Quantitative, high-resolution proteomics for data-driven systems biology

Peptidomics of potato protein hydrolysates: implications of post-translational modifications in food peptide structure and behaviour

Post-translational modifications (PTMs) often occur in proteins and play a regulatory role in protein function. There is an increasing interest in the bioactivity of food protein-derived peptides, but the occurrence of PTMs and their influence on food peptide structure and behaviour remain largely unknown. In this study, the shotgun-based peptidomics strategy was used to identify the occurrence of PTMs in peptides generated from potato protein hydrolysis using digestive proteases. Diverse PTMs were found in the potato peptides, including acetylation of lysine, N-terminal of proteins and peptides, C-terminal amidation, de-amidation of asparagine/glutamine, methylation and trimethylation, methionine oxidation and N-terminal pyro-glutamyl residue formation. The modifications may have been formed naturally or as a result of chemical reactions during isolation and enzymatic processing of the potato proteins. Most of the PTMs were calculated to decrease the isoelectric point and increase molecular hydrophobicity of the peptides, which will influence their bioactivity while also potentially altering their solubility in an aqueous environment. This is the first study to unravel that food-derived peptides can be widely modified by PTMs associated with notable changes in peptide chemical properties. The findings have broader implications on the bioavailability, biomolecular interactions and biological activities of food peptides.

Multiple Reaction Monitoring for quantitative laccase kinetics by LC-MS

Laccases (EC 1.10.3.2) are enzymes known for their ability to catalyse the oxidation of phenolic compounds using molecular oxygen as the final electron acceptor. Lignin is a natural phenylpropanoids biopolymer whose degradation in nature is thought to be aided by enzymatic oxidation by laccases. Laccase activity is often measured spectrophotometrically on compounds such as syringaldazine and ABTS which poorly relate to lignin. We employed natural phenolic hydroxycinnamates having different degree of methoxylations, p-coumaric, ferulic and sinapic acid, and a lignin model OH-dilignol compound as substrates to assess enzyme kinetics by HPLC-MS on two fungal laccases Trametes versicolor laccase, Tv and Ganoderma lucidum laccase, Gl. The method allowed accurate kinetic measurements and detailed insight into the product profiles of both laccases. Both Tv and Gl laccase are active on the hydroxycinnammates and show a preference for substrate with methoxylations. Product profiles were dominated by the presence of dimeric and trimeric species already after 10 minutes of reaction and similar profiles were obtained with the two laccases. This new HPLC-MS method is highly suitable and accurate as a new method for assaying laccase activity on genuine phenolic substrates, as well as a tool for examining laccase oxidation product profiles.

Balanced trafficking between the ER and the Golgi apparatus increases protein secretion in yeast

The yeast Saccharomyces cerevisiae is widely used as a cell factory to produce recombinant proteins. However, S. cerevisiae naturally secretes only a few proteins, such as invertase and the mating alpha factor, and its secretory capacity is limited. It has been reported that engineering protein anterograde trafficking from the endoplasmic reticulum to the Golgi apparatus by the moderate overexpression of SEC16 could increase recombinant protein secretion in S. cerevisiae. In this study, the retrograde trafficking in a strain with moderate overexpression of SEC16 was engineered by overexpression of ADP-ribosylation factor GTP activating proteins, Gcs1p and Glo3p, which are involved in the process of COPI-coated vesicle formation. Engineering the retrograde trafficking increased the secretion of α-amylase but did not induce production of reactive oxygen species. An expanded ER membrane was detected in both the GCS1 and GLO3 overexpression strains. Physiological characterizations during batch fermentation showed that GLO3 overexpression had better effect on recombinant protein secretion than GCS1 overexpression. Additionally, the GLO3 overexpression strain had higher secretion of two other recombinant proteins, endoglucanase I from Trichoderma reesei and glucan-1,4-α-glucosidase from Rhizopus oryzae, indicating overexpression of GLO3 in a SEC16 moderate overexpression strain might be a general strategy for improving production of secreted proteins by yeast.

Comparison of protein expression between human livers and the hepatic cell lines HepG2, Hep3B, and Huh7 using SWATH and MRM-HR proteomics: Focusing on drug-metabolizing enzymes

Human hepatic cell lines are widely used as an in vitro model for the study of drug metabolism and liver toxicity. However, the validity of this model is still a subject of debate because the expressions of various proteins in the cell lines, including drug-metabolizing enzymes (DMEs), can differ significantly from those in human livers. In the present study, we first conducted an untargeted proteomics analysis of the microsomes of the cell lines HepG2, Hep3B, and Huh7, and compared them to human livers using a sequential window acquisition of all theoretical mass spectra (SWATH) method. Furthermore, high-resolution multiple reaction monitoring (MRM-HR), a targeted proteomic approach, was utilized to compare the expressions of pre-selected DMEs between human livers and the cell lines. In general, the SWATH quantifications were in good agreement with the MRM-HR analysis. Over 3000 protein groups were quantified in the cells and human livers, and the proteome profiles of human livers significantly differed from the cell lines. Among the 101 DMEs quantified with MRM-HR, most were expressed at substantially lower levels in the cell lines. Thus, appropriate caution must be exercised when using these cell lines for the study of hepatic drug metabolism and toxicity.

