Quantification of the Proteins of the Bacterial Ribosome Using QconCAT Technology

Toward systems-informed models for biologics disposition: covariates of the abundance of the neonatal Fc Receptor (FcRn) in human tissues and implications for pharmacokinetic modelling

Biologics are a fast-growing therapeutic class, with intertwined pharmacokinetics and pharmacodynamics, affected by the abundance and function of the FcRn receptor. While many investigators assume adequacy of classical models, such as allometry, for pharmacokinetic characterization of biologics, advocates of physiologically-based pharmacokinetics (PBPK) propose consideration of known systems parameters that affect the fate of biologics to enable a priori predictions, which go beyond allometry. The aim of this study was to deploy a systems-informed modelling approach to predict the disposition of Fc-containing biologics. We used global proteomics to quantify the FcRn receptor [p51 and β₂-microglobulin (B2M) subunits] in 167 samples of human tissue (liver, intestine, kidney and skin) and assessed covariates of its expression. FcRn p51 subunit was highest in liver relative to other tissues, and B2M was 1-2 orders of magnitude more abundant than FcRn p51 across all sets. There were no sex-related differences, while higher expression was confirmed in neonate liver compared with adult liver. Trends of expression in liver and kidney indicated a moderate effect of body mass index, which should be confirmed in a larger sample size. Expression of FcRn p51 subunit was approximately 2-fold lower in histologically normal liver tissue adjacent to cancer compared with healthy liver. FcRn mRNA in plasma-derived exosomes correlated moderately with protein abundance in matching liver tissue, opening the possibility of use as a potential clinical tool. Predicted effects of trends in FcRn abundance in healthy and disease (cancer and psoriasis) populations using trastuzumab and efalizumab PBPK models were in line with clinical observations, and global sensitivity analysis revealed endogenous IgG plasma concentration and tissue FcRn abundance as key systems parameters influencing exposure to Fc-conjugated biologics.

Proteomic quantification of receptor tyrosine kinases involved in the development and progression of colorectal cancer liver metastasis

Introduction

Alterations in expression and activity of human receptor tyrosine kinases (RTKs) are associated with cancer progression and in response to therapeutic intervention.

Methods

Thus, protein abundance of 21 RTKs was assessed in 15 healthy and 18 cancerous liver samples [2 primary and 16 colorectal cancer liver metastasis (CRLM)] matched with non-tumorous (histologically normal) tissue, by a validated QconCAT-based targeted proteomic approach.

Results

It was demonstrated, for the first time, that the abundance of EGFR, INSR, VGFR3 and AXL, is lower in tumours relative to livers from healthy individuals whilst the opposite is true for IGF1R. EPHA2 was upregulated in tumour compared with histologically normal tissue surrounding it. PGFRB levels were higher in tumours relative to both histologically normal tissue surrounding tumour and tissues taken from healthy individuals. The abundances of VGFR1/2, PGFRA, KIT, CSF1R, FLT3, FGFR1/3, ERBB2, NTRK2, TIE2, RET, and MET were, however, comparable in all samples. Statistically significant, but moderate correlations were observed (Rs > 0.50, p < 0.05) for EGFR with INSR and KIT. FGFR2 correlated with PGFRA and VGFR1 with NTRK2 in healthy livers. In non-tumorous (histologically normal) tissues from cancer patients, there were correlations between TIE2 and FGFR1, EPHA2 and VGFR3, FGFR3 and PGFRA (p < 0.05). EGFR correlated with INSR, ERBB2, KIT and EGFR, and KIT with AXL and FGFR2. In tumours, CSF1R correlated with AXL, EPHA2 with PGFRA, and NTRK2 with PGFRB and AXL. Sex, liver lobe and body mass index of donors had no impact on the abundance of RTKs, although donor age showed some correlations. RET was the most abundant of these kinases in non-tumorous tissues (~35%), while PGFRB was the most abundant RTK in tumours (~47%). Several correlations were also observed between the abundance of RTKs and proteins relevant to drug pharmacokinetics (enzymes and transporters).

