Comparative LC-MS: a landscape of peaks and valleys

Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects

Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.

Uncovering the seasonal brain: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) as a biochemical approach for studying seasonal social behaviors

Many animals show pronounced changes in physiology and behavior across the annual cycle, and these adaptations enable individuals to prioritize investing in the neuroendocrine mechanisms underlying reproduction and/or survival based on the time of year. While prior research has offered valuable insight into how seasonal variation in neuroendocrine processes regulates social behavior, the majority of these studies have investigated how a single hormone influences a single behavioral phenotype. Given that hormones are synthesized and metabolized via complex biochemical pathways and often act in concert to control social behavior, these approaches provide a limited view of how hormones regulate seasonal changes in behavior. In this review, we discuss how seasonal influences on hormones, the brain, and social behavior can be studied using liquid chromatography-tandem mass spectrometry (LC-MS/MS), an analytical chemistry technique that enables researchers to simultaneously quantify the concentrations of multiple hormones and the activities of their synthetic enzymes. First, we examine studies that have investigated seasonal plasticity in brain-behavior interactions, specifically by focusing on how two groups of hormones, sex steroids and nonapeptides, regulate sexual and aggressive behavior. Then, we explain the operations of LC-MS/MS, highlight studies that have used LC-MS/MS to study the neuroendocrine mechanisms underlying social behavior, both within and outside of a seasonal context, and discuss potential applications for LC-MS/MS in the field of behavioral neuroendocrinology. We propose that this cutting-edge technology will provide a more comprehensive understanding of how the multitude of hormones that comprise complex neuroendocrine networks affect seasonal variation in the brain and behavior.

New advances in quantitative proteomics research and current applications in asthma

INTRODUCTION

Asthma is the most common chronic respiratory disease and has been declared a global public health problem by the World Health Organization. Due to the high heterogeneity and complexity, asthma can be classified into different 'phenotypes' and it is still difficult to assess the phenotypes and stages of asthma by traditional methods. In recent years, mass spectrometry-based proteomics studies have made significant progress in sensitivity and accuracy of protein identification and quantitation, and are able to obtain differences in protein expression across samples, which provides new insights into the mechanisms and classification of asthma.

AREAS COVERED

In this article, we summarize research strategies in quantitative proteomics, including labeled, label-free and targeted quantification, and highlight the advantages and disadvantages of each. In addition, new applications of quantitative proteomics and the current status of research in asthma have also been discussed. In this study, online resources such as PubMed and Google Scholar were used for literature retrieval.

EXPERT OPINION

The application of quantitative proteomics in asthma has an important role in identifying asthma subphenotypes, revealing potential pathogenesis and therapeutic targets. But the proteomic studies on asthma are not sufficient, as most of them are in the phase of biomarker discovery.

Tissue Proteomic Approaches to Understand the Pathogenesis of Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) has become a global disease encompassing a group of progressive disorders characterized by recurrent chronic inflammation of the gut with variable disease courses and complications. Despite recent advances in the knowledge of IBD pathophysiology, the elucidation of its etiopathology and progression is far from fully understood, requiring complex and multiple approaches. Therefore, limited clinical progress in diagnosis, assessment of disease activity, and optimal therapeutic regimens have been made over the past few decades. This review explores recent advances and challenges in tissue proteomics with an emphasis on biomarker discovery and better understanding of the molecular mechanisms underlying IBD pathogenesis. Future multi-omic studies are required for the comprehensive molecular characterization of disease biology in real time with a future impact on early detection, disease monitoring, and prediction of the clinical outcome.

Insight into the Metabolic Profiles of Pb(II) Removing Microorganisms

The objective of the study was to gather insight into the metabolism of lead-removing microorganisms, coupled with Pb(II) removal, biomass viability and nitrate concentrations for Pb(II) bioremoval using an industrially obtained microbial consortium. The consortium used for study has proven to be highly effective at removing aqueous Pb(II) from solution. Anaerobic batch experiments were conducted with Luria-Bertani broth as rich growth medium over a period of 33 h, comparing a lower concentration of Pb(II) with a higher concentration at two different nutrient concentrations. Metabolite profiling and quantification were conducted with the aid of both liquid chromatography coupled with tandem mass spectroscopy (UPLC-HDMS) in a “non-targeted” fashion and high-performance liquid chromatography (HPLC) in a “targeted” fashion. Four main compounds were identified, and a metabolic study was conducted on each to establish their possible significance for Pb(II) bioremoval. The study investigates the first metabolic profile to date for Pb(II) bioremoval, which in turn can result in a clarified understanding for development on an industrial and microbial level.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Proline: an efficient and user-friendly software suite for large-scale proteomics

Motivation

The proteomics field requires the production and publication of reliable mass spectrometry-based identification and quantification results. Although many tools or algorithms exist, very few consider the importance of combining, in a unique software environment, efficient processing algorithms and a data management system to process and curate hundreds of datasets associated with a single proteomics study.

