zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation

Isobaric labeling-based mass spectrometry (ILMS) has been widely used to quantify, on a proteome-wide scale, the relative protein abundance in different biological conditions. However, large-scale ILMS data sets typically involve multiple runs of mass spectrometry, bringing great computational difficulty to the integration of ILMS samples. We present zMAP, a toolset that makes ILMS intensities comparable across mass spectrometry runs by modeling the associated mean-variance dependence and accordingly applying a variance stabilizing z-transformation. The practical utility of zMAP is demonstrated in several case studies involving the dynamics of cell differentiation and the heterogeneity across cancer patients.

Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections

Microbial pathogens have evolved numerous mechanisms to hijack host's systems, thus causing disease. This is mediated by alterations in the combined host-pathogen proteome in time and space. Mass spectrometry-based proteomics approaches have been developed and tailored to map disease progression. The result is complex multidimensional data that pose numerous analytic challenges for downstream interpretation. However, a systematic review of approaches for the downstream analysis of such data has been lacking in the field. In this review, we detail the steps of a typical temporal and spatial analysis, including data pre-processing steps (i.e., quality control, data normalization, the imputation of missing values, and dimensionality reduction), different statistical and machine learning approaches, validation, interpretation, and the extraction of biological information from mass spectrometry data. We also discuss current best practices for these steps based on a collection of independent studies to guide users in selecting the most suitable strategies for their dataset and analysis objectives. Moreover, we also compiled the list of commonly used R software packages for each step of the analysis. These could be easily integrated into one's analysis pipeline. Furthermore, we guide readers through various analysis steps by applying these workflows to mock and host-pathogen interaction data from public datasets. The workflows presented in this review will serve as an introduction for data analysis novices, while also helping established users update their data analysis pipelines. We conclude the review by discussing future directions and developments in temporal and spatial proteomics and data analysis approaches. Data analysis codes, prepared for this review are available from https://github.com/BabuLab-UofR/TempSpac, where guidelines and sample datasets are also offered for testing purposes.

Multi-Q 2 software facilitates isobaric labeling quantitation analysis with improved accuracy and coverage

Mass spectrometry-based proteomics using isobaric labeling for multiplex quantitation has become a popular approach for proteomic studies. We present Multi-Q 2, an isobaric-labeling quantitation tool which can yield the largest quantitation coverage and improved quantitation accuracy compared to three state-of-the-art methods. Multi-Q 2 supports identification results from several popular proteomic data analysis platforms for quantitation, offering up to 12% improvement in quantitation coverage for accepting identification results from multiple search engines when compared with MaxQuant and PatternLab. It is equipped with various quantitation algorithms, including a ratio compression correction algorithm, and results in up to 336 algorithmic combinations. Systematic evaluation shows different algorithmic combinations have different strengths and are suitable for different situations. We also demonstrate that the flexibility of Multi-Q 2 in customizing algorithmic combination can lead to improved quantitation accuracy over existing tools. Moreover, the use of complementary algorithmic combinations can be an effective strategy to enhance sensitivity when searching for biomarkers from differentially expressed proteins in proteomic experiments. Multi-Q 2 provides interactive graphical interfaces to process quantitation and to display ratios at protein, peptide, and spectrum levels. It also supports a heatmap module, enabling users to cluster proteins based on their abundance ratios and to visualize the clustering results. Multi-Q 2 executable files, sample data sets, and user manual are freely available at http://ms.iis.sinica.edu.tw/COmics/Software_Multi-Q2.html.

Identification of a Biomarker Panel for Early Detection of Lung Cancer Patients

Lung cancer is the most common cause of cancer-related mortality worldwide, characterized by late clinical presentation (49-53% of patients are diagnosed at stage IV) and consequently poor outcomes. One challenge in identifying biomarkers of early disease is the collection of samples from patients prior to symptomatic presentation. We used blood collected during surgical resection of lung tumors in an iTRAQ isobaric tagging experiment to identify proteins effluxing from tumors into pulmonary veins. Forty proteins were identified as having an increased abundance in the vein draining from the tumor compared to "healthy" pulmonary veins. These protein markers were then assessed in a second cohort that utilized the mass spectrometry (MS) technique: Sequential window acquisition of all theoretical fragment ion spectra (SWATH) MS. SWATH-MS was used to measure proteins in serum samples taken from 25 patients <50 months prior to and at lung cancer diagnosis and 25 matched controls. The SWATH-MS analysis alone produced an 11 protein marker panel. A machine learning classification model was generated that could discriminate patient samples from patients within 12 months of lung cancer diagnosis and control samples. The model was evaluated as having a mean AUC of 0.89, with an accuracy of 0.89. This panel was combined with the SWATH-MS data from one of the markers from the first cohort to create a 12 protein panel. The proteome signature developed for lung cancer risk can now be developed on further cohorts.

