Absolute Quantification of Molecular Representation and Interaction

分子表示和相互作用的绝对定量

• 批准号: 10692131
• 负责人: Aleksandra Nita-Lazar
• 金额: $ 80.12万
• 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/10692131
• 关键词: 3-Dimensional; Affinity; Behavior; Binding; Biological; Biological Assay; Biological Models; Cell Line; Cells; Chemotaxis; Collaborations; Complex; Computational Biology; Computer Models; Coupled; Data; Data Set; Exposure to; Freedom; G Protein-Coupled Receptor Signaling; Geometry; Immune signaling; Immune system; In Vitro; Inflammasome; Label; Length; Ligands; Liquid Chromatography; Literature; Mammalian Cell; Mass Spectrum Analysis; Measurement; Measures; Mediating; Methodology; Methods; Modeling; Modification; Molecular; Monitor; Mouse Protein; Mus; Mutation; Osteoclasts; Pathway interactions; Peptides; Phosphorylated Peptide; Proteins; Proteome; Proteomics; PubMed; RNA; Reaction; Sampling; Set protein; Shotguns; Signal Pathway; Signal Transduction; Signaling Molecule; Signaling Protein; Site; Sphingosine-1-Phosphate Receptor; Stimulus; Study models; System; TLR4 gene; Testing; Time; Transcript; base; bone; chemokine; deamidation; design; differential expression; in silico; insight; macrophage; mass spectrometer; mathematical model; model building; monocyte; nano; network models; oxidation; pathogen; predictive modeling; protein expression; protein structure; receptor; response; simulation; sphingosine 1-phosphate; success; synthetic peptide; text searching; transcriptome sequencing; transcriptomics

Explanation We have adapted the methods for absolute quantification based on the targeted proteomics combined with the use of peptide standards and used the data for robust predictive modeling of the signaling pathways in the immune system. We used sphingosine-1-phosphate-mediated macriphage chemotaxis as an initial model system. S1P regulates the chemoattraction and chemorepulsion of osteoclast precursors to and from bones. The murine macrophage RAW 264.7 cells, used here as a model, express two receptors for S1P: S1PR1 and S1PR2. These receptors have markedly different affinity to S1P and cause the opposite effects upon exposure to low/high concentrations of S1P. To develop a deeper understanding of mammalian cell chemotaxis, we used transcriptomics, shotgun proteomics, targeted proteomics, and pathway simulation to investigate S1P-mediated chemotaxis of osteoclast precursors. Transcriptomics using RNA-seq enabled the identification and quantitation of RNA transcripts and shotgun proteomics enabled the identification of proteotypic peptides selected based on peptide proteotypic qualities, sequence uniqueness, and vulnerability to modification (e.g., oxidation and deamidation), eliminating many theoretically possible peptides, which could be non-compatible with mass spectrometric analysis. We used the quantitative data obtained from osteoclast precursors by shotgun proteomics to find the peptides amenable to analysis in our mass spectrometers. SPOT synthesis was used to prepare a set of 409 standard, synthetic peptides, which we used to assess the protein expressions in macrophages. Single Reaction Monitoring (SRM) of RAW264.7 cell lysates spiked with the standard peptides resulted in the confident identification and semi-quantitation of 208 of the 409 peptide targets from proteins in the