Discussion

DiscussionThis study quantified perturbation to the abundance of several RTKs in cancer and the value generated in this study can be used as input to systems biology models defining liver cancer metastases and biomarkers of its progression.

Quantitative Assessment of the Impact of Crohn's Disease on Protein Abundance of Human Intestinal Drug-Metabolising Enzymes and Transporters

Crohn's disease affects the mucosal layer of the intestine, predominantly ileum and colon segments, with the potential to affect the expression of intestinal enzymes and transporters, and consequently, oral drug bioavailability. We carried out a quantitative proteomic analysis of inflamed and non-inflamed ileum and colon tissues from Crohn's disease patients and healthy donors. Homogenates from samples in each group were pooled and protein abundance determined by liquid chromatography-mass spectrometry (LC-MS). In inflamed Crohn's ileum, CYP3A4, CYP20A1, CYP51A1, ADH1B, ALPI, FOM1, SULT1A2, SULT1B1 and ABCB7 showed ≥10-fold reduction in abundance compared with healthy baseline. By contrast, only MGST1 showed ≥10 fold reduction in inflamed colon. Ileal UGT1A1, MGST1, MGST2, and MAOA levels increased by ≥2 fold in Crohn's patients, while only ALPI showed ≥2 fold increase in the colon. Counter-intuitively, non-inflamed ileum had a higher magnitude of fold change than inflamed tissue when compared with healthy tissue. Marked but non-uniform alterations were observed in the expression of various enzymes and transporters in ileum and colon compared with healthy samples. Modelling will allow improved understanding of the variable effects of Crohn's disease on bioavailability of orally administered drugs.

Increased carvone production in Escherichia coli by balancing limonene conversion enzyme expression via targeted quantification concatamer proteome analysis

(−)-Carvone is a monoterpenoid with a spearmint flavor. A sustainable biotechnological production process for (−)-carvone is desirable. Although all enzymes in (−)-carvone biosynthesis have been functionally expressed in Escherichia coli independently, the yield was low in previous studies. When cytochrome P450 limonene-6-hydroxylase (P450)/cytochrome P450 reductase (CPR) and carveol dehydrogenase (CDH) were expressed in a single strain, by-product formation (dihydrocarveol and dihydrocarvone) was detected. We hypothesized that P450 and CDH expression levels differ in E. coli. Thus, two strains independently expressing P450/CPR and CDH were mixed with different ratios, confirming increased carvone production and decreased by-product formation when CDH input was reduced. The optimum ratio of enzyme expression to maximize (−)-carvone production was determined using the proteome analysis quantification concatamer (QconCAT) method. Thereafter, a single strain expressing both P450/CPR and CDH was constructed to imitate the optimum expression ratio. The upgraded strain showed a 15-fold improvement compared to the initial strain, showing a 44 ± 6.3 mg/L (−)-carvone production from 100 mg/L (−)-limonene. Our study showed the usefulness of the QconCAT proteome analysis method for strain development in the industrial biotechnology field.

Proteomics of colorectal cancer liver metastasis: A quantitative focus on drug elimination and pharmacodynamics effects

Aims

This study aims to quantify drug‐metabolising enzymes, transporters, receptor tyrosine kinases (RTKs) and protein markers (involved in pathways affected in cancer) in pooled healthy, histologically normal and matched cancerous liver microsomes from colorectal cancer liver metastasis (CRLM) patients.

Methods

Microsomal fractionation was performed and pooled microsomes were prepared. Global and accurate mass and retention time liquid chromatography–mass spectrometry proteomics were used to quantify proteins. A QconCAT (KinCAT) for the quantification of RTKs was designed and applied for the first time. Physiologically based pharmacokinetic (PBPK) simulations were performed to assess the contribution of altered abundance of drug‐metabolising enzymes and transporters to changes in pharmacokinetics.

