微生物蛋白质组学的定量分析

越来越多的微生物基因组序列数据为系统地研究基因的调节和功能创造了有利条件.由于蛋白质是具有生物功能的分子,蛋白质组学在微生物基因组的功能研究中异军突起、蓬勃发展.微生物蛋白质组学的基本原则是,用比较研究来阐明和理解不同微生物之间或不同生长条件下基因的表达水平.显而易见,定量分析技术是比较蛋白质组学中急需发展的核心技术.对蛋白质组学定量分析技术在微生物蛋白质组研究中的进展进行了综述.     

Empirical Bayes Methods Enable Advanced Population-Level Analyses of Single-Molecule FRET Experiments

Many single-molecule experiments aim to characterize biomolecular processes in terms of kinetic models that specify the rates of transition between conformational states of the biomolecule. Estimation of these rates often requires analysis of a population of molecules, in which the conformational trajectory of each molecule is represented by a noisy, time-dependent signal trajectory. Although hidden Markov models (HMMs) may be used to infer the conformational trajectories of individual molecules, estimating a consensus kinetic model from the population of inferred conformational trajectories remains a statistically difficult task, as inferred parameters vary widely within a population. Here, we demonstrate how a recently developed empirical Bayesian method for HMMs can be extended to enable a more automated and statistically principled approach to two widely occurring tasks in the analysis of single-molecule fluorescence resonance energy transfer (smFRET) experiments: 1), the characterization of changes in rates across a series of experiments performed under variable conditions; and 2), the detection of degenerate states that exhibit the same FRET efficiency but differ in their rates of transition. We apply this newly developed methodology to two studies of the bacterial ribosome, each exemplary of one of these two analysis tasks. We conclude with a discussion of model-selection techniques for determination of the appropriate number of conformational states. The code used to perform this analysis and a basic graphical user interface front end are available as open source software.     

Empirical Bayes Methods Enable Advanced Population-Level Analyses of Single-Molecule FRET Experiments

Many single-molecule experiments aim to characterize biomolecular processes in terms of kinetic models that specify the rates of transition between conformational states of the biomolecule. Estimation of these rates often requires analysis of a population of molecules, in which the conformational trajectory of each molecule is represented by a noisy, time-dependent signal trajectory. Although hidden Markov models (HMMs) may be used to infer the conformational trajectories of individual molecules, estimating a consensus kinetic model from the population of inferred conformational trajectories remains a statistically difficult task, as inferred parameters vary widely within a population. Here, we demonstrate how a recently developed empirical Bayesian method for HMMs can be extended to enable a more automated and statistically principled approach to two widely occurring tasks in the analysis of single-molecule fluorescence resonance energy transfer (smFRET) experiments: 1), the characterization of changes in rates across a series of experiments performed under variable conditions; and 2), the detection of degenerate states that exhibit the same FRET efficiency but differ in their rates of transition. We apply this newly developed methodology to two studies of the bacterial ribosome, each exemplary of one of these two analysis tasks. We conclude with a discussion of model-selection techniques for determination of the appropriate number of conformational states. The code used to perform this analysis and a basic graphical user interface front end are available as open source software.     

定量蛋白质组学分析方法及应用

蛋白质组学经历了近10年的发展,现在已经初具规模。但是由于它是动态地观察生物体不断变化的所有蛋白质,所以技术难度非常之大。为使研究简化并更具针对性,人们着重进行比较蛋白质组学的研究。为了具体量化这些蛋白质的变化产生了定量蛋白质组学,近几年各种标记技术的进步使得该学科得以迅猛发展。     

