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| 2. |
- Bludau, Isabell, et al.
(författare)
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Systematic detection of functional proteoform groups from bottom-up proteomic datasets
- 2021
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- To a large extent functional diversity in cells is achieved by the expansion of molecular complexity beyond that of the coding genome. Various processes create multiple distinct but related proteins per coding gene – so-called proteoforms – that expand the functional capacity of a cell. Evaluating proteoforms from classical bottom-up proteomics datasets, where peptides instead of intact proteoforms are measured, has remained difficult. Here we present COPF, a tool for COrrelation-based functional ProteoForm assessment in bottom-up proteomics data. It leverages the concept of peptide correlation analysis to systematically assign peptides to co-varying proteoform groups. We show applications of COPF to protein complex co-fractionation data as well as to more typical protein abundance vs. sample data matrices, demonstrating the systematic detection of assembly- and tissue-specific proteoform groups, respectively, in either dataset. We envision that the presented approach lays the foundation for a systematic assessment of proteoforms and their functional implications directly from bottom-up proteomic datasets.
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- Boersema, P. J., et al.
(författare)
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Biology/Disease-Driven Initiative on Protein-Aggregation Diseases of the Human Proteome Project: Goals and Progress to Date
- 2018
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 17:12, s. 4072-4084
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Tidskriftsartikel (refereegranskat)abstract
- The Biology/Disease-driven (B/D) working groups of the Human Proteome Project are alliances of research groups aimed at developing or improving proteomic tools to support specific biological or disease-related research areas. Here, we describe the activities and progress to date of the B/D working group focused on protein aggregation diseases (PADs). PADs are characterized by the intra- or extracellular accumulation of aggregated proteins and include devastating diseases such as Parkinson's and Alzheimer's disease and systemic amyloidosis. The PAD B/D working group aims for the development of proteomic assays for the quantification of aggregation-prone proteins involved in PADs to support basic and clinical research on PADs. Because the proteins in PADs undergo aberrant conformational changes, a goal is to quantitatively resolve altered protein structures and aggregation states in complex biological specimens. We have developed protein-extraction protocols and a set of mass spectrometric (MS) methods that enable the detection and quantification of proteins involved in the systemic and localized amyloidosis and the probing of aberrant protein conformational transitions in cell and tissue extracts. In several studies, we have demonstrated the potential of MS-based proteomics approaches for specific and sensitive clinical diagnoses and for the subtyping of PADs. The developed methods have been detailed in both protocol papers and manuscripts describing applications to facilitate implementation by nonspecialized laboratories, and assay coordinates are shared through public repositories and databases. Clinicians actively involved in the PAD working group support the transfer to clinical practice of the developed methods, such as assays to quantify specific disease related proteins and their fragments in biofluids and multiplexed MS-based methods for the diagnosis and typing of systemic amyloidosis. We believe that the increasing availability of tools to precisely measure proteins involved in PADs will positively impact research on the molecular bases of these diseases and support early disease diagnosis and a more-confident subtyping.
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| 11. |
- Gustafsson, Mika, et al.
(författare)
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Modules, networks and systems medicine for understanding disease and aiding diagnosis
- 2014
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Ingår i: Genome Medicine. - : BioMed Central. - 1756-994X. ; 6:82
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Forskningsöversikt (refereegranskat)abstract
- Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data have identified modules of disease-associated genes that have been used to obtain both a systems level and a molecular understanding of disease mechanisms. For example, in allergy a module was used to find a novel candidate gene that was validated by functional and clinical studies. Such analyses play important roles in systems medicine. This is an emerging discipline that aims to gain a translational understanding of the complex mechanisms underlying common diseases. In this review, we will explain and provide examples of how network-based analyses of omics data, in combination with functional and clinical studies, are aiding our understanding of disease, as well as helping to prioritize diagnostic markers or therapeutic candidate genes. Such analyses involve significant problems and limitations, which will be discussed. We also highlight the steps needed for clinical implementation.
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| 12. |
- Kanfer, G, et al.
(författare)
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Mitotic redistribution of the mitochondrial network by Miro and Cenp-F
- 2015
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 6, s. 8015-
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Tidskriftsartikel (refereegranskat)abstract
- Although chromosome partitioning during mitosis is well studied, the molecular mechanisms that allow proper segregation of cytoplasmic organelles in human cells are poorly understood. Here we show that mitochondria interact with growing microtubule tips and are transported towards the daughter cell periphery at the end of mitosis. This phenomenon is promoted by the direct and cell cycle-dependent interaction of the mitochondrial protein Miro and the cytoskeletal-associated protein Cenp-F. Cenp-F is recruited to mitochondria by Miro at the time of cytokinesis and associates with microtubule growing tips. Cells devoid of Cenp-F or Miro show decreased spreading of the mitochondrial network as well as cytokinesis-specific defects in mitochondrial transport towards the cell periphery. Thus, Miro and Cenp-F promote anterograde mitochondrial movement and proper mitochondrial distribution in daughter cells.
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| 15. |
- Piazza, I, et al.
(författare)
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A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes
- 2020
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1, s. 4200-
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Tidskriftsartikel (refereegranskat)abstract
- Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identify the protein targets of a compound and also detect the interaction surfaces between ligands and protein targets without prior labeling or modification. To address this limitation, we here develop LiP-Quant, a drug target deconvolution pipeline based on limited proteolysis coupled with mass spectrometry that works across species, including in human cells. We use machine learning to discern features indicative of drug binding and integrate them into a single score to identify protein targets of small molecules and approximate their binding sites. We demonstrate drug target identification across compound classes, including drugs targeting kinases, phosphatases and membrane proteins. LiP-Quant estimates the half maximal effective concentration of compound binding sites in whole cell lysates, correctly discriminating drug binding to homologous proteins and identifying the so far unknown targets of a fungicide research compound.
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| 16. |
- Glasbey, JC, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 18. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 19. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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