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Radiobiology and Hadron Therapy
Published in Manjit Dosanjh, Jacques Bernier, Advances in Particle Therapy, 2018
Eleanor A. Blakely, Manjit Dosanjh
It is not surprising that the novel clustered characteristics of particle-induced DNA damage triggers a unique set of DDR post-lesion formation-signaling cascades and cell cycle arrest and recruitment of DNA repair factors compared to X-ray damage. A systematic study to decipher acute signaling events induced by different radiation qualities using high-resolution mass spectrometry-based proteomics has recently been published by Winter et al. (2017). Two hours after exposure of stable isotope labeling by amino acids in cell culture (SILAC)-labeled human lung adenocarcinoma A549 cells to X-rays, protons or carbon ion showed extensive alterations of the phosphorylation status despite protein expression remaining largely unchanged. Phosphorylation events were similar for proton and carbon irradiation; however, a distinctly different number of sites responded differentially for X-rays. The results were also validated with targeted spike-in experiments. This information will provide unique insight into the differential regulation of phosphorylation sites for radiations of different quality and how they may be further optimised for cancer radiotherapy.
From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology
Published in Shaker A. Mousa, Raj Bawa, Gerald F. Audette, The Road from Nanomedicine to Precision Medicine, 2020
Maria Eugenia Gallo Cantafio, Katia Grillone, Daniele Caracciolo, Francesca Scionti, Mariamena Arbitrio, Vito Barbieri, Licia Pensabene, Pietro Hiram Guzzi, Maria Teresa Di Martino
Proteomics is the study of the entire set of proteins in any given cell, including the set of all protein isoforms and modifications, the interactions between them, the structural description of proteins, and their higher-order complexes [65, 66]. Therefore, proteomics is the next step to study biological systems because proteins are responsible for most cellular processes; their analysis would more accurately reflect cellular status to determine the mechanism that underlies disease initiation, progression, and dissemination [67, 68]. To analyze the complex protein mixtures with higher sensitivity, the main technology presently in use is mass spectrometry (MS) [69], combined with liquid chromatography or matrix-assisted laser desorption ionization (MALDI-TOF/TOF) [68, 70, 71]. However, new methods were recently developed for quantitative proteomic such as isotope-coded affinity tag (ICAT) labeling, stable isotope labeling with amino acids in cell culture (SILAC), and isobaric tag for relative and absolute quantitation (iTRAQ) [72–74]. X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy are two major high-throughput techniques that provide 3D structure of proteins [75]. These technologies are adopted in cancer research to generate data on (i) differential protein expression levels, (ii) protein–gene expression correlation, (iii) differential protein expression comparisons between different cancer phenotypes and subtypes, and (iv) the associations of protein expression with survival prognosis in cancer patients. Proteomic data are collected in several databases including PRIDE (proteomics identification database) and Global Proteome Machine [76]. Other databases such as KEGG, IPA (Ingenuity Pathway Analysis), Pathway Knowledge Base Reactome, or BioCarta include comprehensive data regarding metabolism, signaling, and protein interactions [77–79]. Global proteomic profile is increasingly being carried out in both cancer cell lines and patient-derived samples to provide information that are useful for cancer type classification [80–83] and drug sensitivity/resistance prediction [84, 85].
From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology
Published in Shaker A. Mousa, Raj Bawa, Gerald F. Audette, The Road from Nanomedicine to Precision Medicine, 2019
Maria Eugenia Gallo Cantafio, Katia Grillone, Daniele Caracciolo, Francesca Scionti, Mariamena Arbitrio, Vito Barbieri, Licia Pensabene, Pietro Hiram Guzzi, Maria Teresa Di Martino
Proteomics is the study of the entire set of proteins in any given cell, including the set of all protein isoforms and modifications, the interactions between them, the structural description of proteins, and their higher-order complexes [65, 66]. Therefore, proteomics is the next step to study biological systems because proteins are responsible for most cellular processes; their analysis would more accurately reflect cellular status to determine the mechanism that underlies disease initiation, progression, and dissemination [67, 68]. To analyze the complex protein mixtures with higher sensitivity, the main technology presently in use is mass spectrometry (MS) [69], combined with liquid chromatography or matrix-assisted laser desorption ionization (MALDI-TOF/TOF) [68, 70, 71]. However, new methods were recently developed for quantitative proteomic such as isotope-coded affinity tag (ICAT) labeling, stable isotope labeling with amino acids in cell culture (SILAC), and isobaric tag for relative and absolute quantitation (iTRAQ) [72–74]. X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy are two major high-throughput techniques that provide 3D structure of proteins [75]. These technologies are adopted in cancer research to generate data on (i) differential protein expression levels, (ii) protein–gene expression correlation, (iii) differential protein expression comparisons between different cancer phenotypes and subtypes, and (iv) the associations of protein expression with survival prognosis in cancer patients. Proteomic data are collected in several databases including PRIDE (proteomics identification database) and Global Proteome Machine [76]. Other databases such as KEGG, IPA (Ingenuity Pathway Analysis), Pathway Knowledge Base Reactome, or BioCarta include comprehensive data regarding metabolism, signaling, and protein interactions [77–79]. Global proteomic profile is increasingly being carried out in both cancer cell lines and patient-derived samples to provide information that are useful for cancer type classification [80–83] and drug sensitivity/resistance prediction [84, 85].
Omics to address the opportunities and challenges of nanotechnology in agriculture
Published in Critical Reviews in Environmental Science and Technology, 2021
Sanghamitra Majumdar, Arturo A. Keller
Electrospray ionization (ESI) and Matrix-Assisted Laser Desorption Ionization (MALDI) are the most common platforms used to ionize peptides and proteins. The precise molecular mass of the resulting ions is then analyzed using mass analyzers, such as ion trap, quadrupole, Orbitrap, time-of-flight (TOF), and Fourier transform ion cyclotron resonance (FTICR), which are often used in tandem to achieve higher degrees of ion separation (Lai et al., 2012). In untargeted proteomics using LC-MS/MS, data dependent acquisition is performed, where the highest abundance peptide ions from full MS scans are selected for MS/MS. However, this may generate datasets skewed toward the identification of relatively high abundance proteins, thereby masking and excluding the low abundance proteins from quantification (Hart-Smith et al., 2017). Several labeling techniques such as isobaric Tags for Relative and Absolute Quantitation (iTRAQ) and Stable Isotope Labeling by Amino acids in Cell culture (SILAC) are also available which can help to reduce errors introduced during measurement conditions. However, untargeted proteomic analysis requires extensive data processing and is currently challenged by incomplete and limited nature of plant genomic and proteomic databases. In recent times, the use of label-free shotgun proteomic techniques have become increasingly popular, as they do not restrict the number of proteins identified compared to gel-based methods (Majumdar et al., 2015, 2019; Mirzajani et al., 2014; Vannini et al., 2014; Verano-Braga et al., 2014). However, gel-free methods have several drawbacks, such as masking of low abundant peptides and unavailability of protein database for all species.