Studies of quantitative analysis of protein expression in Saccharomyces cerevisiae

Spot quantification in two dimensional gel electrophoresis image analysis: comparison of different approaches and presentation of a novel compound fitting algorithm

Background

Various computer-based methods exist for the detection and quantification of protein spots in two dimensional gel electrophoresis images. Area-based methods are commonly used for spot quantification: an area is assigned to each spot and the sum of the pixel intensities in that area, the so-called volume, is used a measure for spot signal. Other methods use the optical density, i.e. the intensity of the most intense pixel of a spot, or calculate the volume from the parameters of a fitted function.

Results

In this study we compare the performance of different spot quantification methods using synthetic and real data. We propose a ready-to-use algorithm for spot detection and quantification that uses fitting of two dimensional Gaussian function curves for the extraction of data from two dimensional gel electrophoresis (2-DE) images. The algorithm implements fitting using logical compounds and is computationally efficient. The applicability of the compound fitting algorithm was evaluated for various simulated data and compared with other quantification approaches. We provide evidence that even if an incorrect bell-shaped function is used, the fitting method is superior to other approaches, especially when spots overlap. Finally, we validated the method with experimental data of urea-based 2-DE of Aβ peptides andre-analyzed published data sets. Our methods showed higher precision and accuracy than other approaches when applied to exposure time series and standard gels.

Conclusion

Compound fitting as a quantification method for 2-DE spots shows several advantages over other approaches and could be combined with various spot detection methods.

The algorithm was scripted in MATLAB (Mathworks) and is available as a supplemental file.

The role of phenotypic plasticity on the proteome differences between two sympatric marine snail ecotypes adapted to distinct micro-habitats

Background

The role of phenotypic plasticity is increasingly being recognized in the field of evolutionary studies. In this paper we look at the role of genetic determination versus plastic response by comparing the protein expression profiles between two sympatric ecotypes adapted to different shore levels and habitats using two-dimensional protein maps.

Results

We compared qualitative and quantitative differences in protein expression between pools of both ecotypes from different environments (field and laboratory conditions). The results suggested that ecotype differences may affect about 7% of the proteome in agreement with previous studies, and moreover these differences are basically insensitive to environmental changes. Thus, observed differences between wild ecotypes can be mainly attributed to genetic factors rather than phenotypic plasticity.

Conclusions

These results confirm the mechanism of adaptation already proposed in this species and a minor role of phenotypic plasticity in this ecological speciation process. In addition, this study provides a number of interesting protein spots potentially involved in adaptation, and therefore candidates for a future identification.

Computational methods for analysis of two-dimensional gels

Two-dimensional gel electrophoresis (2D gels) is an essential quantitative proteomics technique that is frequently used to study differences between samples of clinical relevance. Although considered to have a low throughput, 2D gels can separate thousands of proteins in one gel, making it a good complementary method to MS-based protein quantification. The main drawback of the technique is the tendency of large and hydrophobic proteins such as membrane proteins to precipitate in the isoelectric focusing step. Furthermore, tests using different programs with distinct algorithms for 2D-gel analysis have shown inconsistent ratio values. The aim here is therefore to provide a discussion of algorithms described for the analysis of 2D gels.

Identification of secreted glycoproteins of human prostate and bladder stromal cells by comparative quantitative proteomics

BACKGROUND

Functional development of the prostate is governed by stromal mesenchyme induction and epithelial response. Stromal/epithelial signaling can be mediated through direct cell-cell contact and diffusible factors and their cell surface receptors. These inducers are likely secreted or membrane-associated extracellular proteins. Given the importance of intercellular communication, it is possible that diseases like cancer could arise from a loss of this communication. One approach to gain a molecular understanding of stromal cells is to identify, as a first step, secreted stromal signaling factors. We proposed to do this by comparative analysis between bladder and prostate.

METHODS

Secreted proteins were identified from cultured normal prostate and bladder stromal mesenchyme cells by glycopeptide-capture method followed by mass spectrometry. Differences in protein abundance between prostate and bladder were quantified from calculated peptide ion current area (PICA) followed by Western validation. Functional and pathway analyses of the proteins were carried out by Gene Ontology (GO) and Teranode software.

RESULTS

This analysis produced a list of 116 prostate and 84 bladder secreted glycoproteins with ProteinProphet probability scores > or =0.9. Stromal proteins upregulated in the prostate include cathepsin L, follistatin-related protein, neuroendocrine convertase, tumor necrosis factor receptor, and others that are known to be involved in signal transduction, extracellular matrix interaction, differentiation and transport.

