Yaoyang Zhang

Protein Analysis by Shotgun/Bottom-up Proteomics

According to Genome Sequencing Project statistics (http://www.ncbi.nlm.nih.gov/genomes/static/gpstat.html), as of Feb 16, 2012, complete gene sequences have become available for 2816 viruses, 1117 prokaryotes, and 36 eukaryotes.1–2 The availability of full genome sequences has greatly facilitated biological research in many fields, and has greatly contributed to the growth of proteomics. Proteins are important because they are the direct bio-functional molecules in the living organisms. The term “proteomics” was coined from merging “protein” and “genomics” in the 1990s.3–4 As a post-genomic discipline, proteomics encompasses efforts to identify and quantify all the proteins of a proteome, including expression, cellular localization, interactions, post-translational modifications (PTMs), and turnover as a function of time, space and cell type, thus making the full investigation of a proteome more challenging than sequencing a genome. There are possibly 100,000 protein forms encoded by the approximate 20,235 genes of the human genome,5 and determining the explicit function of each form will be a challenge. The progress of proteomics has been driven by the development of new technologies for peptide/protein separation, mass spectrometry analysis, isotope labeling for quantification, and bioinformatics data analysis. Mass spectrometry has emerged as a core tool for large-scale protein analysis. In the past decade, there has been a rapid advance in the resolution, mass accuracy, sensitivity and scan rate of mass spectrometers used to analyze proteins. In addition, hybrid mass analyzers have been introduced recently (e.g. Linear Ion Trap-Orbitrap series6–7) which have significantly improved proteomic analysis. “Bottom-up” protein analysis refers to the characterization of proteins by analysis of peptides released from the protein through proteolysis. When bottom-up is performed on a mixture of proteins it is called shotgun proteomics,8–10 a name coined by the Yates lab because of its analogy to shotgun genomic sequencing.11 Shotgun proteomics provides an indirect measurement of proteins through peptides derived from proteolytic digestion of intact proteins. In a typical shotgun proteomics experiment, the peptide mixture is fractionated and subjected to LC-MS/MS analysis. Peptide identification is achieved by comparing the tandem mass spectra derived from peptide fragmentation with theoretical tandem mass spectra generated from in silico digestion of a protein database. Protein inference is accomplished by assigning peptide sequences to proteins. Because peptides can be either uniquely assigned to a single protein or shared by more than one protein, the identified proteins may be further scored and grouped based on their peptides. In contrast, another strategy, termed ‘top-down’ proteomics, is used to characterize intact proteins (Figure 1). The top-down approach has some potential advantages for PTM and protein isoform determination and has achieved notable success. Intact proteins have been measured up to 200 kDa,12 and a large scale study has identified more than 1,000 proteins by multi-dimensional separations from complex samples.13 However, the top-down method has significant limitations compared with shotgun proteomics due to difficulties with protein fractionation, protein ionization and fragmentation in the gas phase. By relying on the analysis of peptides, which are more easily fractionated, ionized and fragmented, shotgun proteomics can be more universally adopted for protein analysis. In fact, a hybrid of bottom-up and top-down methodologies and instrumentation has been introduced as middle-down proteomics.14 Essentially, middle-down proteomics analyzes larger peptide fragments than bottom-up proteomics, minimizing peptide redundancy between proteins. Additionally the large peptide fragments yield similar advantages as top-down proteomics, such as gaining further insight into post-translational modifications, without the analytical challenges of analyzing intact proteins. Shotgun proteomics has become a workhorse for the analysis of proteins and their modifications and will be increasingly combined with top-down methods in the future. Figure 1 Proteomic strategies: bottom-up vs. top-down vs. middle-down. The bottom-up approach analyzes proteolytic peptides. The top-down method measures the intact proteins. The middle-down strategy analyzes larger peptides resulted from limited digestion or ... In the past decade shotgun proteomics has been widely used by biologists for many different research experiments, advancing biological discoveries. Some applications include, but are not limited to, proteome profiling, protein quantification, protein modification, and protein-protein interaction. There have been several reviews nicely summarizing mass spectrometry history,15 protein quantification with mass spectrometry,16 its biological applications,5,17–26 and many recent advances in methodology.27–32 In this review, we try to provide a full and updated survey of shotgun proteomics, including the fundamental techniques and applications that laid the foundation along with those developed and greatly improved in the past several years.

