Relative quantification methods have dominated the quantitative proteomics field. There is a need, however, to conduct absolute quantification studies to accurately model and understand the complex molecular biology that results in proteome variability among biological samples. A new method of absolute quantification of proteins is described. This method is based on the discovery of an unexpected relationship between MS signal response and protein concentration: the average MS signal response for the three most intense tryptic peptides per mole of protein is constant within a coefficient of variation of less than ؎10%. Given an internal standard, this relationship is used to calculate a universal signal response factor. The universal signal response factor (counts/mol) was shown to be the same for all proteins tested in this study. A controlled set of six exogenous proteins of varying concentrations was studied in the absence and presence of human serum. The absolute quantity of the standard proteins was determined with a relative error of less than ؎15%. The average MS signal responses of the three most intense peptides from each protein were plotted against their calculated protein concentrations, and this plot resulted in a linear relationship with an R 2 value of 0.9939. The analyses were applied to determine the absolute concentration of 11 common serum proteins, and these concentrations were then compared with known values available in the literature. Additionally within an unfractionated Escherichia coli lysate, a subset of identified proteins known to exist as functional complexes was studied. The calculated absolute quantities were used to accurately determine their stoichiometry. Molecular & Cellular Proteomics 5:144 -156, 2006.The study of proteins is crucial in understanding and combating disease through identification of proteins, discovering disease biomarkers, studying protein involvement in specific metabolic pathways, and identifying protein targets in drug discovery (1, 2). An important technique that is used in these studies to quantify and identify peptides and/or proteins present in simple and complex mixtures is ESI-LCMS.To date a majority of the quantitative proteomic analyses have been performed using stable isotope labeling strategies such as ICAT (3), iTRAQ TM (4), SILAC (stable isotope labeling by amino acids in cell culture) (5), and 18 O labeling (6, 7). These methodologies require complex, time-consuming sample preparation and can be relatively expensive.Recently there have been numerous reports applying labelfree methods to monitor the relative abundance of protein between different conditions (8 -11). Relative quantification provides information regarding specific protein abundance changes between two conditions caused by an induced perturbation (environment-induced, drug-induced, and diseaseinduced). These studies require comparison of identical proteolytic peptides in each of the two experiments to accurately determine relative ratios of the particular protein(s) of interest.Rel...

Current methodologies for protein quantitation include 2-dimensional gel electrophoresis techniques, metabolic labeling, and stable isotope labeling methods to name only a few. The current literature illustrates both pros and cons for each of the previously mentioned methodologies. Keeping with the teachings of William of Ockham, "with all things being equal the simplest solution tends to be correct", a simple LC/MS based methodology is presented that allows relative changes in abundance of proteins in highly complex mixtures to be determined. Utilizing a reproducible chromatographic separations system along with the high mass resolution and mass accuracy of an orthogonal time-of-flight mass spectrometer, the quantitative comparison of tens of thousands of ions emanating from identically prepared control and experimental samples can be made. Using this configuration, we can determine the change in relative abundance of a small number of ions between the two conditions solely by accurate mass and retention time. Employing standard operating procedures for both sample preparation and ESI-mass spectrometry, one typically obtains under 5 ppm mass precision and quantitative variations between 10 and 15%. The principal focus of this paper will demonstrate the quantitative aspects of the methodology and continue with a discussion of the associated, complementary qualitative capabilities.

A novel database search algorithm is presented for the qualitative identification of proteins over a wide dynamic range, both in simple and complex biological samples. The algorithm has been designed for the analysis of data originating from data independent acquisitions, whereby multiple precursor ions are fragmented simultaneously. Measurements used by the algorithm include retention time, ion intensities, charge state, and accurate masses on both precursor and product ions from LC-MS data. The search algorithm uses an iterative process whereby each iteration incrementally increases the selectivity, specificity, and sensitivity of the overall strategy. Increased specificity is obtained by utilizing a subset database search approach, whereby for each subsequent stage of the search, only those peptides from securely identified proteins are queried. Tentative peptide and protein identifications are ranked and scored by their relative correlation to a number of models of known and empirically derived physicochemical attributes of proteins and peptides. In addition, the algorithm utilizes decoy database techniques for automatically determining the false positive identification rates. The search algorithm has been tested by comparing the search results from a four-protein mixture, the same four-protein mixture spiked into a complex biological background, and a variety of other "system" type protein digest mixtures. The method was validated independently by data dependent methods, while concurrently relying on replication and selectivity. Comparisons were also performed with other commercially and publicly available peptide fragmentation search algorithms. The presented results demonstrate the ability to correctly identify peptides and proteins from data independent acquisition strategies with high sensitivity and specificity. They also illustrate a more comprehensive analysis of the samples studied; providing approximately 20% more protein identifications, compared to a more conventional data directed approach using the same identification criteria, with a concurrent increase in both sequence coverage and the number of modified peptides.