Results

Most CYPs and UGTs were downregulated in histologically normal relative to healthy samples, and were further reduced in cancer samples (up to 54‐fold). The transporters, MRP2/3, OAT2/7 and OATP2B1/1B3/1B1 were downregulated in CRLM. Application of abundance data in PBPK models for substrates with different attributes indicated substantially lower (up to 13‐fold) drug clearance when using cancer‐specific instead of default parameters in cancer population. Liver function markers were downregulated, while inflammation proteins were upregulated (by up to 76‐fold) in cancer samples. Various pharmacodynamics markers (e.g. RTKs) were altered in CRLM. Using global proteomics, we examined proteins in pathways relevant to cancer (such as metastasis and desmoplasia), including caveolins and collagen chains, and confirmed general over‐expression of such pathways.

Conclusion

This study highlights impaired drug metabolism, perturbed drug transport and altered abundance of cancer markers in CRLM, demonstrating the importance of population‐specific abundance data in PBPK models for cancer.

Selected reaction monitoring for the quantification of Escherichia coli ribosomal proteins

Ribosomes are the sophisticated machinery that is responsible for protein synthesis in a cell. Recently, quantitative mass spectrometry (qMS) have been successfully applied for understanding the dynamics of protein complexes. Here, we developed a highly specific and reproducible method to quantify all ribosomal proteins (r-proteins) by combining selected reaction monitoring (SRM) and isotope labeling. We optimized the SRM methods using purified ribosomes and Escherichia coli lysates and verified this approach as detecting 41 of the 54 r-proteins separately synthesized in E. coli S30 extracts. The SRM methods will enable us to utilize qMS as a highly specific analytical tool in the research of E. coli ribosomes, and this methodology have potential to accelerate the understanding of ribosome biogenesis, function, and the development of engineered ribosomes with additional functions.

Mass spectrometry-based abundance atlas of ABC transporters in human liver, gut, kidney, brain and skin

ABC transporters (ATP-binding cassette transporter) traffic drugs and their metabolites across membranes, making ABC transporter expression levels a key factor regulating local drug concentrations in different tissues and individuals. Yet, quantification of ABC transporters remains challenging because they are large and low-abundance transmembrane proteins. Here, we analysed 200 samples of crude and membrane-enriched fractions from human liver, kidney, intestine, brain microvessels and skin, by label-free quantitative mass spectrometry. We identified 32 (out of 48) ABC transporters: ABCD3 was the most abundant in liver, whereas ABCA8, ABCB2/TAP1 and ABCE1 were detected in all tissues. Interestingly, this atlas unveiled that ABCB2/TAP1 may have TAP2-independent functions in the brain and that biliary atresia (BA) and control livers have quite different ABC transporter profiles. We propose that meaningful biological information can be derived from a direct comparison of these data sets.

Pseudomonas aeruginosa Presents Multiple Vital Changes in Its Proteome in the Presence of 3-Hydroxyphenylacetic Acid, a Promising Antimicrobial Agent

Pseudomonas aeruginosa, a widely distributed opportunistic pathogen, is an important threat to human health for causing serious infections worldwide. Due to its antibiotic resistance and virulence factors, it is so difficult to combat this bacterium; thus, new antimicrobial agents are in search. 3-Hydroxyphenylacetic acid (3-HPAA), which is a phenolic acid mostly found in olive oil wastewater, can be a promising candidate with its dose-dependent antimicrobial properties. Elucidating the molecular mechanism of action is crucial for future examinations and the presentation of 3-HPAA as a new agent. In this study, the antimicrobial activity of 3-HPAA on P. aeruginosa and its action mechanism was investigated via shot-gun proteomics. The data, which are available via ProteomeXchange with identifier PXD016243, were examined by STRING analysis to determine the interaction networks of proteins. KEGG Pathway enrichment analysis via the DAVID bioinformatics tool was also performed to investigate the metabolic pathways that undetected and newly detected groups of the proteins. The results displayed remarkable changes after 3-HPAA exposure in the protein profile of P. aeruginosa related to DNA replication and repair, RNA modifications, ribosomes and proteins, cell envelope, oxidative stress, as well as nutrient availability. 3-HPAA showed its antimicrobial action on P. aeruginosa by affecting multiple bacterial processes; hence, it could be categorized as a multitarget antimicrobial agent.