定量蛋白质组学分析PknG在分枝杆菌中的功能

丝氨酸苏氨酸蛋白激酶G(PknG)是分枝杆菌中一个类似于真核生物蛋白激酶C的蛋白质,对结核分枝杆菌的生长和新陈代谢等生理过程,以及结核分枝杆菌的耐药和在宿主细胞中的存活都起着重要的调节作用.本文在耻垢分枝杆菌(Mycobacterium smegmatis)mc2155中构建了过表达结核分枝杆菌PknG的重组菌株PknG-mc2155,并发现PknG-mc2155的生长速度慢于mc2155.应用化学修饰结合LC-LC-MS/MS的定量蛋白质组学方法,在mc2155和PknG-mc2155中鉴定到了176种有差异表达的蛋白,其中152种蛋白在PknG-mc2155中表达下调,24种蛋白表达上调.这些差异表达的蛋白参与了多个细胞过程,包括代谢、蛋白翻译等.基于这些结果,我们推测PknG-mc2155生长速度慢的原因是因为代谢相关酶如GlpK,ALD和DesA1等蛋白表达的下调;而Ag85A,Ag85C,SecA2等蛋白的上调则增强细菌的感染性;另外KatG蛋白的下调提示PknG的过表达增强了菌株的抗药性.代谢组学分析发现谷氨酸和谷氨酰胺在PknG-mc2155中的水平低于在mc2155中水平,证实了PknG影响谷氨酰胺的稳态平衡.利用蛋白质磷酸化分析,我们发现PknG的苏氨酸残基T-320上有一个自磷酸化修饰,而且在PknG-mc2155菌株中,也鉴定到gltA和glmM上的磷酸化修饰,显示gltA和glmM是PknG的底物.本研究为理解PknG的功能和作用机制提供了新的依据和解释,为深入研究PknG在结核分枝杆菌中的功能奠定了基础,我们的结果也表明蛋白质组学技术是系统研究细菌蛋白质功能的重要工具.     

Empirical Bayes Gibbs sampling

The wide applicability of Gibbs sampling has increased the use of more complex and multi-level hierarchical models. To use these models entails dealing with hyperparameters in the deeper levels of a hierarchy. There are three typical methods for dealing with these hyperparameters: specify them, estimate them, or use a 'flat' prior. Each of these strategies has its own associated problems. In this paper, using an empirical Bayes approach, we show how the hyperparameters can be estimated in a way that is both computationally feasible and statistically valid.     

Empirical Bayes logistic regression

We construct a diagnostic predictor for patient disease status based on a single data set of mass spectra of serum samples together with the binary case-control response. The model is logistic regression with Bernoulli log-likelihood augmented either by quadratic ridge or absolute L1 penalties. For ridge penalization using the singular value decomposition we reduce the number of variables for maximization to the rank of the design matrix. With log-likelihood loss, 10-fold cross-validatory choice is employed to specify the penalization hyperparameter. Predictive ability is judged on a set-aside subset of the data.     

定量蛋白质组学无标记定量方法的研究进展

依靠质谱技术的蛋白质组学快速发展,寻求速度快、重复性好以及准确度高的定量方法是该领域的一项艰巨任务,定量蛋白质组学分支领域应运而生．其中,无标记定量方法以其样品制备简单、耗材费用低廉以及结果数据分析便捷等优点渐露锋芒．无标记定量方法通常分为信号强度法和谱图计数法两大类．本文在这两种无标记定量方法计算原理的基础上,针对各种常用的无标记定量方法及最新进展做一个较为全面的介绍,并将详细讨论两类方法的异同点,以及目前蛋白质组学中无标记定量方法所面临的主要挑战,希望能为这一领域的研究人员在选择无标记定量方法时提供一个合理的参考．     

Systems-wide Analysis of a Phosphatase Knock-down by Quantitative Proteomics and Phosphoproteomics