Results

Here, we present Proline, a robust software suite for analysis of MS-based proteomics data, which collects, processes and allows visualization and publication of proteomics datasets. We illustrate its ease of use for various steps in the validation and quantification workflow, its data curation capabilities and its computational efficiency. The DDA label-free quantification workflow efficiency was assessed by comparing results obtained with Proline to those obtained with a widely used software using a spiked-in sample. This assessment demonstrated Proline’s ability to provide high quantification accuracy in a user-friendly interface for datasets of any size.

Availability and implementation

Proline is available for Windows and Linux under CECILL open-source license. It can be deployed in client–server mode or in standalone mode at http://proline.profiproteomics.fr/#downloads.

Supplementary information

Supplementary data are available at Bioinformatics online.

Update on Proteomic approaches to uncovering virus-induced protein alterations and virus -host protein interactions during the progression of viral infection

INTRODUCTION

Viruses induce profound changes in the cells they infect. Understanding these perturbations will assist in designing better therapeutics to combat viral infection. System-based proteomic assays now provide unprecedented opportunity to monitor large numbers of cellular proteins.

AREAS COVERED

This review will describe various quantitative and functional mass spectrometry-based methods, and complementary non-mass spectrometry-based methods, such as aptamer profiling and proximity extension assays, and examples of how each are used to delineate how viruses affect host cells, identify which viral proteins interact with which cellular proteins, and how these change during the course of a viral infection. PubMed was searched multiple times prior to manuscript submissions and revisions, using virus, viral, proteomics; in combination with each keyword. The most recent examples of published works from each search were then analyzed.

EXPERT OPINION

There has been exponential growth in numbers and types of proteomic analyses in recent years. Continued development of reagents that allow increased multiplexing and deeper proteomic probing of the cell, at quantitative and functional levels, enhancements that target more important protein modifications, and improved bioinformatics software tools and pathway prediction algorithms will accelerate this growth and usher in a new era of host proteome understanding.

The Implementation of Mass Spectrometry-Based Proteomics Workflows in Clinical Routines of Acute Myeloid Leukemia: Applicability and Perspectives

With the current reproducibility of proteome preparation workflows along with the speed and sensitivity of the mass spectrometers, the transition of the mass spectrometry (MS)-based proteomics technology from biomarker discovery to clinical implementation is under appraisal in the biomedicine community. Therefore, this technology might be implemented soon to detect well-known biomarkers in cancers and other diseases. Acute myeloid leukemia (AML) is an aggressive heterogeneous malignancy that requires intensive treatment to cure the patient. Leukemia relapse is still a major challenge even for patients who have favorable genetic abnormalities. MS-based proteomics could be of great help to both describe the proteome changes of individual patients and identify biomarkers that might encourage specific treatments or clinical strategies. Herein, we will review the advances and availability of the MS-based proteomics strategies that could already be used in clinical proteomics. However, the heterogeneity of complex diseases as AML requires consensus to recognize AML biomarkers and to establish MS-based workflows that allow their unbiased identification and quantification. Although our literature review appears promising towards the utilization of MS-based proteomics in clinical AML in a near future, major efforts are required to validate AML biomarkers and agree on clinically approved workflows.

Refinements of LC-MS/MS Spectral Counting Statistics Improve Quantification of Low Abundance Proteins

Mass spectrometry-based spectral count has been a common choice of label-free proteome quantification due to the simplicity for the sample preparation and data generation. The discriminatory nature of spectral count in the MS data-dependent acquisition, however, inherently introduces the spectral count variation for low-abundance proteins in multiplicative LC-MS/MS analysis, which hampers sensitive proteome quantification. As many low-abundance proteins play important roles in cellular processes, deducing low-abundance proteins in a quantitatively reliable manner greatly expands the depth of biological insights. Here, we implemented the Moment Adjusted Imputation error model in the spectral count refinement as a post PLGEM-STN for improving sensitivity for quantitation of low-abundance proteins by reducing spectral count variability. The statistical framework, automated spectral count refinement by integrating the two statistical tools, was tested with LC-MS/MS datasets of MDA-MB468 breast cancer cells grown under normal and glucose deprivation conditions. We identified about 30% more quantifiable proteins that were found to be low-abundance proteins, which were initially filtered out by the PLGEM-STN analysis. This newly developed statistical framework provides a reliable abundance measurement of low-abundance proteins in the spectral count-based label-free proteome quantification and enabled us to detect low-abundance proteins that could be functionally important in cellular processes.