Novel RNA-Affinity Proteogenomics Dissects Tumor Heterogeneity for Revealing Personalized Markers in Precision Prognosis of Cancer

To discriminate the patient subpopulations with different clinical outcomes within each breast cancer (BC) subtype, we introduce a robust, clinical-practical, activity-based proteogenomic method that identifies, in their oncogenically active states, candidate biomarker genes bearing patient-specific transcriptomic/genomic alterations of prognostic value. First, we used the intronic splicing enhancer (ISE) probes to sort ISE-interacting trans-acting protein factors (trans-interactome) directly from a tumor tissue for subsequent mass spectrometry characterization. In the retrospective, proteogenomic analysis of patient datasets, we identified those ISE trans-factor-encoding genes showing interaction-correlated expression patterns (iCEPs) as new BC-subtypic genes. Further, patient-specific co-alterations in mRNA expression of select iCEP genes distinguished high-risk patient subsets/subpopulations from other patients within a single BC subtype. Function analysis further validated a tumor-phenotypic trans-interactome contained the drivers of oncogenic splicing switches, representing the predominant tumor cells in a tissue, from which novel personalized biomarkers were clinically characterized/validated for precise prognostic prediction and subsequent individualized alignment of optimal therapy.

Cell-free synthesis of stable isotope-labeled internal standards for targeted quantitative proteomics

High-sensitivity mass spectrometry approaches using selected reaction monitoring (SRM) or multiple reaction monitoring (MRM) methods are powerful tools for targeted quantitative proteomics-based investigation of dynamics in specific biological systems. Both high-sensitivity detection of low-abundance proteins and their quantification using this technique employ stable isotope-labeled peptide internal standards. Currently, there are various ways for preparing standards, including chemical peptide synthesis, cellular protein expression, and cell-free protein or peptide synthesis. Cell-free protein synthesis (CFPS) or in vitro translation (IVT) systems in particular provide high-throughput and low-cost preparation methods, and various cell types and reconstituted forms are now commercially available. Herein, we review the use of such systems for precise and reliable protein quantification.

Systematic analysis of protein turnover in primary cells

A better understanding of proteostasis in health and disease requires robust methods to determine protein half-lives. Here we improve the precision and accuracy of peptide ion intensity-based quantification, enabling more accurate protein turnover determination in non-dividing cells by dynamic SILAC-based proteomics. This approach allows exact determination of protein half-lives ranging from 10 to >1000 h. We identified 4000-6000 proteins in several non-dividing cell types, corresponding to 9699 unique protein identifications over the entire data set. We observed similar protein half-lives in B-cells, natural killer cells and monocytes, whereas hepatocytes and mouse embryonic neurons show substantial differences. Our data set extends and statistically validates the previous observation that subunits of protein complexes tend to have coherent turnover. Moreover, analysis of different proteasome and nuclear pore complex assemblies suggests that their turnover rate is architecture dependent. These results illustrate that our approach allows investigating protein turnover and its implications in various cell types.

Editorial of the PCCP themed issue on "Physical Chemistry for Life Sciences"

This themed issue features high quality and original research on the theme of Physical Chemistry for Life Sciences.

Universal absolute quantification of biomolecules using element mass spectrometry and generic standards

Standardless highly sensitive ICP-MS approach for the absolute and simultaneous quantification of important target biomolecules in life sciences.

MetaLab: an automated pipeline for metaproteomic data analysis

BACKGROUND

Research involving microbial ecosystems has drawn increasing attention in recent years. Studying microbe-microbe, host-microbe, and environment-microbe interactions are essential for the understanding of microbial ecosystems. Currently, metaproteomics provide qualitative and quantitative information of proteins, providing insights into the functional changes of microbial communities. However, computational analysis of large-scale data generated in metaproteomic studies remains a challenge. Conventional proteomic software have difficulties dealing with the extreme complexity and species diversity present in microbiome samples leading to lower rates of peptide and protein identification. To address this issue, we previously developed the MetaPro-IQ approach for highly efficient microbial protein/peptide identification and quantification.

RESULT

Here, we developed an integrated software platform, named MetaLab, providing a complete and automated, user-friendly pipeline for fast microbial protein identification, quantification, as well as taxonomic profiling, directly from mass spectrometry raw data. Spectral clustering adopted in the pre-processing step dramatically improved the speed of peptide identification from database searches. Quantitative information of identified peptides was used for estimating the relative abundance of taxa at all phylogenetic ranks. Taxonomy result files exported by MetaLab are fully compatible with widely used metagenomics tools. Herein, the potential of MetaLab is evaluated by reanalyzing a metaproteomic dataset from mouse gut microbiome samples.

CONCLUSION

MetaLab is a fully automatic software platform enabling an integrated data-processing pipeline for metaproteomics. The function of sample-specific database generation can be very advantageous for searching peptides against huge protein databases. It provides a seamless connection between peptide determination and taxonomic profiling; therefore, the peptide abundance is readily used for measuring the microbial variations. MetaLab is designed as a versatile, efficient, and easy-to-use tool which can greatly simplify the procedure of metaproteomic data analysis for researchers in microbiome studies.

Quantitative proteomic analysis of amastigotes from Leishmania (L.) amazonensis LV79 and PH8 strains reveals molecular traits associated with the virulence phenotype

BACKGROUND

Leishmaniasis is an antropozoonosis caused by Leishmania parasites that affects around 12 million people in 98 different countries. The disease has different clinical forms, which depend mainly on the parasite genetics and on the immunologic status of the host. The promastigote form of the parasite is transmitted by an infected female phlebotomine sand fly, is internalized by phagocytic cells, mainly macrophages, and converts into amastigotes which replicate inside these cells. Macrophages are important cells of the immune system, capable of efficiently killing intracellular pathogens. However, Leishmania can evade these mechanisms due to expression of virulence factors. Different strains of the same Leishmania species may have different infectivity and metastatic phenotypes in vivo, and we have previously shown that analysis of amastigote proteome can give important information on parasite infectivity. Differential abundance of virulence factors probably accounts for the higher virulence of PH8 strain parasites shown in this work. In order to test this hypothesis, we have quantitatively compared the proteomes of PH8 and LV79 lesion-derived amastigotes using a label-free proteomic approach.