Signal transduction in metazoans regulates almost all aspects of biological function, and aberrant signaling is involved in many diseases. Perturbations in phosphorylation-based signaling networks are typically studied in a hypothesis-driven approach, using phospho-specific antibodies. Here we apply quantitative, high-resolution mass spectrometry to determine the systems response to the depletion of one signaling component. Drosophila cells were metabolically labeled using stable isotope labeling by amino acids in cell culture (SILAC) and the phosphatase Ptp61F, the ortholog of mammalian PTB1B, a drug target for diabetes, was knocked down by RNAi. In total we detected more than 10,000 phosphorylation sites in the phosphoproteome of Drosophila Schneider cells and trained a phosphorylation site predictor with this data. SILAC-based quantitation after phosphatase knock-down showed that apart from the phosphatase, the proteome was minimally affected whereas 288 of 6,478 high-confidence phosphorylation sites changed significantly. Responses at the phosphotyrosine level included the already described Ptp61F substrates Stat92E and Abi. Our analysis highlights a connection of Ptp61F to cytoskeletal regulation through GTPase regulating proteins and focal adhesion components.Information processing in biological systems relies heavily on activation and inactivation of proteins by phosphorylation. This key post-translational modification is involved in the regulation of most cellular processes and mediates many rapid responses as well as long-term gene expression changes in response to stimuli. Protein kinases and protein phosphatases coordinately regulate this highly dynamic and reversible modification. Phosphorylation is usually studied in a candidate-based approach by in vitro kinase assays or by immune techniques employing phospho-specific antibodies. Despite the success of this reductionist approach, it does not afford a systems-wide observation of the effects upon perturbations of signaling networks.Recent advances in MS-based¹ proteomics now allow the identification of thousands of phosphorylation sites from complex protein mixtures (1–3). Most large-scale phosphoproteomics studies have been qualitative rather than quantitative; however, isotope-based methods enable precise quantitation of phosphorylation sites between two or more cellular states (4–6). Our group has applied the metabolic labeling technology termed stable isotope labeling by amino acids in cell culture (SILAC) (7) for the quantitative comparison of phosphoproteomes. For example, we quantified phosphorylation dynamics in response to epidermal growth factor stimulation. Out of a measured phosphoproteome of several thousand sites only a minority (about 10%) was regulated by the signal, highlighting the importance of quantitation in pinpointing specific systems responses (8).Drosophila is a well established model system to study key players in cell signaling and development. Genetic studies have been performed for decades whereas more recently also RNA interference (RNAi) has been employed for gene function studies using a highly efficient silencing protocol (9). A further advantage of Drosophila as a model system is the lower degree of functional redundancy compared with higher vertebrates while maintaining a high level of conservation of human genes linked to disease (10).Two large-scale, non-quantitative Drosophila phosphoproteome studies were carried out in embryonic Kc167 cells (11) and embryos (12). Both studies identified more than 10,000 sites of the Drosophila phosphoproteome.We have recently adapted the SILAC methodology for quantitative proteomics to Drosophila. Schneider line 2 (SL2) cells were treated with either mock dsRNA or dsRNA against ISWI, a component of chromatin remodeling complexes. The combination of RNAi and SILAC allows the unbiased “phenotypization” of the gene knock-down directly at the proteome level (13).Here we determined a high-quality basal phosphoproteome in SL2 cells and characterized its structural and evolutionary properties. We compared kinase substrate motives between Drosophila and human and trained a Drosophila phosphorylation site predictor.To explore the potential of quantitative phosphoproteomics in a systems-wide manner, we focused on the Drosophila non-transmembrane tyrosine phosphatase Ptp61F. This phosphatase is the ortholog of mammalian PTB1B, which is thought to be involved in type 2 diabetes, obesity, and cancer (14), and which is the target of several ongoing drug development projects (15). Ptp61F is a negative regulator of JAK/STAT signaling (16, 17) and, together with the Ableson kinase (Abl), involved in the regulation of the Abl interacting protein (Abi) and lamella formation (18). Both PTP1B and Ptp61F are among the best studied protein tyrosine phosphatases in their respective organisms; however the characterization of their substrates is still far from complete. Two recent mass spectrometric studies employed substrate trapping to identify direct substrates of PTP1B and Ptp61F (19, 20). The PTP1B study was combined with phosphotyrosine peptide enrichment, which led to site-specific detection of potential PTP1B targets. PTP1B function was additionally investigated by quantitative phosphotyrosine proteomics comparing wild type and PTP1B-deficient fibroblasts. In contrast, the Ptp61F study identified potential substrates without site-specific information. One of these was PVR, the Drosophila homolog of VEGFR and PDGFR, suggesting that Ptp61F - like its mammalian counterpart - counteracts receptor tyrosine kinase signaling. Apart from Abi, further components of the SCAR/WAVE complex as well as its regulatory kinase Abl were identified as potential Ptp61F substrates. This supports an involvement of Ptp61F in the regulation of actin reorganization and remodeling.To study the role of Ptp61F in a global and unbiased approach we combined global quantitative phosphoproteome analysis with RNA interference. We profiled tyrosine, serine and threonine phosphorylation changes upon ablation of Ptp61F by RNAi. In parallel, we quantified changes in the proteome, which allowed us to normalize changes in phosphorylation sites to corresponding changes at the protein level. Interestingly, we observed increased tyrosine phosphorylation of the protein tyrosine kinase Abl which suggests an enhanced Abl activity upon Ptp61F RNAi. We additionally detected up-regulated phosphotyrosine sites on GTPase regulating proteins (like RhoGAP15B and Vav) and constituents of focal adhesions (like Paxillin and Lasp) which expand the proposed involvement of Ptp61F in the regulation of cytoskeleton organization. Our work represents proof-of-principle that the combination of large-scale phosphoproteomics and a loss-of-function approach can contribute significantly to elucidating the role of key players in phosphorylation-dependent signaling. Importantly, this systems-wide approach measures the net effect of the perturbation on the entire signaling network, without the need to define specific substrate-kinase or substrate -phosphatase relationships or other direct functional mechanisms.     

