Current Proteomics - Volume 7, Issue 2, 2010

Identifying the disease-related genes of important human diseases from genomics can provide valuable clues for the discovery of potential therapeutic targets. However, discovering the disease-related genes by traditional biological experiments methods is usually laborious and time-consuming. Therefore, it is necessary to develop a powerful computational approach to improve the effectiveness of disease-related gene identification. In this study, multiple sequence features of known disease-related genes in 62 kinds of diseases were extracted, and then the selected features were further optimized and analyzed for disease-related genes prediction. The leave-one-out cross-validation tests demonstrated that 55% of the disease-related genes can be ranked within the top 10 of the prediction results, which confirmed the reliability of this multi-feature fusion approach.

The ability to combine a selective capture strategy with on chip MALDI-TOF analysis allows for rapid, sensitive analysis of a variety of different analytes. In this overview a series of applications of capture enhanced laser desorption ionization time of flight (CELDI-TOF) mass spectrometry are described. The key feature of the assay is an off-chip capture step that utilizes high affinity bacterial binding proteins to capture a selected ligand. This allows large volumes of sample to be used and provides for a concentration step prior to transfer to a gold chip for traditional mass spectral analysis. The approach can also be adapted to utilize specific antibody as the basis of the capture step. The direct and indirect CELDI-TOF assays are rapid, reproducible and can be a valuable proteomic tool for analysis of low abundance molecules present in complex mixtures like blood plasma.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and peptide mass fingerprint (PMF) are one of the most powerful combined tool for protein identification. Frequently it is the method of choice as it is faster and less expensive than protein sequencing. However, sometimes PMF only allows the identification of a subset of peptides, requiring further MS/MS or Edman degradation to attain unequivocal protein identification. This work describes an approach that combines several matrices to improve the fidelity of the protein identification by MALDI-TOF MS. The matrices used for sample preparation are 2,5-dihydroxybenzoic acid (DHB), 2,6- dihydroxyacetophenone (DHAP), CHCA and 2,4,6-Trihydroxy acetophenone (THAP). We have also developed an algorithm called Matrix Assisted Protein Identification (MAPI) that processes Mascot datasets derived from each one of the four matrix preparations, integrating them and achieving a significant improvement in protein identification.

This review focuses on the current status of proteomic techniques that can be specifically applied to heart. Proteomics allows us to study alterations in protein expression in diseased hearts and leads us to develop new diagnostics and therapeutic parameters. The availability of the high resolution capacity of 2-DE can be successfully used to separate proteins in the first dimension according to their charge (isoelectric point) under denaturing conditions followed by their separation according to their molecular mass by SDS PAGE. The separated proteins are then visualized at high sensitivity with SYPRO dyes, especially SYPRO Ruby which is the most appropriate post-electrophoretic stain because of its compatibility for subsequent MS analysis. After the generation of a large protein dataset, they are organized using bioinformatics. Even though proteomics techniques have undergone substantial improvement, it remains a problem to identify phosphorylated proteins, which may be used for early disease detection. The proteomics analysis discussed in this review can be used for drug discovery, development of therapeutic modalities for cardiovascular diseases and the design of clinical trials. Proteins play more dynamic roles compared to DNA and RNA since most biological functions are regulated by protein-protein interactions. Protein-protein interaction mapping is crucial for many degenerative diseases and proteomics play an important role in understanding the molecular mechanisms of cellular functions. Though advancements in equipmentation have been made, it is unlikely to gain although MS is a powerful and evolving technique, the cost of running a sample needs to be considered. For example, regarding the cost of labeling, iTRAC runs about $400/sample and as many as 30 biological samples may be required to reach statistical significance in patient samples. Extensive time is also needed on a MS machine to run a fractionated sample on the order of days (times the number of samples). Once large datasets are generated, a bioinformaticist is required to align and analyze data from multiple treatment groups. An additional limitation is that the protein and splice variants have to be characterized to be identified by search engines. A number of predicted proteins may be identified with limited commercial resources available to follow up on such targets. Finally, though there have been advances in mass spectrometry equipment such as the Fouriertransform ion cyclotron resonance MS that generate higher sensitivity and dynamic range, there is a lack of standardization of protocols from sample collection and processing along the pipeline to data analysis. Unlike genomic data there is no community standard for database sharing. Although there are limitations to the technique, proteomics is likely to have great impact on drug discovery and clinical trial design leading to the development of niche personalized medicine. There is a definite need for early disease detection with appropriate biomarkers and proteomics are the tool to fulfill the requirement. For example, a routine, specific and sensitive serum proteomic pattern for cardiovascular diseases would be useful to clinicians for the early detection of diseases. In this regard, a low-resolution SELDI-TOF proteomic profile could be extremely useful. Compared to mRNAs, proteins are subjected to posttranslational modifications like phosphorylation, glycosylation and cleavage, and thus genomics are likely to miss the correct targets. This is of utmost importance for disease-related proteomics to become an essential component of personalized medicine system, which has great promise for the improvement of disease evaluation and patient care.