METHODOLOGY/PRINCIPAL FINDINGS

In the present work, we have compared lesion development by L. (L.) amazonensis PH8 and LV79 strains in mice, showing that they have different virulence in vivo. Viability and numbers of lesion-derived amastigotes were accordingly significantly different. Proteome profiles can discriminate parasites from the two strains and several proteins were differentially expressed.

CONCLUSIONS/SIGNIFICANCE

This work shows that PH8 strain is more virulent in mice, and that lesion-derived parasites from this strain are more viable and more infective in vitro. Amastigote proteome comparison identified GP63 as highly expressed in PH8 strain, and Superoxide Dismutase, Tryparedoxin Peroxidase and Heat Shock Protein 70 as more abundant in LV79 strain. The expression profile of all proteins and of the differential ones precisely classified PH8 and LV79 samples, indicating that the two strains have proteins with different abundances and that proteome profiles correlate with their phenotypes.

An activity-dependent proximity ligation platform for spatially resolved quantification of active enzymes in single cells

Integration of chemical probes into proteomic workflows enables the interrogation of protein activity, rather than abundance. Current methods limit the biological contexts that can be addressed due to sample homogenization, signal-averaging, and bias toward abundant proteins. Here we report a platform that integrates family-wide chemical probes with proximity-dependent oligonucleotide amplification and imaging to quantify enzyme activity in native contexts with high spatial resolution. Application of this method, activity-dependent proximity ligation (ADPL), to serine hydrolase and cysteine protease enzymes enables quantification of differential enzyme activity resulting from endogenous changes in localization and expression. In a competitive format, small-molecule target engagement with endogenous proteins in live cells can be quantified. Finally, retention of sample architecture enables interrogation of complex environments such as cellular co-culture and patient samples. ADPL should be amenable to diverse probe and protein families to detect active enzymes at scale and resolution out of reach with current methods.

The interrogation of enzyme activity involves the ensemble averaging of many cells, loss of spatial relationships and is often biased to abundant proteins. Here the authors develop activity-dependent proximity ligation to quantify enzyme activity at the cellular and sub-cellular level in relevant biological contexts.

Personalized Proteome Profiles of Healthy and Tumor Human Colon Organoids Reveal Both Individual Diversity and Basic Features of Colorectal Cancer

Diseases at the molecular level are complex and patient dependent, necessitating development of strategies that enable precision treatment to optimize clinical outcomes. Organoid technology has recently been shown to have the potential to recapitulate the in vivo characteristics of the original individual's tissue in a three-dimensional in vitro culture system. Here, we present a quantitative mass-spectrometry-based proteomic analysis and a comparative transcriptomic analysis of human colorectal tumor and healthy organoids derived, in parallel, from seven patients. Although gene and protein signatures can be derived to distinguish the tumor organoid population from healthy organoids, our data clearly reveal that each patient possesses a distinct organoid signature at the proteomic level. We demonstrate that a personalized patient-specific organoid proteome profile can be related to the diagnosis of a patient and with future development contribute to the generation of personalized therapies.

A Perspective on Extracellular Vesicles Proteomics

Increasing attention has been given to secreted extracellular vesicles (EVs) in the past decades, especially in the portrayal of their molecular cargo and role as messengers in both homeostasis and pathophysiological conditions. This review presents the state-of-the-art proteomic technologies to identify and quantify EVs proteins along with their PTMs, interacting partners and structural details. The rapid growth of mass spectrometry-based analytical strategies for protein sequencing, PTMs and structural characterization has improved the level of molecular details that can be achieved from limited amount of EVs isolated from different biological sources. Here we will provide a perspective view on the achievements and challenges on EVs proteome characterization using mass spectrometry. A detailed bioinformatics approach will help us to picture the molecular fingerprint of EVs and understand better their pathophysiological function.

Physiological responses of reared sea bream (Sparus aurata Linnaeus, 1758) to an Amyloodinium ocellatum outbreak

Amyloodiniosis represents a major bottleneck for semi-intensive aquaculture production in Southern Europe, causing extremely high mortalities. Amyloodinium ocellatum is a parasitic dinoflagellate that can infest almost all fish, crustacean and bivalves that live within its ecological range. Fish mortalities are usually attributed to anoxia, associated with serious gill hyperplasia, inflammation, haemorrhage and necrosis in heavy infestations; or with osmoregulatory impairment and secondary microbial infections due to severe epithelial damage in mild infestation. However, physiological information about the host responses to A. ocellatum infestation is scarce. In this work, we analysed the proteome of gilthead sea bream (Sparus aurata) plasma and relate it with haematological and immunological indicators, in order to enlighten the different physiological responses when exposed to an A. ocellatum outbreak. Using 2D-DIGE, immunological and haematological analysis and in response to the A. ocellatum contamination we have identified several proteins associated with acute-phase response, inflammation, lipid transport, homoeostasis, and osmoregulation, wound healing, neoplasia and iron transport. Overall, this preliminary study revealed that amyloodiniosis affects some fish functional pathways as revealed by the changes in the plasma proteome of S. aurata, and that the innate immunological system is not activated in the presence of the parasite.

