PRISM, a generic large scale proteomic investigation strategy for mammals

Co-fractionation-mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain

Human mitochondrial (mt) protein assemblies are vital for neuronal and brain function, and their alteration contributes to many human disorders, e.g., neurodegenerative diseases resulting from abnormal protein-protein interactions (PPIs). Knowledge of the composition of mt protein complexes is, however, still limited. Affinity purification mass spectrometry (MS) and proximity-dependent biotinylation MS have defined protein partners of some mt proteins, but are too technically challenging and laborious to be practical for analyzing large numbers of samples at the proteome level, e.g., for the study of neuronal or brain-specific mt assemblies, as well as altered mtPPIs on a proteome-wide scale for a disease of interest in brain regions, disease tissues or neurons derived from patients. To address this challenge, we adapted a co-fractionation-MS platform to survey native mt assemblies in adult mouse brain and in human NTERA-2 embryonal carcinoma stem cells or differentiated neuronal-like cells. The workflow consists of orthogonal separations of mt extracts isolated from chemically cross-linked samples to stabilize PPIs, data-dependent acquisition MS to identify co-eluted mt protein profiles from collected fractions and a computational scoring pipeline to predict mtPPIs, followed by network partitioning to define complexes linked to mt functions as well as those essential for neuronal and brain physiological homeostasis. We developed an R/CRAN software package, Macromolecular Assemblies from Co-elution Profiles for automated scoring of co-fractionation-MS data to define complexes from mtPPI networks. Presently, the co-fractionation-MS procedure takes 1.5-3.5 d of proteomic sample preparation, 31 d of MS data acquisition and 8.5 d of data analyses to produce meaningful biological insights.

Phosphoproteomic Analysis of Dopamine D2 Receptor Signaling Reveals Interplay of G Protein- and β-Arrestin-Mediated Effects

Leveraging biased signaling of G protein-coupled receptors has been proposed as a promising strategy for the development of drugs with higher specificity. However, the consequences of selectively targeting G protein- or β-arrestin-mediated signaling on cellular functions are not comprehensively understood. In this study, we utilized phosphoproteomics to gain a systematic overview of signaling induced by the four biased and balanced dopamine D2 receptor (D₂R) ligands MS308, BM138, quinpirole, and sulpiride in an in vitro D₂R transfection model. Quantification of 14,160 phosphosites revealed a low impact of the partial G protein agonist MS308 on cellular protein phosphorylation, as well as surprising similarities between the balanced agonist quinpirole and the inverse agonist sulpiride. Analysis of the temporal profiles of ligand-induced phosphorylation events showed a transient impact of the G protein-selective agonist MS308, whereas the β-arrestin-preferring agonist BM138 elicited a delayed, but more pronounced response. Functional enrichment analysis of ligand-impacted phosphoproteins and treatment-linked kinases confirmed multiple known functions of D₂R signaling while also revealing novel effects, for example of MS308 on sterol regulatory element-binding protein-related gene expression. All raw data were deposited in MassIVE (MSV000089457).

Glycoproteomics Identifies Plexin-B3 as a Targetable Cell Surface Protein Required for the Growth and Invasion of Triple-Negative Breast Cancer Cells

Driven by the lack of targeted therapies, triple-negative breast cancers (TNBCs) have the worst overall survival of all breast cancer subtypes. Considering that cell surface proteins are favorable drug targets and are predominantly glycosylated, glycoproteome profiling has significant potential to facilitate the identification of much-needed drug targets for TNBCs. Here, we performed N-glycoproteomics on six TNBCs and five normal control (NC) cell lines using hydrazide-based enrichment. Quantitative proteomics and integrative data mining led to the discovery of Plexin-B3 (PLXNB3), a previously undescribed TNBC-enriched cell surface protein. Furthermore, siRNA knockdown and CRISPR-Cas9 editing of in vitro and in vivo models show that PLXNB3 is required for TNBC cell line growth, invasion, and migration. Altogether, we provide insights into N-glycoproteome remodeling associated with TNBCs and functional evaluation of an extracted target, which indicate the surface protein PLXNB3 as a potential therapeutic target for TNBCs.

Subcellular metabolomics: Isolation, measurement, and applications

Metabolomics, a technique that profiles global small molecules in biological samples, has been a pivotal tool for disease diagnosis and mechanism research. The sample type in metabolomics covers a wide range, including a variety of body fluids, tissues, and cells. However, little attention was paid to the smaller, relatively independent partition systems in cells, namely the organelles. The organelles are specific compartments/places where diverse metabolic activities are happening in an orderly manner. Metabolic disorders of organelles were found to occur in various pathological conditions such as inherited metabolic diseases, diabetes, cancer, and neurodegenerative diseases. However, at the cellular level, the metabolic outcomes of organelles and cytoplasm are superimposed interactively, making it difficult to describe the changes in subcellular compartments. Therefore, characterizing the metabolic pool in the compartmentalized system is of great significance for understanding the role of organelles in physiological functions and diseases. So far, there are very few research articles or reviews related to subcellular metabolomics. In this review, subcellular fractionation and metabolite analysis methods, as well as the application of subcellular metabolomics in the physiological and pathological studies are systematically reviewed, as a practical reference to promote the continued advancement in subcellular metabolomics.

Proteomic discovery of non-invasive biomarkers of localized prostate cancer using mass spectrometry

Prostate cancer is the second most frequently diagnosed non-skin cancer in men worldwide. Patient outcomes are remarkably heterogeneous and the best existing clinical prognostic tools such as International Society of Urological Pathology Grade Group, pretreatment serum PSA concentration and T-category, do not accurately predict disease outcome for individual patients. Thus, patients newly diagnosed with prostate cancer are often overtreated or undertreated, reducing quality of life and increasing disease-specific mortality. Biomarkers that can improve the risk stratification of these patients are, therefore, urgently needed. The ideal biomarker in this setting will be non-invasive and affordable, enabling longitudinal evaluation of disease status. Prostatic secretions, urine and blood can be sources of biomarker discovery, validation and clinical implementation, and mass spectrometry can be used to detect and quantify proteins in these fluids. Protein biomarkers currently in use for diagnosis, prognosis and relapse-monitoring of localized prostate cancer in fluids remain centred around PSA and its variants, and opportunities exist for clinically validating novel and complimentary candidate protein biomarkers and deploying them into the clinic.

