Parallel proteomics to improve coverage and confidence in the partially annotated Oryctolagus cuniculus mitochondrial proteome

High-Field Asymmetric Waveform Ion Mobility Spectrometry: Practical Alternative for Cardiac Proteome Sample Processing

Heart tissue sample preparation for mass spectrometry (MS) analysis that includes prefractionation reduces the cellular protein dynamic range and increases the relative abundance of nonsarcomeric proteins. We previously described "IN-Sequence" (IN-Seq) where heart tissue lysate is sequentially partitioned into three subcellular fractions to increase the proteome coverage more than a single direct tissue analysis by mass spectrometry. Here, we report an adaptation of the high-field asymmetric ion mobility spectrometry (FAIMS) coupled to mass spectrometry, and the establishment of a simple one step sample preparation coupled with gas-phase fractionation. The FAIMS approach substantially reduces manual sample handling, significantly shortens the MS instrument processing time, and produces unique protein identification and quantification approximating the commonly used IN-Seq method in less time.

A Global Profile of Reversible and Irreversible Cysteine Redox Post-Translational Modifications During Myocardial Ischemia/Reperfusion Injury and Antioxidant Intervention

Aims: Cysteine (Cys) is a major target for redox post-translational modifications (PTMs) that occur in response to changes in the cellular redox environment. We describe multiplexed, peptide-based enrichment and quantitative mass spectrometry (MS) applied to globally profile reversible redox Cys PTM in rat hearts during ischemia/reperfusion (I/R) in the presence or absence of an aminothiol antioxidant, N-2-mercaptopropionylglycine (MPG). Parallel fractionation also allowed identification of irreversibly oxidized Cys peptides (Cys-SO₂H/SO₃H). Results: We identified 4505 reversibly oxidized Cys peptides of which 1372 were significantly regulated by ischemia and/or I/R. An additional 219 peptides (247 sites) contained Cys-SO₂H/Cys-SO₃H modifications, and these were predominantly identified from hearts subjected to I/R (n = 168 peptides). Parallel reaction monitoring MS (PRM-MS) enabled relative quantitation of 34 irreversibly oxidized Cys peptides. MPG attenuated a large cluster of I/R-associated reversibly oxidized Cys peptides and irreversible Cys oxidation to less than nonischemic controls (n = 24 and 34 peptides, respectively). PRM-MS showed that Cys sites oxidized during ischemia and/or I/R and "protected" by MPG were largely mitochondrial, and were associated with antioxidant functions (peroxiredoxins 5 and 6) and metabolic processes, including glycolysis. Metabolomics revealed I/R induced changes in glycolytic intermediates that were reversed in the presence of MPG, which were consistent with irreversible PTM of triose phosphate isomerase and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), altered GAPDH enzyme activity, and reduced I/R glycolytic payoff as evidenced by adenosine triphosphate and NADH levels. Innovation: Novel enrichment and PRM-MS approaches developed here enabled large-scale relative quantitation of Cys redox sites modified by reversible and irreversible PTM during I/R and antioxidant remediation. Conclusions: Cys sites identified here are targets of reactive oxygen species that can contribute to protein dysfunction and the pathogenesis of I/R.

N-terminomics - its past and recent advancements

N-terminomics is a rapidly evolving branch of proteomics that encompasses the study of protein N-terminal sequence. A proteome-wide collection of such sequences has been widely used to understand the proteolytic cascades and in annotating the genome. Over the last two decades, various N-terminomic strategies have been developed for achieving high sensitivity, greater depth of coverage, and high-throughputness. We, in this review, cover how the field of N-terminomics has evolved to date, including discussion on various sample preparation and N-terminal peptide enrichment strategies. We also compare different N-terminomic methods and highlight their relative benefits and shortcomings in their implementation. In addition, an overview of the currently available bioinformatics tools and data analysis pipelines for the annotation of N-terminomic datasets is also included. SIGNIFICANCE: It has been recognized that proteins undergo several post-translational modifications (PTM), and a number of perturbed biological pathways are directly associated with modifications at the terminal sites of a protein. In this regard, N-terminomics can be applied to generate a proteome-wide landscape of mature N-terminal sequences, annotate their source of generation, and recognize their significance in the biological pathways. Besides, a system-wide study can be used to study complicated proteolytic machinery and protease cleavage patterns for potential therapeutic targets. Moreover, due to unprecedented improvements in the analytical methods and mass spectrometry instrumentation in recent times, the N-terminomic methodologies now offers an unparalleled ability to study proteoforms and their implications in clinical conditions. Such approaches can further be applied for the detection of low abundant proteoforms, annotation of non-canonical protein coding sites, identification of candidate disease biomarkers, and, last but not least, the discovery of novel drug targets.

Efficient identification of multiple pathways: RNA-Seq analysis of livers from 56Fe ion irradiated mice

Background

mRNA interaction with other mRNAs and other signaling molecules determine different biological pathways and functions. Gene co-expression network analysis methods have been widely used to identify correlation patterns between genes in various biological contexts (e.g., cancer, mouse genetics, yeast genetics). A challenge remains to identify an optimal partition of the networks where the individual modules (clusters) are neither too small to make any general inferences, nor too large to be biologically interpretable. Clustering thresholds for identification of modules are not systematically determined and depend on user-settable parameters requiring optimization. The absence of systematic threshold determination may result in suboptimal module identification and a large number of unassigned features.