Proteomics and Metabolomics Analysis of a Trait Anxiety Mouse Model Reveals Divergent Mitochondrial Pathways

BACKGROUND Although anxiety disorders are the most prevalent psychiatric disorders, no molecular biomarkers exist for their premorbid diagnosis, accurate patient subcategorization, or treatment efficacy prediction. To unravel the neurobiological underpinnings and identify candidate biomarkers and affected pathways for anxiety disorders, we interrogated the mouse model of high anxiety-related behavior (HAB), normal anxiety-related behavior (NAB), and low anxiety-related behavior (LAB) employing a quantitative proteomics and metabolomics discovery approach. METHODS We compared the cingulate cortex synaptosome proteomes of HAB and LAB mice by in vivo (15)N metabolic labeling and mass spectrometry and quantified the cingulate cortex metabolomes of HAB/NAB/LAB mice. The combined data sets were used to identify divergent protein and metabolite networks by in silico pathway analysis. Selected differentially expressed proteins and affected pathways were validated with immunochemical and enzymatic assays. RESULTS Altered levels of up to 300 proteins and metabolites were found between HAB and LAB mice. Our data reveal alterations in energy metabolism, mitochondrial import and transport, oxidative stress, and neurotransmission, implicating a previously nonhighlighted role of mitochondria in modulating anxiety-related behavior. CONCLUSIONS Our results offer insights toward a molecular network of anxiety pathophysiology with a focus on mitochondrial contribution and provide the basis for pinpointing affected pathways in anxiety-related behavior.

Protein kinase C-η controls CTLA-4–mediated regulatory T cell function

Regulatory T (Treg) cells, which maintain immune homeostasis and self-tolerance, form an immunological synapse (IS) with antigen-presenting cells (APCs). However, signaling events at the Treg cell IS remain unknown. Here we show that the kinase PKC-η associated with CTLA-4 and was recruited to the Treg cell IS. PKC-η–deficient Treg cells displayed defective suppressive activity, including suppression of tumor immunity but not of autoimmune colitis. Phosphoproteomic and biochemical analysis revealed an association between CTLA-4–PKC-η and the GIT2-αPIX-PAK complex, an IS-localized focal adhesion complex. Defective activation of this complex in PKC-η–deficient Treg cells was associated with reduced depletion of CD86 from APCs by Treg cells. These results reveal a CTLA-4–PKC-η signaling axis required for contact-dependent suppression and implicate this pathway as a potential cancer immunotherapy target.

Proteomic and Metabolomic Profiling of a Trait Anxiety Mouse Model Implicate Affected Pathways

Depression and anxiety disorders affect a great number of people worldwide. Whereas singular factors have been associated with the pathogenesis of psychiatric disorders, growing evidence emphasizes the significance of dysfunctional neural circuits and signaling pathways. Hence, a systems biology approach is required to get a better understanding of psychiatric phenotypes such as depression and anxiety. Furthermore, the availability of biomarkers for these disorders is critical for improved diagnosis and monitoring treatment response. In the present study, a mouse model presenting with robust high versus low anxiety phenotypes was subjected to thorough molecular biomarker and pathway discovery analyses. Reference animals were metabolically labeled with the stable 15N isotope allowing an accurate comparison of protein expression levels between the high anxiety-related behavior versus low anxiety-related behavior mouse lines using quantitative mass spectrometry. Plasma metabolomic analyses identified a number of small molecule biomarkers characteristic for the anxiety phenotype with particular focus on myo-inositol and glutamate as well as the intermediates involved in the tricarboxylic acid cycle. In silico analyses suggested pathways and subnetworks as relevant for the anxiety phenotype. Our data demonstrate that the high anxiety-related behavior and low anxiety-related behavior mouse model is a valuable tool for anxiety disorder drug discovery efforts.

Proteome Scale Turnover Analysis in Live Animals Using Stable Isotope Metabolic Labeling

At present most quantitative proteomics investigations are focused on the analysis of protein expression differences between two or more sample specimens. With each analysis a static snapshot of a cellular state is captured with regard to protein expression. However, any information on protein turnover cannot be obtained using classic methodologies. Protein turnover, the result of protein synthesis and degradation, represents a dynamic process, which is of equal importance to understanding physiological processes. Methods employing isotopic tracers have been developed to measure protein turnover. However, applying these methods to live animals is often complicated by the fact that an assessment of precursor pool relative isotope abundance is required. Also, data analysis becomes difficult in case of low label incorporation, which results in a complex convolution of labeled and unlabeled peptide mass spectrometry signals. Here we present a protein turnover analysis method that circumvents this problem using a (15)N-labeled diet as an isotopic tracer. Mice were fed with the labeled diet for limited time periods and the resulting partially labeled proteins digested and subjected to tandem mass spectrometry. For the interpretation of the mass spectrometry data, we have developed the ProTurnyzer software that allows the determination of protein fractional synthesis rates without the need of precursor relative isotope abundance information. We present results validating ProTurnyzer with Escherichia coli protein data and apply the method to mouse brain and plasma proteomes for automated turnover studies.

QuantiSpec - Quantitative mass spectrometry data analysis of 15N-metabolically labeled proteins

For relative protein quantitation by mass spectrometry we metabolically labeled E. coli bacteria with (15)N-enriched diets. Proteins extracted from (15)N-labeled and unlabeled E. coli bacteria were mixed, separated by two-dimensional gel electrophoresis and enzymatically digested. The resulting tryptic peptides were analyzed by MALDI mass spectrometry. For the relative protein quantitation we developed fully automated software, QuantiSpec (Quantitative Mass Spectrometry Analysis Software), which uses data from MALDI TOF mass spectrometry and the Mascot database search engine. QuantiSpec detects natural as well as partially or fully labeled peptide isotope distributions. For each identified peptide the (15)N incorporation rate is determined by comparing the experimental to a set of theoretical isotope patterns based on the peptide sequence. Relative quantitation is accomplished by calculating the signal intensity ratios for each (14)N/(15)N peptide pair.