Mass Spectrometry of Human Transporters

Transporters are key to understanding how an individual will respond to a particular dose of a drug. Two patients with similar systemic concentrations may have quite different local concentrations of a drug at the required site. The transporter profile of any individual depends upon a variety of genetic and environmental factors, including genotype, age, and diet status. Robust models (virtual patients) are therefore required and these models are data hungry. Necessary data include quantitative transporter profiles at the relevant organ. Liquid chromatography with tandem mass spectrometry (LC-MS/MS) is currently the most powerful method available for obtaining this information. Challenges include sourcing the tissue, isolating the hydrophobic membrane-embedded transporter proteins, preparing the samples for MS (including proteolytic digestion), choosing appropriate quantification methodology, and optimizing the LC-MS/MS conditions. Great progress has been made with all of these, especially within the last few years, and is discussed here.

Quantitative Proteomics of Clinically Relevant Drug-Metabolizing Enzymes and Drug Transporters and Their Intercorrelations in the Human Small Intestine

The levels of drug-metabolizing enzymes (DMEs) and transporter proteins in the human intestine are pertinent to determine oral drug bioavailability. Despite the paucity of reports on such measurements, it is well recognized that these values are essential for translating in vitro data on drug metabolism and transport to predict drug disposition in gut wall. In the current study, clinically relevant DMEs [cytochrome P450 (P450) and uridine 5′-diphospho-glucuronosyltransferase (UGT)] and drug transporters were quantified in total mucosal protein preparations from the human jejunum (n = 4) and ileum (n = 12) using quantification concatemer–based targeted proteomics. In contrast to previous reports, UGT2B15 and organic anion-transporting polypeptide 1 (OATP1A2) were quantifiable in all our samples. Overall, no significant disparities in protein expression were observed between jejunum and ileum. Relative mRNA expression for drug transporters did not correlate with the abundance of their cognate protein, except for P-glycoprotein 1 (P-gp) and organic solute transporter subunit alpha (OST-α), highlighting the limitations of RNA as a surrogate for protein expression in dynamic tissues with high turnover. Intercorrelations were found within P450 [2C9-2C19 (P = 0.002, R² = 0.63), 2C9–2J2 (P = 0.004, R² = 0.40), 2D6-2J2 (P = 0.002, R² = 0.50)] and UGT [1A1-2B7 (P = 0.02, R² = 0.87)] family of enzymes. There were also correlations between P-gp and several other proteins [OST-α (P < 0.0001, R² = 0.77), UGT1A6 (P = 0.009, R² = 0.38), and CYP3A4 (P = 0.007, R² = 0.30)]. Incorporating such correlations into building virtual populations is crucial for obtaining plausible characteristics of simulated individuals.

Toward a Consensus on Applying Quantitative Liquid Chromatography-Tandem Mass Spectrometry Proteomics in Translational Pharmacology Research: A White Paper

Quantitative translation of information on drug absorption, disposition, receptor engagement, and drug-drug interactions from bench to bedside requires models informed by physiological parameters that link in vitro studies to in vivo outcomes. To predict in vivo outcomes, biochemical data from experimental systems are routinely scaled using protein quantity in these systems and relevant tissues. Although several laboratories have generated useful quantitative proteomic data using state-of-the-art mass spectrometry, no harmonized guidelines exit for sample analysis and data integration to in vivo translation practices. To address this gap, a workshop was held on September 27 and 28, 2018, in Cambridge, MA, with 100 experts attending from academia, the pharmaceutical industry, and regulators. Various aspects of quantitative proteomics and its applications in translational pharmacology were debated. A summary of discussions and best practices identified by this expert panel are presented in this "White Paper" alongside unresolved issues that were outlined for future debates.

