MPLEx: a Robust and Universal Protocol for Single-Sample Integrative Proteomic, Metabolomic, and Lipidomic Analyses.

Integrative multi-omics analyses can empower more effective investigation and complete understanding of complex biological systems. Despite recent advances in a range of omics analyses, multi-omic measurements of the same sample are still challenging and current methods have not been well evaluated in terms of reproducibility and broad applicability. Here we adapted a solvent-based method, widely applied for extracting lipids and metabolites, to add proteomics to mass spectrometry-based multi-omics measurements. The metabolite, protein, and lipid extraction (MPLEx) protocol proved to be robust and applicable to a diverse set of sample types, including cell cultures, microbial communities, and tissues. To illustrate the utility of this protocol, an integrative multi-omics analysis was performed using a lung epithelial cell line infected with Middle East respiratory syndrome coronavirus, which showed the impact of this virus on the host glycolytic pathway and also suggested a role for lipids during infection. The MPLEx method is a simple, fast, and robust protocol that can be applied for integrative multi-omic measurements from diverse sample types (e.g., environmental, in vitro, and clinical). IMPORTANCE In systems biology studies, the integration of multiple omics measurements (i.e., genomics, transcriptomics, proteomics, metabolomics, and lipidomics) has been shown to provide a more complete and informative view of biological pathways. Thus, the prospect of extracting different types of molecules (e.g., DNAs, RNAs, proteins, and metabolites) and performing multiple omics measurements on single samples is very attractive, but such studies are challenging due to the fact that the extraction conditions differ according to the molecule type. Here, we adapted an organic solvent-based extraction method that demonstrated broad applicability and robustness, which enabled comprehensive proteomics, metabolomics, and lipidomics analyses from the same sample. Author Video: An author video summary of this article is available.

Proteomic analysis reveals down-regulation of surfactant protein B in murine type II pneumocytes infected with influenza A virus.

Infection of type II alveolar epithelial (ATII) cells by influenza A viruses (IAV) correlates with severe respiratory disease in humans and mice. To understand pathogenic mechanisms during IAV infection of ATII cells, murine ATII cells were cultured to maintain a differentiated phenotype, infected with IAV-PR8, which causes severe lung pathology in mice, and proteomics analyses were performed using liquid chromatography-mass spectrometry. PR8 infection increased levels of proteins involved in interferon signaling, antigen presentation, and cytoskeleton regulation. Proteins involved in mitochondrial membrane permeability, energy metabolism, and chromatin formation had reduced levels in PR8-infected cells. Phenotypic markers of ATII cells in vivo were identified, confirming the differentiation status of the cultures. Surfactant protein B had decreased levels in PR8-infected cells, which was confirmed by immunoblotting and immunofluorescence assays. Analysis of ATII cell protein profiles will elucidate cellular processes in IAV pathogenesis, which may provide insight into potential therapies to modulate disease severity.

Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics.

In this review, we apply selected imputation strategies to label-free liquid chromatography-mass spectrometry (LC-MS) proteomics datasets to evaluate the accuracy with respect to metrics of variance and classification. We evaluate several commonly used imputation approaches for individual merits and discuss the caveats of each approach with respect to the example LC-MS proteomics data. In general, local similarity-based approaches, such as the regularized expectation maximization and least-squares adaptive algorithms, yield the best overall performances with respect to metrics of accuracy and robustness. However, no single algorithm consistently outperforms the remaining approaches, and in some cases, performing classification without imputation sometimes yielded the most accurate classification. Thus, because of the complex mechanisms of missing data in proteomics, which also vary from peptide to protein, no individual method is a single solution for imputation. On the basis of the observations in this review, the goal for imputation in the field of computational proteomics should be to develop new approaches that work generically for this data type and new strategies to guide users in the selection of the best imputation for their dataset and analysis objectives.

Data integration reveals key homeostatic mechanisms following low dose radiation exposure.

The goal of this study was to define pathways regulated by low dose radiation to understand how biological systems respond to subtle perturbations in their environment and prioritize pathways for human health assessment. Using an in vitro 3-D human full thickness skin model, we have examined the temporal response of dermal and epidermal layers to 10 cGy X-ray using transcriptomic, proteomic, phosphoproteomic and metabolomic platforms. Bioinformatics analysis of each dataset independently revealed potential signaling mechanisms affected by low dose radiation, and integrating data shed additional insight into the mechanisms regulating low dose responses in human tissue. We examined direct interactions among datasets (top down approach) and defined several hubs as significant regulators, including transcription factors (YY1, MYC and CREB1), kinases (CDK2, PLK1) and a protease (MMP2). These data indicate a shift in response across time - with an increase in DNA repair, tissue remodeling and repression of cell proliferation acutely (24-72h). Pathway-based integration (bottom up approach) identified common molecular and pathway responses to low dose radiation, including oxidative stress, nitric oxide signaling and transcriptional regulation through the SP1 factor that would not have been identified by the individual data sets. Significant regulation of key downstream metabolites of nitrative stress was measured within these pathways. Among the features identified in our study, the regulation of MMP2 and SP1 was experimentally validated. Our results demonstrate the advantage of data integration to broadly define the pathways and networks that represent the mechanisms by which complex biological systems respond to perturbation.