Proteomics and metabolomics analysis of hepatic mitochondrial metabolism in alcohol-preferring and non-preferring rats

Alcohol preference induced tolerance in humans and animals when their bodily functions adapt to compensate for the disruption caused by alcohol consumption. This was thought to be an important component of the genetic predisposition to alcoholism. To investigate the underlying mechanisms of hepatic metabolic tolerance during alcohol preference, the alcohol preferring and alcohol non-preferring rats were used in this study. The liver mitochondria were purified for comparative quantitative proteomics analysis, and the liver metabolite extracts were collected for metabolomics analysis. Our study identified 96 differentially expressed hepatic mitochondrial proteins that associated with alcohol preference, the further gene ontology and protein interaction network analysis suggest a down-regulation of amino acid metabolism and up-regulation of lipid metabolism. We found alcohol preference induced a series of enzymes decreased (e.g. SSADH and GABA-T) and several amino acids increased (e.g. glutamate and aspartate) in rat liver, indicating down-regulations of glutamate degradation occurred during alcohol preference. Most of these changes were due to the genetic differences between alcohol preferring and non-preferring animals. Furthermore, this study would provided new insights to further clarify the mechanisms of hepatic metabolic tolerance during alcohol preference.

Serial-omics characterization of equine urine

Horse urine is easily collected and contains molecules readily measurable using mass spectrometry that can be used as biomarkers representative of health, disease or drug tampering. This study aimed at analyzing microliter levels of horse urine to purify, identify and quantify proteins, polar metabolites and non-polar lipids. Urine from a healthy 12 year old quarter horse mare on a diet of grass hay and vitamin/mineral supplements with limited pasture access was collected for serial-omics characterization. The urine was treated with methyl tert-butyl ether (MTBE) and methanol to partition into three distinct layers for protein, non-polar lipid and polar metabolite content from a single liquid-liquid extraction and was repeated two times. Each layer was analyzed by high performance liquid chromatography—high resolution tandem mass spectrometry (LC-MS/MS) to obtain protein sequence and relative protein levels as well as identify and quantify small polar metabolites and lipids. The results show 46 urine proteins, many related to normal kidney function, structural and circulatory proteins as well as 474 small polar metabolites but only 10 lipid molecules. Metabolites were mostly related to urea cycle and ammonia recycling as well as amino acid related pathways, plant diet specific molecules, etc. The few lipids represented triglycerides and phospholipids. These data show a complete mass spectrometry based—omics characterization of equine urine from a single 333 μL mid-stream urine aliquot. These omics data help serve as a baseline for healthy mare urine composition and the analyses can be used to monitor disease progression, health status, monitor drug use, etc.

Comparative 3-Sample DIGE Analysis of Skeletal Muscles

The skeletal muscle proteome consists of a large number of diverse protein species with a broad and dynamic concentration range. Since mature skeletal muscles are characterized by a specific combination of contractile cells with differing physiological and biochemical properties, it is essential to determine specific differences in the protein composition of fast, slow, and hybrid fibers. Fluorescence two-dimensional gel electrophoresis (DIGE) is a powerful comparative tool to analyze fiber type-specific differences between fast and slow muscles. In this chapter, the application of the DIGE method for the comparative analysis of different subtypes of skeletal muscles is outlined in detail. A standardized proteomic workflow is described, involving sample preparation, protein extraction, differential fluorescence labeling using a 3-dye system, first-dimension isoelectric focusing, second-dimension slab gel electrophoresis, DIGE image analysis, protein digestion, and mass spectrometry.

Label-free, direct localization and relative quantitation of the RNA nucleobase methylations m6A, m5C, m3U, and m5U by top-down mass spectrometry

Nucleobase methylations are ubiquitous posttranscriptional modifications of ribonucleic acids (RNA) that can substantially increase the structural diversity of RNA in a highly dynamic fashion with implications for gene expression and human disease. However, high throughput, deep sequencing does not generally provide information on posttranscriptional modifications (PTMs). A promising alternative approach for the characterization of PTMs, i.e. their identification, localization, and relative quantitation, is top-down mass spectrometry (MS). In this study, we have investigated how specific nucleobase methylations affect RNA ionization in electrospray ionization (ESI), and backbone cleavage in collisionally activated dissociation (CAD) and electron detachment dissociation (EDD). For this purpose, we have developed two new approaches for the characterization of RNA methylations in mixtures of either isomers of RNA or nonisomeric RNA forms. Fragment ions from dissociation experiments were analyzed to identify the modification type, to localize the modification sites, and to reveal the site-specific, relative extent of modification for each site.

Integrated analysis of proteome, phosphotyrosine-proteome, tyrosine-kinome, and tyrosine-phosphatome in acute myeloid leukemia

Reversible protein-tyrosine phosphorylation is catalyzed by the antagonistic actions of protein-tyrosine kinases (PTKs) and phosphatases (PTPs), and represents a major form of cell regulation. Acute myeloid leukemia (AML) is an aggressive hematological malignancy that results from the acquisition of multiple genetic alterations, which in some instances are associated with deregulated protein-phosphotyrosine (pY) mediated signaling networks. However, although individual PTKs and PTPs have been linked to AML and other malignancies, analysis of protein-pY networks as a function of activated PTKs and PTPs has not been done. In this study, MS was used to characterize AML proteomes, and phospho-proteome-subsets including pY proteins, PTKs, and PTPs. AML proteomes resolved into two groups related to high or low degrees of maturation according to French-American-British classification, and reflecting differential expression of cell surface antigens. AML pY proteomes reflect canonical, spatially organized signaling networks, unrelated to maturation, with heterogeneous expression of activated receptor and nonreceptor PTKs. We present the first integrated analysis of the pY-proteome, activated PTKs, and PTPs. Every PTP and most PTKs have both positive and negative associations with the pY-proteome. pY proteins resolve into groups with shared PTK and PTP correlations. These findings highlight the importance of pY turnover and the PTP phosphatome in shaping the pY-proteome in AML.