The regulon of the RNA chaperone CspA and its auto-regulation in Staphylococcus aureus

RNA-binding proteins (RBPs) are essential to fine-tune gene expression. RBPs containing the cold-shock domain are RNA chaperones that have been extensively studied. However, the RNA targets and specific functions for many of them remain elusive. Here, combining comparative proteomics and RBP-immunoprecipitation-microarray profiling, we have determined the regulon of the RNA chaperone CspA of Staphylococcus aureus. Functional analysis revealed that proteins involved in carbohydrate and ribonucleotide metabolism, stress response and virulence gene expression were affected by cspA deletion. Stress-associated phenotypes such as increased bacterial aggregation and diminished resistance to oxidative-stress stood out. Integration of the proteome and targetome showed that CspA post-transcriptionally modulates both positively and negatively the expression of its targets, denoting additional functions to the previously proposed translation enhancement. One of these repressed targets was its own mRNA, indicating the presence of a negative post-transcriptional feedback loop. CspA bound the 5′UTR of its own mRNA disrupting a hairpin, which was previously described as an RNase III target. Thus, deletion of the cspA 5′UTR abrogated mRNA processing and auto-regulation. We propose that CspA interacts through a U-rich motif, which is located at the RNase III cleavage site, portraying CspA as a putative RNase III-antagonist.

De Novo Design and Implementation of a Tandem Acyl Carrier Protein Domain in a Type I Modular Polyketide Synthase

During polyketide and fatty acid biosynthesis, the growing acyl chain is attached to the acyl carrier protein via a thioester linkage. The acyl carrier protein interacts with other enzymes that perform chain elongation and chain modification on the bound acyl chain. Most type I polyketide synthases and fatty acid synthases contain only one acyl carrier protein. However, polyunsaturated fatty acid synthases from deep-sea bacteria contain anywhere from two to nine acyl carrier proteins. Recent studies have shown that this tandem acyl carrier protein feature is responsible for the unusually high fatty acid production rate of deep-sea bacteria. To investigate if a similar strategy can be used to increase the production rate of type I polyketide synthases, a 3×ACP domain was rationally designed and genetically installed in module 6 of 6-deoxyerythronolide B synthase, which is a prototypical type I modular polyketide synthase that naturally harbors just one acyl carrier protein. This modification resulted in an ∼2.5-fold increase in the total amount of polyketide produced in vitro, demonstrating that installing a tandem acyl carrier domain in a type I polyketide synthase is an effective strategy for enhancing the rate of polyketide natural product biosynthesis.

Efficient Quantitative Comparisons of Plasma Proteomes Using Label-Free Analysis with MaxQuant

Mass spectrometry (MS)-based quantitation of plasma proteomes is challenging due to the extremely wide dynamic range and molecular heterogeneity of plasma samples. However, recent advances in technology, MS instrumentation, and bioinformatics have enabled in-depth quantitative analyses of very complex proteomes, including plasma. Specifically, recent improvements in both label-based and label-free quantitation strategies have allowed highly accurate quantitative comparisons of expansive proteome datasets. Here we present a method for in-depth label-free analysis of human plasma samples using MaxQuant.

BatMass: a Java Software Platform for LC-MS Data Visualization in Proteomics and Metabolomics

Mass spectrometry (MS) coupled to liquid chromatography (LC) is a commonly used technique in metabolomic and proteomic research. As the size and complexity of LC-MS-based experiments grow, it becomes increasingly more difficult to perform quality control of both raw data and processing results. In a practical setting, quality control steps for raw LC-MS data are often overlooked, and assessment of an experiment's success is based on some derived metrics such as "the number of identified compounds". The human brain interprets visual data much better than plain text, hence the saying "a picture is worth a thousand words". Here, we present the BatMass software package, which allows for performing quick quality control of raw LC-MS data through its fast visualization capabilities. It also serves as a testbed for developers of LC-MS data processing algorithms by providing a data access library for open mass spectrometry file formats and a means of visually mapping processing results back to the original data. We illustrate the utility of BatMass with several use cases of quality control and data exploration.

Proteome catalog of Zymoseptoria tritici captured during pathogenesis in wheat

Zymoseptoria tritici is an economically important pathogen of wheat. However, the molecular basis of pathogenicity on wheat is still poorly understood. Here, we present a global survey of the proteins secreted by this fungus in the apoplast of resistant (cv. Shafir) and susceptible (cv. Obelisk) wheat cultivars after inoculation with reference Z. tritici strain IPO323. The fungal proteins present in apoplastic fluids were analyzed by gel electrophoresis and by data-independent acquisition liquid chromatography/mass spectrometry (LC/MS(E)) combined with data-dependent acquisition LC-MS/MS. Subsequent mapping mass spectrometry-derived peptide sequence data against the genome sequence of strain IPO323 identified 665 peptides in the MS(E) and 93 in the LC-MS/MS mode that matched to 85 proteins. The identified fungal proteins, including cell-wall degrading enzymes and proteases, might function in pathogenicity, but the functions of many remain unknown. Most fungal proteins accumulated in cv. Obelisk at the onset of necrotrophy. This inventory provides an excellent basis for future detailed studies on the role of these genes and their encoded proteins during pathogenesis in wheat.