The detection, correlation, and comparison of peptide and product ions from a data independent LC-MS acquisition strategy with data dependent LC-MS/MS is described. The data independent mode of acquisition differs from an LC-MS/MS data acquisition since no ion transmission window is applied with the first mass analyzer prior to collision induced disassociation. Alternating the energy applied to the collision cell, between low and elevated energy, on a scan-to-scan basis, provides accurate mass precursor and associated product ion spectra from every ion above the LOD of the mass spectrometer. The method therefore provides a near 100% duty cycle, with an inherent increase in signal intensity due to the fact that both precursor and product ion data are collected on all isotopes of every charge-state across the entire chromatographic peak width. The correlation of product to precursor ions, after deconvolution, is achieved by using reconstructed retention time apices and chromatographic peak shapes. Presented are the results from the comparison of a simple four protein mixture, in the presence and absence of an enzymatically digested protein extract from Escherichia coli. The samples were run in triplicate by both data dependant analysis (DDA) LC-MS/MS and data-independent, alternate scanning LC-MS. The detection and identification of precursor and product ions from the combined DDA search results of the four protein mixture were used for comparison to all other data. Each individual set of data-independent LC-MS data provides a more comprehensive set of detected ions than the combined peptide identifications from the DDA LC-MS/MS experiments. In the presence of the complex E. coli background, over 90% of the monoisotopic masses from the combined LC-MS/MS identifications were detected at the appropriate retention time. Moreover, the fragmentation pattern and number of associated elevated energy product ions in each replicate experiment was found to be very similar to the DDA identifications. In the case of the corresponding individual DDA LC-MS/MS experiment, 43% of the possible detectable peptides of interest were identified. The presented data illustrates that the time-aligned data from data-independent alternate scanning LC-MS experiments is highly comparable to the data obtained via DDA. The obtained information can therefore be effectively and correctly deconvolved to correlate product ions with parent precursor ions. The ability to generate precursor-product ion tables from this information and subsequently identify the correct parent precursor peptide will be illustrated in a companion manuscript.

We describe a novel LCMS approach to the relative quantitation and simultaneous identification of proteins within the complex milieu of unfractionated Escherichia coli. This label-free, LCMS acquisition method observes all detectable, eluting peptides and their corresponding fragment ions. Postacquisition data analysis methods extract both the chromatographic and the mass spectrometric information on the tryptic peptides to provide time-resolved, accurate mass measurements, which are subsequently used for quantitation and identification of constituent proteins. The response of E. coli to carbon source variation is well understood, and it is thus commonly used as a model biological system when validating an analytical method. Using this LCMS approach, we characterized proteins isolated from E. coli grown in glucose, lactose, and acetate. The change in relative abundance of the corresponding proteins was measured from peptides common to both conditions. Protein identities were also determined for those peptides that were unique to each condition, and these identities were found to be consistent with the underlying biochemical restrictions imposed by the growth conditions. The relative change in abundance of the characterized proteins ranged from 0.1-to 90-fold among the three binary comparisons. The overall coverage of the characterized proteins ranged from 10 to Escherichia coli is a microbial symbiote found in the colon and large intestine of most warm blooded animals that plays a critical role in vertebrate anabolism and catabolism. The environment in which E. coli lives is subject to rapid changes in the availability of the carbon and nitrogen compounds necessary to provide its energy and primary building blocks. E. coli survival hinges on the ability to successfully control the expression of genes coding for enzymes and proteins required for growth in response to environmental changes. Because of its simple cellular structure and its relative ease of maintenance and manipulation in the laboratory, E. coli has become the "workhorse host" for most research in molecular biology and microbiology. As a result, it is regarded as one of the most completely characterized organisms in all biology. The ease with which recombinant proteins can be expressed in E. coli has made this bacterium useful in the study of many basic biological processes as well as in the production of heterologous proteins for research and therapeutic purposes. For these reasons, E. coli has become a model system for testing new analytical technologies. For example, the relatively small genome size and prevalent laboratory use made E. coli genome one of the first to be completely sequenced (1). Likewise E. coli genome microarrays were among the first to be commercially available with sequences for the complete set of open reading frames as well as intergenic regions (2). The origins of proteomics can also be traced back to E. coli when pioneering two-dimensional gel electrophoresis experiments enabled the investigation of proteins on an organism...