Human-Soybean Allergies: Elucidation of the Seed Proteome and Comprehensive Protein-Protein Interaction Prediction

The soybean crop, Glycine max (L.) Merr., is consumed by humans, Homo sapiens, worldwide. While the respective bodies of literature and -omics data for each of these organisms are extensive, comparatively few studies investigate the molecular biological processes occurring between the two. We are interested in elucidating the network of protein-protein interactions (PPIs) involved in human-soybean allergies. To this end, we leverage state-of-the-art sequence-based PPI predictors amenable to predicting the enormous comprehensive interactome between human and soybean. A network-based analytical approach is proposed, leveraging similar interaction profiles to identify candidate allergens and proteins involved in the allergy response. Interestingly, the predicted interactome can be explored from two complementary perspectives: which soybean proteins are predicted to interact with specific human proteins and which human proteins are predicted to interact with specific soybean proteins. A total of eight proteins (six specific to the human proteome and two to the soy proteome) have been identified and supported by the literature to be involved in human health, specifically related to immunological and neurological pathways. This study, beyond generating the most comprehensive human-soybean interactome to date, elucidated a soybean seed interactome and identified several proteins putatively consequential to human health.

A proteomic investigation of isogenic radiation resistant prostate cancer cell lines

To model the problem of radiation resistance in prostate cancer, cell lines mimicking a clinical course of conventionally fractionated or hypofractionated radiotherapy have been generated. Proteomic analysis of radiation resistant and radiosensitive DU145 prostate cancer cells detected 4410 proteins. Over 400 proteins were differentially expressed across both radiation resistant cell lines and pathway analysis revealed enrichment in epithelial to mesenchymal transition, glycolysis and hypoxia. From the radiation resistant protein candidates, the cell surface protein CD44 was identified in the glycolysis and epithelial to mesenchymal transition pathways and may serve as a potential therapeutic target.

Proximity labeling and other novel mass spectrometric approaches for spatiotemporal protein dynamics

BACKGROUND

Proteins are highly dynamic and their biological function is controlled by not only temporal abundance changes but also via regulated protein-protein interaction networks, which respond to internal and external perturbations. A wealth of novel analytical reagents and workflows allow studying spatiotemporal protein environments with great granularity while maintaining high throughput and ease of analysis.

AREAS COVERED

We review technology advances for measuring protein-protein proximity interactions with an emphasis on proximity labeling, and briefly summarize other spatiotemporal approaches including protein localization, and their dynamic changes over time, specifically in human cells and mammalian tissues. We focus especially on novel technologies and workflows emerging within the past 5 years. This includes enrichment-based techniques (proximity labeling and crosslinking), separation-based techniques (organelle fractionation and size exclusion chromatography), and finally sorting-based techniques (laser capture microdissection and mass spectrometry imaging).

EXPERT OPINION

Spatiotemporal proteomics is a key step in assessing biological complexity, understanding refined regulatory mechanisms, and forming protein complexes and networks. Studying protein dynamics across space and time holds promise for gaining deep insights into how protein networks may be perturbed during disease and aging processes, and offer potential avenues for therapeutic interventions, drug discovery, and biomarker development.

Rat Sciatic Nerve Axoplasm Proteome Is Enriched with Ribosomal Proteins during Regeneration Processes

Axons are complex subcellular compartments that are extremely long in relation to cell bodies, especially in peripheral nerves. Many processes are required and regulated during axon injury, including anterograde and retrograde transport, glia-to-axon macromolecular transfer, and local axonal protein synthesis. Many in vitro omics approaches have been used to gain insight into these processes, but few have been applied in vivo. Here we adapted the osmotic ex vivo axoplasm isolation method and analyzed the adult rat sciatic-nerve-extruded axoplasm by label-free quantitative proteomics before and after injury. 2087 proteins groups were detected in the axoplasm, revealing translation machinery and microtubule-associated proteins as the most overrepresented biological processes. Ribosomal proteins (73) were detected in the uninjured axoplasm and increased their levels after injury but not within whole sciatic nerves. Meta-analysis showed that detected ribosomal proteins were present in in vitro axonal proteomes. Because local protein synthesis is important for protein localization, we were interested in detecting the most abundant newly synthesized axonal proteins in vivo. With an MS/MS-BONCAT approach, we detected 42 newly synthesized protein groups. Overall, our work indicates that proteomics profiling is useful for local axonal interrogation and suggests that ribosomal proteins may play an important role, especially during injury.

Anticancer auranofin engages 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) as a target

Auranofin (AuRF) has been reported to display anticancer activity and has entered several clinical trials; however, its mechanism of action remains largely unknown. In this work, the anticancer mechanism of auranofin was investigated using a proteomics strategy entailing subcellular fractionation prior to mass spectrometric analysis. Bioinformatics analysis of the nuclear sub-proteomes revealed that tumor suppressor p14^ARF is a key regulator of transcription. Through independent analysis, we validated that up-regulation of p14^ARF is associated with E2F-dependent transcription and increased p53 expression. Our analyses further reveal that 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR), which is the rate-determining enzyme of the mevalonate pathway, is a novel target of auranofin with half maximal inhibitory concentration at micromolar levels. The auranofin-induced cancer cell death could be partially reverted by the addition of downstream products of the mevalonate pathway (mevalonolactone or geranyleranyl pyrophosphate (GGPP)), implying that auranofin may target the mevalonate pathway to exert its anticancer effect.

EPIC: software toolkit for elution profile-based inference of protein complexes

Protein complexes are key macromolecular machines of the cell, but their description remains incomplete. We and others previously reported an experimental strategy for global characterization of native protein assemblies based on chromatographic fractionation of biological extracts coupled to precision mass spectrometry analysis (chromatographic fractionation-mass spectrometry, CF-MS), but the resulting data are challenging to process and interpret. Here, we describe EPIC (elution profile-based inference of complexes), a software toolkit for automated scoring of large-scale CF-MS data to define high-confidence multi-component macromolecules from diverse biological specimens. As a case study, we used EPIC to map the global interactome of Caenorhabditis elegans, defining 612 putative worm protein complexes linked to diverse biological processes. These included novel subunits and assemblies unique to nematodes that we validated using orthogonal methods. The open source EPIC software is freely available as a Jupyter notebook packaged in a Docker container (https://hub.docker.com/r/baderlab/bio-epic/).