Empirical Bayes analysis of single nucleotide polymorphisms

Background  

An important goal of whole-genome studies concerned with single nucleotide polymorphisms (SNPs) is the identification of SNPs associated with a covariate of interest such as the case-control status or the type of cancer. Since these studies often comprise the genotypes of hundreds of thousands of SNPs, methods are required that can cope with the corresponding multiple testing problem. For the analysis of gene expression data, approaches such as the empirical Bayes analysis of microarrays have been developed particularly for the detection of genes associated with the response. However, the empirical Bayes analysis of microarrays has only been suggested for binary responses when considering expression values, i.e. continuous predictors.     

定量蛋白质组学研究技术

随着蛋白质组研究的深入发展,人们已不满足对一个混合体系中蛋白质进行定性和简单定量分析,要求更加准确的定量分析。为此,有人提出了“定量蛋白质组学”概念。目前,应用于定量蛋白质学的研究技术主要有：蛋白质荧光染色技术,同位素标记技术,同位素亲和标签技术,蛋白质芯片技术。     

Bayes and Empirical Bayes Estimators of Abundance and Density from Spatial Capture-Recapture Data

In capture-recapture and mark-resight surveys, movements of individuals both within and between sampling periods can alter the susceptibility of individuals to detection over the region of sampling. In these circumstances spatially explicit capture-recapture (SECR) models, which incorporate the observed locations of individuals, allow population density and abundance to be estimated while accounting for differences in detectability of individuals. In this paper I propose two Bayesian SECR models, one for the analysis of recaptures observed in trapping arrays and another for the analysis of recaptures observed in area searches. In formulating these models I used distinct submodels to specify the distribution of individual home-range centers and the observable recaptures associated with these individuals. This separation of ecological and observational processes allowed me to derive a formal connection between Bayes and empirical Bayes estimators of population abundance that has not been established previously. I showed that this connection applies to every Poisson point-process model of SECR data and provides theoretical support for a previously proposed estimator of abundance based on recaptures in trapping arrays. To illustrate results of both classical and Bayesian methods of analysis, I compared Bayes and empirical Bayes esimates of abundance and density using recaptures from simulated and real populations of animals. Real populations included two iconic datasets: recaptures of tigers detected in camera-trap surveys and recaptures of lizards detected in area-search surveys. In the datasets I analyzed, classical and Bayesian methods provided similar – and often identical – inferences, which is not surprising given the sample sizes and the noninformative priors used in the analyses.     

