Current Concepts and State of Knowledge

The analysis of proteomes is significantly more challenging than that of genomes. Measuring ► gene expression at the protein level is potentially more informative than the corresponding measurement at the mRNA level. Though certain RNAs are known to function as effector molecules, proteins are the major actors and catalysts of biological function. Proteins contain several dimensions of information, which represent the actual rather than the potential functional state as indicated by mRNA analysis. These dimensions include the abundance, state of PTM, (sub) cellular localization and association and interaction with each other. PTMs of a protein can determine its activity state, localization, turnover, and interactions with other proteins. More than 300 different types of PTMs are currently known. Changes in gene expression at the level of the message, mRNA expression, may not directly correlate with protein expression since mRNA is not the functional endpoint of gene expression. Recent investigations show that differences in protein concentrations are only 20-40% assigned to variable mRNA levels, emphasizing the importance of posttranscriptional regulation. In general, protein concentrations depend on the translation rate and the degradation rate, i.e., the protein turnover. In addition to the complexity at the transcriptional level, proteome approaches have to deal with the considerable increase in isoforms due to multiple PTMs (Tyers and Mann 2003).

Proteomics Methodologies

Proteomics technologies (both expression profiling and functional approaches) have widely expanded in recent years. Because of protein diversity, a range of techniques have emerged, which depend on integration of biological, chemical and analytical methods. The main proteomic technologies of today utilize ► mass spectrometry (MS) coupled with global protein separations (Aebersold and Mann 2003) and methods based on protein arrays (Phizicky et al. 2003). The global protein separation methods are conventionally divided into gel-based and gel-free, where the gel-free are all MS based. The protein arrays are not global in the sense that they typically depend on the availability of antibodies. The methodologies are complementary and are increasingly used in combination with each other. Even though their analytical windows overlap, each of them covers selected sets of proteins that are not identified by the other techniques (Choudhary and Grant 2004).

► Two-dimensional gel electrophoresis. The global protein separation method two-dimensional gel electro-phoresis (2-DE) enables the possibility to resolve several thousand proteins in a single sample. 2-DE mainly produces data which allow the investigator to determine whether a particular protein shows an increase or decrease when comparing two different conditions. The limited dynamic range and poor reproducibility between gels have been of major concern with traditional 2-DE experiments (Westermeier et al. 2008).

In a typical gel-based proteomics experiment the proteins in a sample are separated by 2-DE, stained, and each observed protein spot is quantified by its staining intensity. Selected spots are excised, and analyzed by MS after "digestion" (see below). Pattern-matching algorithms as well as interpretation by skilled researchers are required to relate the 2-DE patterns to each other in order to detect characteristic patterns and differences among samples. The major limitation of 2-DE techniques is a relatively low throughput particularly in cases where many proteins have to be identified with subsequent MS analysis which is very time-consuming (Westermeier et al. 2008).

Detecting changes in protein expression is improved by the introduction of fluorescence difference gel electrophoresis (DIGE). 2-D-DIGE enables the pre-labeling and separation of up to three samples on a single 2-D gel providing quantitation of proteins. Up to three protein samples are labeled with size- and charge-matched CyDye DIGE fluorochromes and co-separated on the same 2-D electrophoresis gel. Gels using the 2-D-DIGE method usually contain three samples labeled with three distinct fluorescent dyes, Cy2, Cy3 and Cy5. The Cy2 dye is typically used to label an internal standard, which is a mix of all samples in the experiment, and the other two dyes are then employed to label two biological samples of interest. The strength of the internal standard is to help the mapping of spots/proteins between gels and thus make the different gels more comparable. The internal standard is also used in some methods for normalization within and between gels (Westermeier et al. 2008) (Fig. 1).

Mass spectrometry-based proteomics. The coupling of chromatographic separation methods with MS is commonly utilized for qualitative and quantitative characterization of highly complex protein mixtures. The advances in chemical tagging and isotope labeling techniques have

Pooled internal

Standard label with Cy™2

Protein extract 1 label with Cy3

Protein extract 2 label with Cy5

Proteomics. Fig. 1. Workflow for a standard 2-D difference gel electrophoresis (DIGE) experiment. Samples are labeled with molecular weight and charge matched CyDye DIGE Fluors, minimal dyes. This permits multiplexing of up to two samples and a pooled internal standard on the same first and second dimension gel. Gels are scanned on an imager and processed using analysis software.

improved the quantitative analysis of proteomes. ► High performance liquid chromatography (► HPLC) methods provide powerful tools of protein and peptide separation of protein mixtures. Primary advantages of liquid chro-matographic (LC) separation are the flexibility of the methods and the possibility to link LC directly to MS. Proteins and peptides can be separated based on their physical properties, including affinity, charge (ion exchange), hydrophobicity (reversed phase), and size (size exclusion) (Aebersold and Mann 2003).