Systems NMR: single-sample quantification of RNA, proteins and metabolites for biomolecular network analysis

Cellular behavior is controlled by the interplay of diverse biomolecules. Most experimental methods, however, can only monitor a single molecule class or reaction type at a time. We developed an in vitro nuclear magnetic resonance spectroscopy (NMR) approach, which permitted dynamic quantification of an entire 'heterotypic' network-simultaneously monitoring three distinct molecule classes (metabolites, proteins and RNA) and all elementary reaction types (bimolecular interactions, catalysis, unimolecular changes). Focusing on an eight-reaction co-transcriptional RNA folding network, in a single sample we recorded over 35 time points with over 170 observables each, and accurately determined five core reaction constants in multiplex. This reconstruction revealed unexpected cross-talk between the different reactions. We further observed dynamic phase-separation in a system of five distinct RNA-binding domains in the course of the RNA transcription reaction. Our Systems NMR approach provides a deeper understanding of biological network dynamics by combining the dynamic resolution of biochemical assays and the multiplexing ability of 'omics'.

Proteomic Response of Human Umbilical Vein Endothelial Cells to Histamine Stimulation

The histamine receptors (HRs) represent a subclass of G protein-coupled receptors (GPCRs) and comprise four subtypes. Due to their numerous physiological and pathological effects, HRs are popular drug targets for the treatment of allergic reactions or the regulation of gastric acid secretion. Hence, an understanding of the functional selectivity of HR ligands has gained importance. These ligands can bind to specific GPCRs and selectively activate defined pathways. Supporting the activation of a therapeutically necessary pathway without the activation of other signaling cascades can result in drugs with more specific activity and fewer side effects. To evaluate the cellular consequences resulting from receptor binding, comprehensive analyses of cellular protein alterations upon incubation with ligands are required. For this purpose, endothelial cells are treated with histamine, as the endogenous ligand of HRs, to obtain a global overview of its cellular effects. Quantitative proteomics and pathway analyses of histamine-treated and untreated cells reveal enrichment of the nuclear factor-κB and tumor necrosis factor signaling pathways, cytokine-cytokine receptor interactions, complement and coagulation cascades, and acute inflammatory processes upon histamine treatment. This strategy offers the opportunity to monitor HR-mediated signaling in a multidimensional manner.

Detecting protein variants by mass spectrometry: a comprehensive study in cancer cell-lines

Background

Onco-proteogenomics aims to understand how changes in a cancer’s genome influences its proteome. One challenge in integrating these molecular data is the identification of aberrant protein products from mass-spectrometry (MS) datasets, as traditional proteomic analyses only identify proteins from a reference sequence database.

Methods

We established proteomic workflows to detect peptide variants within MS datasets. We used a combination of publicly available population variants (dbSNP and UniProt) and somatic variations in cancer (COSMIC) along with sample-specific genomic and transcriptomic data to examine proteome variation within and across 59 cancer cell-lines.

Results

We developed a set of recommendations for the detection of variants using three search algorithms, a split target-decoy approach for FDR estimation, and multiple post-search filters. We examined 7.3 million unique variant tryptic peptides not found within any reference proteome and identified 4771 mutations corresponding to somatic and germline deviations from reference proteomes in 2200 genes among the NCI60 cell-line proteomes.

Conclusions

We discuss in detail the technical and computational challenges in identifying variant peptides by MS and show that uncovering these variants allows the identification of druggable mutations within important cancer genes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0454-9) contains supplementary material, which is available to authorized users.

DLG5 connects cell polarity and Hippo signaling protein networks by linking PAR-1 with MST1/2

Here, Kwan et al. investigated the mechanisms connecting cell polarity proteins with intracellular signaling pathways. They found that DLG5 functions as an evolutionarily conserved scaffold and negative regulator of Hippo signaling, demonstrating a direct connection between cell polarity proteins and Hippo that is needed for proper development of multicellular organisms.

Disruption of apical–basal polarity is implicated in developmental disorders and cancer; however, the mechanisms connecting cell polarity proteins with intracellular signaling pathways are largely unknown. We determined previously that membrane-associated guanylate kinase (MAGUK) protein discs large homolog 5 (DLG5) functions in cell polarity and regulates cellular proliferation and differentiation via undefined mechanisms. We report here that DLG5 functions as an evolutionarily conserved scaffold and negative regulator of Hippo signaling, which controls organ size through the modulation of cell proliferation and differentiation. Affinity purification/mass spectrometry revealed a critical role of DLG5 in the formation of protein assemblies containing core Hippo kinases mammalian ste20 homologs 1/2 (MST1/2) and Par-1 polarity proteins microtubule affinity-regulating kinases 1/2/3 (MARK1/2/3). Consistent with this finding, Hippo signaling is markedly hyperactive in mammalian Dlg5^−/− tissues and cells in vivo and ex vivo and in Drosophila upon dlg5 knockdown. Conditional deletion of Mst1/2 fully rescued the phenotypes of brain-specific Dlg5 knockout mice. Dlg5 also interacts genetically with Hippo effectors Yap1/Taz. Mechanistically, we show that DLG5 inhibits the association between MST1/2 and large tumor suppressor homologs 1/2 (LATS1/2), uses its scaffolding function to link MST1/2 with MARK3, and inhibits MST1/2 kinase activity. These data reveal a direct connection between cell polarity proteins and Hippo, which is essential for proper development of multicellular organisms.

Evolutionarily conserved intercalated disc protein Tmem65 regulates cardiac conduction and connexin 43 function

Membrane proteins are crucial to heart function and development. Here we combine cationic silica-bead coating with shotgun proteomics to enrich for and identify plasma membrane-associated proteins from primary mouse neonatal and human fetal ventricular cardiomyocytes. We identify Tmem65 as a cardiac-enriched, intercalated disc protein that increases during development in both mouse and human hearts. Functional analysis of Tmem65 both in vitro using lentiviral shRNA-mediated knockdown in mouse cardiomyocytes and in vivo using morpholino-based knockdown in zebrafish show marked alterations in gap junction function and cardiac morphology. Molecular analyses suggest that Tmem65 interaction with connexin 43 (Cx43) is required for correct localization of Cx43 to the intercalated disc, since Tmem65 deletion results in marked internalization of Cx43, a shorter half-life through increased degradation, and loss of Cx43 function. Our data demonstrate that the membrane protein Tmem65 is an intercalated disc protein that interacts with and functionally regulates ventricular Cx43.

