Protein identification and expression analysis using mass spectrometry

Quantification of SARS-CoV-2 spike protein expression from mRNA vaccines using isotope dilution mass spectrometry

The advent of mRNA vaccine technology has been vital in rapidly creating and manufacturing COVID-19 vaccines at an industrial scale. To continue to accelerate this leading vaccine technology, an accurate method is needed to quantify antigens produced by the transfection of cells with a mRNA vaccine product. This will allow monitoring of protein expression during mRNA vaccine development and provide information on how changes to vaccine components affects the expression of the desired antigen. Developing novel approaches that allow for high-throughput screening of vaccines to detect changes in antigen production in cell culture prior to in vivo studies could aid vaccine development. We have developed and optimized an isotope dilution mass spectrometry method to detect and quantify the spike protein expressed after transfection of baby hamster kidney cells with expired COVID-19 mRNA vaccines. Five peptides of the spike protein are simultaneously quantified and provide assurance that protein digestion in the region of the target peptides is complete since results between the five peptides had a relative standard deviation of less than 15 %. In addition, two housekeeping proteins, actin and GAPDH, are quantified in the same analytical run to account for any variation in cell growth within the experiment. IDMS allows a precise and accurate means to quantify protein expression by mammalian cells transfected with an mRNA vaccine.

Advances and perspectives in the analytical technology for small peptide hormones analysis: A glimpse to gonadorelin

In the last twenty years, we have witnessed an important evolution of bioanalytical approaches moving from conventional lab bench instrumentation to simpler, easy-to-use techniques to deliver analytical responses on-site, with reduced analysis times and costs. In this frame, affinity reagents production has also jointly advanced from natural receptors to biomimetic, abiotic receptors, animal-free produced. Among biomimetic ones, aptamers, and molecular imprinted polymers (MIPs) play a leading role. Herein, our motivation is to provide insights into the evolution of conventional and innovative analytical approaches based on chromatography, immunochemistry, and affinity sensing referred to as peptide hormones. Indeed, the analysis of peptide hormones represents a current challenge for biomedical, pharmaceutical, and anti-doping analysis. Specifically, as a paradigmatic example, we report the case of gonadorelin, a neuropeptide that in recent years has drawn a lot of attention as a therapeutic drug misused in doping practices during sports competitions.

Rapid Targeted Quantitation of Protein Overexpression with Direct Infusion Shotgun Proteome Analysis (DISPA-PRM)

While much effort has been placed on comprehensive quantitative proteome analysis, certain applications demand the measurement of only a few target proteins from complex systems. Traditional approaches to targeted proteomics rely on nanoliquid chromatography (nLC) and targeted mass spectrometry (MS) methods, e.g., parallel reaction monitoring (PRM). However, the time requirement for nLC can limit the throughput of targeted proteomics. To achieve rapid and high-throughput targeted methods, here we show that nLC separations can be eliminated and replaced with direct infusion shotgun proteome analysis (DISPA) using high-field asymmetric waveform ion mobility spectrometry (FAIMS) with PRM. We demonstrate the application of DISPA-PRM for rapid targeted quantification of bacterial enzymes utilized in the production of biofuels by monitoring temporal expression in 72 metabolically engineered bacterial cultures in less than 2.5 h, with a measured dynamic range >1200-fold. We conclude that DISPA-PRM presents a valuable innovative tool with results comparable to nLC-MS/MS, enabling fast and rapid detection of targeted proteins in complex mixtures.

Proteomic profiling in extracellular vesicles for cancer detection and monitoring

Extracellular vesicles (EVs) are nanometer-size lipid vesicles released by cells, which play essential biological functions in intercellular communication. Increasing evidence indicates that EVs participate in cancer development, including invasion, migration, metastasis, and cancer immune modulation. One of the key mechanisms is that EVs affect different cells in the tumor microenvironment through surface-anchor proteins and protein cargos. Moreover, proteins specifically expressed in tumor-derived EVs can be applied in cancer diagnosis and monitoring. Besides, the EV proteome also helps to understand drug resistance in cancers and to guide clinical medication. With the development of mass spectrometry and array-based multi-protein detection, the research of EV proteomics has entered a new era. The high-throughput parallel proteomic profiling based on these new platforms allows us to study the impact of EV proteome on cancer progression more comprehensively and to describe the proteomic landscape in cancers with more details. In this article, we review the role and function of different types of EVs in cancer progression. More importantly, we summarize the proteomic profiling of EVs based on different methods and the application of EV proteome in cancer detection and monitoring.