Analysis of the Cerebrospinal Fluid Proteome in Alzheimer's Disease

Alzheimer’s disease is a neurodegenerative disorder accounting for more than 50% of cases of dementia. Diagnosis of Alzheimer’s disease relies on cognitive tests and analysis of amyloid beta, protein tau, and hyperphosphorylated tau in cerebrospinal fluid. Although these markers provide relatively high sensitivity and specificity for early disease detection, they are not suitable for monitor of disease progression. In the present study, we used label-free shotgun mass spectrometry to analyse the cerebrospinal fluid proteome of Alzheimer’s disease patients and non-demented controls to identify potential biomarkers for Alzheimer’s disease. We processed the data using five programs (DecyderMS, Maxquant, OpenMS, PEAKS, and Sieve) and compared their results by means of reproducibility and peptide identification, including three different normalization methods. After depletion of high abundant proteins we found that Alzheimer’s disease patients had lower fraction of low-abundance proteins in cerebrospinal fluid compared to healthy controls (p<0.05). Consequently, global normalization was found to be less accurate compared to using spiked-in chicken ovalbumin for normalization. In addition, we determined that Sieve and OpenMS resulted in the highest reproducibility and PEAKS was the programs with the highest identification performance. Finally, we successfully verified significantly lower levels (p<0.05) of eight proteins (A2GL, APOM, C1QB, C1QC, C1S, FBLN3, PTPRZ, and SEZ6) in Alzheimer’s disease compared to controls using an antibody-based detection method. These proteins are involved in different biological roles spanning from cell adhesion and migration, to regulation of the synapse and the immune system.

Jasmonic acid induced protein response to biophoton emissions and flooding stress in soybean

Biophoton emissions were elevated by the exogenous plant hormone application such as jasmonic (JA) and salicylic acid (SA). To reveal the molecular mechanisms underlying flooding stress responses in soybean treated with JA and SA, biophoton emissions from plants were quantified in combination with proteomic analyses. Furthermore, treatment with exogenous JA inhibited lateral root growth and markedly reduced root weight. Out of 649 proteins identified in the JA- and JA/SA-treated plants, 44 were unique to JA-treated plants, 403 were unique to JA/SA-treated plants, and 202 were shared between the groups. These proteins were involved in stress, signaling, degradation, glycolysis, fermentation, and hormone metabolism. The abundances of glutathione-S-transferase, alanine aminotransferase, and malate dehydrogenase were decreased; however, the activities of these enzymes were increased. In contrast, the abundance and activity of monodehydroascorbate reductase increased in the roots of plants treated with JA and SA under flooding stress. This suggests that the quantity of lateral roots, total root mass, and free radicals generated during oxidation and reduction reactions and reactive oxygen species scavenging largely contribute to biophoton emission. Furthermore, monodehydroascorbate reductase, which is involved in detoxification and controlling hydrogen peroxide levels, may protect plant cells against oxidative damage during flooding.

BIOLOGICAL SIGNIFICANCE

To understand the source of biophoton emission and molecular mechanism by the application of jasmonic and salicylic acid under flooding conditions in soybean plants, the label-free quantitative techniques were performed in roots. Root lengths and weights were significantly reduced by the effect of jasmonic and salicylic acid while it inhibited growth of the lateral roots in normal conditions using the jasmonic acid. Finally, identified proteins were functionally annotated by MAPMAN software application; that were assigned to different functional categories, such as stress, signaling, protein, glycolysis, metabolism, cell wall, and cell organization. Consequently, this study offers to learn the photon emission in plants and to know the molecular mechanism under flooding stress in soybean.

DeMix-Q: Quantification-Centered Data Processing Workflow

For historical reasons, most proteomics workflows focus on MS/MS identification but consider quantification as the end point of a comparative study. The stochastic data-dependent MS/MS acquisition (DDA) gives low reproducibility of peptide identifications from one run to another, which inevitably results in problems with missing values when quantifying the same peptide across a series of label-free experiments. However, the signal from the molecular ion is almost always present among the MS(1)spectra. Contrary to what is frequently claimed, missing values do not have to be an intrinsic problem of DDA approaches that perform quantification at the MS(1)level. The challenge is to perform sound peptide identity propagation across multiple high-resolution LC-MS/MS experiments, from runs with MS/MS-based identifications to runs where such information is absent. Here, we present a new analytical workflow DeMix-Q (https://github.com/userbz/DeMix-Q), which performs such propagation that recovers missing values reliably by using a novel scoring scheme for quality control. Compared with traditional workflows for DDA as well as previous DIA studies, DeMix-Q achieves deeper proteome coverage, fewer missing values, and lower quantification variance on a benchmark dataset. This quantification-centered workflow also enables flexible and robust proteome characterization based on covariation of peptide abundances.

Comprehensive protein profiling of synovial fluid in osteoarthritis following protein equalization

OBJECTIVE

The aim of the study was to characterise the protein complement of synovial fluid (SF) in health and osteoarthritis (OA) using liquid chromatography mass spectrometry (LC-MS/MS) following peptide-based depletion of high abundance proteins.

DESIGN

SF was used from nine normal and nine OA Thoroughbred horses. Samples were analysed with LC-MS/MS using a NanoAcquity™ LC coupled to an LTQ Orbitrap Velos. In order to enrich the lower-abundance protein fractions protein equalisation was first undertaken using ProteoMiner™. Progenesis-QI™ LC-MS software was used for label-free quantification. In addition immunohistochemistry, western blotting and mRNA expression analysis was undertaken on selected joint tissues.