CD24 tracks divergent pluripotent states in mouse and human cells

Reprogramming is a dynamic process that can result in multiple pluripotent cell types emerging from divergent paths. Cell surface protein expression is a particularly desirable tool to categorize reprogramming and pluripotency as it enables robust quantification and enrichment of live cells. Here we use cell surface proteomics to interrogate mouse cell reprogramming dynamics and discover CD24 as a marker that tracks the emergence of reprogramming-responsive cells, while enabling the analysis and enrichment of transgene-dependent (F-class) and -independent (traditional) induced pluripotent stem cells (iPSCs) at later stages. Furthermore, CD24 can be used to delineate epiblast stem cells (EpiSCs) from embryonic stem cells (ESCs) in mouse pluripotent culture. Importantly, regulated CD24 expression is conserved in human pluripotent stem cells (PSCs), tracking the conversion of human ESCs to more naive-like PSC states. Thus, CD24 is a conserved marker for tracking divergent states in both reprogramming and standard pluripotent culture.

Characterizing the cellular stages that lead to induced reprogramming is of much interest and cell surface markers could offer unique advantages for this. Here the authors use surface proteomics and discover CD24 as a marker that tracks reprogramming-responsive cells and enables the analysis and enrichment of transgene-dependent and -independent induced pluriopotent stem cells.

The effect of aging on mitochondrial and cytosolic hepatic intrinsic death pathway and apoptosis associated proteins in Fischer 344 rats

Apoptosis is increased in the liver in old age and is a common pathological feature of liver disease. The mitochondria play a key role in regulating apoptosis via the intrinsic death pathway. As the effect of aging on this pathway is unclear, we aimed to characterize the impact of aging on the hepatic intrinsic death pathway and apoptosis. Livers from young adult (6.6 ± 0.3 months, n = 9) and old (25.4 ± 0.7 months, n = 9) male Fischer 344 rats were extracted for cellular fractionation and immunobloting. In old age there were lower mitochondrial protein levels of pro-apoptotic BAK, BID, tBID and VDAC1 (p < 0.05) and of anti-apoptotic Bcl-2. Compared to young, old rats had lower cytosolic protein levels of pro-apoptotic BAX, BAK, BID, tBID and anti-apoptotic Bcl-xL (p < 0.05). BAK, Bcl-2 and Bcl-xL were found in the cytosol. Furthermore with old age, cytosolic protein levels of cytochrome C, AIF and cleaved caspase-9 did not change but activation of caspase-3, -6 and -7 increased (p < 0.05) and DNA fragmentation trended to increase. Our results suggest an age-related decline in the levels of a number of proteins involved in the intrinsic death pathway, an uncoupling of intermediate apoptosis signaling and increased cellular apoptosis in the liver in old age.

openBEB: open biological experiment browser for correlative measurements

Background

New experimental methods must be developed to study interaction networks in systems biology. To reduce biological noise, individual subjects, such as single cells, should be analyzed using high throughput approaches. The measurement of several correlative physical properties would further improve data consistency. Accordingly, a considerable quantity of data must be acquired, correlated, catalogued and stored in a database for subsequent analysis.

Results

We have developed openBEB (open Biological Experiment Browser), a software framework for data acquisition, coordination, annotation and synchronization with database solutions such as openBIS. OpenBEB consists of two main parts: A core program and a plug-in manager. Whereas the data-type independent core of openBEB maintains a local container of raw-data and metadata and provides annotation and data management tools, all data-specific tasks are performed by plug-ins. The open architecture of openBEB enables the fast integration of plug-ins, e.g., for data acquisition or visualization. A macro-interpreter allows the automation and coordination of the different modules. An update and deployment mechanism keeps the core program, the plug-ins and the metadata definition files in sync with a central repository.

Conclusions

The versatility, the simple deployment and update mechanism, and the scalability in terms of module integration offered by openBEB make this software interesting for a large scientific community. OpenBEB targets three types of researcher, ideally working closely together: (i) Engineers and scientists developing new methods and instruments, e.g., for systems-biology, (ii) scientists performing biological experiments, (iii) theoreticians and mathematicians analyzing data. The design of openBEB enables the rapid development of plug-ins, which will inherently benefit from the “house keeping” abilities of the core program. We report the use of openBEB to combine live cell microscopy, microfluidic control and visual proteomics. In this example, measurements from diverse complementary techniques are combined and correlated.

Quantitative proteomic profiling reveals novel region-specific markers in the adult mouse brain

Despite major advances in neuroscience, a comprehensive understanding of the structural and functional components of the adult brain compartments remains to be fully elucidated at a quantitative molecular level. Indeed, over half of the soluble- and membrane-annotated proteins are currently unmapped within online digital brain atlases. In this study, two complementary approaches were used to assess the unique repertoire of proteins enriched within select regions of the adult mouse CNS, including the brain stem, cerebellum, and remaining brain hemispheres. Of the 1200 proteins visualized by 2D-DIGE, approximately 150 (including cytosolic and membrane proteins) were found to exhibit statistically significant changes in relative abundance thus representing putative region-specific brain markers. In addition to using a high-precision (18) O-labeling strategy for the quantitative LC-MS/MS mapping of membrane proteins isolated from myelin-enriched fractions, we have identified over 1000 proteins that have yet to be described in any other mammalian myelin proteome. A comparison of our myelin proteome was made to an existing transcriptome database containing mRNA abundance profiles during oligodendrocyte differentiation and has confirmed statistically significant abundance changes for ∼500 of these newly mapped proteins, thus revealing new roles in oligodendrocyte and myelin biology. These data offer a resource for the neuroscience community studying the molecular basis for specialized neuronal activities in the CNS and myelin-related disorders. The MS proteomics data associated with this manuscript have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD000327 (http://proteomecentral.proteomexchange.org/dataset/PXD000327).