Approaches to Computational Strain Design in the Multiomics Era

Modern omics analyses are able to effectively characterize the genetic, regulatory, and metabolic phenotypes of engineered microbes, yet designing genetic interventions to achieve a desired phenotype remains challenging. With recent developments in genetic engineering techniques, timelines associated with building and testing strain designs have been greatly reduced, allowing for the first time an efficient closed loop iteration between experiment and analysis. However, the scale and complexity associated with multi-omics datasets complicates manual biological reasoning about the mechanisms driving phenotypic changes. Computational techniques therefore form a critical part of the Design-Build-Test-Learn (DBTL) cycle in metabolic engineering. Traditional statistical approaches can reduce the dimensionality of these datasets and identify common motifs among high-performing strains. While successful in many studies, these methods do not take full advantage of known connections between genes, proteins, and metabolic networks. There is therefore a growing interest in model-aided design, in which modeling frameworks from systems biology are used to integrate experimental data and generate effective and non-intuitive design predictions. In this mini-review, we discuss recent progress and challenges in this field. In particular, we compare methods augmenting flux balance analysis with additional constraints from fluxomic, genomic, and metabolomic datasets and methods employing kinetic representations of individual metabolic reactions, and machine learning. We conclude with a discussion of potential future directions for improving strain design predictions in the omics era and remaining experimental and computational hurdles.

Determining the Plasmodium vivax VCG-1 strain blood stage proteome

Plasmodium vivax is the second most prevalent parasite species causing malaria in humans living in tropical and subtropical areas throughout the world. There have been few P. vivax proteomic studies to date and they have focused on using clinical isolates, given the technical difficulties concerning how to maintain an in vitro culture of this species. This study was thus focused on identifying the P. vivax VCG-1 strain proteome during its blood lifecycle through LC-MS/MS; this led to identifying 734 proteins, thus increasing the overall number reported for P. vivax to date. Some of them have previously been related to reticulocyte invasion, parasite virulence and growth and others are new molecules possibly playing a functional role during metabolic processes, as predicted by Database for Annotation, Visualization and Integrated Discovery (DAVID) functional analysis. This is the first large-scale proteomic analysis of a P. vivax strain adapted to a non-human primate model showing the parasite protein repertoire during the blood lifecycle. Database searches facilitated the in silico prediction of proteins proposed for evaluation in further experimental assays regarding their potential as pharmacologic targets or as component of a totally efficient vaccine against malaria caused by P. vivax.

BIOLOGICAL SIGNIFICANCE

P. vivax malaria continues being a public health problem around world. Although considerable progress has been made in understanding genome- and transcriptome-related P. vivax biology, there are few proteome studies, currently representing only 8.5% of the predicted in silico proteome reported in public databases. A high-throughput proteomic assay was used for discovering new P. vivax intra-reticulocyte asexual stage molecules taken from parasites maintained in vivo in a primate model. The methodology avoided the main problem related to standardising an in vitro culture system to obtain enough samples for protein identification and annotation. This study provides a source of potential information contributing towards a basic understanding of P. vivax biology related to parasite proteins which are of significant importance for the malaria research community.