Results

In this study, we propose a new pipeline to perform gene co-expression network analysis. The proposed pipeline employs WGCNA, a software widely used to perform different aspects of gene co-expression network analysis, and Modularity Maximization algorithm, to analyze novel RNA-Seq data to understand the effects of low-dose ⁵⁶Fe ion irradiation on the formation of hepatocellular carcinoma in mice. The network results, along with experimental validation, show that using WGCNA combined with Modularity Maximization, provides a more biologically interpretable network in our dataset, than that obtainable using WGCNA alone. The proposed pipeline showed better performance than the existing clustering algorithm in WGCNA, and identified a module that was biologically validated by a mitochondrial complex I assay.

Conclusions

We present a pipeline that can reduce the problem of parameter selection that occurs with the existing algorithm in WGCNA, for applicable RNA-Seq datasets. This may assist in the future discovery of novel mRNA interactions, and elucidation of their potential downstream molecular effects.

Clinical implications from proteomic studies in neurodegenerative diseases: lessons from mitochondrial proteins

Mitochondria play a key role in eukaryotic cells, being mediators of energy, biosynthetic and regulatory requirements of these cells. Emerging proteomics techniques have allowed scientists to obtain the differentially expressed proteome or the proteomic redox status in mitochondria. This has unmasked the diversity of proteins with respect to subcellular location, expression and interactions. Mitochondria have become a research 'hot spot' in subcellular proteomics, leading to identification of candidate clinical targets in neurodegenerative diseases in which mitochondria are known to play pathological roles. The extensive efforts to rapidly obtain differentially expressed proteomes and unravel the redox proteomic status in mitochondria have yielded clinical insights into the neuropathological mechanisms of disease, identification of disease early stage and evaluation of disease progression. Although current technical limitations hamper full exploitation of the mitochondrial proteome in neurosciences, future advances are predicted to provide identification of specific therapeutic targets for neurodegenerative disorders.

Post-translational processing targets functionally diverse proteins in Mycoplasma hyopneumoniae

Mycoplasma hyopneumoniae is a genome-reduced, cell wall-less, bacterial pathogen with a predicted coding capacity of less than 700 proteins and is one of the smallest self-replicating pathogens. The cell surface of M. hyopneumoniae is extensively modified by processing events that target the P97 and P102 adhesin families. Here, we present analyses of the proteome of M. hyopneumoniae-type strain J using protein-centric approaches (one- and two-dimensional GeLC–MS/MS) that enabled us to focus on global processing events in this species. While these approaches only identified 52% of the predicted proteome (347 proteins), our analyses identified 35 surface-associated proteins with widely divergent functions that were targets of unusual endoproteolytic processing events, including cell adhesins, lipoproteins and proteins with canonical functions in the cytosol that moonlight on the cell surface. Affinity chromatography assays that separately used heparin, fibronectin, actin and host epithelial cell surface proteins as bait recovered cleavage products derived from these processed proteins, suggesting these fragments interact directly with the bait proteins and display previously unrecognized adhesive functions. We hypothesize that protein processing is underestimated as a post-translational modification in genome-reduced bacteria and prokaryotes more broadly, and represents an important mechanism for creating cell surface protein diversity.

A novel method to detect translation of membrane proteins following microvesicle intercellular transfer of nucleic acids

Microvesicles (MVs) serve as vectors of nucleic-acid dissemination and are important mediators of intercellular communication. However, the functionality of packaged nucleic acids on recipient cells following transfer of MV cargo has not been clearly elucidated. This limitation is attributed to a lack of methodology available in assessing protein translation following homotypic intercellular transfer of nucleic acids. Using surface peptide shaving we have demonstrated that MVs derived from human leukaemic cells transfer functional P-glycoprotein transcripts, conferring drug-efflux capacity to recipient cells. We demonstrate expression of newly synthesized protein using Western blot. Furthermore, we show functionality of translated P-gp protein in recipient cells using Calcein-AM dye exclusion assays on flow cytometry. Newly synthesized 170 kDa P-gp was detected in recipient cells after coculture with shaven MVs and these proteins were functional, conferring drug efflux. This is the first demonstration of functionality of transferred nucleic acids between human homotypic cells as well as the translation of the cancer multidrug-resistance protein in recipient cells following intercellular transfer of its transcript. This study supports the significant role of MV's in the transfer of deleterious traits in cancer populations and describes a new paradigm in mechanisms governing the acquisition of traits in cancer cell populations.