Revisiting biomarker discovery by plasma proteomics

Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very slow rate. As described in this review, mass spectrometry (MS)-based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous "triangular strategies" aimed at discovering single biomarker candidates in small cohorts, followed by classical immunoassays in much larger validation cohorts. We propose a "rectangular" plasma proteome profiling strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in health and disease. Translating such concepts into clinical practice will require restructuring several aspects of diagnostic decision-making, and we discuss some first steps in this direction.

Optomechanical devices for deep plasma cancer proteomics

Most of the cancer deaths could be avoided by early detection of the tumor when it is confined to its primary site and it has not metastasized. To this aim, one of the most promising strategies is the discovery and detection of protein biomarkers shed by the young tumor to the bloodstream. Proteomic technologies, mainly mass spectrometry and multiplexed immunoassays, have rapidly developed during last years with improved limits of detection and multiplexing capability. Unfortunately, these developments together major investments and large international efforts have not resulted into new useful protein biomarkers. Here, we analyze the potential and limitations of current proteomic technologies for detecting protein biomarkers released into circulation by the tumor. We find that these technologies can hardly probe the deepest region of the plasma proteome, at concentrations below the pg/mL level, where protein biomarkers for early cancer detection may exist. This clearly indicates the need of incorporating novel ultrasensitive techniques to the proteomic tool-box that can cover the inaccessible regions of the plasma proteome. We here propose biological detectors based on nanomechanical systems for discovery and detection of cancer protein biomarkers in plasma. We review the modes of operation of these devices, putting our focus on recent developments on nanomechanical sandwich immunoassays and nanomechanical spectrometry. The first technique enables reproducible immunodetection of proteins at concentrations well below the pg/mL level, with a limit of detection on the verge of 10 ag/mL. This technology can potentially detect low abundance tumor-associated proteins in plasma at the very early stages of the tumor. The second technique enables the identification of individual intact proteins by two physical coordinates, the mass and stiffness, instead of the mass-to-charge ratio of the protein constituents. This technology enormously simplifies the identification of proteins and it can provide useful information on interactions and posttranslational modifications, that otherwise is lost in mass spectrometry.

Identification of the PAK4 interactome reveals PAK4 phosphorylation of N-WASP and promotion of Arp2/3-dependent actin polymerization

p21-activated kinase 4 (PAK4) regulates cell proliferation, apoptosis, cell motility and F-actin remodeling, but the PAK4 interactome has not been systematically analyzed. Here, we comprehensively characterized the human PAK4 interactome by iTRAQ quantitative mass spectrometry of PAK4-immunoprecipitations. Consistent with its multiple reported functions, the PAK4 interactome was enriched in diverse protein networks, including the 14-3-3, proteasome, replication fork, CCT and Arp2/3 complexes. Because PAK4 co-immunoprecipitated most subunits of the Arp2/3 complex, we hypothesized that PAK4 may play a role in Arp2/3 dependent actin regulation. Indeed, we found that PAK4 interacts with and phosphorylates the nucleation promoting factor N-WASP at Ser484/Ser485 and promotes Arp2/3-dependent actin polymerization in vitro. Also, PAK4 ablation in vivo reduced N-WASP Ser484/Ser485 phosphorylation and altered the cellular balance between G- and F-actin as well as the actin organization. By presenting the PAK4 interactome, we here provide a powerful resource for further investigations and as proof of principle, we also indicate a novel mechanism by which PAK4 regulates actin cytoskeleton remodeling.

Multi-level and hybrid modelling approaches for systems biology

During the last decades, high-throughput techniques allowed for the extraction of a huge amount of data from biological systems, unveiling more of their underling complexity. Biological systems encompass a wide range of space and time scales, functioning according to flexible hierarchies of mechanisms making an intertwined and dynamic interplay of regulations. This becomes particularly evident in processes such as ontogenesis, where regulative assets change according to process context and timing, making structural phenotype and architectural complexities emerge from a single cell, through local interactions. The information collected from biological systems are naturally organized according to the functional levels composing the system itself. In systems biology, biological information often comes from overlapping but different scientific domains, each one having its own way of representing phenomena under study. That is, the different parts of the system to be modelled may be described with different formalisms. For a model to have improved accuracy and capability for making a good knowledge base, it is good to comprise different system levels, suitably handling the relative formalisms. Models which are both multi-level and hybrid satisfy both these requirements, making a very useful tool in computational systems biology. This paper reviews some of the main contributions in this field.

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

OBJECTIVE

Pregnancy is accompanied by dramatic physiological changes in maternal plasma proteins. Characterization of the maternal plasma proteome in normal pregnancy is an essential step for understanding changes to predict pregnancy outcome. The objective of this study was to describe maternal plasma proteins that change in abundance with advancing gestational age and determine biological processes that are perturbed in normal pregnancy.

STUDY DESIGN

A longitudinal study included 43 normal pregnancies that had a term delivery of an infant who was appropriate for gestational age without maternal or neonatal complications. For each pregnancy, 3 to 6 maternal plasma samples (median, 5) were profiled to measure the abundance of 1125 proteins using multiplex assays. Linear mixed-effects models with polynomial splines were used to model protein abundance as a function of gestational age, and the significance of the association was inferred via likelihood ratio tests. Proteins considered to be significantly changed were defined as having the following: (1) >1.5-fold change between 8 and 40 weeks of gestation; and (2) a false discovery rate-adjusted value of P < .1. Gene ontology enrichment analysis was used to identify biological processes overrepresented among the proteins that changed with advancing gestation.

