jmb.2018.06.032 10。 Int J Mol Sci20。https://doi.org/10.3390/ijms20061354