Unraveling the Complexities of Life Sciences Data

The life sciences have entered into the realm of big data and data-enabled science, where data can either empower or overwhelm. These data bring the challenges of the 5 Vs of big data: volume, veracity, velocity, variety, and value. Both independently and through our involvement with DELSA Global (Data-Enabled Life Sciences Alliance, DELSAglobal.org), the Kolker Lab ( kolkerlab.org ) is creating partnerships that identify data challenges and solve community needs. We specialize in solutions to complex biological data challenges, as exemplified by the community resource of MOPED (Model Organism Protein Expression Database, MOPED.proteinspire.org ) and the analysis pipeline of SPIRE (Systematic Protein Investigative Research Environment, PROTEINSPIRE.org ). Our collaborative work extends into the computationally intensive tasks of analysis and visualization of millions of protein sequences through innovative implementations of sequence alignment algorithms and creation of the Protein Sequence Universe tool (PSU). Pushing into the future together with our collaborators, our lab is pursuing integration of multi-omics data and exploration of biological pathways, as well as assigning function to proteins and porting solutions to the cloud. Big data have come to the life sciences; discovering the knowledge in the data will bring breakthroughs and benefits.

Application of on-line nanoLC-IT-TOF in the identification of serum β-catenin complex in mice scald model

Severe burn shock remains an unresolved clinical problem with an urgent need to explore novel therapeutic treatments. Intracellular β-catenin, through interaction with other proteins, has been reported to be able to regulate the size of cutaneous wounds. Higher expression of β-catenin is associated with larger sized wounds. However, the identification of serum β-catenin complex is difficult and has been rarely reported. The exploitation of more binding partners can contribute to uncovering the exact mechanisms behind serum β-catenin mediated biological effects. Here, we describe a method that consists of immunoprecipitation, SDS-PAGE, in-gel digestion, and nanoLC coupled to LCMS-IT-TOF for the investigation of serum β-catenin complex in mice scald model. Among selected gel bands obtained from the protein gels, a total of 31 peptides were identified and sequenced with high statistical significance (p<0.01). Three proteins (alpha-2-marcoglobulin, serine protease inhibitor A3K, and serine protease inhibitor A1A) were identified and validated with high reliability and high reproducibility. It was inferred that these proteins might interact with serum β-catenin, which could affect the wound healing resulting from burn shock. Our study demonstrated that the on-line coupling of nano-LC with a LCMS-IT-TOF mass spectrometer was capable of sensitive and automated characterization of the serum β-catenin complex in mice scald model.

MOPED: Model Organism Protein Expression Database

Large numbers of mass spectrometry proteomics studies are being conducted to understand all types of biological processes. The size and complexity of proteomics data hinders efforts to easily share, integrate, query and compare the studies. The Model Organism Protein Expression Database (MOPED, htttp://moped.proteinspire.org) is a new and expanding proteomics resource that enables rapid browsing of protein expression information from publicly available studies on humans and model organisms. MOPED is designed to simplify the comparison and sharing of proteomics data for the greater research community. MOPED uniquely provides protein level expression data, meta-analysis capabilities and quantitative data from standardized analysis. Data can be queried for specific proteins, browsed based on organism, tissue, localization and condition and sorted by false discovery rate and expression. MOPED empowers users to visualize their own expression data and compare it with existing studies. Further, MOPED links to various protein and pathway databases, including GeneCards, Entrez, UniProt, KEGG and Reactome. The current version of MOPED contains over 43,000 proteins with at least one spectral match and more than 11 million high certainty spectra.

Effect of cleavage enzyme, search algorithm and decoy database on mass spectrometric identification of wheat gluten proteins

While tandem mass spectrometry (MS/MS) is routinely used to identify proteins from complex mixtures, certain types of proteins present unique challenges for MS/MS analyses. The major wheat gluten proteins, gliadins and glutenins, are particularly difficult to distinguish by MS/MS. Each of these groups contains many individual proteins with similar sequences that include repetitive motifs rich in proline and glutamine. These proteins have few cleavable tryptic sites, often resulting in only one or two tryptic peptides that may not provide sufficient information for identification. Additionally, there are less than 14,000 complete protein sequences from wheat in the current NCBInr release. In this paper, MS/MS methods were optimized for the identification of the wheat gluten proteins. Chymotrypsin and thermolysin as well as trypsin were used to digest the proteins and the collision energy was adjusted to improve fragmentation of chymotryptic and thermolytic peptides. Specialized databases were constructed that included protein sequences derived from contigs from several assemblies of wheat expressed sequence tags (ESTs), including contigs assembled from ESTs of the cultivar under study. Two different search algorithms were used to interrogate the database and the results were analyzed and displayed using a commercially available software package (Scaffold). We examined the effect of protein database content and size on the false discovery rate. We found that as database size increased above 30,000 sequences there was a decrease in the number of proteins identified. Also, the type of decoy database influenced the number of proteins identified. Using three enzymes, two search algorithms and a specialized database allowed us to greatly increase the number of detected peptides and distinguish proteins within each gluten protein group.

