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      Clinically Relevant Post-Translational Modification Analyses—Maturing Workflows and Bioinformatics Tools

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          Abstract

          Post-translational modifications (PTMs) can occur soon after translation or at any stage in the lifecycle of a given protein, and they may help regulate protein folding, stability, cellular localisation, activity, or the interactions proteins have with other proteins or biomolecular species. PTMs are crucial to our functional understanding of biology, and new quantitative mass spectrometry (MS) and bioinformatics workflows are maturing both in labelled multiplexed and label-free techniques, offering increasing coverage and new opportunities to study human health and disease. Techniques such as Data Independent Acquisition (DIA) are emerging as promising approaches due to their re-mining capability. Many bioinformatics tools have been developed to support the analysis of PTMs by mass spectrometry, from prediction and identifying PTM site assignment, open searches enabling better mining of unassigned mass spectra—many of which likely harbour PTMs—through to understanding PTM associations and interactions. The remaining challenge lies in extracting functional information from clinically relevant PTM studies. This review focuses on canvassing the options and progress of PTM analysis for large quantitative studies, from choosing the platform, through to data analysis, with an emphasis on clinically relevant samples such as plasma and other body fluids, and well-established tools and options for data interpretation.

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          Most cited references148

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          In silico prediction of protein-protein interactions in human macrophages

          Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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            Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer.

            Despite the success of tyrosine kinase-based cancer therapeutics, for most solid tumors the tyrosine kinases that drive disease remain unknown, limiting our ability to identify drug targets and predict response. Here we present the first large-scale survey of tyrosine kinase activity in lung cancer. Using a phosphoproteomic approach, we characterize tyrosine kinase signaling across 41 non-small cell lung cancer (NSCLC) cell lines and over 150 NSCLC tumors. Profiles of phosphotyrosine signaling are generated and analyzed to identify known oncogenic kinases such as EGFR and c-Met as well as novel ALK and ROS fusion proteins. Other activated tyrosine kinases such as PDGFRalpha and DDR1 not previously implicated in the genesis of NSCLC are also identified. By focusing on activated cell circuitry, the approach outlined here provides insight into cancer biology not available at the chromosomal and transcriptional levels and can be applied broadly across all human cancers.
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              alpha-Synuclein is phosphorylated in synucleinopathy lesions.

              The deposition of the abundant presynaptic brain protein alpha-synuclein as fibrillary aggregates in neurons or glial cells is a hallmark lesion in a subset of neurodegenerative disorders. These disorders include Parkinson's disease (PD), dementia with Lewy bodies (DLB) and multiple system atrophy, collectively referred to as synucleinopathies. Importantly, the identification of missense mutations in the alpha-synuclein gene in some pedigrees of familial PD has strongly implicated alpha-synuclein in the pathogenesis of PD and other synucleinopathies. However, specific post-translational modifications that underlie the aggregation of alpha-synuclein in affected brains have not, as yet, been identified. Here, we show by mass spectrometry analysis and studies with an antibody that specifically recognizes phospho-Ser 129 of alpha-synuclein, that this residue is selectively and extensively phosphorylated in synucleinopathy lesions. Furthermore, phosphorylation of alpha-synuclein at Ser 129 promoted fibril formation in vitro. These results highlight the importance of phosphorylation of filamentous proteins in the pathogenesis of neurodegenerative disorders.
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                Author and article information

                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                20 December 2018
                January 2019
                : 20
                : 1
                : 16
                Affiliations
                [1 ]Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia; dana.pascovici@ 123456mq.edu.au (D.P.); jemma.wu@ 123456mq.edu.au (J.X.W.); matthew.mckay@ 123456mq.edu.au (M.J.M.); zainab.noor@ 123456students.mq.edu.au (Z.N.); Karthik.kamath@ 123456mq.edu.au (K.K.); Yunqi.wu@ 123456mq.edu.au (Y.W.); Shoba.ranganathan@ 123456mq.edu.au (S.R.)
                [2 ]Australian Proteome Analysis Facility, Macquarie University, Sydney, NSW 2109, Australia
                [3 ]Department of Clinical Medicine, Macquarie University, Sydney, NSW 2109, Australia; chitra.joseph@ 123456hdr.mq.edu.au (C.J.); vivek.gupta@ 123456mq.edu.au (V.G.)
                Author notes
                [* ]Correspondence: Mehdi.mirzaei@ 123456mq.edu.au ; Tel.: +61-2-98508284
                Author information
                https://orcid.org/0000-0003-4449-8413
                https://orcid.org/0000-0002-8290-813X
                Article
                ijms-20-00016
                10.3390/ijms20010016
                6337699
                30577541
                6294ea9b-daab-4d6c-830f-0db48dc39dd7
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 15 November 2018
                : 17 December 2018
                Categories
                Review

                Molecular biology
                post translational modification,quantitative proteomics,body fluids,clinical samples,ptm

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