Discovery of novel disease-specific and membrane-associated candidate markers in a mouse model of multiple sclerosis

Multiple sclerosis is a chronic demyelinating disorder characterized by the infiltration of auto-reactive immune cells from the periphery into the central nervous system resulting in axonal injury and neuronal cell death. Experimental autoimmune encephalomyelitis represents the best characterized animal model as common clinical, histological, and immunological features are recapitulated. A label-free mass spectrometric proteomics approach was used to detect differences in protein abundance within specific fractions of disease-affected tissues including the soluble lysate derived from the spinal cord and membrane protein-enriched peripheral blood mononuclear cells. Tissues were harvested from actively induced experimental autoimmune encephalomyelitis mice and sham-induced ("vehicle" control) counterparts at the disease peak followed by subsequent analysis by nanoflow liquid chromatography tandem mass spectrometry. Relative protein quantitation was performed using both intensity- and fragmentation-based approaches. After statistical evaluation of the data, over 500 and 250 differentially abundant proteins were identified in the spinal cord and peripheral blood mononuclear cell data sets, respectively. More than half of these observations have not previously been linked to the disease. The biological significance of all candidate disease markers has been elucidated through rigorous literature searches, pathway analysis, and validation studies. Results from comprehensive targeted mass spectrometry analyses have confirmed the differential abundance of ∼ 200 candidate markers (≥ twofold dysregulated expression) at a 70% success rate. This study is, to our knowledge, the first to examine the cell-surface proteome of peripheral blood mononuclear cells in experimental autoimmune encephalomyelitis. These data provide a unique mechanistic insight into the dynamics of peripheral immune cell infiltration into CNS-privileged sites at a molecular level and has identified several candidate markers, which represent promising targets for future multiple sclerosis therapies. The mass spectrometry proteomics data associated with this manuscript have been deposited to the ProteomeXchange Consortium with the data set identifier PXD000255.

Exploring mitochondrial system properties of neurodegenerative diseases through interactome mapping

Mitochondria are double membraned, dynamic organelles that are required for a large number of cellular processes, and defects in their function have emerged as causative factors for a growing number of human disorders and are highly associated with cancer, metabolic, and neurodegenerative (ND) diseases. Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in ND disease, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. However, high-throughput proteomic and genomic approaches developed in genetically tractable model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins, including cytosolic and membrane proteins. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We discuss how the knowledge from the resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus further clarify the role of mitochondrial biology and the complex etiologies of ND disease.

BIOLOGICAL SIGNIFICANCE

Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in neurodegenerative (ND) diseases, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. Large-scale proteomic and genomic approaches developed in model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins. Extension of this new framework to the mitochondrial sub-system in human will likewise provide a universally informative systems-level view of the physical and functional landscape for exploring the evolutionary principles underlying mitochondrial function. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We anticipate that the knowledge from these resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus foster a deeper molecular understanding of mitochondrial biology as well as the etiology of mitochondrial diseases. This article is part of a Special Issue: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

In vivo knockdown of intersectin-1s alters endothelial cell phenotype and causes microvascular remodeling in the mouse lungs

Intersectin-1s (ITSN-1s) is a general endocytic protein involved in regulating lung vascular permeability and endothelial cells (ECs) survival, via MEK/Erk1/2^MAPK signaling. To investigate the in vivo effects of ITSN-1s deficiency and the resulting ECs apoptosis on pulmonary vasculature and lung homeostasis, we used an ITSN-1s knocked-down (KD_ITSN) mouse generated by repeated delivery of a specific siRNA targeting ITSN-1 gene (siRNA_ITSN). Biochemical and histological analyses as well as electron microscopy (EM) revealed that acute KD_ITSN [3-days (3d) post-siRNA_ITSN treatment] inhibited Erk1/2^MAPK pro-survival signaling, causing significant ECs apoptosis and lung injury; at 10d of KD_ITSN, caspase-3 activation was at peak, terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL)-positive ECs showed 3.4-fold increase, the mean linear intercept (MLI) showed 48 % augment and pulmonary microvessel density as revealed by aquaporin-1 staining (AQP-1) decreased by 30 %, all compared to controls; pulmonary function was altered. Concomitantly, expression of several growth factors known to activate Erk1/2^MAPK and suppress Bad pro-apoptotic activity increased. KD_ITSN altered Smads activity, downstream of the transforming growth factor beta-receptor-1 (TβR1), as shown by subcellular fractionation and immunoblot analyses. Moreover, 24d post-siRNA_ITSN, surviving ECs became hyper-proliferative and apoptotic-resistant against ITSN-1s deficiency, as demonstrated by EM imaging, 5-bromo-deoxyuridine (BrdU) incorporation and Bad-Ser^112/155 phosphorylation, respectively, leading to increased microvessel density and repair of the injured lungs, as well as matrix deposition. In sum, ECs endocytic dysfunction and apoptotic death caused by KD_ITSN contribute to the initial lung injury and microvascular loss, followed by endothelial phenotypic changes and microvascular remodeling in the remaining murine pulmonary microvascular bed.

ProteoMirExpress: inferring microRNA and protein-centered regulatory networks from high-throughput proteomic and mRNA expression data

MicroRNAs (miRNAs) regulate gene expression through translational repression and RNA degradation. Recently developed high-throughput proteomic methods measure gene expression changes at protein level and therefore can reveal the direct effects of miRNAs' translational repression. Here, we present a web server, ProteoMirExpress, that integrates proteomic and mRNA expression data together to infer miRNA-centered regulatory networks. With both types of high-throughput data from the users, ProteoMirExpress is able to discover not only miRNA targets that have decreased mRNA, but also subgroups of targets with suppressed proteins whose mRNAs are not significantly changed or with decreased mRNA whose proteins are not significantly changed, which are usually ignored by most current methods. Furthermore, both direct and indirect targets of miRNAs can be detected. Therefore, ProteoMirExpress provides more comprehensive miRNA-centered regulatory networks. We used several published data to assess the quality of our inferred networks and prove the value of our server. ProteoMirExpress is available online, with free access to academic users.

ER exit sites are physical and functional core autophagosome biogenesis components

ERES function is required for assembly of the autophagy machinery immediately downstream of the Atg1 kinase complex and is associated with formation of autophagosomes at every stage of the process. ERES are core components of the autophagy machinery for the biogenesis of autophagosomes.

