High Throughput Sequencing and Proteomics to Identify Immunogenic Proteins of a New Pathogen: The Dirty Genome Approach

Abstract

Accurate diagnosis and proper monitoring of cancer patients remain a key obstacle for successful cancer treatment and prevention. Therein comes the need for biomarker discovery, which is crucial to the current oncological and other clinical practices having the potential to impact the diagnosis and prognosis. In fact, most of the biomarkers have been discovered utilizing the proteomicsbased approaches. Although high-throughput mass spectrometry-based proteomic approaches like SILAC, 2D-DIGE, and iTRAQ are filling up the pitfalls of the conventional techniques, still serum proteomics importunately poses hurdle in overcoming a wide range of protein concentrations, and also the availability of patient tissue samples is a limitation for the biomarker discovery. Thus, researchers have looked for alternatives, and profiling of candidate biomarkers through tissue culture of tumor cell lines comes up as a promising option. It is a rich source of tumor cell-derived proteins, thereby, representing a wide array of potential biomarkers. Interestingly, most of the clinical biomarkers in use today (CA 125, CA 15.3, CA 19.9, and PSA) were discovered through tissue culture-based system and tissue extracts. This paper tries to emphasize the tissue culture-based discovery of candidate biomarkers through various mass spectrometry-based proteomic approaches.

Differential Neuroproteomic and Systems Biology Analysis of Spinal Cord Injury

“…Proteomic methods are the most powerful techniques that can aid in the discovery of novel biomarker candidates; it utilizes extensive sample procedure and Data Dependent Acquisition to follow disease-specific proteins (identity and concentration). It facilitates the identification of all differentially expressed proteins at any given time in a proteome and correlates these patterns with the healthy ones during disease progression (13). It has been used to study protein expression at the molecular level with a dynamic perspective that helps to understand the mechanisms of the disease (14).…”

“…It has been used to study protein expression at the molecular level with a dynamic perspective that helps to understand the mechanisms of the disease (14). The complexity, immense size and variability of the neuroproteome, extensive protein-protein and proteinlipid interactions, all limit the ability of the mass spectrometer to detect all peptides/proteins contained within the sample; further, some peptides/proteins are extraordinarily resistant to isolation (15,16). Therefore, the analytical methods for the separation and identification of peptides/proteins must manage all of these issues by using separation techniques combined with the powerful new mass spectrometry technologies to expand the scope of protein identification, quantitation and characterization.…”

Proteomics and peptidomics: moving toward precision medicine in urological malignancies

“…Two-dimensional gel electrophoresis mass spectrometry (2DE-MS) used to be a traditional protein profiling strategy [23]. To correct for poor throughput of 2DE-MS, two-dimensional difference gel electrophoresis mass spectrometry (2D-DIGE-MS) was developed [24]. Capillary electrophoresis-mass spectrometry (CE-MS) is another protein profiling technique.…”

Implementation of Proteomics for Cancer Research: Past, Present, and Future

“…Reagents: There is a growing need in the field of proteomics for high-quality, well-characterized, and standard reagents that can improve the specificity and reproducibility of proteomic technologies (Paul et al, 2013). One widely used reagent in proteomic research is (Jacquemier et al, 2005;Castronovo et al, 2007;Gonçalves et al, 2008;Montazery-Kordy et al, 2008;Fan et al, 2010;Hooshmand et al, 2013) Fibrinogen A Fragment; S100A9; 21-protein signature; GCDFP-15 AAG; PARK7; S10A7; GDIR; DDAH1; DDAH2; Versican core protein precursor; AGR2; Ubiquitin; Ferritin light chain; CD13, OSF-2; RS/DJ-1 autoantibody Esophageal cancer (Fujita et al, 2006;Hatakeyama et al, 2006;Jazii, Najafi et al, 2006;Uemura et al, 2009;Moghanibashi, Jazii et al 2012;Moghanibashi et al, 2013) Peroxiredoxin VI autoantibody; Alpha -actinin 4; 67 ku laminin receptor; Rho GDP dissociation inhibitor 2; alpha-enolase ; Lamin A/C; nucleodisediphosphate kinase A Gastric cancer (Bai et al, 2011;Kočevar et al, 2012;Sousa et al, 2012;Karimi et al, 2014) α1-antitrypsin precursor; Pepsinogen C; Cathepsin B; MAWBP; Vimentin; galectin 1; DEAD-box protein 48 autoantibody; hnRNP A2/B1…”