RESULTS

The following results were found: (1) Ten percent (112 of 1125) of the profiled proteins changed in abundance as a function of gestational age; (2) of the 1125 proteins analyzed, glypican-3, sialic acid-binding immunoglobulin-type lectin-6, placental growth factor, C-C motif-28, carbonic anhydrase 6, prolactin, interleukin-1 receptor 4, dual-specificity mitogen-activated protein kinase 4, and pregnancy-associated plasma protein-A had more than a 5-fold change in abundance across gestation (these 9 proteins are known to be involved in a wide range of both physiological and pathological processes, such as growth regulation, embryogenesis, angiogenesis immunoregulation, inflammation etc); and (3) biological processes associated with protein changes in normal pregnancy included defense response, defense response to bacteria, proteolysis, and leukocyte migration (false discovery rate, 10%).

CONCLUSION

The plasma proteome of normal pregnancy demonstrates dramatic changes in both the magnitude of changes and the fraction of the proteins involved. Such information is important to understand the physiology of pregnancy and the development of biomarkers to differentiate normal vs abnormal pregnancy and determine the response to interventions.

The hepatocyte proteome in organotypic rat liver models and the influence of the local microenvironment

BACKGROUND

Liver models that closely mimic the in vivo microenvironment are useful for understanding liver functions, capabilities, and intercellular communication processes. Three-dimensional (3D) liver models assembled using hepatocytes and liver sinusoidal endothelial cells (LSECs) separated by a polyelectrolyte multilayer (PEM) provide a functional system while also permitting isolation of individual cell types for proteomic analyses.

METHODS

To better understand the mechanisms and processes that underlie liver model function, hepatocytes were maintained as monolayers and 3D PEM-based formats in the presence or absence of primary LSECs. The resulting hepatocyte proteomes, the proteins in the PEM, and extracellular levels of urea, albumin and glucose after three days of culture were compared.

RESULTS

All systems were ketogenic and found to release glucose. The presence of the PEM led to increases in proteins associated with both mitochondrial and peroxisomal-based β-oxidation. The PEMs also limited production of structural and migratory proteins associated with dedifferentiation. The presence of LSECs increased levels of Phase I and Phase II biotransformation enzymes as well as several proteins associated with the endoplasmic reticulum and extracellular matrix remodeling. The proteomic analysis of the PEMs indicated that there was no significant change after three days of culture. These results are discussed in relation to liver model function.

CONCLUSIONS

Heterotypic cell-cell and cell-ECM interactions exert different effects on hepatocyte functions and phenotypes.

In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values

Considering as one of the major goals in quantitative proteomics, detection of the differentially expressed proteins (DEPs) plays an important role in biomarker selection and clinical diagnostics. There have been plenty of algorithms and tools focusing on DEP detection in proteomics research. However, due to the different application scopes of these methods, and various kinds of experiment designs, it is not very apparent about the best choice for large-scale proteomics data analyses. Moreover, given the fact that proteomics data usually contain high percentage of missing values (MVs), but few replicates, a systematic evaluation of the DEP detection methods combined with the MV imputation methods is essential and urgent. Here, we analyzed a total of four representative imputation methods and five DEP methods on different experimental and simulated datasets. The results showed that (i) MV imputation could not always improve the performances of DEP detection methods and the imputation effects differed in the missing value percentages; (ii) the DEP detection methods had different statistical powers affected by the percentage of MVs. Two statistical methods (i.e. the empirical Bayesian random censoring threshold model, and the significance analysis of microarray) performed better than the other evaluated methods in terms of accuracy and sensitivity.

Harnessing Integrative Omics to Facilitate Molecular Imaging of the Human Epidermal Growth Factor Receptor Family for Precision Medicine

Cancer is a growing problem worldwide. The cause of death in cancer patients is often due to treatment-resistant metastatic disease. Many molecularly targeted anticancer drugs have been developed against 'oncogenic driver' pathways. However, these treatments are usually only effective in properly selected patients. Resistance to molecularly targeted drugs through selective pressure on acquired mutations or molecular rewiring can hinder their effectiveness. This review summarizes how molecular imaging techniques can potentially facilitate the optimal implementation of targeted agents. Using the human epidermal growth factor receptor (HER) family as a model in (pre)clinical studies, we illustrate how molecular imaging may be employed to characterize whole body target expression as well as monitor drug effectiveness and the emergence of tumor resistance. We further discuss how an integrative omics discovery platform could guide the selection of 'effect sensors' - new molecular imaging targets - which are dynamic markers that indicate treatment effectiveness or resistance.

Dynamic changes in the mouse skeletal muscle proteome during denervation-induced atrophy

Loss of neuronal stimulation enhances protein breakdown and reduces protein synthesis, causing rapid loss of muscle mass. To elucidate the pathophysiological adaptations that occur in atrophying muscles, we used stable isotope labelling and mass spectrometry to quantify protein expression changes accurately during denervation-induced atrophy after sciatic nerve section in the mouse gastrocnemius muscle. Additionally, mice were fed a stable isotope labelling of amino acids in cell culture (SILAC) diet containing ¹³C₆-lysine for 4, 7 or 11 days to calculate relative levels of protein synthesis in denervated and control muscles. Ubiquitin remnant peptides (K-ε-GG) were profiled by immunoaffinity enrichment to identify potential substrates of the ubiquitin-proteasomal pathway. Of the 4279 skeletal muscle proteins quantified, 850 were differentially expressed significantly within 2 weeks after denervation compared with control muscles. Moreover, pulse labelling identified Lys6 incorporation in 4786 proteins, of which 43 had differential Lys6 incorporation between control and denervated muscle. Enrichment of diglycine remnants identified 2100 endogenous ubiquitination sites and revealed a metabolic and myofibrillar protein diglycine signature, including myosin heavy chains, myomesins and titin, during denervation. Comparative analysis of these proteomic data sets with known atrogenes using a random forest approach identified 92 proteins subject to atrogene-like regulation that have not previously been associated directly with denervation-induced atrophy. Comparison of protein synthesis and proteomic data indicated that upregulation of specific proteins in response to denervation is mainly achieved by protein stabilization. This study provides the first integrated analysis of protein expression, synthesis and ubiquitin signatures during muscular atrophy in a living animal.