Autophagy is a central homeostasis and stress response pathway conserved in all eukaryotes. One hallmark of autophagy is the de novo formation of autophagosomes. These double-membrane vesicular structures form around and deliver cargo for degradation by the vacuole/lysosome. Where and how autophagosomes form are outstanding questions. Here we show, using proteomic, cytological, and functional analyses, that autophagosomes are spatially, physically, and functionally linked to endoplasmic reticulum exit sites (ERES), which are specialized regions of the endoplasmic reticulum where COPII transport vesicles are generated. Our data demonstrate that ERES are core autophagosomal biogenesis components whose function is required for the hierarchical assembly of the autophagy machinery immediately downstream of the Atg1 kinase complex at phagophore assembly sites.

The effect of aging on acetaminophen pharmacokinetics, toxicity and Nrf2 in Fischer 344 rats

We investigated the effect of aging on hepatic pharmacokinetics and the degree of hepatotoxicity following a toxic dose of acetaminophen. Young and old male Fischer 344 rats were treated with 800 mg/kg acetaminophen (young n = 8, old n = 5) or saline (young n = 9, old n = 9). Serum measurements showed old rats treated with acetaminophen had significantly lower serum alanine aminotransferase and higher acetaminophen and acetaminophen glucuronide levels and creatinine, compared with acetaminophen treated young rats (p < .05). Immunoblotting and activity assays showed old saline-treated rats had twofold lower cytochrome P450 2E1 activity and threefold higher NAD(P)H quinone oxireductase 1 protein expression and activity than young saline-treated rats (p < .05), although Nrf2, glutathione cysteine ligase-modulatory subunit, glutathione cysteine ligase-catalytic subunit, and cytochrome P450 2E1 protein expressions were unchanged. Primary hepatocytes isolated from young rats treated with 10 mM acetaminophen had lower survival than those from old rats (52.4% ± 5.8%, young; 83.6% ± 1.7%, old, p < .05). The pharmacokinetic changes described may decrease susceptibility to acetaminophen-induced hepatotoxicity but may increase risk of nephrotoxicity in old age.

High-throughput capillary electrophoresis-mass spectrometry: from analysis of amino acids to analysis of protein complexes

Recent advances in capillary electrophoresis-mass spectrometry (CE-MS) interfacing using porous tip is leading to commercialization of CE-MS with a sheathless interface for the first time. The new sheathless interface in conjunction with CE capillary coatings using self-coating background electrolytes (BGE) has significantly simplified CE-MS analysis of complex mixtures. CE-MS, with its high separation efficiency, compound identification capability, and ability to rapidly separate compounds with a wide range of mass and charge while consuming only nanoliters of samples, has become a valuable analytical technique for the analysis of complex biological mixtures. These advances have allowed a single capillary to analyze a range of compounds including amino acids, their D/L enantiomers, protein digests, intact proteins, and protein complexes. With these capabilities, CE-MS is poised to become the multipurpose tool of separation scientists. More recently, an eight-capillary CE in conjunction with an 8-inlet mass spectrometry has allowed 8 CE-MS analyses to be performed concurrently, significantly increasing throughput.

Identification of protein complexes in Escherichia coli using sequential peptide affinity purification in combination with tandem mass spectrometry

Since most cellular processes are mediated by macromolecular assemblies, the systematic identification of protein-protein interactions (PPI) and the identification of the subunit composition of multi-protein complexes can provide insight into gene function and enhance understanding of biological systems(1, 2). Physical interactions can be mapped with high confidence vialarge-scale isolation and characterization of endogenous protein complexes under near-physiological conditions based on affinity purification of chromosomally-tagged proteins in combination with mass spectrometry (APMS). This approach has been successfully applied in evolutionarily diverse organisms, including yeast, flies, worms, mammalian cells, and bacteria(1-6). In particular, we have generated a carboxy-terminal Sequential Peptide Affinity (SPA) dual tagging system for affinity-purifying native protein complexes from cultured gram-negative Escherichia coli, using genetically-tractable host laboratory strains that are well-suited for genome-wide investigations of the fundamental biology and conserved processes of prokaryotes(1, 2, 7). Our SPA-tagging system is analogous to the tandem affinity purification method developed originally for yeast(8, 9), and consists of a calmodulin binding peptide (CBP) followed by the cleavage site for the highly specific tobacco etch virus (TEV) protease and three copies of the FLAG epitope (3X FLAG), allowing for two consecutive rounds of affinity enrichment. After cassette amplification, sequence-specific linear PCR products encoding the SPA-tag and a selectable marker are integrated and expressed in frame as carboxy-terminal fusions in a DY330 background that is induced to transiently express a highly efficient heterologous bacteriophage lambda recombination system(10). Subsequent dual-step purification using calmodulin and anti-FLAG affinity beads enables the highly selective and efficient recovery of even low abundance protein complexes from large-scale cultures. Tandem mass spectrometry is then used to identify the stably co-purifying proteins with high sensitivity (low nanogram detection limits). Here, we describe detailed step-by-step procedures we commonly use for systematic protein tagging, purification and mass spectrometry-based analysis of soluble protein complexes from E. coli, which can be scaled up and potentially tailored to other bacterial species, including certain opportunistic pathogens that are amenable to recombineering. The resulting physical interactions can often reveal interesting unexpected components and connections suggesting novel mechanistic links. Integration of the PPI data with alternate molecular association data such as genetic (gene-gene) interactions and genomic-context (GC) predictions can facilitate elucidation of the global molecular organization of multi-protein complexes within biological pathways. The networks generated for E. coli can be used to gain insight into the functional architecture of orthologous gene products in other microbes for which functional annotations are currently lacking.

A census of human soluble protein complexes

Cellular processes often depend on stable physical associations between proteins. Despite recent progress, knowledge of the composition of human protein complexes remains limited. To close this gap, we applied an integrative global proteomic profiling approach, based on chromatographic separation of cultured human cell extracts into more than one thousand biochemical fractions that were subsequently analyzed by quantitative tandem mass spectrometry, to systematically identify a network of 13,993 high-confidence physical interactions among 3,006 stably associated soluble human proteins. Most of the 622 putative protein complexes we report are linked to core biological processes and encompass both candidate disease genes and unannotated proteins to inform on mechanism. Strikingly, whereas larger multiprotein assemblies tend to be more extensively annotated and evolutionarily conserved, human protein complexes with five or fewer subunits are far more likely to be functionally unannotated or restricted to vertebrates, suggesting more recent functional innovations.