IPM: An integrated protein model for false discovery rate estimation and identification in high-throughput proteomics

In high-throughput mass spectrometry proteomics, peptides and proteins are not simply identified as present or not present in a sample, rather the identifications are associated with differing levels of confidence. The false discovery rate (FDR) has emerged as an accepted means for measuring the confidence associated with identifications. We have developed the Systematic Protein Investigative Research Environment (SPIRE) for the purpose of integrating the best available proteomics methods. Two successful approaches to estimating the FDR for MS protein identifications are the MAYU and our current SPIRE methods. We present here a method to combine these two approaches to estimating the FDR for MS protein identifications into an integrated protein model (IPM). We illustrate the high quality performance of this IPM approach through testing on two large publicly available proteomics datasets. MAYU and SPIRE show remarkable consistency in identifying proteins in these datasets. Still, IPM results in a more robust FDR estimation approach and additional identifications, particularly among low abundance proteins. IPM is now implemented as a part of the SPIRE system.

Identification of outer membrane proteins from an Antarctic bacterium Pseudomonas syringae Lz4W

Subcellular fractionation of proteins is a preferred method of choice for detection and identification of proteins from complex mixtures such as bacterial cells. To characterize the membrane proteins of the Antarctic bacterium Pseudomonas syringae Lz4W, the membrane fractions were prepared using three different methods, namely Triton X-100 solubilization, sucrose density gradient, and carbonate extraction methods. The proteins were separated on one-dimensional polyacrylamide gels and analyzed using a combination of liquid chromatography-coupled electrospray ionization-MS. The membrane proteins that were prepared by carbonate extraction were separated on two-dimensional PAGE in different pI ranges using the detergent 2% amidosulfobetaine (ASB). The proteins were then subjected to matrix-assisted laser desorption ionization-time-of-flight/time-of-flight for analysis and identification. Because the genome sequence of P. syringae Lz4W is not known, the proteins were identified by using the relevant sequence databases of the Pseudomonas sp available at National Centre for Biotechnology Information (NCBI). The sequence identification of some tryptic peptides were validated by de novo sequencing and others by chemical modification and mass spectrometry. The peptide sequences of P. syringae Lz4W were then matched with the sequences of the peptides from different Pseudomonas sp. by similarity search of the proteins from different species using clustal W2 program. Thus by using a combination of the methods, we have been able to identify large number of proteins of this bacterial strain, which include most of the outer membrane proteins.

Design and initial characterization of the SC-200 proteomics standard mixture

High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards. The absence of appropriate standards and data sets for validating experimental and computational workflows hinders the development of HTP proteomics methods. Most protein standards are simple mixtures of proteins or peptides, or undercharacterized reference standards in which the identity and concentration of the constituent proteins is unknown. The Seattle Children's 200 (SC-200) proposed proteomics standard mixture is the next step toward developing realistic, fully characterized HTP proteomics standards. The SC-200 exhibits a unique modular design to extend its functionality, and consists of 200 proteins of known identities and molar concentrations from 6 microbial genomes, distributed into 10 molar concentration tiers spanning a 1,000-fold range. We describe the SC-200's design, potential uses, and initial characterization. We identified 84% of SC-200 proteins with an LTQ-Orbitrap and 65% with an LTQ-Velos (false discovery rate = 1% for both). There were obvious trends in success rate, sequence coverage, and spectral counts with protein concentration; however, protein identification, sequence coverage, and spectral counts vary greatly within concentration levels.