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Viral encephalitis is still very prominent around the world, and traditional antiviral therapies still have shortcomings. Some patients cannot get effective relief or suffer from serious sequelae. At present, people are studying the role of the innate immune system in viral encephalitis. Microglia, as resident cells of the central nervous system (CNS), can respond quickly to various CNS injuries including trauma, ischemia, and infection and maintain the homeostasis of CNS, but this response is not always good; sometimes, it will exacerbate damage. Studies have shown that microglia also act as a double-edged sword during viral encephalitis. On the one hand, microglia can sense ATP signals through the purinergic receptor P2Y12 and are recruited around infected neurons to exert phagocytic activity. Microglia can exert a direct antiviral effect by producing type 1 interferon (IFN-1) to induce IFN-stimulated gene (ISG) expression of themselves or indirect antiviral effects by IFN-1 acting on other cells to activate corresponding signaling pathways. In addition, microglia can also exert an antiviral effect by inducing autophagy or secreting cytokines. On the other hand, microglia mediate presynaptic membrane damage in the hippocampus through complement, resulting in long-term memory impairment and cognitive dysfunction in patients with encephalitis. Microglia mediate fetal congenital malformations caused by Zika virus (ZIKV) infection. The gene expression profile of microglia in HIV encephalitis changes, and they tend to be a pro-inflammatory type. Microglia inhibited neuronal autophagy and aggravated the damage of CNS in HIV encephalitis; E3 ubiquitin ligase Pellino (pelia) expressed by microglia promotes the replication of virus in neurons. The interaction between amyloid-β peptide (Aβ) produced by neurons and activated microglia during viral infection is uncertain. Although neurons can mediate antiviral effects by activating receptor-interacting protein kinases 3 (RIPK3) in a death-independent pathway, the RIPK3 pathway of microglia is unknown. Different brain regions have different susceptibility to viruses, and the gene expression of microglia in different brain regions is specific. The relationship between the two needs to be further confirmed. How to properly regulate the function of microglia and make it exert more anti-inflammatory effects is our next research direction.

S pontaneous intracerebral hemorrhage (ICH) accounts for 10% to 15% of all strokes and is one of the leading causes of stroke-related mortality and morbidity worldwide. Patients with ICH are generally at risk of developing stroke-associated pneumonia (SAP) during acute hospitalization. Evidence has shown that SAP not only increases the length of hospital stay (LOS) and medical cost 1,2 but also is an important risk factor of mortality and morbidity after acute stroke. 3,4 Several risk factors for SAP have been identified, such as older age, 4-12 male sex, 5,6,10,11,13 current smoking, 12 diabetes mellitus, 6 hypertension, 14 atrial fibrillation, 7,10,12 congestive heart failure, 7,12,13,15 chronic obstructive pulmonary disease, 8,[12][13][14] preexisting dependency, 8,12,13,16 stroke severity, 5,6,8,12,17,18 dysphagia, [8][9][10][11][12]14,[18][19][20] and blood glucose. 12 Meanwhile, based on these risk factors, a few risk models have been developed for SAP after acute ischemic stroke. [8][9][10][11][12] Currently, no valid scoring system is available for predicting SAP after ICH in routine clinical practice or clinical trial. We hypothesized that there might be some common grounds for the development of pneumonia after acute ischemic stroke and ICH, and those predictors for SAP after acute ischemic stroke might also be useful for predicting SAP after ICH. For clinical practice, an effective risk-stratification and prognostic model for SAP after ICH would be helpful to identify vulnerable patients, allocate relevant medical resources, and implement tailored preventive strategies. In addition, for clinical trial, it could be used in nonrandomized studies to control for case-mix variation and in controlled studies as a selection criterion.Background and Purpose-We aimed to develop a risk score (intracerebral hemorrhage-associated pneumonia score, ICH-APS) for predicting hospital-acquired stroke-associated pneumonia (SAP) after ICH. Methods-The ICH-APS was developed based on the China National Stroke Registry (CNSR), in which eligible patients were randomly divided into derivation (60%) and validation (40%) cohorts. Variables routinely collected at presentation were used for predicting SAP after ICH. For testing the added value of hematoma volume measure, we separately developed 2 models with (ICH-APS-B) and without (ICH-APS-A) hematoma volume included. Multivariable logistic regression was performed to identify independent predictors. The area under the receiver operating characteristic curve (AUROC), Hosmer-Lemeshow goodness-of-fit test, and integrated discrimination index were used to assess model discrimination, calibration, and reclassification, respectively. Results-The SAP was 16.4% and 17.7% in the overall derivation (n=2998) and validation (n=2000) cohorts, respectively.A 23-point ICH-APS-A was developed based on a set of predictors and showed good discrimination in the overall derivation (AUROC, 0.75; 95% confidence interval, 0. Ji et al Risk Score to Predict SAP After ICH 2621In the study, we aimed to ...