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

ABSTRACT 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 m etabolite, p rotein, and l ipid ex traction (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

Improved quality control processing of peptide-centric LC-MS proteomics data

Motivation: In the analysis of differential peptide peak intensities (i.e. abundance measures), LC-MS analyses with poor quality peptide abundance data can bias downstream statistical analyses and hence the biological interpretation for an otherwise high-quality dataset. Although considerable effort has been placed on assuring the quality of the peptide identification with respect to spectral processing, to date quality assessment of the subsequent peptide abundance data matrix has been limited to a subjective visual inspection of run-by-run correlation or individual peptide components. Identifying statistical outliers is a critical step in the processing of proteomics data as many of the downstream statistical analyses [e.g. analysis of variance (ANOVA)] rely upon accurate estimates of sample variance, and their results are influenced by extreme values

Mechanisms of Severe Acute Respiratory Syndrome Coronavirus-Induced Acute Lung Injury

ABSTRACT 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.IMPORTANCESevere 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

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 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

Pathogenic Influenza Viruses and Coronaviruses Utilize Similar and Contrasting Approaches To Control Interferon-Stimulated Gene Responses

ABSTRACT 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.IMPORTANCEThis 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

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 log_e(ED100000) and log_e(BaPeq) emissions for high (<i>p</i>-value <0.01) and for the combination of upper-middle and high (<i>p</i>-value <0.05) socioeconomic country groups. A similar relationship was found between log_e(ASDR100000) and log_e(BaPeq) emissions for the combination of upper-middle and high (<i>p</i>-value <0.01) socioeconomic country groups. Conversely, for log_e(ED100000) and log_e(ASDR100000), smoking prevalence was the only significant independent variable in the low socioeconomic country group (<i>p</i>-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

Combined Statistical Analyses of Peptide Intensities and Peptide Occurrences Improves Identification of Significant Peptides from MS-Based Proteomics Data

Liquid chromatography−mass spectrometry-based (LC−MS) proteomics uses peak intensities of proteolytic peptides to infer the differential abundance of peptides/proteins. However, substantial run-to-run variability in intensities and observations (presence/absence) of peptides makes data analysis quite challenging. The missing observations in LC−MS proteomics data are difficult to address with traditional imputation-based approaches because the mechanisms by which data are missing are unknown a priori. Data can be missing due to random mechanisms such as experimental error or nonrandom mechanisms such as a true biological effect. We present a statistical approach that uses a test of independence known as a G-test to test the null hypothesis of independence between the number of missing values across experimental groups. We pair the G-test results, evaluating independence of missing data (IMD) with an analysis of variance (ANOVA) that uses only means and variances computed from the observed data. Each peptide is therefore represented by two statistical confidence metrics, one for qualitative differential observation and one for quantitative differential intensity. We use three LC−MS data sets to demonstrate the robustness and sensitivity of the IMD−ANOVA approach

Effect of Native American Fish Smoking Methods on Dietary Exposure to Polycyclic Aromatic Hydrocarbons and Possible Risks to Human Health

Although it is known that polycyclic aromatic hydrocarbons (PAHs) can be found in smoked meats, little is known about their prevalence in Native American smoked fish. In this work, the effect of traditional Native American fish smoking methods on dietary exposure to PAHs and possible risks to human health has been assessed. Smoking methods considered smoking structure (tipi or shed) and wood type (apple or alder). Neither smoking structure nor wood type accounted for differences in smoked salmon content of 33 PAHs. Carcinogenic and noncarcinogenic PAH loads in traditionally smoked salmon were 40–430 times higher than those measured in commercial products. Dietary exposure to PAHs could result in excess lifetime cancer risks between 1 × 10^–5 and 1 × 10^–4 at a daily consumption rate of 5 g d^–1 and could approach 1 × 10^–2 at 300 g d^–1. Hazard indexes approached 0.005 at 5 g d^–1, or approximately 0.3 at 300 g d^–1. Levels of PAHs present in smoked salmon prepared using traditional Native American methods may pose elevated cancer risks if consumed at high consumption rates over many years