Presently two ionization techniques, ► electrospray ionization (► ESI) and ► matrix-assisted laser desorption ionization (► MALDI) are playing a significant role in the field of MS-based proteome analysis. ESI ionizes the ana-lytes out of a solution and is therefore readily coupled to chromatographic and electrophoretic separation tools. MALDI sublimates and ionizes the samples out of a dry, crystalline matrix via laser pulses. There are four basic types of mass analyzers currently used in proteomics research. These are the ion-trap, time-of-flight (TOF), quad-rupole, and Fourier transform ion cyclotron (FT-MS) analyzers. These analyzers can be stand alone or, in some cases, put together in tandem to take advantage of the strengths of each (Aebersold and Mann 2003).

There are two complementary approaches for the MS analysis of proteins (Aebersold and Mann 2003). The bottom-up method is generally used for identifying proteins and determining details of their sequence and PTMs. Proteins of interest are digested with a sequence specific enzyme such as trypsin, and the resulting tryptic peptides are analyzed by ESI or MALDI, which allow intact peptide molecular ions to be put into the gas phase. The MS analysis takes place in two steps. The masses of the tryptic peptides are determined; next, these peptide ions are fragmented in the gas phase. The masses of the peptide fragments yield information on their sequence and modifications. Prior to being introduced to the MS the tryptic peptides usually are separated on reversed phase LC directly coupled to the ESI source (Fig. 2). For quantitative analysis of proteins by MS stable-isotope labeling of proteins can be used. These methods utilize either metabolic labeling, tagging by chemical reaction, or stable-isotope incorporation via enzyme reaction of proteins or peptides (Aebersold and Mann 2003). Labeled proteins or peptides are combined, separated and analyzed by MS and/or tandem MS for identifying the proteins and determining their relative abundance.

In the top-down approach, intact protein ions are introduced into the gas phase and are then fragmented in the mass spectrometer. If enough numbers of informative fragment ions are observed, the analysis can provide a complete description of the primary structure of the protein and reveal all of its modifications. Until recently, it has proved difficult to produce extensive gas-phase fragmentation of intact large protein ions, but novel techniques such as electron transfer dissociation promise to drastically improve this situation.

► Neuropeptidomics. The core proteomics tools, including 2-DE in combination with MS, are limited to the analysis of proteins >10 kDa. Other technologies are

Proteomics. Fig. 2. Typical MS-based proteomics experiment. The general proteomics experiment consists of five stages. (a) the proteins to be analyzed are isolated from cell lysate or tissues by biochemical fractionation (such as SDS polyacrylamide gel electrophoresis (PAGE) or multidimensional LC) for reduction of the sample complexity or affinity selection (for enrichment of a sub-proteome), (b) the proteins are degraded enzymatically to peptides, usually by trypsin, (c) the peptides are separated by one or more steps of high performance liquid chromatography (HPLC) in very fine capillaries and eluted into an electrospray ionization (ESI) ion source, (d) after evaporation, multiply protonated peptides enter the mass spectrometer and a mass spectrum of the peptides eluting at this time point is taken, (e) the computer generates a prioritized list of these peptides for fragmentation and a series of tandem mass spectrometric (MS/MS) experiments follows. The outcome of the experiment is the identity of the peptides and therefore the proteins making up the purified protein population (modified from Aebersold and Mann 2003).

therefore necessary to identify small endogenous proteins and peptides such as present in brain samples. Neuro-peptidomics is the technological approach for detailed analyses of endogenous peptides from the nervous system/brain. It is a relatively new direction in proteomics research that covers the gap between proteomics and ► metabolomics and overlaps with both areas. Peptido-mics methodologies are generally based on separating complex endogenous peptide mixtures by multistep LC approaches, usually nL/min flow capillary reversed-phase LC (nanoLC), or gel- or liquid-based isoelectric focusing combined with MS for sequence analysis. The levels of peptides in the brain reflect certain information about physiological status; this information can be revealed when MS is used to generate broad profiles of the dynamic neuropeptide patterns (Svensson et al. 2007).