Integrating Phosphoproteomics and Bioinformatics to Study Brassinosteroid-Regulated Phosphorylation Dynamics in Arabidopsis

BACKGROUND

Protein phosphorylation regulated by plant hormone is involved in the coordination of fundamental plant development. Brassinosteroids (BRs), a group of phytohormones, regulated phosphorylation dynamics remains to be delineated in plants. In this study, we performed a mass spectrometry (MS)-based phosphoproteomics to conduct a global and dynamic phosphoproteome profiling across five time points of BR treatment in the period between 5 min and 12 h. MS coupling with phosphopeptide enrichment techniques has become the powerful tool for profiling protein phosphorylation. However, MS-based methods tend to have data consistency and coverage issues. To address these issues, bioinformatics approaches were used to complement the non-detected proteins and recover the dynamics of phosphorylation events.

RESULTS

A total of 1104 unique phosphorylated peptides from 739 unique phosphoproteins were identified. The time-dependent gene ontology (GO) analysis shows the transition of biological processes from signaling transduction to morphogenesis and stress response. The protein-protein interaction analysis found that most of identified phosphoproteins have strongly connections with known BR signaling components. The analysis by using Motif-X was performed to identify 15 enriched motifs, 11 of which correspond to 6 known kinase families. To uncover the dynamic activities of kinases, the enriched motifs were combined with phosphorylation profiles and revealed that the substrates of casein kinase 2 and mitogen-activated protein kinase were significantly phosphorylated and dephosphorylated at initial time of BR treatment, respectively. The time-dependent kinase-substrate interaction networks were constructed and showed many substrates are the downstream of other signals, such as auxin and ABA signaling. While comparing BR responsive phosphoproteome and gene expression data, we found most of phosphorylation changes were not led by gene expression changes. Our results suggested many downstream proteins of BR signaling are induced by phosphorylation via various kinases, not through transcriptional regulation.

CONCLUSIONS

Through a large-scale dynamic profile of phosphoproteome coupled with bioinformatics, a complicated kinase-centered network related to BR-regulated growth was deciphered. The phosphoproteins and phosphosites identified in our study provide a useful dataset for revealing signaling networks of BR regulation, and also expanded our knowledge of protein phosphorylation modification in plants as well as further deal to solve the plant growth problems.

Characterization, design, and function of the mitochondrial proteome: from organs to organisms

Mitochondria are a common energy source for organs and organisms; their diverse functions are specialized according to the unique phenotypes of their hosting environment. Perturbation of mitochondrial homeostasis accompanies significant pathological phenotypes. However, the connections between mitochondrial proteome properties and function remain to be experimentally established on a systematic level. This uncertainty impedes the contextualization and translation of proteomic data to the molecular derivations of mitochondrial diseases. We present a collection of mitochondrial features and functions from four model systems, including two cardiac mitochondrial proteomes from distinct genomes (human and mouse), two unique organ mitochondrial proteomes from identical genetic codons (mouse heart and mouse liver), as well as a relevant metazoan out-group (drosophila). The data, composed of mitochondrial protein abundance and their biochemical activities, capture the core functionalities of these mitochondria. This investigation allowed us to redefine the core mitochondrial proteome from organs and organisms, as well as the relevant contributions from genetic information and hosting milieu. Our study has identified significant enrichment of disease-associated genes and their products. Furthermore, correlational analyses suggest that mitochondrial proteome design is primarily driven by cellular environment. Taken together, these results connect proteome feature with mitochondrial function, providing a prospective resource for mitochondrial pathophysiology and developing novel therapeutic targets in medicine.

Comparative mitochondrial proteomics: perspective in human diseases

Mitochondria are the most complex and the most important organelles of eukaryotic cells, which are involved in many cellular processes, including energy metabolism, apoptosis, and aging. And mitochondria have been identified as the "hot spot" by researchers for exploring relevant associated dysfunctions in many fields. The emergence of comparative proteomics enables us to have a close look at the mitochondrial proteome in a comprehensive and effective manner under various conditions and cellular circumstances. Two-dimensional electrophoresis combined with mass spectrometry is still the most popular techniques to study comparative mitochondrial proteomics. Furthermore, many new techniques, such as ICAT, MudPIT, and SILAC, equip researchers with more flexibilities inselecting proper methods. This article also reviews the recent development of comparative mitochondrial proteomics on diverse human diseases. And the results of mitochondrial proteomics enhance a better understanding of the pathogenesis associated with mitochondria and provide promising therapeutic targets.

How advancement in biological network analysis methods empowers proteomics

Proteomics provides important information--that may not be inferable from indirect sources such as RNA or DNA--on key players in biological systems or disease states. However, it suffers from coverage and consistency problems. The advent of network-based analysis methods can help in overcoming these problems but requires careful application and interpretation. This review considers briefly current trends in proteomics technologies and understanding the causes of critical issues that need to be addressed--i.e., incomplete data coverage and inter-sample inconsistency. On the coverage issue, we argue that holistic analysis based on biological networks provides a suitable background on which more robust models and interpretations can be built upon; and we introduce some recently developed approaches. On consistency, group-based approaches based on identified clusters, as well as on properly integrated pathway databases, are particularly useful. Despite that protein interactions and pathway networks are still largely incomplete, given proper quality checks, applications and reasonably sized data sets, they yield valuable insights that greatly complement data generated from quantitative proteomics.