Absolute Quantification of Molecular Representation and Interaction

分子表示和相互作用的绝对定量

基本信息

批准号：
10927839
负责人：
Aleksandra Nita-Lazar
金额：
$ 89.92万
依托单位：
NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES
依托单位国家：
美国
项目类别：
财政年份：
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/10927839
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 Macrophage 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 Receptor Signaling Sampling Set protein Shotguns Signal Pathway Signal Transduction Signaling Molecule Site Sphingosine-1-Phosphate Receptor Stimulus Study models System TLR4 gene Testing Transcript bone chemokine deamidation design differential expression in silico insight 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

项目摘要

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 macrophage 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 constructing 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. The current efforts are focused on determining the association constants for the complexes in the TLR pathway in collaboration with Dr. Sergio Hassan, using Alpha Fold. We have also 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.

解释我们已经根据靶向蛋白质组学结合使用肽标准的靶向蛋白质组学，调整了绝对定量的方法，并将数据用于免疫系统中信号通路的鲁棒预测建模。我们使用鞘氨醇1-磷酸介导的巨噬细胞趋化性作为初始模型系统。 S1P调节骨骼前体和从骨骼的化学提取和化学粘液。在此用作模型的鼠巨噬细胞RAW 264.7细胞为S1P：S1PR1和S1PR2表达两个受体。这些受体与S1P的亲和力明显不同，并对暴露于低/高浓度S1P的影响会产生相反的影响。为了对哺乳动物细胞趋化性有更深入的了解，我们使用了转录组学，shot弹枪蛋白质组学，靶向蛋白质组学和途径模拟来研究骨细胞前体的S1P介导的趋化性。使用RNA-SEQ的转录组学启用了RNA转录和shot弹枪蛋白质组学的识别和定量，从而使基于肽蛋白质型蛋白质肽选择的蛋白质型肽可以识别，从而消除了许多可能的分析，可以分析peptides，从而消除了许多可能的分析（例如，氧化和脱氨基）。我们使用了通过shot弹枪蛋白质组学从破骨细胞前体获得的定量数据来找到质谱仪中分析的肽。斑点合成用于制备一组409个标准的合成肽，我们用来评估巨噬细胞中的蛋白质表达。 RAW264.7细胞裂解物的单个反应监测（SRM）用标准肽尖峰导致对趋化因子信号网络中蛋白质的409肽靶标的208个自信鉴定和半定量。对在不同实验条件下差异表达的蛋白质的65个重型标记的，定量的内部肽标准品的SRM分析提供了绝对数量的分子。然后，这些数据被用于设计小鼠趋化途径蛋白质的靶向蛋白质组学测定。使用纳米流液色谱法与选定的反应监测质谱法（LC-SRM）结合的靶向蛋白质组学测定，以生成原始264.7细胞中每个靶蛋白的绝对丰度值（以副本/细胞的单位/细胞为单位）。由于它们具有非常相似的S1P定向趋化性行为，因此RAW细胞再次被用作模型破骨细胞前体。基于规则的途径建模能够基于在硅生生细胞中三维的几何形状中基于双分子相互作用的小鼠趋化途径的模拟。用作模拟输入参数的测得的蛋白质丰度值导致在体外测量中匹配的硅途径行为。此外，一旦建立了模型参数，即使是对参数化的刺激的模拟响应也与实验发现一致。这些发现证明了将靶向质谱与途径建模相结合的可行性和价值，以推进生物学洞察力，并定义了我们对其他免疫系统信号通路进行建模的实验方法（1、2、3）。在TLR信号网络建模研究中，我们利用具有转录组学的靶向蛋白质组学来有助于在小鼠单核细胞 - 摩托噬细胞系RAW264.7中构建LPS-TLR4信号通路的计算模型。从描述LPS-TLR4信号传导的当前文献和KEGG途径的回顾中确定了一组蛋白质靶标。根据几个标准，包括长度，shot弹枪蛋白质组学识别和潜在的PTM位点在得分后选择相应的肽，这些肽由基序预测（PubMed）确定的潜在PTM位点。在shot弹枪模式和SRM模式中分析了肽，以确定生物样品成功的潜力。用LPS在不同时间刺激的原细胞样品为选定的肽分析。我们使用外部肽标准品进行了半定量分析，并为规范TLR信号网络中的大多数蛋白质获得了蛋白质肽。基于这些结果，我们已经获得了针对相应的蛋白质靶标的绝对定量测量的且重型标记的内部肽标准。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上磷酸化的肽。我们已经用尖刺到细胞裂解物的重肽标准标准进行了强大的定量测量。使用SRM和PRM（平行反应监测），我们检查了未刺激的对照和LPS刺激的细胞30分钟。我们获得了两者的绝对蛋白质测量和磷材料的占用测量结果，并使全面的数据集公开可用（4）。我们为基于Simmune的模型创建了必需蛋白质的网络及其在先天免疫信号传导（5）中的相互作用，并开始对TLR刺激LPS进行建模。我们正在与Meier-Schellersheim博士合作构建的模型，计算生物学部分还结合了从项目AI001084-11获得的PTM变化的测量以及我们使用蛋白质结构数据建模的结合常数。目前的工作重点是使用Alpha Fold与Sergio Hassan博士合作确定TLR途径中复合物的关联常数。我们还开始使用Slavov和Budnik（Scope-MS）最近开发的单细胞蛋白质组学方法的组合来研究单细胞水平的蛋白质组变化。在该项目中，我们可以超出基础水平的量化，以进一步开发和测试TLR信号网络模型在各种生物学相关的扰动下（不同的和修饰的TLR配体，整个病原体和具有特定信号分子突变的细胞）。 1。ManesNP等。（2015）mol细胞蛋白质组学。 2015年10月; 14（10）：2661-81。 doi：10.1074/MCP.M115.048918。 2。ManesNP，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。ManesNP等。（2022）SCI数据。 2022年8月12日； 9（1）：491。 5。ManesNP，Nita-Lazar A（2021）MSYSTEMS，6月29日； 6（3）：E0033621。