chemokine signaling network. The SRM analysis of a smaller set of 65 heavy-labeled, quantitated internal peptide standards from proteins differentially expressed under different experimental conditions provided absolute numbers of molecules. These data were then used to design targeted proteomics assays of the proteins of the mouse chemotaxis pathway. Targeted proteomics assays using nano-flow liquid chromatography coupled to selected reaction monitoring mass spectrometry (LC-SRM) were performed to produce absolute abundance values (in units of copies/cell) for each of the target proteins within RAW 264.7 cells. RAW cells were again used as model osteoclast precursors because they have very similar S1P-directed chemotaxis behavior. Rules-based pathway modeling enabled the simulation of the mouse chemotaxis pathway based on bi-molecular interactions within the geometry of a three-dimensional in silico RAW cell. Measured protein abundance values, used as simulation input parameters, led to in silico pathway behavior matching in vitro measurements. Moreover, once model parameters were established, even simulated responses towards stimuli that were not used for parameterization were consistent with experimental findings. These findings demonstrated the feasibility and value of combining targeted mass spectrometry with pathway modeling for advancing biological insight and defined our experimental approach to modeling other immune system signaling pathways (1, 2, 3). In the TLR signaling network modeling study, we utilize targeted proteomics with transcriptomics to aid in contructing a computational model of the LPS-TLR4 signaling pathway in a mouse monocyte-macrophage cell line RAW264.7. A set of protein targets was identified from a review of current literature and KEGG pathways describing LPS-TLR4 signaling. Corresponding peptides were selected after scoring based on several criteria including length, shotgun proteomics identification, and potential PTM sites as determined by literature mining by motif prediction (Pubmed). Peptides were analyzed in both shotgun-mode and SRM-mode to determine the potential for success in biological samples. RAW cell samples stimulated with LPS for different times were analyzed for the selected peptides. We performed semi-quantitative analysis with the external peptide standards and obtained proteotypic peptides for most of the proteins in the canonical TLR signaling network. Based on these results, we have obtained and heavy-labeled internal peptide standards against corresponding protein targets for absolute quantitation measurements. Additionally, we designed, obtained and tested a set of peptides phosphorylated at the crucial regulatory residues of the proteins in the TLR signaling network. We have performed robust quantitative measurements with the heavy-labeled peptide standards spiked into the cell lysates. Using SRM and PRM (Parallel Reaction Monitoring) we examined unstimulated controls and cells stimulated with LPS for 30 minutes. We obtained absolute protein measurements and phosphosite occupancy measurements for both and made the comprehensive datasets publicly available (4). We have created the network of essential proteins and their interactions in the innate immune signaling (5) for the Simmune-based model and began modeling the network changes following TLR