Practical Integration of Multi-Run iTRAQ Data

In this chapter, we describe some of the approaches we employ in the analysis of iTRAQ data in our group, with an emphasis on practical issues that can occur in larger multi-run projects. Our pipeline starts with a well-established iTRAQ workflow, makes use of protein level quantitation using ProteinPilot, and continues either via a global analysis in the presence of a common reference, or by identifying pairwise comparisons of interest and applying a method taking the protein ratios and protein ratio confidence measures into consideration. Additionally we describe what issues can occur in the more subtle scenarios involving composite databases in multi-run situations, and an approach applicable in that setting.

MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes

Isotope-labeling-based mass spectrometry (MS) is widely used in quantitative proteomic studies. With this technique, the relative abundance of thousands of proteins can be efficiently profiled in parallel, greatly facilitating the detection of proteins differentially expressed across samples. However, this task remains computationally challenging. Here we present a new approach, termed Model-based Analysis of Proteomic data (MAP), for this task. Unlike many existing methods, MAP does not require technical replicates to model technical and systematic errors, and instead utilizes a novel step-by-step regression analysis to directly assess the significance of observed protein abundance changes. We applied MAP to compare the proteomic profiles of undifferentiated and differentiated mouse embryonic stem cells (mESCs), and found it has superior performance compared with existing tools in detecting proteins differentially expressed during mESC differentiation. A web-based application of MAP is provided for online data processing at http://bioinfo.sibs.ac.cn/shaolab/MAP.

Proteomic analysis of labial saliva of the generalist cabbage looper (Trichoplusia ni) and its role in interactions with host plants

Insect saliva is one of the first secretions to come in contact with plants during feeding. The composition and role of caterpillar saliva has not been as thoroughly studied as that of sucking insects. This study focuses on characterizing the proteome of the cabbage looper (Trichoplusia ni) saliva using iTRAQ labeling and LC-MS/MS. We also measured how the saliva proteome changed when larvae were reared on different diets - cabbage, tomato, and an artificial pinto bean diet. We identified 254 proteins in the saliva out of which 63 were differentially expressed. A large percentage (56%) of the proteins identified function in protein metabolism, followed by proteins involved in vesicle transport (6%) and oxidoreductase activity (5%), among other categories. Several proteins identified are antioxidants or reactive oxygen species (ROS) scavengers. Among these ROS scavengers, we identified a catalase and further analyzed its gene expression and enzymatic activity. We also applied commercial, purified catalase on tomato and measured the activity of defensive proteins - trypsin proteinase inhibitor, polyphenol oxidase and peroxidase. Catalase gene expression was significantly higher in the salivary glands of larvae fed on tomato. Also, catalase suppressed the induction of tomato trypsin proteinase inhibitor levels, but not the induction of polyphenol oxidase or peroxidase. These results add to our understanding of proteomic plasticity in saliva and its role in herbivore offense against plant defenses.

Quantitative proteomic analysis of the fall armyworm saliva

Lepidopteran larvae secrete saliva on plant tissues during feeding. Components in the saliva may aid in food digestion, whereas other components are recognized by plants as cues to elicit defense responses. Despite the ecological and economical importance of these plant-feeding insects, knowledge of their saliva composition is limited to a few species. In this study, we identified the salivary proteins of larvae of the fall armyworm (FAW), Spodoptera frugiperda; determined qualitative and quantitative differences in the salivary proteome of the two host races-corn and rice strains-of this insect; and identified changes in total protein concentration and relative protein abundance in the saliva of FAW larvae associated with different host plants. Quantitative proteomic analyses were performed using labeling with isobaric tags for relative and absolute quantification followed by liquid chromatography-tandem mass spectrometry. In total, 98 proteins were identified (>99% confidence) in the FAW saliva. These proteins were further categorized into five functional groups: proteins potentially involved in (1) plant defense regulation, (2) herbivore offense, (3) insect immunity, (4) detoxification, (5) digestion, and (6) other functions. Moreover, there were differences in the salivary proteome between the FAW strains that were identified by label-free proteomic analyses. Thirteen differentially identified proteins were present in each strain. There were also differences in the relative abundance of eleven salivary proteins between the two FAW host strains as well as differences within each strain associated with different diets. The total salivary protein concentration was also different for the two strains reared on different host plants. Based on these results, we conclude that the FAW saliva contains a complex mixture of proteins involved in different functions that are specific for each strain and its composition can change plastically in response to diet type.