RESULTS

The number of protein identifications was increased by 33% in the ProteoMiner™ treated SF compared to undepleted SF. A total of 764 proteins (462 with≥2 significant peptides) were identified in SF. A subset of 10 proteins were identified which were differentially expressed in OA SF. S100-A10, a calcium binding protein was upregulated in OA and validated with western blotting and immunohistochemistry. Several new OA specific peptide fragments (neopeptides) were identified.

CONCLUSION

The protein equalisation method compressed the dynamic range of the synovial proteins identifying the most comprehensive SF proteome to date. A number of proteins were identified for the first time in SF which may be involved in the pathogenesis of OA. We identified a distinct set of proteins and neopeptides that may act as potential biomarkers to distinguish between normal and OA joints.

Involvement of Reactive Oxygen Species and Mitochondrial Proteins in Biophoton Emission in Roots of Soybean Plants under Flooding Stress

To understand the mechanism of biophoton emission, ROS and mitochondrial proteins were analyzed in soybean plants under flooding stress. Enzyme activity and biophoton emission were increased in the flooding stress samples when assayed in reaction mixes specific for antioxidant enzymes and reactive oxygen species; although the level of the hydroxyl radicals was increased at day 4 (2 days of flooding) compared to nonflooding at day 4, the emission of biophotons did not change. Mitochondria were isolated and purified from the roots of soybean plants grown under flooding stress by using a Percoll gradient, and proteins were analyzed by a gel-free proteomic technique. Out of the 98 mitochondrial proteins that significantly changed abundance under flooding stress, 47 increased and 51 decreased at day 4. The mitochondrial enzymes fumarase, glutathione-S-transferase, and aldehyde dehydrogenase increased at day 4 in protein abundance and enzyme activity. Enzyme activity and biophoton emission decreased at day 4 by the assay of lipoxygenase under stress. Aconitase, acyl CoA oxidase, succinate dehydrogenase, and NADH ubiquinone dehydrogenase were up-regulated at the transcription level. These results indicate that oxidation and peroxide scavenging might lead to biophoton emission and oxidative damage in the roots of soybean plants under flooding stress.

Gel-free quantitative proteomic approach to identify cotyledon proteins in soybean under flooding stress

Flooding stress causes growth inhibition and ultimately death in most crop species by limiting of energy production. To better understand plant responses to flooding stress, here, flooding-responsive proteins in the cotyledons of soybean were identified using a gel-free quantitative proteomic approach. One hundred forty six proteins were commonly observed in both control and flooding-stressed plants, and 19 were identified under only flooding stress conditions. The main functional categories were protein and development-related proteins. Protein-protein interaction analysis revealed that zincin-like metalloprotease and cupin family proteins were found to highly interact with other proteins under flooding stress. Plant stearoyl acyl-carrier protein, ascorbate peroxidase 1, and secretion-associated RAS superfamily 2 were down-regulated, whereas ferretin 1 was up-regulated at the transcription level. Notably, the levels of all corresponding proteins were decreased, indicating that mRNA translation to proteins is impaired under flooding conditions. Decreased levels of ferritin may lead to a strong deregulation of the expression of several metal transporter genes and over-accumulation of iron, which led to increased levels of reactive oxygen species, resulting to detoxification of these reactive species. Taken together, these results suggest that ferritin might have an essential role in protecting plant cells against oxidative damage under flooding conditions.

BIOLOGICAL SIGNIFICANCE

This study reported the comparative proteomic analysis of cotyledon of soybean plants between non-flooding and flooding conditions using the gel-free quantitative techniques. Mass spectrometry analysis of the proteins from cotyledon resulted in the identification of a total of 165 proteins under flooding stress. These proteins were assigned to different functional categories, such as protein, development, stress, redox, and glycolysis. Therefore, this study provides not only the comparative proteomic analysis but also the molecular mechanism underlying the flooding responsive protein functions in the cotyledon.

Determination of supplier-to-supplier and lot-to-lot variability in glycation of recombinant human serum albumin expressed in Oryza sativa

The use of different expression systems to produce the same recombinant human protein can result in expression-dependent chemical modifications (CMs) leading to variability of structure, stability and immunogenicity. Of particular interest are recombinant human proteins expressed in plant-based systems, which have shown particularly high CM variability. In studies presented here, recombinant human serum albumins (rHSA) produced in Oryza sativa (Asian rice) (OsrHSA) from a number of suppliers have been extensively characterized and compared to plasma-derived HSA (pHSA) and rHSA expressed in yeast (Pichia pastoris and Saccharomyces cerevisiae). The heterogeneity of each sample was evaluated using size exclusion chromatography (SEC), reversed-phase high-performance liquid chromatography (RP-HPLC) and capillary electrophoresis (CE). Modifications of the samples were identified by liquid chromatography-mass spectrometry (LC-MS). The secondary and tertiary structure of the albumin samples were assessed with far U/V circular dichroism spectropolarimetry (far U/V CD) and fluorescence spectroscopy, respectively. Far U/V CD and fluorescence analyses were also used to assess thermal stability and drug binding. High molecular weight aggregates in OsrHSA samples were detected with SEC and supplier-to-supplier variability and, more critically, lot-to-lot variability in one manufactures supplied products were identified. LC-MS analysis identified a greater number of hexose-glycated arginine and lysine residues on OsrHSA compared to pHSA or rHSA expressed in yeast. This analysis also showed supplier-to-supplier and lot-to-lot variability in the degree of glycation at specific lysine and arginine residues for OsrHSA. Both the number of glycated residues and the degree of glycation correlated positively with the quantity of non-monomeric species and the chromatographic profiles of the samples. Tertiary structural changes were observed for most OsrHSA samples which correlated well with the degree of arginine/lysine glycation. The extensive glycation of OsrHSA from multiple suppliers may have further implications for the use of OsrHSA as a therapeutic product.