Large-scale quantitative assessment of different in-solution protein digestion protocols reveals superior cleavage efficiency of tandem Lys-C/trypsin proteolysis over trypsin digestion

The complete and specific proteolytic cleavage of protein samples into peptides is crucial for the success of every shotgun LC-MS/MS experiment. In particular, popular peptide-based label-free and targeted mass spectrometry approaches rely on efficient generation of fully cleaved peptides to ensure accurate and sensitive protein quantification. In contrast to previous studies, we globally and quantitatively assessed the efficiency of different digestion strategies using a yeast cell lysate, label-free quantification, and statistical analysis. Digestion conditions include double tryptic, surfactant-assisted, and tandem-combinatorial Lys-C/trypsin digestion. In comparison to tryptic digests, Lys-C/trypsin digests were found most efficient to yield fully cleaved peptides while reducing the abundance of miscleaved peptides. Subsequent sequence context analysis revealed improved digestion performances of Lys-C/trypsin for miscleaved sequence stretches flanked by charged basic and particulary acidic residues. Furthermore, targeted MS analysis demonstrated a more comprehensive protein cleavage only after Lys-C/trypsin digestion, resulting in a more accurrate absolute protein quantification and extending the number of peptides suitable for SRM assay development. Therefore, we conclude that a serial Lys-C/trypsin digestion is highly attractive for most applications in quantitative MS-based proteomics building on in-solution digestion schemes.

Control of the RNA polymerase II phosphorylation state in promoter regions by CTD interaction domain-containing proteins RPRD1A and RPRD1B

RNA polymerase II (RNAP II) C-terminal domain (CTD) phosphorylation is important for various transcription-related processes. Here, we identify by affinity purification and mass spectrometry three previously uncharacterized human CTD-interaction domain (CID)-containing proteins, RPRD1A, RPRD1B and RPRD2, which co-purify with RNAP II and three other RNAP II-associated proteins, RPAP2, GRINL1A and RECQL5, but not with the Mediator complex. RPRD1A and RPRD1B can accompany RNAP II from promoter regions to 3'-untranslated regions during transcription in vivo, predominantly interact with phosphorylated RNAP II, and can reduce CTD S5- and S7-phosphorylated RNAP II at target gene promoters. Thus, the RPRD proteins are likely to have multiple important roles in transcription.

Target identification by chromatographic co-elution: monitoring of drug-protein interactions without immobilization or chemical derivatization

Bioactive molecules typically mediate their biological effects through direct physical association with one or more cellular proteins. The detection of drug-target interactions is therefore essential for the characterization of compound mechanism of action and off-target effects, but generic label-free approaches for detecting binding events in biological mixtures have remained elusive. Here, we report a method termed target identification by chromatographic co-elution (TICC) for routinely monitoring the interaction of drugs with cellular proteins under nearly physiological conditions in vitro based on simple liquid chromatographic separations of cell-free lysates. Correlative proteomic analysis of drug-bound protein fractions by shotgun sequencing is then performed to identify candidate target(s). The method is highly reproducible, does not require immobilization or derivatization of drug or protein, and is applicable to diverse natural products and synthetic compounds. The capability of TICC to detect known drug-protein target physical interactions (K(d) range: micromolar to nanomolar) is demonstrated both qualitatively and quantitatively. We subsequently used TICC to uncover the sterol biosynthetic enzyme Erg6p as a novel putative anti-fungal target. Furthermore, TICC identified Asc1 and Dak1, a core 40 S ribosomal protein that represses gene expression, and dihydroxyacetone kinase involved in stress adaptation, respectively, as novel yeast targets of a dopamine receptor agonist.

Identification of novel ryanodine receptor 1 (RyR1) protein interaction with calcium homeostasis endoplasmic reticulum protein (CHERP)

The ryanodine receptor type 1 (RyR1) is a homotetrameric Ca(2+) release channel located in the sarcoplasmic reticulum of skeletal muscle where it plays a role in the initiation of skeletal muscle contraction. A soluble, 6×-histidine affinity-tagged cytosolic fragment of RyR1 (amino acids 1-4243) was expressed in HEK-293 cells, and metal affinity chromatography under native conditions was used to purify the peptide together with interacting proteins. When analyzed by gel-free liquid chromatography mass spectrometry (LC-MS), 703 proteins were identified under all conditions. This group of proteins was filtered to identify putative RyR interacting proteins by removing those proteins found in only 1 RyR purification and proteins for which average spectral counts were enriched by less than 4-fold over control values. This resulted in 49 potential RyR1 interacting proteins, and 4 were selected for additional interaction studies: calcium homeostasis endoplasmic reticulum protein (CHERP), endoplasmic reticulum-Golgi intermediate compartment 53-kDa protein (LMAN1), T-complex protein, and phosphorylase kinase. Western blotting showed that only CHERP co-purified with affinity-tagged RyR1 and was eluted with imidazole. Immunofluorescence showed that endogenous CHERP co-localizes with endogenous RyR1 in the sarcoplasmic reticulum of rat soleus muscle. A combination of overexpression of RyR1 in HEK-293 cells with siRNA-mediated suppression of CHERP showed that CHERP affects Ca(2+) release from the ER via RyR1. Thus, we propose that CHERP is an RyR1 interacting protein that may be involved in the regulation of excitation-contraction coupling.

Hierarchical clustering of shotgun proteomics data

A new result report for Mascot search results is described. A greedy set cover algorithm is used to create a minimal set of proteins, which is then grouped into families on the basis of shared peptide matches. Protein families with multiple members are represented by dendrograms, generated by hierarchical clustering using the score of the nonshared peptide matches as a distance metric. The peptide matches to the proteins in a family can be compared side by side to assess the experimental evidence for each protein. If the evidence for a particular family member is considered inadequate, the dendrogram can be cut to reduce the number of distinct family members.

The Asia Oceania Human Proteome Organisation Membrane Proteomics Initiative. Preparation and characterisation of the carbonate-washed membrane standard

The Asia Oceania Human Proteome Organisation (AOHUPO) has embarked on a Membrane Proteomics Initiative with goals of systematic comparison of strategies for analysis of membrane proteomes and discovery of membrane proteins. This multilaboratory project is based on the analysis of a subcellular fraction from mouse liver that contains endoplasmic reticulum and other organelles. In this study, we present the strategy used for the preparation and initial characterization of the membrane sample, including validation that the carbonate-washing step enriches for integral and lipid-anchored membrane proteins. Analysis of 17 independent data sets from five types of proteomic workflows is in progress.