Bayesian proteoform modeling improves protein quantification of global proteomic measurements.

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that, with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian Proteoform Quantification model (BP-Quant)(1) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern or the existence of multiple overexpressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab® and R packages.

Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements.

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally-driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian model (BP-Quant) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern, or the existence of multiple over-expressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab ® and R packages at https://github.com/PNNL-Comp-Mass-Spec/BP-Quant.

Pathogenic influenza viruses and coronaviruses utilize similar and contrasting approaches to control interferon-stimulated gene responses.

UNLABELLED: The broad range and diversity of interferon-stimulated genes (ISGs) function to induce an antiviral state within the host, impeding viral pathogenesis. While successful respiratory viruses overcome individual ISG effectors, analysis of the global ISG response and subsequent viral antagonism has yet to be examined. Employing models of the human airway, transcriptomics and proteomics datasets were used to compare ISG response patterns following highly pathogenic H5N1 avian influenza (HPAI) A virus, 2009 pandemic H1N1, severe acute respiratory syndrome coronavirus (SARS-CoV), and Middle East respiratory syndrome CoV (MERS-CoV) infection. The results illustrated distinct approaches utilized by each virus to antagonize the global ISG response. In addition, the data revealed that highly virulent HPAI virus and MERS-CoV induce repressive histone modifications, which downregulate expression of ISG subsets. Notably, influenza A virus NS1 appears to play a central role in this histone-mediated downregulation in highly pathogenic influenza strains. Together, the work demonstrates the existence of unique and common viral strategies for controlling the global ISG response and provides a novel avenue for viral antagonism via altered histone modifications. IMPORTANCE: This work combines systems biology and experimental validation to identify and confirm strategies used by viruses to control the immune response. Using a novel screening approach, specific comparison between highly pathogenic influenza viruses and coronaviruses revealed similarities and differences in strategies to control the interferon and innate immune response. These findings were subsequently confirmed and explored, revealing both a common pathway of antagonism via type I interferon (IFN) delay as well as a novel avenue for control by altered histone modification. Together, the data highlight how comparative systems biology analysis can be combined with experimental validation to derive novel insights into viral pathogenesis.

High and low doses of ionizing radiation induce different secretome profiles in a human skin model.

It is postulated that secreted soluble factors are important contributors of bystander effect and adaptive responses observed in low dose ionizing radiation. Using multidimensional liquid chromatography-mass spectrometry based proteomics, we quantified the changes of skin tissue secretome--the proteins secreted from a full thickness, reconstituted 3-dimensional skin tissue model 48 hr after exposure to 3, 10 and 200 cGy of X-rays. Overall, 135 proteins showed statistical significant difference between the sham (0 cGy) and any of the irradiated groups (3, 10 or 200 cGy) on the basis of Dunnett adjusted t-test; among these, 97 proteins showed a trend of downregulation and 9 proteins showed a trend of upregulation with increasing radiation dose. In addition, there were 21 and 8 proteins observed to have irregular trends with the 10 cGy irradiated group either having the highest or the lowest level among all three radiated doses. Moreover, two proteins, carboxypeptidase E and ubiquitin carboxyl-terminal hydrolase isozyme L1 were sensitive to ionizing radiation, but relatively independent of radiation dose. Conversely, proteasome activator complex subunit 2 protein appeared to be sensitive to the dose of radiation, as rapid upregulation of this protein was observed when radiation doses were increased from 3, to 10 or 200 cGy. These results suggest that different mechanisms of action exist at the secretome level for low and high doses of ionizing radiation.

Advancing the high throughput identification of liver fibrosis protein signatures using multiplexed ion mobility spectrometry.