Summary: Comprehensive proteomic profiling of protein expression, synthesis and ubiquitination during skeletal muscle atrophy reveals that complex regulatory networks are activated during muscle wasting.

Automatic assignment of metal-containing peptides in proteomic LC-MS and MS/MS data sets

Transition metal-containing proteins and enzymes are critical for the maintenance of cellular function and metal-based (metallo)drugs are commonly used for the treatment of many diseases, such as cancer. Detection and characterisation of metallodrug targets is crucial for improving drug-design and therapeutic efficacy. Due to the unique isotopic ratios of many metal species, and the complexity of proteomic samples, standard MS data analysis of these species is unsuitable for accurate assignment. Herein a new method for differentiating metal-containing species within complex LCMS data is presented based upon the Smart Numerical Annotation Procedure (SNAP). SNAP-LC accounts for the change in isotopic envelopes for analytes containing non-standard species, such as metals, and will accurately identify, record, and display the particular spectra within extended LCMS runs that contain target species, and produce accurate lists of matched peaks, greatly assisting the identification and assignment of modified species and tailored to the metals of interest. Analysis of metallated species obtained from tryptic digests of common blood proteins after reactions with three candidate metallodrugs is presented as proof-of-concept examples and demonstrates the effectiveness of SNAP-LC for the fast and accurate elucidation of metallodrug targets.

Hippocampal proteomics defines pathways associated with memory decline and resilience in normal aging and Alzheimer's disease mouse models

Alzheimer's disease (AD), the most common form of dementia in the elderly, has no cure. Thus, the identification of key molecular mediators of cognitive decline in AD remains a top priority. As aging is the most significant risk factor for AD, the goal of this study was to identify altered proteins and pathways associated with the development of normal aging and AD memory deficits, and identify unique proteins and pathways that may contribute to AD-specific symptoms. We used contextual fear conditioning to diagnose 8-month-old 5XFAD and non-transgenic (Ntg) mice as having either intact or impaired memory, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) to quantify hippocampal membrane proteins across groups. Subsequent analysis detected 113 proteins differentially expressed relative to memory status (intact vs impaired) in Ntg mice and 103 proteins in 5XFAD mice. Thirty-six proteins, including several involved in neuronal excitability and synaptic plasticity (e.g., GRIA1, GRM3, and SYN1), were altered in both normal aging and AD. Pathway analysis highlighted HDAC4 as a regulator of observed protein changes in both genotypes and identified the REST epigenetic regulatory pathway and Gi intracellular signaling as AD-specific pathways involved in regulating the onset of memory deficits. Comparing the hippocampal membrane proteome of Ntg versus AD, regardless of cognitive status, identified 138 differentially expressed proteins, including confirmatory proteins APOE and CLU. Overall, we provide a novel list of putative targets and pathways with therapeutic potential, including a set of proteins associated with cognitive status in normal aging mice or gene mutations that cause AD.

Microchip-based single-cell functional proteomics for biomedical applications

Cellular heterogeneity has been widely recognized but only recently have single cell tools become available that allow characterizing heterogeneity at the genomic and proteomic levels. We review the technological advances in microchip-based toolkits for single-cell functional proteomics. Each of these tools has distinct advantages and limitations, and a few have advanced toward being applied to address biological or clinical problems that traditional population-based methods fail to address. High-throughput single-cell proteomic assays generate high-dimensional data sets that contain new information and thus require developing new analytical frameworks to extract new biology. In this review article, we highlight a few biological and clinical applications in which microchip-based single-cell proteomic tools provide unique advantages. The examples include resolving functional heterogeneity and dynamics of immune cells, dissecting cell-cell interaction by creating a well-controlled on-chip microenvironment, capturing high-resolution snapshots of immune system functions in patients for better immunotherapy and elucidating phosphoprotein signaling networks in cancer cells for guiding effective molecularly targeted therapies.

Cell signaling as a cognitive process

Cellular identity as defined through morphology and function emerges from intracellular signaling networks that communicate between cells. Based on recursive interactions within and among these intracellular networks, dynamical solutions in terms of biochemical behavior are generated that can differ from those in isolated cells. In this way, cellular heterogeneity in tissues can be established, implying that cell identity is not intrinsically predetermined by the genetic code but is rather dynamically maintained in a cognitive manner. We address how to experimentally measure the flow of information in intracellular biochemical networks and demonstrate that even simple causality motifs can give rise to rich, context-dependent dynamic behavior. The concept how intercellular communication can result in novel dynamical solutions is applied to provide a contextual perspective on cell differentiation and tumorigenesis.

A relative quantitative positive/negative ion switching method for untargeted lipidomics via high resolution LC-MS/MS from any biological source

INTRODUCTION

Advances in high-resolution mass spectrometry have created renewed interest for studying global lipid biochemistry in disease and biological systems.

OBJECTIVES

Here, we present an untargeted 30 min. LC-MS/MS platform that utilizes positive/negative polarity switching to perform unbiased data dependent acquisitions (DDA) via higher energy collisional dissociation (HCD) fragmentation to profile more than 1000-1500 lipid ions mainly from methyl-tert-butyl ether (MTBE) or chloroform:methanol extractions.

METHODS

The platform uses C18 reversed-phase chromatography coupled to a hybrid QExactive Plus/HF Orbitrap mass spectrometer and the entire procedure takes ~10 h from lipid extraction to identification/quantification for a data set containing 12 samples (~4 h for a single sample). Lipids are identified by both accurate precursor ion mass and fragmentation features and quantified using Lipid-Search and Elements software.