MALDI ► imaging mass spectrometry. MALDI mass spectrometric tissue imaging (► MALDI-IMS) of pep-tides and proteins in the brain is performed in thin (10-20 mm) tissue sections in situ (Stoeckli et al. 2001). The sections are typically coated with a raster of matrix droplets before an ordered array of mass spectra is acquired from each matrix spot. This way each spectrum reflects the local molecular composition at known x y coordinates. Image profiles of selected peptides and proteins in the section are generated by extracting their corresponding mass-to-charge (m/z) ranges from the spatially acquired MS data files. The approach yields information on the spatial localization of the peptides and proteins in the tissue analyzed, without the requirement of extensive sample manipulation. Applications of MALDI-IMS range from low-resolution peptide and protein profile images of selected areas in e.g., mouse brain to single neural cell peptide profiling analyzes and highresolution imaging of proteins and drugs.

► Protein microarrays. Protein and peptide microar-rays involve the spotting of proteins (including antibodies or other affinity reagents directed against defined proteins) and peptides at high density on surfaces such as glass slides and can be used for both profiling and functional proteo-mics. Antibody microarrays hold potential for high-throughput protein profiling. A complex mixture, such as a brain cell lysate, is passed over the microarray surface to allow the antigens present to bind to their cognate antibodies or targeted reagents. The bound antigen is detected either by using lysates containing fluorescently tagged or radioactively labeled proteins, or by using a secondary antibody against each antigen of interest. Functional protein arrays allows for testing of activities and interactions with lipids, nucleic acids and small molecules as well as other proteins (Phizicky et al. 2003).

Understanding the Brain Molecular Organization and Complexity

There are a variety of applications for proteomic technology in psychopharmacology and neuroscience. These range from defining the proteome of a particular cell type, identifying changes in brain protein expression under different experimental (including pathological) conditions, profiling protein modifications and mapping protein-protein interactions. All of them have their strengths and limitations and a major challenge is to determine the most appropriate proteomic technology to the system studied. Large-scale proteomic analysis can help unravel the complexities of brain function as many of the activities of the brain involve intricate signaling networks and changes in PTMs (Choudhary and Grant 2004). Clinical research aims to benefit from proteomics by both the identification of new drug targets and the development of new diagnostic markers.

Proteome global mapping. Detailed analysis of the mouse brain proteome has established 2-DE protein indices of thousands of proteins, with MS annotations of ~500. Proteins from all functional classes have been identified by such analyses. However, membrane proteins are typically underrepresented in 2-DE due to poor solubility in the initial isoelectric focusing step of the method, which requires relatively large amounts of starting material. Another approach using multidimensional LC coupled to ESI MS/MS does not have this bias against membrane proteins and identified close to 5,000 proteins from 1.8 mg of rat brain homogenate with an average of 25% protein sequence coverage. The proteins identified included membrane proteins, such as neurotransmitter receptors and ion channels implicated in important physiological functions and disease.

Neuropeptidomics has been used to profile a large number of neuropeptides from the brain and the central nervous system. Strategies that reduce complexity and increase the dynamic range of endogenous peptide detection, particularly fractionation methods and separations based on the peptides' chemical and physical properties and bioinformatics approaches, have resulted in the discovery and chemical characterization of novel endogenous peptides. Some of these, such as peptides originated from the secretogranin-1, ► somatostatin, prodynorphin (► endogenous opioids), and ► cholecysto-kinin precursors appear differentially expressed in the ► striatum with and without 3,4-dihydroxy-L-phenylalanine (► l-Dopa) administration in 6-hydroxydopamine (► 6-OHDA) ► animal models for ► Parkinson's disease (Nilsson et al. 2009).

The MALDI IMS technique has been applied to animal models of ► neurodegenerative (► neurodegeneration) disorders to investigate peptide and protein expression, particularly to compare patterns in pre- and post-lesions using 6-OHDA and 1-methyl-4-phenyl-1,2,3,6-tetrahydro-pyridine (MPTP) (Fig. 3). In these affected brains, the IMS molecular tool identified changes in complex protein patterns and identified specific proteins involved in specific brain regions (Svensson et al. 2007). IMS has also been utilized to reveal the regional distribution of psycho-pharmacology agents in the brain such as ► clozapine (► atypical antipsychotic drugs). IMS images revealing the spatial localization in rat brain tissue sections following administration of ► clozapine were found to be in good correlation with those using an autoradiographic approach. The results are encouraging for the potential applicability of this technique for the direct analysis of drug candidates in intact tissue slices (Cornett et al. 2007).