Proteomic analysis of oral cavity squamous cell carcinoma specimens identifies patient outcome-associated proteins

CONTEXT

Global proteomic analysis of oral cavity squamous cell carcinoma was performed to identify changes that reflect patient outcomes.

OBJECTIVES

To identify differentially expressed proteins associated with patient outcomes and to explore the use of imaging mass spectrometry as a clinical tool to identify clinically relevant proteins.

DESIGN

Two-dimensional separation of digested peptides generated from 43 specimens with high-resolution mass spectrometry identified proteins associated with disease-specific death, distant metastasis, and loco-regional recurrence. RNA expressions had been correlated to protein levels to test transcriptional regulation of clinically relevant proteins. Imaging mass spectrometry explored an alternative platform for assessing clinically relevant proteins that would complement surgical pathologic diagnosis.

RESULTS

Seventy-two peptide features were found to be associated with 3 patient outcomes: disease-specific death (9), distant metastasis (16), and loco-regional recurrence (39); 8 of them were associated with multiple outcomes. Functional ontology revealed major changes in cell adhesion and calcium binding. Thirteen RNAs showed strong correlation with their encoded proteins, implying transcriptional control. Reduction of DSP, PKP1, and TRIM29 was associated with significantly shorter time to onset of distant metastasis. Reduction of PKP1 and TRIM29 correlated with poorer disease-specific survival. Additionally, S100A8 and S100A9 reductions were verified for their association with poor prognosis using imaging mass spectrometry, a platform more adaptable for use with surgical pathology.

CONCLUSIONS

Using global proteomic analysis, we have identified proteins associated with clinical outcomes. The list of clinically relevant proteins observed will provide a means to develop clinical assays for prognosis and optimizing treatment selection.

(Post-)genomics approaches in fungal research

To date, hundreds of fungal genomes have been sequenced and many more are in progress. This wealth of genomic information has provided new directions to study fungal biodiversity. However, to further dissect and understand the complicated biological mechanisms involved in fungal life styles, functional studies beyond genomes are required. Thanks to the developments of current -omics techniques, it is possible to produce large amounts of fungal functional data in a high-throughput fashion (e.g. transcriptome, proteome, etc.). The increasing ease of creating -omics data has also created a major challenge for downstream data handling and analysis. Numerous databases, tools and software have been created to meet this challenge. Facing such a richness of techniques and information, hereby we provide a brief roadmap on current wet-lab and bioinformatics approaches to study functional genomics in fungi.

Reproducible ion-current-based approach for 24-plex comparison of the tissue proteomes of hibernating versus normal myocardium in swine models

Hibernating myocardium is an adaptive response to repetitive myocardial ischemia that is clinically common, but the mechanism of adaptation is poorly understood. Here we compared the proteomes of hibernating versus normal myocardium in a porcine model with 24 biological replicates. Using the ion-current-based proteomic strategy optimized in this study to expand upon previous proteomic work, we identified differentially expressed proteins in new molecular pathways of cardiovascular interest. The methodological strategy includes efficient extraction with detergent cocktail; precipitation/digestion procedure with high, quantitative peptide recovery; reproducible nano-LC/MS analysis on a long, heated column packed with small particles; and quantification based on ion-current peak areas. Under the optimized conditions, high efficiency and reproducibility were achieved for each step, which enabled a reliable comparison of 24 the myocardial samples. To achieve confident discovery of differentially regulated proteins in hibernating myocardium, we used highly stringent criteria to define “quantifiable proteins”. These included the filtering criteria of low peptide FDR and S/N > 10 for peptide ion currents, and each protein was quantified independently from ≥2 distinct peptides. For a broad methodological validation, the quantitative results were compared with a parallel, well-validated 2D-DIGE analysis of the same model. Excellent agreement between the two orthogonal methods was observed (R = 0.74), and the ion-current-based method quantified almost one order of magnitude more proteins. In hibernating myocardium, 225 significantly altered proteins were discovered with a low false-discovery rate (∼3%). These proteins are involved in biological processes including metabolism, apoptosis, stress response, contraction, cytoskeleton, transcription, and translation. This provides compelling evidence that hibernating myocardium adapts to chronic ischemia. The major metabolic mechanisms include a down-regulation of mitochondrial respiration and an increase in glycolysis. Meanwhile, cardioprotective and cytoskeletal proteins are increased, while cardiomyocyte contractile proteins are reduced. These intrinsic adaptations to regional ischemia maintain long-term cardiomyocyte viability at the expense of contractile function.