Rapid diagnosis of disease states using less invasive, safer, and more clinically acceptable approaches than presently employed is a crucial direction for the field of medicine. While MS-based proteomics approaches have attempted to meet these objectives, challenges such as the enormous dynamic range of protein concentrations in clinically relevant biofluid samples coupled with the need to address human biodiversity have slowed their employment. Herein, we report on the use of a new instrumental platform that addresses these challenges by coupling technical advances in rapid gas phase multiplexed ion mobility spectrometry separations with liquid chromatography and MS to dramatically increase measurement sensitivity and throughput, further enabling future high throughput MS-based clinical applications. An initial application of the liquid chromatography--ion mobility spectrometry-MS platform analyzing blood serum samples from 60 postliver transplant patients with recurrent fibrosis progression and 60 nontransplant patients illustrates its potential utility for disease characterization.

A comprehensive collection of systems biology data characterizing the host response to viral infection.

The Systems Biology for Infectious Diseases Research program was established by the U.S. National Institute of Allergy and Infectious Diseases to investigate host-pathogen interactions at a systems level. This program generated 47 transcriptomic and proteomic datasets from 30 studies that investigate in vivo and in vitro host responses to viral infections. Human pathogens in the Orthomyxoviridae and Coronaviridae families, especially pandemic H1N1 and avian H5N1 influenza A viruses and severe acute respiratory syndrome coronavirus (SARS-CoV), were investigated. Study validation was demonstrated via experimental quality control measures and meta-analysis of independent experiments performed under similar conditions. Primary assay results are archived at the GEO and PeptideAtlas public repositories, while processed statistical results together with standardized metadata are publically available at the Influenza Research Database (www.fludb.org) and the Virus Pathogen Resource (www.viprbrc.org). By comparing data from mutant versus wild-type virus and host strains, RNA versus protein differential expression, and infection with genetically similar strains, these data can be used to further investigate genetic and physiological determinants of host responses to viral infection.

A halotyrosine antibody that detects increased protein modifications in asthma patients.

Airway inflammation has a pathophysiological role in asthma. Eosinophils, which are commonly increased in asthmatic airways, express eosinophil peroxidase and thereby produce hypobromite and bromotyrosine. Bromotyrosine is believed to be a specific marker for eosinophil activity, but developing an antibody against monobromotyrosine, the predominant brominated tyrosine residue found in vivo has proven difficult. We evaluated whether a 3-bromobenozoic acid hapten antigen produced antibodies that recognized halogenated tyrosine residues. Studies with small-molecule inhibitors or brominated or chlorinated protein suggested that a mouse monoclonal antibody (BTK-94C) selectively bound free and protein mono- and dibromotyrosine and, to a lesser degree, chlorotyrosine, and thus was designated a general halotyrosine antibody. We evaluated if this antibody had potential for characterizing human asthma using an enzyme-linked immunosorbent assay (ELISA) microarray platform to examine the halogenation of 23 proteins in three independent sets of sputum samples (52 samples total). In 15 healthy control or asthmatic subjects, ICAM, PDGF and RANTES had greater proportional amounts of halogenation in asthmatic subjects and the halogenation signal was associated with the severity of exercise-induced airway hyperresponsiveness. In 17 severe asthma patients treated with placebo or mepolizumab to suppress eosinophils, drug-related decreases in halogenation were observed with p values ranging from 0.006 to 0.11 for these 3 proteins. Analysis of 20 subjects that either had neutrophilic asthma or were healthy controls demonstrated a broad increase in halotyrosine (possibly chlorotyrosine) in neutrophilic asthmatics. Overall, these results suggest that an ELISA utilizing BTK-94C could prove useful for assessing airway inflammation in asthma patients.

A semiautomated framework for integrating expert knowledge into disease marker identification.

BACKGROUND: The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. OBJECTIVE: To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. METHODS: The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. RESULTS: The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. CONCLUSIONS: Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.

A network integration approach to predict conserved regulators related to pathogenicity of influenza and SARS-CoV respiratory viruses.

Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel "crowd-based" approach to identify and combine ranking metrics that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse 'omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models.

Mechanisms of severe acute respiratory syndrome coronavirus-induced acute lung injury.

