Advances in proteomics analytical techniques

   Proteins are fundamental components of cells which mediate many essential biological processes. Proteomics is a rapidly growing field for the study of proteome, the protein complement expressed by the genome of an organism or cell type. The large-scale analysis of proteins leads to a more comprehensive view of molecular and cellular pathways that improves the overall understanding of the complex processes supporting the living systems. The analysis of proteome is significantly challenging due to high dynamic range and difficulties in assessment of low abundance proteins and the absence of efficient purification and identification techniques. A variety of methods have been utilized for protein studies including gel-based techniques, protein microarrays, mass spectrometry-based approaches such as MALDI and SELDI, high and ultra-performance liquid chromatography and fourier transform ion cyclotron resonance mass spectrometry. NMR spectroscopy and X-Ray crystallography methods are also used for structural study of proteins. This review aims to give a brief overview of some of the above techniques and their most recent advances. We also introduce Proteominer, a recent protein enrichment technology for the exploration of the entire proteome conten

Pathway and Network Analysis in Primary Open Angle Glaucoma

Glaucoma, a group of multifactor ocular diseases, is the second leading cause of blindness worldwide. Primary open angle (POA) is the most common type of glaucoma, characterized by progressive optic nerve degeneration. Numerous genes and proteins have been revealed to be associated with POAG, but the pathologic mechanisms of the disease are still poorly understood. Proteomics, the collective study of proteins in an organism at a given condition, has extensively been used for the high-throughput identification of proteins related to POAG. A significant obstacle in proteomics studies is the data variability which makes it hard to interpret the results. Pathway analysis and network topological information can help address the challenge and provide a greater appreciation of the disease mechanism and progression. The purpose of this paper is to determine POAG biological and network information to further understand the mechanisms associated with POAG. PANTHER classification system was used, including classification with gene ontology, protein class and pathway. 474 gene/protein IDs were extracted from previous proteomic studies. Among pathways found by PANTHER classification, apoptosis signaling pathway was the most significant pathway (with the p-value of 5.54E-12). Other PANTHER categories results demonstrated that developmental processes, receptor binding, extracellular region and extracellular matrix proteins were the most significant biological process, molecular function, cellular component and protein class respectively. Pathway analysis aids to find probable mechanisms involved in POAG. A network analysis on proteins was also performed using STRING database and cytoscape software. From network analysis, candidate biomarkers for the disease were introduced.

Serum-based metabolic alterations in patients with papillary thyroid carcinoma unveiled by non-targeted 1H-NMR metabolomics approach

Objective(s): As the most prevalent endocrine system malignancy, papillary thyroid carcinoma had a very fast rising incidence in recent years for unknown reasons besides the fact that the current methods in thyroid cancer diagnosis still hold some limitations. Therefore, the aim of this study was to improve the potential molecular markers for diagnosis of benign and malignant thyroid nodules to prevent unnecessary surgeries for benign tumors. Materials and Methods: In this study, 1H-NMR metabolomics platform was used to seek the discriminating serum metabolites in malignant papillary thyroid carcinoma (PTC) compared to benign multinodular goiter (MNG) and healthy subjects and also to better understand the disease mechanisms using bioinformatics analysis. Multivariate statistical analysis showed that PTC and MNG samples could be successfully discriminated in PCA and OPLS-DA score plots. Results: Significant metabolites that differentiated malignant and benign thyroid lesions included citrate, acetylcarnitine, glutamine, homoserine, glutathione, kynurenine, nicotinic acid, hippurate, tyrosine, tryptophan, β-alanine, and xanthine. The significant metabolites in the PTC group compared to healthy subjects also included scyllo- and myo-inositol, tryptophan, propionate, lactate, homocysteine, 3-methyl glutaric acid, asparagine, aspartate, choline, and acetamide. The metabolite sets enrichment analysis demonstrated that aspartate metabolism and urea cycle were the most important pathways in papillary thyroid cancer progression. Conclusion: The study results demonstrated that serum metabolic fingerprinting could serve as a viable method for differentiating various thyroid lesions and for proposing novel potential markers for thyroid cancers. Obviously, further studies are needed for the validation of the results

Study of PKA binding sites in cAMP-signaling pathway using structural protein-protein interaction networks

Backgroud: Protein-protein interaction, plays a key role in signal transduction in signaling pathways. Different approaches are used for prediction of these interactions including experimental and computational approaches. In conventional node-edge protein-protein interaction networks, we can only see which proteins interact but ‘structural networks’ show us how these proteins interact which can give us so much information about the network. Structural networks help us understand the molecular basis of cellular functions and regulatory mechanisms in signaling pathways. In this study, we aimed to construct a structural network for a part of cAMP signaling pathway which has PKA (cAMP-dependent protein kinase catalytic subunit alpha) as the hub. Materials and Methods: A part of cAMP signaling pathway was selected from kegg database and interactions of PKA as hub protein with some of its partners were achieved using Hex8.00 software. The interfaces of the resulted complexes were predicted by KFC2 server. Results: Hex8.00, as a docking software, gave us the complexes from the interaction of PKA with 15 proteins of its partners. For each complex, the KFC2 server gave us the amino acid composition of the interfaces. Using this amino acid composition, we draw a structural network which shows the binding sites on PKA surface. Conclusion: We have constructed a structural network for cAMP signaling pathway which shows how PKA interacts with its partners. This network can be used for understanding the mechanisms of signal transduction and also for drug design purposes

suppl._Figure_1 – Supplemental material for Decreased apolipoprotein A4 and increased complement component 3 as potential markers for papillary thyroid carcinoma: A proteomic study

<p>Supplemental material, suppl._Figure_1 for Decreased apolipoprotein A4 and increased complement component 3 as potential markers for papillary thyroid carcinoma: A proteomic study by Reyhaneh Farrokhi Yekta, Afsaneh Arefi Oskouie, Mostafa Rezaei Tavirani, Mohammad R. Mohajeri-Tehrani and Ahmad R. Soroush in The International Journal of Biological Markers</p

suppl._Figure_2 – Supplemental material for Decreased apolipoprotein A4 and increased complement component 3 as potential markers for papillary thyroid carcinoma: A proteomic study

<p>Supplemental material, suppl._Figure_2 for Decreased apolipoprotein A4 and increased complement component 3 as potential markers for papillary thyroid carcinoma: A proteomic study by Reyhaneh Farrokhi Yekta, Afsaneh Arefi Oskouie, Mostafa Rezaei Tavirani, Mohammad R. Mohajeri-Tehrani and Ahmad R. Soroush in The International Journal of Biological Markers</p

suppl._Table_1 – Supplemental material for Decreased apolipoprotein A4 and increased complement component 3 as potential markers for papillary thyroid carcinoma: A proteomic study

<p>Supplemental material, suppl._Table_1 for Decreased apolipoprotein A4 and increased complement component 3 as potential markers for papillary thyroid carcinoma: A proteomic study by Reyhaneh Farrokhi Yekta, Afsaneh Arefi Oskouie, Mostafa Rezaei Tavirani, Mohammad R. Mohajeri-Tehrani and Ahmad R. Soroush in The International Journal of Biological Markers</p