Protein misfolding and aggregation into nano-scale ordered amyloid fibrils is one of the most exciting field of Biochemistry. The correct folding is essential for function of proteins and thus, existence of living systems. An effective protein quality control in living system recognizes and degrades misfolded protein quickly and prevents aggregation. Dysfunction or impaired function of this quality control system may result in deposition of protein aggregates. A number of diseases have been linked to deposition of insoluble amyloid aggregates in the brain or other organs. Recently, researchers have reported nano-based therapeutic options and early detection techniques for amyloid diseases. Moreover, thermodynamics of misfolding and aggregation has provided important clue for drug development. Researchers across the globe are working on the development of therapeutic strategies to combat protein aggregation diseases. In the current review, we aim to bring together recent developments about protein misfolding, aggregation and therapeutics against protein misfolding/ aggregation diseases.

Global demand on energy combined with dwindling fuel reserves has led to record fuel prices around the world and resulted in a concerted effort to identify alternate and sustainable fuel supplies. One such alternative is to produce cellulosic biofuels through the conversion of complex sugars found in plant cell walls (plant biomass) into fuels. While the synthesis of cellulosic biofuels is currently an achievable technology, associated production costs due to biomass recalcitrance, sugar composition and ineffectual conversion make their production impractical. In order to overcome these issues significant research will be required in areas ranging from plant cell wall biosynthesis, microbial host metabolism and tolerance that enable targeted engineering of these systems. Proteomics will play a central role in implementing this strategy by identifying new targets for biofuel crop engineering, analyzing engineered biochemical pathways and characterizing plant cell wall biosynthesis. This review will examine the current use of proteomics to fast-track cellulosic biofuel production and evaluate the potential of this technology to provide significant breakthroughs in this area.

Silicon (Si) is a micronutrient. Its amount has been found to vary from plant to plant. Grasses contain much higher Si than Arabidopsis. Interestingly, Si in plants has been shown to enhance their tolerance against various abiotic and biotic stresses. Silicon induced resistance in rice against pathogenic fungi Magnaporthe grisea and Rhizoctonia solini have been well demonstrated. In addition, Si also plays an important role in providing tolerance to heavy metal toxicity and water stress. Systematic identification and characterization of Si-responsive genes responsive genes and proteins will help in better understanding the underlying mechanism of Si-induced tolerance in plants. High-throughput technologies, such as transcriptomics and proteomics, have tremendous potential in establishing the Si-responsive genes and proteins network in order to design next generation crop plants. Here, we will focus on the role of Si in conferring tolerance in plants against various environmental stressors. We highlight the importance of genomics and potential of proteomics and metabolomics in investigating Si responses in plants and discuss its suitability in crop improvement.

Mass spectrometry has become a routine instrument to identify proteins and peptides from simple or complex samples. Although identification can be confidently determined from a single experiment, quantitation requires multiple replicates and careful analysis. Alternatively, stable isotopes can be used to obtain relative quantitation of proteins and peptides from fewer replicates. Conventionally, half of a sample is labeled with stable isotope and mixed with the other half of unlabeled sample. The mixed sample is analyzed by mass spectrometry and because the stable isotope does not change the chemical properties of the peptide, the intensities of the unlabeled and labeled peptide can be directly compared. Absolute quantitation is obtained by adding a known amount of stable isotope labeled peptide or protein and comparing to an unlabeled counterpart. Stable isotope labeling methodologies can be divided into three categories: Chemical, enzymatic and metabolic. Here we provide an up-to-date review comparing the benefits and drawbacks of all three stable isotope labeling methodologies and briefly describe quantitation software solutions. In addition to quantitation, stable isotopes have also been used to identify post-translational modifications in proteins, identify components of DNA-protein and protein-protein complexes and to distinguish background contaminants from experimental results. Finally, we describe how fragmentation patterns from stable isotope labeled peptide and unlabeled peptides can improve peptide and protein identification and validation.

This is with reference to the article entitled, “Protein Identification in Sub Proteome Fractions of Breast Cancer Cells by OFFGEL-IEF and iTRAQ Labeling”, by K.H. Chandramouli, Pushpa Agrawal, K.N. Thimmaiah, published in Current Proteomics, April 2009, Vol. 6, No. 1, pp. 43-50. Some of the content of the manuscript has been mistakenly copied and there were absolutely no intentions to copy the text. This oversight has been deeply regretted by the authors.