The necessity of adjusting tests of protein category enrichment in discovery proteomics

MOTIVATION

Enrichment tests are used in high-throughput experimentation to measure the association between gene or protein expression and membership in groups or pathways. The Fisher's exact test is commonly used. We specifically examined the associations produced by the Fisher test between protein identification by mass spectrometry discovery proteomics, and their Gene Ontology (GO) term assignments in a large yeast dataset. We found that direct application of the Fisher test is misleading in proteomics due to the bias in mass spectrometry to preferentially identify proteins based on their biochemical properties. False inference about associations can be made if this bias is not corrected. Our method adjusts Fisher tests for these biases and produces associations more directly attributable to protein expression rather than experimental bias.

RESULTS

Using logistic regression, we modeled the association between protein identification and GO term assignments while adjusting for identification bias in mass spectrometry. The model accounts for five biochemical properties of peptides: (i) hydrophobicity, (ii) molecular weight, (iii) transfer energy, (iv) beta turn frequency and (v) isoelectric point. The model was fit on 181 060 peptides from 2678 proteins identified in 24 yeast proteomics datasets with a 1% false discovery rate. In analyzing the association between protein identification and their GO term assignments, we found that 25% (134 out of 544) of Fisher tests that showed significant association (q-value ≤0.05) were non-significant after adjustment using our model. Simulations generating yeast protein sets enriched for identification propensity show that unadjusted enrichment tests were biased while our approach worked well.

Analysis of protein-protein interactions and proteomic profiles of normal human lenses

PURPOSE

To investigate proteomic profiles of normal human lenses and their key proteins in protein-protein interactions (PPIs).

MATERIALS AND METHODS

Water-soluble and water-insoluble proteins extracted from human lenses were first separated by one-dimensional sodium dodecyl sulfate polyacrylamide gel, and then in-gel digested with trypsin into peptides eluted by reversed-phase high-performance liquid chromatography. The eluted peptides were analyzed by linear ion trap tandem mass spectrometry (MS/MS). The raw data was filtered by TurboSEQUEST algorithm. The reverse database was used for peptide false-positive rate estimation. A network chart was constructed by the identified lens PPIs in accordance with interaction database systems.

RESULTS

From normal human lenses 339 proteins in total were identified, including many formerly unidentified low-abundance proteins. Key proteins we recognized included plectin, actin, spectrin (alpha, beta), vimentin, 14-3-3 protein (beta/alpha, zeta/delta, epsilon, gamma, eta), TSC2, guanine nucleotide-releasing protein, laminin gamma, mitogen-activated protein kinase, alpha-A-crystallin, heat-shock protein (alpha, beta), glyceraldehyde 3-phosphate dehydrogenase, and collagen IV alpha.

CONCLUSIONS

Key proteins of normal human lenses were studied by constructing a network chart of the identified lens PPIs. The results suggest that linear ion trap MS/MS is an effective tool for detecting low-abundance proteins of human lenses. This study provides valuable data for further proteomic research of the human lens development and lens diseases.

Estimating false discovery rates for peptide and protein identification using randomized databases

MS-based proteomics characterizes protein contents of biological samples. The most common approach is to first match observed MS/MS peptide spectra against theoretical spectra from a protein sequence database and then to score these matches. The false discovery rate (FDR) can be estimated as a function of the score by searching together the protein sequence database and its randomized version and comparing the score distributions of the randomized versus nonrandomized matches. This work introduces a straightforward isotonic regression-based method to estimate the cumulative FDRs and local FDRs (LFDRs) of peptide identification. Our isotonic method not only performed as well as other methods used for comparison, but also has the advantages of being: (i) monotonic in the score, (ii) computationally simple, and (iii) not dependent on assumptions about score distributions. We demonstrate the flexibility of our approach by using it to estimate FDRs and LFDRs for protein identification using summaries of the peptide spectra scores. We reconfirmed that several of these methods were superior to a two-peptide rule. Finally, by estimating both the FDRs and LFDRs, we showed for both peptide and protein identification, moderate FDR values (5%) corresponded to large LFDR values (53 and 60%).