Fall Armyworm-Associated Gut Bacteria Modulate Plant Defense Responses

Mechanical damage caused by insect feeding along with components present in insect saliva and oral secretions are known to induce jasmonic acid-mediated defense responses in plants. This study investigated the effects of bacteria from oral secretions of the fall armyworm Spodoptera frugiperda on herbivore-induced defenses in tomato and maize plants. Using culture-dependent methods, we identified seven different bacterial isolates belonging to the family Enterobacteriacea from the oral secretions of field-collected caterpillars. Two isolates, Pantoea ananatis and Enterobacteriaceae-1, downregulated the activity of the plant defensive proteins polyphenol oxidase and trypsin proteinase inhibitors (trypsin PI) but upregulated peroxidase (POX) activity in tomato. A Raoultella sp. and a Klebsiella sp. downregulated POX but upregulated trypsin PI in this plant species. Conversely, all of these bacterial isolates upregulated the expression of the herbivore-induced maize proteinase inhibitor (mpi) gene in maize. Plant treatment with P. ananatis and Enterobacteriaceae-1 enhanced caterpillar growth on tomato but diminished their growth on maize plants. Our results highlight the importance of herbivore-associated microbes and their ability to mediate insect plant interactions differently in host plants fed on by the same herbivore.

Differential expression analysis of the broiler tracheal proteins responsible for the immune response and muscle contraction induced by high concentration of ammonia using iTRAQ-coupled 2D LC-MS/MS

Ammonia has been considered the contaminant primarily responsible for respiratory disease in poultry. Even though it can cause tracheal lesions, its adverse effects on the trachea have not been sufficiently studied. The present study investigated tracheal changes in Arbor Acres broilers (Gallus gallus) induced by high concentration of ammonia using isobaric tag for relative and absolute quantification (iTRAQ)-based proteome analysis. In total, 3,706 proteins within false discovery rate of 1% were identified, including 119 significantly differentially expressed proteins. Functional analysis revealed that proteins related to immune response and muscle contraction were significantly enriched. With respect to the immune response, up-regulated proteins (like FGA) were pro-inflammatory, while down-regulated proteins participated in antigen processing and antigen presenting (like MYO1G), immunoglobulin and cathelicidin production (like fowlicidin-2), and immunodeficiency (like PTPRC). Regarding muscle contraction, all differentially expressed proteins (like TPM1) were up-regulated. An over-expression of mucin, which is a common feature of airway disease, was also observed. Additionally, the transcriptional alterations of 6 selected proteins were analyzed by quantitative RT-PCR. Overall, proteomic changes suggested the onset of airway obstruction and diminished host defense in trachea after ammonia exposure. These results may serve as a valuable reference for future interventions against ammonia toxicity.

On the Statistical Significance of Compressed Ratios in Isobaric Labeling: A Cross-Platform Comparison

Isobaric labeling is gaining popularity in proteomics due to its multiplexing capacity. However, copeptide fragmentation introduces a bias that undermines its accuracy. Several strategies have been shown to partially and, in some cases, completely solve this issue. However, it is still not clear how ratio compression affects the ability to identify a protein's change of abundance as statistically significant. Here, by using the "two proteomes" approach (E. coli lysates with fixed 2.5 ratios in the presence or absence of human lysates acting as the background interference) and manipulating isolation width values, we were able to model isobaric data with different levels of accuracy and precision in three types of mass spectrometers: LTQ Orbitrap Velos, Impact, and Q Exactive. We determined the influence of these variables on the statistical significance of the distorted ratios and compared them to the ratios measured without impurities. Our results confirm previous findings1-4 regarding the importance of optimizing acquisition parameters in each instrument in order to minimize interference without compromising precision and identification. We also show that, under these experimental conditions, the inclusion of a second replicate increases statistical sensitivity 2-3-fold and counterbalances to a large extent the issue of ratio compression.