Comparative proteomic label-free analysis of Campylobacter jejuni NCTC 11168 cultured with porcine mucin

Campylobacter jejuni is a major gastrointestinal pathogen in humans. Poultry is a primary reservoir for C. jejuni, and C. jejuni appears to be highly adapted to the gastrointestinal tracts of avian species. We determined the protein expression profiles of C. jejuni NCTC 11168 cultured in medium containing porcine mucin. Differentially expressed proteins in the presence and absence of porcine mucin were identified using the label-free method. We identified 52 proteins with expression that was either upregulated (32 proteins) or downregulated (20 proteins) by porcine mucin. These proteins are involved in diverse cellular functions, such as motility, cell wall synthesis, iron transport, energy production, and amino acid metabolism. In particular, the upregulated proteins were involved in chemotaxis (CheV and CetA), motility (FlaA), colonization and adherence (CadF, FrdA, CfrA, MapA, and HydA), and stress tolerance (TrxB and ClpB). These results suggest that C. jejuni changes its protein expression in response to porcine mucin and that this change in expression may contribute to host adaptation of C. jejuni NCTC 11168.

Nonlinear alignment of chromatograms by means of moving window fast Fourier transfrom cross-correlation

The preprocessing of chromatograms is essential to modern chromatography for further qualitative and quantitative analysis, especially when chromatographic instruments are used for herb products analysis involving large number of samples. To accurately compare and analyze the obtained chromatograms, it is necessary to preprocess, especially align retention time shifts. Here moving window fast Fourier transform (FFT) cross-correlation is introduced to perform nonlinear alignment of high-throughput chromatograms. Since elution characteristics of chromatograms will produce local similarity in retention time shifts, moving window procedure seems to be a better substitute of segmentation steps. The retention time shifts can be calculated and accelerated by FFT cross-correlation. The artifacts can be detected and eliminated from the retention time shifts profile since the continuity of moving window procedure. The proposed method is demonstrated in comparison with recursive alignment by FFT on chromatographic datasets from herb products analysis. It is shown that the proposed method can address nonlinear retention time shift problem in chromatograms with the simple moving window procedure, which will not introduce segments size optimization problem. In additional, the parameters are intuitive and easy to adjust, which makes it off-the-shelf toolbox for alignment of chromatograms.

A decade of plant proteomics and mass spectrometry: translation of technical advancements to food security and safety issues

Tremendous progress in plant proteomics driven by mass spectrometry (MS) techniques has been made since 2000 when few proteomics reports were published and plant proteomics was in its infancy. These achievements include the refinement of existing techniques and the search for new techniques to address food security, safety, and health issues. It is projected that in 2050, the world's population will reach 9-12 billion people demanding a food production increase of 34-70% (FAO, 2009) from today's food production. Provision of food in a sustainable and environmentally committed manner for such a demand without threatening natural resources, requires that agricultural production increases significantly and that postharvest handling and food manufacturing systems become more efficient requiring lower energy expenditure, a decrease in postharvest losses, less waste generation and food with longer shelf life. There is also a need to look for alternative protein sources to animal based (i.e., plant based) to be able to fulfill the increase in protein demands by 2050. Thus, plant biology has a critical role to play as a science capable of addressing such challenges. In this review, we discuss proteomics especially MS, as a platform, being utilized in plant biology research for the past 10 years having the potential to expedite the process of understanding plant biology for human benefits. The increasing application of proteomics technologies in food security, analysis, and safety is emphasized in this review. But, we are aware that no unique approach/technology is capable to address the global food issues. Proteomics-generated information/resources must be integrated and correlated with other omics-based approaches, information, and conventional programs to ensure sufficient food and resources for human development now and in the future.

Graph-based peak alignment algorithms for multiple liquid chromatography-mass spectrometry datasets

Liquid chromatography coupled to mass spectrometry (LC-MS) is the dominant technological platform for proteomics. An LC-MS analysis of a complex biological sample can be visualized as a 'map' of which the positional coordinates are the mass-to-charge ratio (m/z) and chromatographic retention time (RT) of the chemical species profiled. Label-free quantitative proteomics requires the alignment and comparison of multiple LC-MS maps to ascertain the reproducibility of experiments or reveal proteome changes under different conditions. The main challenge in this task lies in correcting inevitable RT shifts. Similar, but not identical, LC instruments and settings can cause peptides to elute at very different times and sometimes in a different order, violating the assumptions of many state-of-the-art alignment tools. To meet this challenge, we developed LWBMatch, a new algorithm based on weighted bipartite matching. Unlike existing tools, which search for accurate warping functions to correct RT shifts, we directly seek a peak-to-peak mapping by maximizing a global similarity function between two LC-MS maps. For alignment tasks with large RT shifts (>500 s), an approximate warping function is determined by locally weighted scatterplot smoothing of potential matched features, detected using a novel voting scheme based on co-elution. For validation, we defined the ground truth for alignment success based on tandem mass spectrometry identifications from sequence searching. We showed that our method outperforms several existing tools in terms of precision and recall, and is capable of aligning maps from different instruments and settings.