Quantitative Proteomics and Metabolomics Analysis of Normal Human Cerebrospinal Fluid Samples*

The analysis of cerebrospinal fluid (CSF) is used in biomarker discovery studies for various neurodegenerative central nervous system (CNS) disorders. However, little is known about variation of CSF proteins and metabolites between patients without neurological disorders. A baseline for a large number of CSF compounds appears to be lacking. To analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples of patients undergoing routine, non-neurological surgical procedures, we determined the variation of various proteins and metabolites by multiple analytical platforms. A total of 126 common proteins were assessed for biological variations between individuals by ESI-Orbitrap. A large spread in inter-individual variation was observed (relative standard deviations [RSDs] ranged from 18 to 148%) for proteins with both high abundance and low abundance. Technical variation was between 15 and 30% for all 126 proteins. Metabolomics analysis was performed by means of GC-MS and nuclear magnetic resonance (NMR) imaging and amino acids were specifically analyzed by LC-MS/MS, resulting in the detection of more than 100 metabolites. The variation in the metabolome appears to be much more limited compared with the proteome: the observed RSDs ranged from 12 to 70%. Technical variation was less than 20% for almost all metabolites. Consequently, an understanding of the biological variation of proteins and metabolites in CSF of neurologically normal individuals appears to be essential for reliable interpretation of biomarker discovery studies for CNS disorders because such results may be influenced by natural inter-individual variations. Therefore, proteins and metabolites with high variation between individuals ought to be assessed with caution as candidate biomarkers because at least part of the difference observed between the diseased individuals and the controls will not be caused by the disease, but rather by the natural biological variation between individuals.The analysis of CSF¹ is indispensable in the diagnosis and understanding of various neurodegenerative CNS disorders (1–3). CSF is a fluid that has different functions, such as the protection of the brain from outside forces, transport of biological substances, and excretion of toxic and waste substances. It is in close contact with the extracellular fluid of the brain. Therefore, the composition of CSF can reflect biological processes of the brain (4). By discovering the characterization of the proteome and metabolome of CSF we may gain better insight on the pathogenesis of CNS disorders. This would be significant because, for many of these disorders, the etiology is still unclear.CSF is produced in the ventricles of the brain and in the subarachnoidal spaces. Humans normally produce around 500 mL of CSF each day, and the total volume of CSF at a given time is approximately 150 mL. CSF reflects the composition of blood plasma, although the concentrations of most proteins and metabolites in CSF are lower. However, individual proteins and metabolites can act differently. Active transport from blood and secretion from the brain contribute to the specific composition of CSF. This composition can be disturbed in neurological disorders (5–6). Since CNS-specific proteins and metabolites are typically low in abundance compared with their levels in blood, this change in composition is more likely to be found in CSF because in blood the more abundant plasma proteins can completely mask the signal of the less abundant proteins. Also, if the disease markers do not cross the blood-brain-barrier, then the CSF is the only viable biofluid source. Therefore, CSF might be an excellent source for biomarker discovery for CNS disorders if we follow the hypothesis that neurological diseases induce alterations in CSF protein and metabolite levels.Analysis of metabolites in CSF has been common practice in clinical chemistry for decades to analyze biomarkers for inborn errors of metabolism. The approaches used are either metabolite profiling of CSF using NMR (7), or targeted analysis of one or a few metabolites using specific analytical methods (8). Metabolomics includes the analysis of metabolites in biofluids by NMR or MS-based approaches, i.e. LC-MS or GC-MS. Several metabolite profiling studies were performed on CSF using NMR, some of which were published only recently (9,10). Surprisingly, very few metabolomics studies using MS-based methods have been performed on CSF to date (11,12). One of the reasons is the fact that the human CSF metabolome has not yet been characterized very well. Many CSF metabolites remain unidentified, and for those that have been identified there is not much known about normal concentration ranges. A systematic categorization of the