Characterization and sequence identification of angiotensin II by a novel method involving ultra-fast liquid chromatography assay coupled with matrix-assisted laser desorption/ionization quadrupole ion trap time-of-flight five tandem mass spectrometry analysis

High-throughput proteomics aims to investigate dynamically changing proteins expressed by a full organism, specific tissue or cellular compartment under certain conditions. High-sensitivity mass spectrometry has gradually become a significant tool for characterizing peptides. Here, we analyzed angiotensin II using ultra-fast liquid chromatography (UFLC) coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS). First, we applied UFLC in isolating and collecting the angiotensin II, and then Axima-Resonance (MALDI-QIT-ToF MS(5)) was adopted, which enables collision-induced dissociation-MS(5) analysis for fine structural characterization of angiotensin II. Resultant MS, MS(2), MS(3) and MS(4) spectra of interested [M+H](+) ions selected as precursor ions yielded detailed information about the sites of fragmentation as well as the amino acid sequence for angiotensin II; meanwhile, the average deviation between theoretical mass and actually measured mass from MS to MS(5) spectra was only 0.32 Da. It indicated that Axima-Resonance was capable of analyzing the peptide sequence accurately and provide the corresponding fragmentation information thoroughly, thus suggesting a potential strategy involving UFLC assay coupled with MALDI-QIT-ToF MS(5) analysis on high-throughput proteomics study in future.

Genome-environment interactions and prospective technology assessment: evolution from pharmacogenomics to nutrigenomics and ecogenomics

The relationships between food, nutrition science, and health outcomes have been mapped over the past century. Genomic variation among individuals and populations is a new factor that enriches and challenges our understanding of these complex relationships. Hence, the confluence of nutritional science and genomics-nutrigenomics--was the focus of the OMICS: A Journal of Integrative Biology in December 2008 (Part 1). The 2009 Special Issue (Part 2) concludes the analysis of nutrigenomics research and innovations. Together, these two issues expand the scope and depth of critical scholarship in nutrigenomics, in keeping with an integrated multidisciplinary analysis across the bioscience, omics technology, social, ethical, intellectual property and policy dimensions. Historically, the field of pharmacogenetics provided the first examples of specifically identifiable gene variants predisposing to unexpected responses to drugs since the 1950s. Brewer coined the term ecogenetics in 1971 to broaden the concept of gene-environment interactions from drugs and nutrition to include environmental agents in general. In the mid-1990s, introduction of high-throughput technologies led to the terms pharmacogenomics, nutrigenomics and ecogenomics to describe, respectively, the contribution of genomic variability to differential responses to drugs, food, and environment defined in the broadest sense. The distinctions, if any, between these newer fields (e.g., nutrigenomics) and their predecessors (e.g., nutrigenetics) remain to be delineated. For nutrigenomics, its reliance on genome-wide analyses may lead to detection of new biological mechanisms governing host response to food. Recognizing "genome-environment interactions" as the conceptual thread that connects and runs through pharmacogenomics, nutrigenomics, and ecogenomics may contribute toward anticipatory governance and prospective real-time analysis of these omics fields. Such real-time analysis of omics technologies and innovations is crucial, because it can influence and positively shape them as these approaches develop, and help avoid predictable pitfalls, and thus ensure their effective and ethical application in the laboratory, clinic, and society.