Proteomic Quantification of Human Blood-Brain Barrier SLC and ABC Transporters in Healthy Individuals and Dementia Patients

The blood-brain barrier (BBB) maintains brain homeostasis by controlling traffic of molecules from the circulation into the brain. This function is predominantly dependent on proteins expressed at the BBB, especially transporters and tight junction proteins. Alterations to the level and function of BBB proteins can impact the susceptibility of the central nervous system to exposure to xenobiotics in the systemic circulation with potential consequent effects on brain function. In this study, expression profiles of drug transporters and solute carriers in the BBB were assessed in tissues from healthy individuals ( n = 12), Alzheimer's patients ( n = 5), and dementia with Lewy bodies patients ( n = 5), using targeted, accurate mass retention time (AMRT) and global proteomic methods. A total of 53 transporters were quantified, 19 for the first time in the BBB. A further 20 novel transporters were identified but not quantified. The global proteomic method identified another 3333 BBB proteins. Transporter abundances, taken together with the scaling factor, microvessel protein content per unit tissue (BMvPGB also measured here), can be used in quantitative systems pharmacology models predicting drug disposition in the brain and permitting dose adjustment (precision dosing) in special populations of patients, such as those with dementia. Even in this small study, we see differences in transporter profile between healthy and diseased brain tissue.

A Guide to Mass Spectrometry-Based Quantitative Proteomics

Proteomics has become an attractive science in the postgenomic era, given its capacity to identify up to thousands of molecules in a single, complex sample and quantify them in an absolute and/or relative manner. The use of these techniques enables understanding of cellular and molecular mechanisms of diseases and other biological conditions, as well as identification and screening of protein biomarkers. Here we provide a straightforward, up-to-date compilation and comparison of the main quantitation techniques used in comparative proteomics such as in vitro and in vivo stable isotope labeling and label-free techniques. Additionally, this chapter includes common methods for data acquisition in proteomics and some appropriate methods for data processing. This compilation can serve as a reference for scientists who are new to, or already familiar with, quantitative proteomics.

Dose adjustment in orphan disease populations: the quest to fulfill the requirements of physiologically based pharmacokinetics

INTRODUCTION

While the media is engaged and fascinated by the idea of 'Precision Medicine', the nuances related to 'Precision Dosing' seem to be largely ignored. Assuming the 'right drug' is selected, clinicians still need to decide on the 'right dose' for individuals. Ideally, optimal dosing should be studied in clinical trials; however, many drugs on the market lack evidence-based dosing recommendations, and small groups of patients (orphan disease populations) are dependent on local guidance and clinician experience to determine drug dosage adjustments. Areas Covered: This report explores the current understanding of dosing adjustment in special populations and examines the requirements for developing 'in silico' models for pediatric, elderly and pregnant patients. The report also highlights current use of modeling to provide evidence-based recommendations for drug labeling in the absence of complete clinical trials in orphan disease populations. Expert Opinion: Physiologically based pharmacokinetics (PBPK) is an attractive prospect for determining the best drug dosage adjustments in special populations. However, it is not sufficient for individualized, or even stratified dosing, unless the systems (drug-independent) data required to build robust PBPK models are obtained. Such models are not a substitute for clinical trials, but they are an alternative to undocumented and inconsistent guesswork.

Application of targeted mass spectrometry in bottom-up proteomics for systems biology research

The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).

Quantitative Characterization of Major Hepatic UDP-Glucuronosyltransferase Enzymes in Human Liver Microsomes: Comparison of Two Proteomic Methods and Correlation with Catalytic Activity