CSF metabolome is necessary and expected to be beneficial for future biomarker discoveries. Recently, Wishart et al. made a good start in exploring the human CSF metabolome with their computer-aided literature survey that resulted in 308 detectable metabolites in human CSF (13).The CSF proteome has been characterized to a much larger extent than the CSF metabolome and is currently the topic of investigations in several research groups worldwide. Recently, studies have been published with numerous identities and quantities of CSF proteins. Pan and co-workers were able to identify 2,594 proteins in well-characterized pooled human CSF samples using strict proteomics criteria with a combination of linear trap quadrupole LTQ-FT (Thermo Fisher Scientific, Bremen, Germany) and MALDI TOF/TOF equipment (14). They were also able to quantify several proteins using a targeted LC MALDI TOF/TOF approach (15). Hu et al. have studied the intra- and inter-individual variation in human CSF and found large variations in protein concentrations in six patients by means of two dimensional–gel electrophoresis (16), focusing mainly on the variations within individuals at two different time-points. Although only a limited number of proteins was analyzed, the variation between the time-points was profound, exceeding 200% for seven proteins.Unique CSF biomarkers may contribute to a deeper understanding of the mechanisms of CNS disorders. However, for this assumption to come true, there are still challenges ahead. Although CSF is not as complex as blood (almost missing the cellular part and the clotting system present in blood), it is expected to consist of thousands of organic- and non-organic salts, sugars, lipids, and proteins. A large part of the CSF consists of a few highly abundant metabolites and proteins, which hamper, if no precautions are undertaken, the identification and quantification of metabolites and proteins that occur in lower amounts. The analysis of the CSF metabolome is complicated because of the diverse chemical nature of metabolites and the lower concentration of metabolites compared with blood. Analytical method development is still required because it is not possible to identify the entire range of CSF metabolites with one single analytical method. Although in proteome research efforts have been made to quantify proteins, metabolomics studies up to now either do not provide quantitative information or they only give information for the most abundant metabolites.Another challenge is the sample amount obtained by lumbar puncture to collect CSF. Lumbar puncture is an invasive method that is not performed as frequently as blood sampling. However, often after the analysis of various clinical parameters, only a limited amount of CSF sample is available for biomarker discovery. Metabolomics studies are hampered by limited CSF sample amount. Therefore, analytical methods are required that are suitable to handle relatively small sample volumes.The main objectives of this study were (1) to analyze the variation in CSF protein and metabolite abundances in a number of well-defined individual samples by multiple analytical platforms; and (2) to integrate metabolomics and proteomics to present biological variations in metabolite and protein abundances and compare these with technical variations with the currently used analytical methods. The results will facilitate and increase the application of CSF for future biomarker discovery studies in the field of neurodegenerative diseases and neuro-oncology.     

Label-Free Quantitative Proteomics and N-terminal Analysis of Human Metastatic Lung Cancer Cells

Proteomic analysis is helpful in identifying cancer-associated proteins that are differentially expressed and fragmented that can be annotated as dysregulated networks and pathways during metastasis. To examine meta-static process in lung cancer, we performed a proteomics study by label-free quantitative analysis and N-terminal analysis in 2 human non-small-cell lung cancer cell lines with disparate metastatic potentials—NCI-H1703 (primary cell, stage I) and NCI-H1755 (metastatic cell, stage IV). We identified 2130 proteins, 1355 of which were common to both cell lines. In the label-free quantitative analysis, we used the NSAF normalization method, resulting in 242 differential expressed proteins. For the N-terminal proteome analysis, 325 N-terminal peptides, including 45 novel fragments, were identified in the 2 cell lines. Based on two proteomic analysis, 11 quantitatively expressed proteins and 8 N-terminal peptides were enriched for the focal adhesion pathway. Most proteins from the quantitative analysis were upregulated in metastatic cancer cells, whereas novel fragment of CRKL was detected only in primary cancer cells. This study increases our understanding of the NSCLC metastasis proteome.