The utility of functional proteomics has been recently exploited to elucidate cellular mechanisms in the brain, of particular importance in the area of signal transduction. Reversible phosphorylation of proteins is the most widely

Control MPTP

Proteomics. Fig. 3. IMS analysis of brain tissue sections after 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) treatment. The relative ion density of PEP-19 from one control (a) and one MPTP-treated animal (b) shows that there is a reduction of PEP-19 expression in the striatum. Typically, in this experiment, about 400 distinct mass signals were detected in the mass range of 2-30 kDa (modified from Svensson et al. 2007).

studied type of signal transduction. Recent synapse phos-phoproteomics studies analyzing phosphorylation sites of proteins identified 974 sites in mouse synaptosomes and 1,563 in postsynaptic densities isolated from mouse cortex, midbrain, cerebellum and hippocampus. Phosphoproteo-mics has identified phosphorylation sites that are altered in Alzheimer's disease, particularly in the microtubule-asso-ciated ► protein Tau (Bayes and Grant 2009).

Characterization of protein complexes provided information about molecular organization as well as cellular pathways. A multi-bait yeast two hybrid screen of proteins relevant to ► Huntington's disease, found 186 proteinprotein interactions, of which 165 had not been previously described and which might be relevant to the pathology of Huntington's disease (Bayes and Grant 2009).

Advantages and Limitations in Neuroproteomics

While many of the proteomics approaches have focused on determining the proteins or peptides present in the cell and their relative expression levels, the specific aim of proteo-mics would be to simultaneously identify, quantify and analyze a large number of proteins within their functional context. The shift in focus from a proteomics discovery mode to a functional approach creates challenges that are at present unmet in all aspects ofthe proteomic experiment, including the experimental design, data analysis, storage, and publication of data. (Choudhary and Grant 2004).

Sample preparation is a major source of variation in the outcome of proteomic experiments. After tissue or body fluid sampling, proteases and other protein-modifying enzymes can rapidly change composition of the pro-teome. As a direct consequence, analytical results will reflect a mix of in vivo proteome and ex vivo degradation products. Vital information about the pre-sampling state may be destroyed or distorted, leading to variation between samples and incorrect conclusions. Sample stabilization and standardization of sample handling are imperative to reduce or eliminate this problem. A recently introduced tissue stabilization system which utilizes a combination of heat and pressure under vacuum has been used to stop degradation in brain and loss of PTMs (e.g., phosphorylations) tissue immediately after sampling (Soloview et al. 2008; Svensson et al. 2007). This methodology provides an improvement to proteomics by greatly reducing the complexity and dynamic range of the proteome in tissue samples and enables enhanced possibilities for discovery and analysis of clinically relevant protein and peptide biomarkers. Rapid removal of neuronal tissue, dissection, and freezing are obvious important procedures for the maintenance of the proteome state in the animal. Human post mortem studies present problematical challenges in neuroproteomics, where careful documentation of post mortem time interval, brain pH, and agonal state is of greatest importance (Soloview et al. 2008).

Proteomic studies by definition result in large amounts of data. The analysis as well as interpretation of the enormous volumes of proteomic data to effectively use their content remains an unsolved challenge, particularly for

MS-based proteomics. The development of tools for the integration of different experimental approaches enabling analyses of such proteomic data sets using statistical principles is an important task for the future (Aebersold and Mann 2003).

MS-based peptidomics technologies in combination with sophisticated bioinformatics tools have great potential for the discovery of novel biologically relevant neuro-peptides. It is likely that a considerable number of

► neuropeptides are still to be discovered. The human genome contains ~550 genes belonging to the G protein-coupled receptor class of proteins. For 25% of these the natural endogenous ligands remain elusive until today, and novel neuropeptides are very plausible candidates. Improved peptidomics approaches and technologies may therefore identify novel biologically important neuropeptides (Svensson et al. 2007).

MALDI-IMS has become an important tool for assessing the spatial distribution of molecular species in brain tissue sections and for the elucidation of molecular signatures indicative of disease progression and drug treatment. The technique allows simultaneous measurements of hundreds of different molecules in tissue specimens without disrupting the integrity of samples. It can trace the distribution of pharmaceuticals and their various metabolites in the brains of dosed animals and can be successfully applied to monitor the changes in the proteome organization upon drug application. Functional information obtained in MALDI-IMS studies can be correlated with proteomic profiles and routine immunohistochemical staining, thereby providing an in-depth comprehension of molecular mechanisms underlying health and disease (Cornett et al. 2007; Stoeckli et al. 2001).

A catalog of the complete neuroproteome will propose new directions to understand brain function. Differential proteomics permits correlations to be drawn between the range of proteins produced by a cell or tissue and the initiation or progression of a disease state. It permits the discovery of new protein markers for diagnostic purposes and the study of novel molecular targets for brain drug discovery. The markers identified may have a wide range of potential applications, such as clinical diagnostic and prognostic tools. Proteomic information has superior functional value and can generate knowledge of cellular protein networks. Proteomics technologies are progressing fast and their increasing usage as a functional high-throughput approach is adding to vital biological findings in areas not accessible to genomics studies.

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