UNLABELLED: Systems biology offers considerable promise in uncovering novel pathways by which viruses and other microbial pathogens interact with host signaling and expression networks to mediate disease severity. In this study, we have developed an unbiased modeling approach to identify new pathways and network connections mediating acute lung injury, using severe acute respiratory syndrome coronavirus (SARS-CoV) as a model pathogen. We utilized a time course of matched virologic, pathological, and transcriptomic data within a novel methodological framework that can detect pathway enrichment among key highly connected network genes. This unbiased approach produced a high-priority list of 4 genes in one pathway out of over 3,500 genes that were differentially expressed following SARS-CoV infection. With these data, we predicted that the urokinase and other wound repair pathways would regulate lethal versus sublethal disease following SARS-CoV infection in mice. We validated the importance of the urokinase pathway for SARS-CoV disease severity using genetically defined knockout mice, proteomic correlates of pathway activation, and pathological disease severity. The results of these studies demonstrate that a fine balance exists between host coagulation and fibrinolysin pathways regulating pathological disease outcomes, including diffuse alveolar damage and acute lung injury, following infection with highly pathogenic respiratory viruses, such as SARS-CoV. IMPORTANCE: Severe acute respiratory syndrome coronavirus (SARS-CoV) emerged in 2002 and 2003, and infected patients developed an atypical pneumonia, acute lung injury (ALI), and acute respiratory distress syndrome (ARDS) leading to pulmonary fibrosis and death. We identified sets of differentially expressed genes that contribute to ALI and ARDS using lethal and sublethal SARS-CoV infection models. Mathematical prioritization of our gene sets identified the urokinase and extracellular matrix remodeling pathways as the most enriched pathways. By infecting Serpine1-knockout mice, we showed that the urokinase pathway had a significant effect on both lung pathology and overall SARS-CoV pathogenesis. These results demonstrate the effective use of unbiased modeling techniques for identification of high-priority host targets that regulate disease outcomes. Similar transcriptional signatures were noted in 1918 and 2009 H1N1 influenza virus-infected mice, suggesting a common, potentially treatable mechanism in development of virus-induced ALI.

Comparative developmental toxicity of environmentally relevant oxygenated PAHs.

Oxygenated polycyclic aromatic hydrocarbons (OPAHs) are byproducts of combustion and photo-oxidation of parent PAHs. OPAHs are widely present in the environment and pose an unknown hazard to human health. The developing zebrafish was used to evaluate a structurally diverse set of 38 OPAHs for malformation induction, gene expression changes and mitochondrial function. Zebrafish embryos were exposed from 6 to 120h post fertilization (hpf) to a dilution series of 38 different OPAHs and evaluated for 22 developmental endpoints. AHR activation was determined via CYP1A immunohistochemistry. Phenanthrenequinone (9,10-PHEQ), 1,9-benz-10-anthrone (BEZO), xanthone (XAN), benz(a)anthracene-7,12-dione (7,12-B[a]AQ), and 9,10-anthraquinone (9,10-ANTQ) were evaluated for transcriptional responses at 48hpf, prior to the onset of malformations. qRT-PCR was conducted for a number of oxidative stress genes, including the glutathione transferase(gst), glutathione peroxidase(gpx), and superoxide dismutase(sod) families. Bioenergetics was assayed to measure in vivo oxidative stress and mitochondrial function in 26hpf embryos exposed to OPAHs. Hierarchical clustering of the structure-activity outcomes indicated that the most toxic of the OPAHs contained adjacent diones on 6-carbon moieties or terminal, para-diones on multi-ring structures. 5-carbon moieties with adjacent diones were among the least toxic OPAHs while the toxicity of multi-ring structures with more centralized para-diones varied considerably. 9,10-PHEQ, BEZO, 7,12-B[a]AQ, and XAN exposures increased expression of several oxidative stress related genes and decreased oxygen consumption rate (OCR), a measurement of mitochondrial respiration. Comprehensive in vivo characterization of 38 structurally diverse OPAHs indicated differential AHR dependency and a prominent role for oxidative stress in the toxicity mechanisms.

Association of carcinogenic polycyclic aromatic hydrocarbon emissions and smoking with lung cancer mortality rates on a global scale.

The objective of this research was to investigate the relationship between lung cancer mortality rates, carcinogenic polycyclic aromatic hydrocarbon (PAH) emissions, and smoking on a global scale, as well as for different socioeconomic country groups. The estimated lung cancer deaths per 100,000 people (ED100000) and age standardized lung cancer death rate per 100,000 people (ASDR100000) in 2004 were regressed on PAH emissions in benzo[a]pyrene equivalence (BaPeq), smoking prevalence, cigarette price, gross domestic product per capita, percentage of people with diabetes, and average body mass index using simple and multiple linear regression for 136 countries. Using stepwise multiple linear regression, a statistically significant positive linear relationship was found between loge(ED100000) and loge(BaPeq) emissions for high (p-value <0.01) and for the combination of upper-middle and high (p-value <0.05) socioeconomic country groups. A similar relationship was found between loge(ASDR100000) and loge(BaPeq) emissions for the combination of upper-middle and high (p-value <0.01) socioeconomic country groups. Conversely, for loge(ED100000) and loge(ASDR100000), smoking prevalence was the only significant independent variable in the low socioeconomic country group (p-value <0.001). These results suggest that reducing BaPeq emissions in the U.S., Canada, Australia, France, Germany, Brazil, South Africa, Poland, Mexico, and Malaysia could reduce ED100000, while reducing smoking prevalence in Democratic People's Republic of Korea, Nepal, Mongolia, Cambodia, and Bangladesh could significantly reduce the ED100000 and ASDR100000.