Risk assessment and communication tools for genotype associations with multifactorial phenotypes: the concept of "edge effect" and cultivating an ethical bridge between omics innovations and society

Applications of omics technologies in the postgenomics era swiftly expanded from rare monogenic disorders to multifactorial common complex diseases, pharmacogenomics, and personalized medicine. Already, there are signposts indicative of further omics technology investment in nutritional sciences (nutrigenomics), environmental health/ecology (ecogenomics), and agriculture (agrigenomics). Genotype-phenotype association studies are a centerpiece of translational research in omics science. Yet scientific and ethical standards and ways to assess and communicate risk information obtained from association studies have been neglected to date. This is a significant gap because association studies decisively influence which genetic loci become genetic tests in the clinic or products in the genetic test marketplace. A growing challenge concerns the interpretation of large overlap typically observed in distribution of quantitative traits in a genetic association study with a polygenic/multifactorial phenotype. To remedy the shortage of risk assessment and communication tools for association studies, this paper presents the concept of edge effect. That is, the shift in population edges of a multifactorial quantitative phenotype is a more sensitive measure (than population averages) to gauge the population level impact and by extension, policy significance of an omics marker. Empirical application of the edge effect concept is illustrated using an original analysis of warfarin pharmacogenomics and the VKORC1 genetic variation in a Brazilian population sample. These edge effect analyses are examined in relation to regulatory guidance development for association studies. We explain that omics science transcends the conventional laboratory bench space and includes a highly heterogeneous cast of stakeholders in society who have a plurality of interests that are often in conflict. Hence, communication of risk information in diagnostic medicine also demands attention to processes involved in production of knowledge and human values embedded in scientific practice, for example, why, how, by whom, and to what ends association studies are conducted, and standards are developed (or not). To ensure sustainability of omics innovations and forecast their trajectory, we need interventions to bridge the gap between omics laboratory and society. Appreciation of scholarship in history of omics science is one remedy to responsibly learn from the past to ensure a sustainable future in omics fields, both emerging (nutrigenomics, ecogenomics), and those that are more established (pharmacogenomics). Another measure to build public trust and sustainability of omics fields could be legislative initiatives to create a multidisciplinary oversight body, at arm's length from conflict of interests, to carry out independent, impartial, and transparent innovation analyses and prospective technology assessment.

The plasma proteome, adductome and idiosyncratic toxicity in toxicoproteomics research

Toxicoproteomics uses the discovery potential of proteomics in toxicology research by applying global protein measurement technologies to biofluids and tissues after host exposure to injurious agents. Toxicoproteomic studies thus far have focused on protein profiling of major organs and biofluids such as liver and blood in preclinical species exposed to model toxicants. The slow pace of discovery for new biomarkers, toxicity signatures and mechanistic insights is partially due to the limited proteome coverage derived from analysis of native organs, tissues and body fluids by traditional proteomic platforms. Improved toxicoproteomic analysis would result by combining higher data density LC-MS/MS platforms with stable isotope labelled peptides and parallel use of complementary platforms. Study designs that remove abundant proteins from biofluids, enrich subcellular structures and include cell specific isolation from heterogeneous tissues would greatly increase differential expression capabilities. By leveraging resources from immunology, cell biology and nutrition research communities, toxicoproteomics could make particular contributions in three inter-related areas to advance mechanistic insights and biomarker development: the plasma proteome and circulating microparticles, the adductome and idiosyncratic toxicity.

Development of BIATECH-54 standard mixtures for assessment of protein identification and relative expression

Mixtures of known proteins have been very useful in the assessment and validation of methods for high-throughput (HTP) MS (MS/MS) proteomics experiments. However, these test mixtures have generally consisted of few proteins at near equal concentration or of a single protein at varied concentrations. Such mixtures are too simple to effectively assess the validity of error rates for protein identification and differential expression in HTP MS/MS studies. This work aimed at overcoming these limitations and simulating studies of complex biological samples. We introduced a pair of 54-protein standard mixtures of variable concentrations with up to a 1000-fold dynamic range in concentration and up to ten-fold expression ratios with additional negative controls (infinite expression ratios). These test mixtures comprised 16 off-the-shelf Sigma-Aldrich proteins and 38 Shewanella oneidensis proteins produced in-house. The standard proteins were systematically distributed into three main concentration groups (high, medium, and low) and then the concentrations were varied differently for each mixture within the groups to generate different expression ratios. The mixtures were analyzed with both low mass accuracy LCQ and high mass accuracy FT-LTQ instruments. In addition, these 54 standard proteins closely follow the molecular weight distributions of both bacterial and human proteomes. As a result, these new standard mixtures allow for a much more realistic assessment of approaches for protein identification and label-free differential expression than previous mixtures. Finally, methodology and experimental design developed in this work can be readily applied in future to development of more complex standard mixtures for HTP proteomics studies.