stimulation with LPS. The model we are building in collaboration with Dr. Meier-Schellersheim and the Computational Biology Section incorporates also the measurements of PTM changes obtained from project AI001084-11 and the binding constants we are getting using the modeling with protein structure data. We have begun to look at proteome changes at the single cell level using the combination of a single-cell proteomics methodology recently developed by Slavov and Budnik (SCoPE-MS). In this project, we can reach beyond basal level quantification to further develop and test the TLR signaling network model under a variety of biologically relevant perturbations (different and modified TLR ligands, whole pathogens, and cells with mutations in specific signaling molecules). 1. Manes NP, et al. (2015) Mol Cell Proteomics. 2015 Oct;14(10):2661-81. doi: 10.1074/mcp.M115.048918. 2. Manes NP, Mann JM, and Nita-Lazar A. (2015) J Vis Exp 102, doi: 10.3791/529 3. Manes NP, Nita-Lazar A (2018) J Proteomics. 2018 Oct 30;189:75-90. doi: 10.1016/j.jprot.2018.02. 4. Manes NP et al. (2022) Sci Data. 2022 Aug 12;9(1):491. 5. Manes NP, Nita-Lazar A (2021) mSystems, Jun 29;6(3):e0033621.

解释 我们根据目标蛋白质组学并结合肽标准品的使用，调整了绝对定量方法，并使用这些数据对免疫系统中的信号通路进行稳健的预测建模。 我们使用 1-磷酸鞘氨醇介导的巨噬细胞趋化作用作为初始模型系统。 S1P 调节破骨细胞前体进出骨骼的化学吸引和化学排斥。此处用作模型的鼠巨噬细胞 RAW 264.7 细胞表达两种 S1P 受体：S1PR1 和 S1PR2。这些受体对 S1P 的亲和力明显不同，并且在暴露于低/高浓度的 S1P 时会产生相反的效果。为了更深入地了解哺乳动物细胞趋化性，我们使用转录组学、鸟枪蛋白质组学、靶向蛋白质组学和途径模拟来研究 S1P 介导的破骨细胞前体趋化性。使用 RNA-seq 的转录组学能够识别和定量 RNA 转录本，而鸟枪法蛋白质组学能够识别基于肽蛋白型质量、序列独特性和修饰脆弱性（例如氧化和脱酰胺）而选择的蛋白型肽，消除了许多理论上可能的肽，这可能与质谱分析不兼容。我们使用通过鸟枪蛋白质组学从破骨细胞前体获得的定量数据来寻找适合在我们的质谱仪中分析的肽。 SPOT 合成用于制备一组 409 个标准合成肽，我们用它们来评估巨噬细胞中的蛋白质表达。对掺有标准肽的 RAW264.7 细胞裂解物进行单反应监测 (SRM)，可对趋化因子信号网络中蛋白质的 409 个肽靶点中的 208 个进行可靠的鉴定和半定量。对来自不同实验条件下差异表达的蛋白质的较小组 65 个重标记定量内肽标准品进行 SRM 分析，提供了分子的绝对数量。然后将这些数据用于设计小鼠趋化途径蛋白质的靶向蛋白质组学测定。 使用纳流液相色谱结合选择反应监测质谱 (LC-SRM) 进行靶向蛋白质组学测定，以产生 RAW 264.7 细胞内每种靶蛋白的绝对丰度值（以拷贝/细胞为单位）。 RAW 细胞再次被用作破骨细胞前体模型，因为它们具有非常相似的 S1P 定向趋化行为。基于规则的通路建模能够基于三维计算机原始细胞几何结构内的双分子相互作用模拟小鼠趋化通路。测量的蛋白质丰度值用作模拟输入参数，导致计算机模拟途径行为与体外测量结果相匹配。此外，一旦建立了模型参数，即使对未用于参数化的刺激的模拟反应也与实验结果一致。这些发现证明了将靶向质谱与通路建模相结合以推进生物学洞察的可行性和价值，并定义了我们模拟其他免疫系统信号通路的实验方法 (1,2,3)。 在 TLR 信号网络建模研究中，我们利用靶向蛋白质组学和转录组学来帮助构建小鼠单核巨噬细胞系 RAW264.7 中 LPS-TLR4 信号通路的计算模型。通过对当前文献和描述 LPS-TLR4 信号传导的 KEGG 通路的回顾，确定了一组蛋白质靶标。根据几个标准评分后选择相应的肽，这些标准包括长度、鸟枪法蛋白质组学鉴定以及通过基序预测（Pubmed）进行文献挖掘确定的潜在 PTM 位点。在鸟枪模式和 SRM 模式下对肽进行分析，以确定在生物样品中成功的潜力。分析用 LPS 刺激不同时间的原始细胞样品中选定的肽。我们使用外部肽标准品进行了半定量分析，并获得了规范 TLR 信号网络中大多数蛋白质的蛋白肽。基于这些结果，我们获得了针对相应蛋白质靶标的重标记内肽标准品，以进行绝对定量测量。此外，我们设计、获得并测试了一组在 TLR 信号网络中蛋白质的关键调节残基处磷酸化的肽。我们使用添加到细胞裂解物中的重标记肽标准品进行了可靠的定量测量。使用 SRM 和 PRM（平行反应监测），我们检查了未刺激的对照和用 LPS 刺激 30 分钟的细胞。我们获得了两者的绝对蛋白质测量值和磷酸位点占据测量值，并公开了综合数据集 (4)。我们为基于 Simmune 的模型创建了必需蛋白网络及其在先天免疫信号传导中的相互作用 (5)，并开始对用 LPS 刺激 TLR 后的网络变化进行建模。我们与 Meier-Schellersheim 博士和计算生物学部分合作构建的模型还包含从项目 AI001084-11 获得的 PTM 变化的测量值以及我们使用蛋白质结构数据建模获得的结合常数。我们已经开始结合 Slavov 和 Budnik 最近开发的单细胞蛋白质组学方法 (SCoPE-MS) 来研究单细胞水平的蛋白质组变化。在这个项目中，我们可以超越基础水平定量，进一步开发和测试各种生物学相关扰动（不同和修饰的 TLR 配体、整个病原体和特定信号分子突变的细胞）下的 TLR 信号网络模型。 1.Manes NP 等人。 (2015)摩尔细胞蛋白质组学。 2015 年 10 月；14(10):2661-81。 doi：10.1074/mcp.M115.048918。 2.Manes NP、Mann JM 和 Nita-Lazar A. (2015) J Vis Exp 102，doi：10.3791/529 3.Manes NP，Nita-Lazar A (2018) J 蛋白质组学。 2018 年 10 月 30 日；189:75-90。 doi：10.1016/j.jprot.2018.02。 4.Manes NP 等人。 （2022）科学数据。 2022 年 8 月 12 日；9(1):491。 5.Manes NP，Nita-Lazar A (2021) mSystems，6 月 29 日；6(3):e0033621。