Protein Z: A putative novel biomarker for early detection of ovarian cancer

Ovarian cancer (OC) has the highest mortality of all gynaecological cancers. Early diagnosis offers an approach to achieving better outcomes. We conducted a blinded-evaluation of prospectively collected preclinical serum from participants in the multimodal group of the United Kingdom Collaborative Trial of Ovarian Cancer Screening. Using isobaric tags (iTRAQ) we identified 90 proteins differentially expressed between OC cases and controls. A second targeted mass spectrometry analysis of twenty of these candidates identified Protein Z as a potential early detection biomarker for OC. This was further validated by ELISA analysis in 482 serial serum samples, from 80 individuals, 49 OC cases and 31 controls, spanning up to 7 years prior to diagnosis. Protein Z was significantly down-regulated up to 2 years pre-diagnosis (p = 0.000000411) in 8 of 19 Type I patients whilst in 5 Type II individuals, it was significantly up-regulated up to 4 years before diagnosis (p = 0.01). ROC curve analysis for CA-125 and CA-125 combined with Protein Z showed a statistically significant (p = 0.00033) increase in the AUC from 77 to 81% for Type I and a statistically significant (p= 0.00003) increase in the AUC from 76 to 82% for Type II. Protein Z is a novel independent early detection biomarker for Type I and Type II ovarian cancer; which can discriminate between both types. Protein Z also adds to CA-125 and potentially the Risk of Ovarian Cancer algorithm in the detection of both subtypes.

iPQF: a new peptide-to-protein summarization method using peptide spectra characteristics to improve protein quantification

MOTIVATION

Isobaric labelling techniques such as iTRAQ and TMT are popular methods for relative protein abundance estimation in proteomic studies. However, measurements are assessed at the peptide spectrum level and exhibit substantial heterogeneity per protein. Hence, clever summarization strategies are required to infer protein ratios. So far, current methods rely exclusively on quantitative values, while additional information on peptides is available, yet it is not considered in these methods.

METHODS

We present iPQF ( I: sobaric P: rotein Q: uantification based on F: eatures) as a novel peptide-to-protein summarization method, which integrates peptide spectra characteristics as well as quantitative values for protein ratio estimation. We investigate diverse features characterizing spectra reliability and reveal significant correlations to ratio accuracy in spectra. As a result, we developed a feature-based weighting of peptide spectra.

RESULTS

A performance evaluation of iPQF in comparison to nine different protein ratio inference methods is conducted on five published MS2 and MS3 datasets with predefined ground truth. We demonstrate the benefit of using peptide feature information to improve protein ratio estimation. Compared to purely quantitative approaches, our proposed strategy achieves increased accuracy by addressing peptide spectra reliability.

AVAILABILITY AND IMPLEMENTATION

The iPQF algorithm is available within the established R/Bioconductor package MSnbase (version ≥ 1.17.8).

CONTACT

renardB@rki.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Discovery and Validation of Predictive Biomarkers of Survival for Non-small Cell Lung Cancer Patients Undergoing Radical Radiotherapy: Two Proteins With Predictive Value

Lung cancer is the most frequent cause of cancer-related death world-wide. Radiotherapy alone or in conjunction with chemotherapy is the standard treatment for locally advanced non-small cell lung cancer (NSCLC). Currently there is no predictive marker with clinical utility to guide treatment decisions in NSCLC patients undergoing radiotherapy. Identification of such markers would allow treatment options to be considered for more effective therapy. To enable the identification of appropriate protein biomarkers, plasma samples were collected from patients with non-small cell lung cancer before and during radiotherapy for longitudinal comparison following a protocol that carries sufficient power for effective discovery proteomics. Plasma samples from patients pre- and during radiotherapy who had survived > 18 mo were compared to the same time points from patients who survived < 14 mo using an 8 channel isobaric tagging tandem mass spectrometry discovery proteomics platform. Over 650 proteins were detected and relatively quantified. Proteins which showed a change during radiotherapy were selected for validation using an orthogonal antibody-based approach. Two of these proteins were verified in a separate patient cohort: values of CRP and LRG1 combined gave a highly significant indication of extended survival post one week of radiotherapy treatment.