CONCLUSIONS

We have identified a number of potential proteins for stromal signaling and bladder or prostate differentiation program. The prostate stromal/epithelial signaling may be accomplished through activation of the ECM-receptor interaction, complement and coagulation cascades, focal adhesion and cell adhesion pathways.

ICAT-based comparative proteomic analysis of non-replicating persistent Mycobacterium tuberculosis

The non-replicating persistence (NRP) phenotype of Mycobacterium tuberculosis (NRP-TB) is assumed to be responsible for the maintenance of latent infection and the requirement of a long treatment duration for active tuberculosis. Isotope coded affinity tag-based proteomic analysis was used for the determination of the relative expression of large numbers of M. tuberculosis proteins during oxygen self-depletion under controlled conditions in a multi-chambered fermentor. Expression of the alpha-crystallin homolog protein, acr, was monitored and quantified to confirm entry into NRP. Relative expression of 586 and 628 proteins was determined in log phase vs. early stage NRP (NRP-1) and log phase vs. later stage NRP (NRP-2), respectively. Relative to expression in log phase and using an abundance ratio of +/-2.0 as a cutoff, 6.5% and 20.4% of proteins were found to be upregulated in NRP-1 and NRP-2, respectively while 20.3% and 13.4% were downregulated, respectively. Functional profiling revealed that 42.1%/39.8% of upregulated proteins and 41.2%/45.2% of downregulated proteins in NRP-1/NRP-2, respectively, were involved in small molecule metabolism. Among those proteins the highest proportions of 37.5% in NRP-1 were involved with degradation and of 45.1% in NRP-2 with energy metabolism. These results suggest distinct protein expression profiles in NRP-1 and NRP-2.

Molecular biologist's guide to proteomics

The emergence of proteomics, the large-scale analysis of proteins, has been inspired by the realization that the final product of a gene is inherently more complex and closer to function than the gene itself. Shortfalls in the ability of bioinformatics to predict both the existence and function of genes have also illustrated the need for protein analysis. Moreover, only through the study of proteins can posttranslational modifications be determined, which can profoundly affect protein function. Proteomics has been enabled by the accumulation of both DNA and protein sequence databases, improvements in mass spectrometry, and the development of computer algorithms for database searching. In this review, we describe why proteomics is important, how it is conducted, and how it can be applied to complement other existing technologies. We conclude that currently, the most practical application of proteomics is the analysis of target proteins as opposed to entire proteomes. This type of proteomics, referred to as functional proteomics, is always driven by a specific biological question. In this way, protein identification and characterization has a meaningful outcome. We discuss some of the advantages of a functional proteomics approach and provide examples of how different methodologies can be utilized to address a wide variety of biological problems.

The scaled volume as an image analysis variable for detecting changes in protein expression levels by silver stain

A new variable for measuring the relative intensities of silver stained protein spots on two-dimensional gels is described. The scaled volume (SV) more accurately measures the intensity of protein spots and accounts for differences frequently encountered when trying to compare two gels than other variables such as relative volume ratio, optical density, or relative optical density. The SV scales the signal of interest by the noise (gel background) with secondary signals removed (spots not of interest, e.g., technical artifacts). The SV of spot intensities offers a better dynamic response to protein amount for the model proteins studied here. Depending on the quantity of protein loaded onto gels, we have observed a coefficient of variation range of 0.2 to 1.3. We also observe that the SV silver stain response follows a characteristic exponential profile for different proteins.

Postelectrophoretic staining of proteins separated by two-dimensional gel electrophoresis using SYPRO dyes

While the classical silver stain has been the method of choice for high sensitivity protein visualization on two-dimensional gel electrophoresis (2-D PAGE), post-electrophoretic fluorescent staining with the SYPRO group of dyes has emerged to challenge silver staining for proteome analysis. The latter offers improved sensitivity, higher dynamic range and easy handling. However, most of the published data were derived from analysis of 1-D gel separations. In this work, we have focused on three commercially available fluorescent dyes, SYPRO Ruby, SYPRO Orange and SYPRO Red (Molecular Probes, Eugene, OR, USA) and studied their sensitivity and dynamic range on 2-D PAGE. The use of a multiwavelength fluorescent scanner to image 2-D protein profiles visualized with fluorescent staining is discussed, and a detailed comparison with analysis by silver staining is also provided. These results demonstrate the advantages of using SYPRO dyes, which are in agreement with the literature based on 1-D gel electrophoresis, and give a more realistic understanding of the performance of these fluorescent dyes with 2-D PAGE.