Quantitative characterization of UDP-glucuronosyltransferase (UGT) enzymes is valuable in glucuronidation reaction phenotyping, predicting metabolic clearance and drug-drug interactions using extrapolation exercises based on pharmacokinetic modeling. Different quantitative proteomic workflows have been employed to quantify UGT enzymes in various systems, with reports indicating large variability in expression, which cannot be explained by interindividual variability alone. To evaluate the effect of methodological differences on end-point UGT abundance quantification, eight UGT enzymes were quantified in 24 matched liver microsomal samples by two laboratories using stable isotope-labeled (SIL) peptides or quantitative concatemer (QconCAT) standard, and measurements were assessed against catalytic activity in seven enzymes (n = 59). There was little agreement between individual abundance levels reported by the two methods; only UGT1A1 showed strong correlation [Spearman rank order correlation (Rs) = 0.73, P < 0.0001; R2 = 0.30; n = 24]. SIL-based abundance measurements correlated well with enzyme activities, with correlations ranging from moderate for UGTs 1A6, 1A9, and 2B15 (Rs = 0.52-0.59, P < 0.0001; R2 = 0.34-0.58; n = 59) to strong correlations for UGTs 1A1, 1A3, 1A4, and 2B7 (Rs = 0.79-0.90, P < 0.0001; R2 = 0.69-0.79). QconCAT-based data revealed generally poor correlation with activity, whereas moderate correlations were shown for UGTs 1A1, 1A3, and 2B7. Spurious abundance-activity correlations were identified in the cases of UGT1A4/2B4 and UGT2B7/2B15, which could be explained by correlations of protein expression between these enzymes. Consistent correlation of UGT abundance with catalytic activity, demonstrated by the SIL-based dataset, suggests that quantitative proteomic data should be validated against catalytic activity whenever possible. In addition, metabolic reaction phenotyping exercises should consider spurious abundance-activity correlations to avoid misleading conclusions.

Global Proteomic Analysis of Human Liver Microsomes: Rapid Characterization and Quantification of Hepatic Drug-Metabolizing Enzymes

Many genetic and environmental factors lead to interindividual variations in the metabolism and transport of drugs, profoundly affecting efficacy and toxicity. Precision dosing, that is, targeting drug dose to a well characterized subpopulation, is dependent on quantitative models of the profiles of drug-metabolizing enzymes (DMEs) and transporters within that subpopulation, informed by quantitative proteomics. We report the first use of ion mobility-mass spectrometry for this purpose, allowing rapid, robust, label-free quantification of human liver microsomal (HLM) proteins from distinct individuals. Approximately 1000 proteins were identified and quantified in four samples, including an average of 70 DMEs. Technical and biological variabilities were distinguishable, with technical variability accounting for about 10% of total variability. The biological variation between patients was clearly identified, with samples showing a range of expression profiles for cytochrome P450 and uridine 5'-diphosphoglucuronosyltransferase enzymes. Our results showed excellent agreement with previous data from targeted methods. The label-free method, however, allowed a fuller characterization of the in vitro system, showing, for the first time, that HLMs are significantly heterogeneous. Further, the traditional units of measurement of DMEs (pmol mg^-1 HLM protein) are shown to introduce error arising from variability in unrelated, highly abundant proteins. Simulations of this variability suggest that up to 1.7-fold variation in apparent CYP3A4 abundance is artifactual, as are background positive correlations of up to 0.2 (Spearman correlation coefficient) between the abundances of DMEs. We suggest that protein concentrations used in pharmacokinetic predictions and scaling to in vivo clinical situations (physiologically based pharmacokinetics and in vitro-in vivo extrapolation) should be referenced instead to tissue mass.

QconCAT: Internal Standard for Protein Quantification

Protein quantification based on stable isotope labeling-mass spectrometry involves adding known quantities of stable isotope-labeled internal standards into biological samples. The internal standards are analogous to analyte molecules and quantification is achieved by comparing signals from isotope-labeled and analyte molecules. This methodology is broadly applicable to proteomics research, biomarker discovery and validation, and clinical studies, which require accurate and precise protein abundance measurements. One such internal standard platform for protein quantification is concatenated peptides (QconCAT). This chapter describes a protocol for the design, expression, characterization, and application of the QconCAT strategy for protein quantification.