Microproteomics: quantitative proteomic profiling of small numbers of laser-captured cells

During the last decade, significant progress in the analysis of whole genomes and transcriptomes has triggered efforts to analyze the proteome. Advancements in protein extraction, purification, and identification have been driven by the development of mass spectrometers with greater sensitivity and resolution. Nevertheless, comparative and quantitative proteomic technologies have not progressed to the extent of genomic and transcriptomic technologies for accessing gene expression differences. Unlike the genome, which is similar throughout all cells in a given organism, the proteome varies in different cells. Also, there is no self-replicating amplification mechanism for proteins such as the polymerase chain reaction (PCR) for DNA. Therefore, developing methods that extract, separate, detect, and identify proteins from extremely small samples are needed. The advent of laser capture microdissection (LCM) has expanded the analytical capabilities of proteomics. LCM has proven an effective technique to harvest pure cell populations from tissue sections. This protocol describes a microproteomic platform that uses nanoscale liquid chromatography/tandem mass spectrometry (nano-LC-MS/MS) to simultaneously identify and quantify hundreds of proteins from LCMs of tissue sections from small tissue samples containing as few as 1000 cells. The LCM-dissected tissues are subjected to protein extraction, reduction, alkylation, and digestion, followed by injection into a nano-LC-MS/MS system for chromatographic separation and protein identification. The approach can be validated by secondary screening using immunological techniques such as immunohistochemistry or immunoblots.

Computational refinement of post-translational modifications predicted from tandem mass spectrometry

Motivation: A post-translational modification (PTM) is a chemical modification of a protein that occurs naturally. Many of these modifications, such as phosphorylation, are known to play pivotal roles in the regulation of protein function. Henceforth, PTM perturbations have been linked to diverse diseases like Parkinson's, Alzheimer's, diabetes and cancer. To discover PTMs on a genome-wide scale, there is a recent surge of interest in analyzing tandem mass spectrometry data, and several unrestrictive (so-called ‘blind’) PTM search methods have been reported. However, these approaches are subject to noise in mass measurements and in the predicted modification site (amino acid position) within peptides, which can result in false PTM assignments.

Results: To address these issues, we devised a machine learning algorithm, PTMClust, that can be applied to the output of blind PTM search methods to improve prediction quality, by suppressing noise in the data and clustering peptides with the same underlying modification to form PTM groups. We show that our technique outperforms two standard clustering algorithms on a simulated dataset. Additionally, we show that our algorithm significantly improves sensitivity and specificity when applied to the output of three different blind PTM search engines, SIMS, InsPecT and MODmap. Additionally, PTMClust markedly outperforms another PTM refinement algorithm, PTMFinder. We demonstrate that our technique is able to reduce false PTM assignments, improve overall detection coverage and facilitate novel PTM discovery, including terminus modifications. We applied our technique to a large-scale yeast MS/MS proteome profiling dataset and found numerous known and novel PTMs. Accurately identifying modifications in protein sequences is a critical first step for PTM profiling, and thus our approach may benefit routine proteomic analysis.

Availability: Our algorithm is implemented in Matlab and is freely available for academic use. The software is available online from http://genes.toronto.edu.

Supplementary Information:Supplementary data are available at Bioinformatics online.

Contact:frey@psi.utoronto.ca

Involvement of the Shewanella oneidensis decaheme cytochrome MtrA in the periplasmic stability of the beta-barrel protein MtrB

The Shewanella oneidensis outer membrane β-barrel protein MtrB is part of a membrane-spanning protein complex (MtrABC) which is necessary for dissimilatory iron reduction. Quantitative PCR, heterologous gene expression, and mutant studies indicated that MtrA is required for periplasmic stability of MtrB. DegP depletion compensated for this MtrA dependence.

The beginning of a beautiful friendship: cross-linking/mass spectrometry and modelling of proteins and multi-protein complexes

After more than a decade of method development, cross-linking in combination with mass spectrometry and bioinformatics is finally coming of age. This technology now provides improved opportunities for modelling by mapping structural details of functional complexes in solution. The structure of proteins or protein complexes is ascertained by identifying amino acid pairs that are positioned in close proximity to each other. The validity of this technique has recently been benchmarked for large multi-protein complexes, by comparing cross-link data with that from a crystal structure of RNA polymerase II. Here, the specific nature of this cross-linking data will be discussed to assess the technical challenges and opportunities for model building. We believe that once remaining technological challenges of cross-linking/mass spectrometry have been addressed and cross-linking/mass spectrometry data has been incorporated into modelling algorithms it will quickly become an indispensable companion of protein and protein complex modelling and a corner-stone of integrated structural biology.

Targeted proteomics by selected reaction monitoring mass spectrometry: applications to systems biology and biomarker discovery

Mass Spectrometry-based proteomics is now considered a relatively established strategy for protein analysis, ranging from global expression profiling to the identification of protein complexes and specific post-translational modifications. Recently, Selected Reaction Monitoring Mass Spectrometry (SRM-MS) has become increasingly popular in proteome research for the targeted quantification of proteins and post-translational modifications. Using triple quadrupole instrumentation (QqQ), specific analyte molecules are targeted in a data-directed mode. Used routinely for the quantitative analysis of small molecular compounds for at least three decades, the technology is now experiencing broadened application in the proteomics community. In the current review, we will provide a detailed summary of current developments in targeted proteomics, including some of the recent applications to biological research and biomarker discovery.

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues.

Proteome analysis of mouse model systems: A tool to model human disease and for the investigation of tissue-specific biology

The molecular dissections of the mechanistic pathways involved in human disease have always relied on the use of model organisms. Among the higher mammalian organisms, the laboratory mouse (Mus musculus) is the most widely used model. A large number of commercially-available, inbred strains are available to the community, including an ever growing collection of transgenic, knock-out, and disease models. Coupled to availability is the fact that animal colonies can be kept under standardized housing condition at most major universities and research institutes, with relative ease and cost efficiency (compared to larger vertebrates). As such, mouse models to study human biology and disease remains extremely attractive. In the current review we will provide an historic overview of the use of mouse models in proteome research with a focus on general tissue and organelle biology, comparative proteomics of human and mouse and the use of mouse models to study cardiac disease.