The potential of proteomics for providing new insights into environmental impacts on human health

The effects of environmental chemicals have traditionally been detected by monitoring biomarkers of exposure or biomarkers of effect. Proteomics, the study of the complete profile of proteins in a given cell, tissue or biological system, is a new approach using a set of high-throughput methodologies with a wide dynamic range that makes possible the discovery of novel biomarkers. This article reviews the application of two-dimensional electrophoresis and mass-spectrometry methods to environmental toxicology. Emphasis is placed on the protein-expression signature approach and on identifying redox-based post-translational protein modifications. The methodological links between studies in sentinel organisms and humans are explored. Significant limitations and challenges are placed on this approach by the shortage of genome sequence data necessary for protein identification and the growing requirement for more stringent study design. Proteomics will continue to be an important toolkit to help address the growing environmental threat posed by nanoparticles and endocrine disrupting agents.

Precolumn Isotope Dilution Analysis in nanoHPLC−ICPMS for Absolute Quantification of Sulfur-Containing Peptides

A novel generic approach based on precolumn isotope dilution nanoHPLC-ICPMS analysis was developed for the accurate absolute quantification of sulfur-containing peptides. A 34S-labeled, species-unspecific sulfur spike (sulfate), noninteracting with analyte peptides under the optimized HPLC condition, was added directly to the chromatographic eluents. Thus a generic sulfur standard permanently present during analysis was used for peptide quantification. Interference-free detection of the 32S and 34S isotopes in ICPMS was achieved by eliminating O2+ ions in a collision cell using Xe gas at 130 microL min-1. The detection limit for sulfur was 45 microg L-1 which corresponded to 1-2 pmol of individual peptides. The method was validated by the analysis of a standard peptide solution showing high accuracy (recovery 103%) and good precision (RSD 2.1%). The combination of nanoHPLC-ICP IDMS with nanoHPLC-ESI MS/MS allowed the precise quantification and identification of sulfur-containing peptides in tryptic digests of human serum albumin and salt-induced yeast protein (SIP18) at the picomole level.

A predictive model for identifying proteins by a single peptide match

MOTIVATION

Tandem mass-spectrometry of trypsin digests, followed by database searching, is one of the most popular approaches in high-throughput proteomics studies. Peptides are considered identified if they pass certain scoring thresholds. To avoid false positive protein identification, > or = 2 unique peptides identified within a single protein are generally recommended. Still, in a typical high-throughput experiment, hundreds of proteins are identified only by a single peptide. We introduce here a method for distinguishing between true and false identifications among single-hit proteins. The approach is based on randomized database searching and usage of logistic regression models with cross-validation. This approach is implemented to analyze three bacterial samples enabling recovery 68-98% of the correct single-hit proteins with an error rate of < 2%. This results in a 22-65% increase in number of identified proteins. Identifying true single-hit proteins will lead to discovering many crucial regulators, biomarkers and other low abundance proteins.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Global and targeted quantitative proteomics for biomarker discovery

The extraordinary developments made in proteomic technologies in the past decade have enabled investigators to consider designing studies to search for diagnostic and therapeutic biomarkers by scanning complex proteome samples using unbiased methods. The major technology driving these studies is mass spectrometry (MS). The basic premises of most biomarker discovery studies is to use the high data-gathering capabilities of MS to compare biological samples obtained from healthy and disease-afflicted patients and identify proteins that are differentially abundant between the two specimen. To meet the need to compare the abundance of proteins in different samples, a number of quantitative approaches have been developed. In this article, many of these will be described with an emphasis on their advantageous and disadvantageous for the discovery of clinically useful biomarkers.