Proteome Profiles of Outer Membrane Vesicles and Extracellular Matrix of Pseudomonas aeruginosa Biofilms

In the present work, two different proteomic platforms, gel-based and gel-free, were used to map the matrix and outer membrane vesicle exoproteomes of Pseudomonas aeruginosa PAO1 biofilms. These two proteomic strategies allowed us a confident identification of 207 and 327 proteins from enriched outer membrane vesicles and whole matrix isolated from biofilms. Because of the physicochemical characteristics of these subproteomes, the two strategies showed complementarity, and thus, the most comprehensive analysis of P. aeruginosa exoproteome to date was achieved. Under our conditions, outer membrane vesicles contribute approximately 20% of the whole matrix proteome, demonstrating that membrane vesicles are an important component of the matrix. The proteomic profiles were analyzed in terms of their biological context, namely, a biofilm. Accordingly relevant metabolic processes involved in cellular adaptation to the biofilm lifestyle as well as those related to P. aeruginosa virulence capabilities were a key feature of the analyses. The diversity of the matrix proteome corroborates the idea of high heterogeneity within the biofilm; cells can display different levels of metabolism and can adapt to local microenvironments making this proteomic analysis challenging. In addition to analyzing our own primary data, we extend the analysis to published data by other groups in order to deepen our understanding of the complexity inherent within biofilm populations.

The complexity of protein semiochemistry in mammals

The high degree of protein sequence similarity in the MUPs (major urinary proteins) poses considerable challenges for their individual differentiation, analysis and quantification. In the present review, we discuss MS approaches for MUP quantification, at either the protein or the peptide level. In particular, we describe an approach to multiplexed quantification based on the design and synthesis of novel proteins (QconCATs) that are concatamers of quantification standards, providing a simple route to the generation of a set of stable-isotope-labelled peptide standards. The MUPs pose a particular challenge to QconCAT design, because of their sequence similarity and the limited number of peptides that can be used to construct the standards. Such difficulties can be overcome by careful attention to the analytical workflow.

Choice of LC-MS methods for the absolute quantification of drug-metabolizing enzymes and transporters in human tissue: a comparative cost analysis

The quantification of drug-metabolizing enzymes and transporters is important for in vitro-in vivo extrapolation (IVIVE) of xenobiotic clearance, which has become an integral part of drug development. There are different mass spectrometry-based techniques used for quantitative proteomics, and as more laboratories are opting for the use of these methods, selecting the most appropriate tool is becoming a concern. For the first time, we attempt to determine the significance of cost of different LC-MS methods of quantitative analysis of these proteins and to present a framework to objectively assess the choice of the techniques. Based on our analysis, quantification using labeled internal standards is more expensive per sample but provides higher quality data than label-free quantification. Quantification using absolute quantification synthetic peptides is the approach of choice for analyzing less than nine proteins, whereas when quantifying a defined set of proteins (10-50), such as enzymes, in a reasonably large number of samples (20-100), the quantification concatemer technique is more economical, followed by label-free quantification. When analyzing proteomes or sub-proteomes (≥500 proteins), label-free quantification is more cost-effective than the use of labeled internal standards. A cost-benefit approach is described to assess the choice of the most appropriate mass spectrometry-based approach for the quantification of proteins relevant to IVIVE.

Studying macromolecular complex stoichiometries by peptide-based mass spectrometry

A majority of cellular functions are carried out by macromolecular complexes. A host of biochemical and spectroscopic methods exists to characterize especially protein/protein complexes, however there has been a lack of a universal method to determine protein stoichiometries. Peptide‐based MS, especially as a complementary method to the MS analysis of intact protein complexes, has now been developed to a point where it can be employed to assay protein stoichiometries in a routine manner. While the experimental demands are still significant, peptide‐based MS has been successfully applied to analyze stoichiometries for a variety of protein complexes from very different biological backgrounds. In this review, we discuss the requirements especially for targeted MS acquisition strategies to be used in this context, with a special focus on the interconnected experimental aspects of sample preparation, protein digestion, and peptide stability. In addition, different strategies for the introduction of quantitative peptide standards and their suitability for different scenarios are compared.