A MS data search method for improved 15N‐labeled protein identification

Quantitative proteomics using stable isotope labeling strategies combined with MS is an important tool for biomarker discovery. Methods involving stable isotope metabolic labeling result in optimal quantitative accuracy, since they allow the immediate combination of two or more samples. Unfortunately, stable isotope incorporation rates in metabolic labeling experiments using mammalian organisms usually do not reach 100%. As a consequence, protein identifications in 15N database searches have poor success rates. We report on a strategy that significantly improves the number of 15N‐labeled protein identifications and results in a more comprehensive and accurate relative peptide quantification workflow.

A TRAF-like motif of the inducible costimulator ICOS controls development of germinal center TFH cells via the kinase TBK1

Signaling via the inducible costimulator ICOS fuels the stepwise development of follicular helper T cells (TFH cells). However, a signaling pathway unique to ICOS has not been identified. We found here that the kinase TBK1 associated with ICOS via a conserved motif, IProx, that shares homology with the tumor-necrosis-factor receptor (TNFR)-associated factors TRAF2 and TRAF3. Disruption of this motif abolished the association of TBK1 with ICOS, TRAF2 and TRAF3, which identified a TBK1-binding consensus. Alteration of this motif in ICOS or depletion of TBK1 in T cells severely impaired the differentiation of germinal center (GC) TFH cells and the development of GCs, interfered with B cell differentiation and disrupted the development of antibody responses, but the IProx motif and TBK1 were dispensable for the early differentiation of TFH cells. These results reveal a previously unknown ICOS-TBK1 signaling pathway that specifies the commitment of GC TFH cells.Inducible costimulator (ICOS) signaling fuels the stepwise development of T follicular helper (TFH) cells. However, a signaling pathway unique to ICOS has not been identified. We show that TANK-binding kinase 1 (TBK1) associates with ICOS via a conserved motif, IProx, which shares homology with tumor necrosis factor receptor (TNFR)-associated factors, TRAF2 and TRAF3. Disruption of this motif abolishes the association with TBK1, thus identifying a TBK1-binding consensus. Mutation of this motif in ICOS, or depletion of TBK1 in T cells severely impaired the differentiation of germinal center (GC) TFH, B cell and antibody responses, but was dispensable for early TFH differentiation. These results reveal a novel ICOS-TBK1 signaling pathway that specifies GC TFH cell commitment.

The 15N isotope effect as a means for correlating phenotypic alterations and affected pathways in a trait anxiety mouse model

Stable isotope labeling techniques hold great potential for accurate quantitative proteomics comparisons by MS. To investigate the effect of stable isotopes in vivo, we metabolically labeled high anxiety‐related behavior (HAB) mice with the heavy nitrogen isotope 15N. 15N‐labeled HAB mice exhibited behavioral alterations compared to unlabeled (14N) HAB mice in their depression‐like phenotype. To correlate behavioral alterations with changes on the molecular level, we explored the 15N isotope effect on the brain proteome by comparing protein expression levels between 15N‐labeled and 14N HAB mouse brains using quantitative MS. By implementing two complementary in silico pathway analysis approaches, we were able to identify altered networks in 15N‐labeled HAB mice, including major metabolic pathways such as the tricarboxylic acid (TCA) cycle and oxidative phosphorylation. Here, we discuss the affected pathways with regard to their relevance for the behavioral phenotype and critically assess the utility of exploiting the 15N isotope effect for correlating phenotypic and molecular alterations.

Quantitative Proteomic Analysis Identifies Targets and Pathways of a 2-Aminobenzamide HDAC Inhibitor in Friedreich’s Ataxia Patient iPSC-Derived Neural Stem Cells

Members of the 2-aminobenzamide class of histone deacetylase (HDAC) inhibitors show promise as therapeutics for the neurodegenerative diseases Friedreich’s ataxia (FRDA) and Huntington’s disease (HD). While it is clear that HDAC3 is one of the important targets of the 2-aminobenzamide HDAC inhibitors, inhibition of other class I HDACs (HDACs 1 and 2) may also be involved in the beneficial effects of these compounds in FRDA and HD, and other HDAC interacting proteins may be impacted by the compound. To this end, we synthesized activity-based profiling probe (ABPP) versions of one of our HDAC inhibitors (compound 106), and in the present study we used a quantitative proteomic method coupled with multidimensional protein identification technology (MudPIT) to identify the proteins captured by the ABPP 106 probe. Nuclear proteins were extracted from FRDA patient iPSC-derived neural stem cells, and then were reacted with control and ABPP 106 probe. After reaction, the bound proteins were digested on the beads, and the peptides were modified using stable isotope-labeled formaldehyde to form dimethyl amine. The selectively bound proteins determined by mass spectrometry were subjected to functional and pathway analysis. Our findings suggest that the targets of compound 106 are involved not only in transcriptional regulation but also in posttranscriptional processing of mRNA.