Sequential projection pursuit principal component analysis--dealing with missing data associated with new -omics technologies.

Principal Component Analysis (PCA) is a common exploratory tool used to evaluate large complex data sets. The resulting lower-dimensional representations are often valuable for pattern visualization, clustering, or classification of the data. However, PCA cannot be applied directly to many -omics data sets generated by newer technologies such as label-free mass spectrometry due to large numbers of non-random missing values. Here we present a sequential projection pursuit PCA (sppPCA) method for defining principal components in the presence of missing data. Our results demonstrate that this approach generates robust and informative low-dimensional data representations compared to commonly used imputation approaches.

Release of severe acute respiratory syndrome coronavirus nuclear import block enhances host transcription in human lung cells.

The severe acute respiratory syndrome coronavirus accessory protein ORF6 antagonizes interferon signaling by blocking karyopherin-mediated nuclear import processes. Viral nuclear import antagonists, expressed by several highly pathogenic RNA viruses, likely mediate pleiotropic effects on host gene expression, presumably interfering with transcription factors, cytokines, hormones, and/or signaling cascades that occur in response to infection. By bioinformatic and systems biology approaches, we evaluated the impact of nuclear import antagonism on host expression networks by using human lung epithelial cells infected with either wild-type virus or a mutant that does not express ORF6 protein. Microarray analysis revealed significant changes in differential gene expression, with approximately twice as many upregulated genes in the mutant virus samples by 48 h postinfection, despite identical viral titers. Our data demonstrated that ORF6 protein expression attenuates the activity of numerous karyopherin-dependent host transcription factors (VDR, CREB1, SMAD4, p53, EpasI, and Oct3/4) that are critical for establishing antiviral responses and regulating key host responses during virus infection. Results were confirmed by proteomic and chromatin immunoprecipitation assay analyses and in parallel microarray studies using infected primary human airway epithelial cell cultures. The data strongly support the hypothesis that viral antagonists of nuclear import actively manipulate host responses in specific hierarchical patterns, contributing to the viral pathogenic potential in vivo. Importantly, these studies and modeling approaches not only provide templates for evaluating virus antagonism of nuclear import processes but also can reveal candidate cellular genes and pathways that may significantly influence disease outcomes following severe acute respiratory syndrome coronavirus infection in vivo.

A comparative analysis of computational approaches to relative protein quantification using peptide peak intensities in label-free LC-MS proteomics experiments.

Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used to identify and quantify peptides in complex biological samples. In particular, label-free shotgun proteomics is highly effective for the identification of peptides and subsequently obtaining a global protein profile of a sample. As a result, this approach is widely used for discovery studies. Typically, the objective of these discovery studies is to identify proteins that are affected by some condition of interest (e.g. disease, exposure). However, for complex biological samples, label-free LC-MS proteomics experiments measure peptides and do not directly yield protein quantities. Thus, protein quantification must be inferred from one or more measured peptides. In recent years, many computational approaches to relative protein quantification of label-free LC-MS data have been published. In this review, we examine the most commonly employed quantification approaches to relative protein abundance from peak intensity values, evaluate their individual merits, and discuss challenges in the use of the various computational approaches.

An approach for calculating a confidence interval from a single aquatic sample for monitoring hydrophobic organic contaminants.

The use of passive sampling devices (PSDs) for monitoring hydrophobic organic contaminants in aquatic environments can entail logistical constraints that often limit a comprehensive statistical sampling plan, thus resulting in a restricted number of samples. The present study demonstrates an approach for using the results of a pilot study designed to estimate sampling variability, which in turn can be used as variance estimates for confidence intervals for future n = 1 PSD samples of the same aquatic system. Sets of three to five PSDs were deployed in the Portland Harbor Superfund site for three sampling periods over the course of two years. The PSD filters were extracted and, as a composite sample, analyzed for 33 polycyclic aromatic hydrocarbon compounds. The between-sample and within-sample variances were calculated to characterize sources of variability in the environment and sampling methodology. A method for calculating a statistically reliable and defensible confidence interval for the mean of a single aquatic passive sampler observation (i.e., n = 1) using an estimate of sample variance derived from a pilot study is presented. Coverage probabilities are explored over a range of variance values using a Monte Carlo simulation.