RESULTS

Using this approach, we are able to profile intact lipid ions from up to 18 different main lipid classes and 66 subclasses. We show several studies from different biological sources, including cultured cancer cells, resected tissues from mice such as lung and breast tumors and biological fluids such as plasma and urine.

CONCLUSIONS

Using mouse embryonic fibroblasts, we showed that TSC2-/- KD significantly abrogates lipid biosynthesis and that rapamycin can rescue triglyceride (TG) lipids and we show that SREBP-/- shuts down lipid biosynthesis significantly via mTORC1 signaling pathways. We show that in mouse EGFR driven lung tumors, a large number of TGs and phosphatidylmethanol (PMe) lipids are elevated while some phospholipids (PLs) show some of the largest decrease in lipid levels from ~ 2000 identified lipid ions. In addition, we identified more than 1500 unique lipid species from human blood plasma.

Human Nup98 regulates the localization and activity of DExH/D-box helicase DHX9

Beyond their role at nuclear pore complexes, some nucleoporins function in the nucleoplasm. One such nucleoporin, Nup98, binds chromatin and regulates gene expression. To gain insight into how Nup98 contributes to this process, we focused on identifying novel binding partners and understanding the significance of these interactions. Here we report on the identification of the DExH/D-box helicase DHX9 as an intranuclear Nup98 binding partner. Various results, including in vitro assays, show that the FG/GLFG region of Nup98 binds to N- and C-terminal regions of DHX9 in an RNA facilitated manner. Importantly, binding of Nup98 stimulates the ATPase activity of DHX9, and a transcriptional reporter assay suggests Nup98 supports DHX9-stimulated transcription. Consistent with these observations, our analysis revealed that Nup98 and DHX9 bind interdependently to similar gene loci and their transcripts. Based on our results, we propose that Nup98 functions as a co-factor that regulates DHX9 and, potentially, other RNA helicases.

Differential proteomics profiling identifies LDPs and biological functions in high-fat diet-induced fatty livers

Eukaryotic cells store neutral lipids in cytoplasmic lipid droplets (LDs) enclosed in a monolayer of phospholipids and associated proteins [LD proteins (LDPs)]. Growing evidence has demonstrated that LDPs play important roles in the pathogenesis of liver diseases. However, the composition of liver LDPs and the role of their alterations in hepatosteatosis are not well-understood. In this study, we performed liver proteome and LD sub-proteome profiling to identify enriched proteins in LDs as LDPs, and quantified their changes in a high-fat diet (HFD)-induced fatty liver model. Among 5,000 quantified liver proteins, 101 were enriched by greater than 10-fold in the LD sub-proteome and were classified as LDPs. Differential profiling of LDPs in HFD-induced fatty liver provided a list of candidate LDPs for functional investigation. We tested the function of an upregulated LDP, S100a10, in vivo with adenovirus-mediated gene silencing and found, unexpectedly, that knockdown of S100a10 accelerated progression of HFD-induced liver steatosis. The S100A10 interactome revealed a connection between S100A10 and lipid transporting proteins, suggesting that S100A10 regulates the development and formation of LDs by transporting and trafficking. This study identified LD-enriched sub-proteome in homeostatic as well as HFD-induced fatty livers, providing a rich resource for the LDP research field.

Quantitative Analysis of Yeast Checkpoint Protein Kinase Activity by Combined Mass Spectrometry Enzyme Assays

Virtually all eukaryotic cell functions and signaling pathways are regulated by protein phosphorylation. The Rad53 kinase plays crucial roles in the DNA damage response in Saccharomyces cerevisiae and is widely used as a surrogate marker for DNA damage checkpoint activation by diverse genotoxic agents. Most currently available assays for Rad53 activation are based on either electrophoretic mobility shifts or semiquantitative in situ autophosphorylation activity on protein blots. Here, we describe direct quantitative measures to assess Rad53 activity using immunoprecipitation kinase assays and quantitative mass spectrometric analysis of Rad53 activation loop autophosphorylation states. Both assays employ a highly specific Rad53 antibody, and thus enable the analysis of the untagged endogenous protein under physiological conditions. The principles of these assays are readily transferable to other protein kinases for which immunoprecipitation-grade antibodies are available, and thus potentially applicable to a wide range of eukaryotic signaling pathways beyond yeast.

Proteomics insights into DNA damage response and translating this knowledge to clinical strategies

Genomic instability is a critical driver in the process of cancer formation. At the same time, inducing DNA damage by irradiation or genotoxic compounds constitutes a key therapeutic strategy to kill fast-dividing cancer cells. Sensing of DNA lesions initiates a complex set of signalling pathways, collectively known as the DNA damage response (DDR). Deciphering DDR signalling pathways with high-throughput technologies could provide insights into oncogenic transformation, metastasis formation and therapy responses, and could build a basis for better therapeutic interventions in cancer treatment. Mass spectrometry (MS)-based proteomics emerged as a method of choice for global studies of proteins and their posttranslational modifications (PTMs). MS-based studies of the DDR have aided in delineating DNA damage-induced signalling responses. Those studies identified changes in abundance, interactions and modification of proteins in the context of genotoxic stress. Here we review ground-breaking MS-based proteomics studies, which analysed changes in protein abundance, protein-protein and protein-DNA interactions, phosphorylation, acetylation, ubiquitylation, SUMOylation and Poly(ADP-ribose)ylation (PARylation) in the DDR. Finally, we provide an outlook on how proteomics studies of the DDR could aid clinical developments on multiple levels.