AVAILABILITY

Available at https://sourceforge.net/projects/rt-alignment/.

Fast photochemical oxidation of proteins for comparing solvent-accessibility changes accompanying protein folding: data processing and application to barstar

Mass spectrometry-based protein footprinting reveals regional and even amino-acid structural changes and fills the gap for many proteins and protein interactions that cannot be studied by X-ray crystallography or NMR spectroscopy. Hydroxyl radical-mediated labeling has proven to be particularly informative in this pursuit because many solvent-accessible residues can be labeled by OH in a protein or protein complex, thus providing more coverage than does specific amino-acid modifications. Finding all the OH-labeling sites requires LC/MS/MS analysis of a proteolyzed sample, but data processing is daunting without the help of automated software. We describe here a systematic means for achieving a comprehensive residue-resolved analysis of footprinting data in an efficient manner, utilizing software common to proteomics core laboratories. To demonstrate the method and the utility of OH-mediated labeling, we show that FPOP easily distinguishes the buried and exposed residues of barstar in its folded and unfolded states. This article is part of a Special Issue entitled: Mass spectrometry in structural biology.

Candidate biomarker discovery for angiogenesis by automatic integration of Orbitrap MS1 spectral- and X!Tandem MS2 sequencing information

Candidate protein biomarker discovery by full automatic integration of Orbitrap full MS1 spectral peptide profiling and X!Tandem MS2 peptide sequencing is investigated by analyzing mass spectra from brain tumor samples using Peptrix. Potential protein candidate biomarkers found for angiogenesis are compared with those previously reported in the literature and obtained from previous Fourier transform ion cyclotron resonance (FT-ICR) peptide profiling. Lower mass accuracy of peptide masses measured by Orbitrap compared to those measured by FT-ICR is compensated by the larger number of detected masses separated by liquid chromatography (LC), which can be directly linked to protein identifications. The number of peptide sequences divided by the number of unique sequences is 9248/6911 ≈ 1.3. Peptide sequences appear 1.3 times redundant per up-regulated protein on average in the peptide profile matrix, and do not seem always up-regulated due to tailing in LC retention time (40%), modifications (40%) and mass determination errors (20%). Significantly up-regulated proteins found by integration of X!Tandem are described in the literature as tumor markers and some are linked to angiogenesis. New potential biomarkers are found, but need to be validated independently. Eventually more proteins could be found by actively involving MS2 sequence information in the creation of the MS1 peptide profile matrix.

Current Omics Technologies in Biomarker Discovery

Biomarkers are playing an increasingly important role in drug discovery and development and can be applied for many purposes, including disease mechanism study, diagnosis, prognosis, staging, and treatment selection. Advances in high-throughput “omics” technologies, including genomics, transcriptomics, proteomics and metabolomics, significantly accelerate the pace of biomarker discovery. Comprehensive molecular profiling using these “omics” technology has become a field of intensive research aiming at identifying biomarkers relevant for improved diagnostics and therapeutics. Although each “omics” technology plays important roles in biomarker research, different “omics” platforms have different strengths and limitations. This chapter aims to give an overview of these “omics” technologies and their current application in the biomarker discovery.

An adaptive alignment algorithm for quality-controlled label-free LC-MS

Label-free quantification using precursor-based intensities is a versatile workflow for large-scale proteomics studies. The method however requires extensive computational analysis and is therefore in need of robust quality control during the data mining stage. We present a new label-free data analysis workflow integrated into a multiuser software platform. A novel adaptive alignment algorithm has been developed to minimize the possible systematic bias introduced into the analysis. Parameters are estimated on the fly from the data at hand, producing a user-friendly analysis suite. Quality metrics are output in every step of the analysis as well as actively incorporated into the parameter estimation. We furthermore show the improvement of this system by comprehensive comparison to classical label-free analysis methodology as well as current state-of-the-art software.

Tissue proteomics in pancreatic cancer study: discovery, emerging technologies, and challenges

Pancreatic cancer is a highly lethal disease that is difficult to diagnose and treat. The advances in proteomics technology, especially quantitative proteomics, have stimulated a great interest in applying this technology for pancreatic cancer study. A variety of tissue proteomics approaches have been applied to investigate pancreatic cancer and the associated diseases. These studies were carried out with various goals, aiming to better understand the molecular mechanisms underlying pancreatic tumorigenesis, to improve therapeutic treatment and to identify cancer associated protein signatures, signaling events as well as interactions between cancer cells and tumor microenvironment. Here, we provide an overview on the tissue proteomics studies of pancreatic cancer reported in the past few years in light of discovery and technology development.