Identification of AMPK Phosphorylation Sites Reveals a Network of Proteins Involved in Cell Invasion and Facilitates Large-Scale Substrate Prediction

Diffuse invasion into adjacent brain matter by glioblastoma (GBM) is largely responsible for their dismal prognosis. Previously, we showed that the TWIST1 (TW) bHLH transcription factor and its regulated gene periostin (POSTN) promote invasive phenotypes of GBM cells. Since TW functional effects are regulated by phosphorylation and dimerization, we investigated how phosphorylation of serine 68 in TW regulates TW dimerization, POSTN expression, and invasion in glioma cells. Compared with wild-type TW, the hypophosphorylation mutant, TW(S68A), impaired TW heterodimerization with the E12 bHLH transcription factor and cell invasion in vitro but had no effect on TW homodimerization. Overexpression of TW:E12 forced dimerization constructs (FDCs) increased glioma cell invasion and upregulated pro-invasive proteins, including POSTN, in concert with cytoskeletal reorganization. By contrast, TW:TW homodimer FDCs inhibited POSTN expression and cell invasion in vitro. Further, phosphorylation of analogous PXSP phosphorylation sites in TW:E12 FDCs (TW S68 and E12 S139) coordinately regulated POSTN and PDGFRa mRNA expression. These results suggested that TW regulates pro-invasive phenotypes in part through coordinated phosphorylation events in TW and E12 that promote heterodimer formation and regulate downstream targets. This new mechanistic understanding provides potential therapeutic strategies to inhibit TW-POSTN signaling in GBM and other cancers.

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Large-Scale Identification and Evolution Indexing of Tyrosine Phosphorylation Sites from Murine Brain

Journal of Proteome Research ◽

10.1021/pr0701254 ◽

2008 ◽

Vol 7 (1) ◽

pp. 311-318 ◽

Cited By ~ 122

Author(s):

Bryan A. Ballif ◽

G. Richard Carey ◽

Shamil R. Sunyaev ◽

Steven P. Gygi

Keyword(s):

Tyrosine Phosphorylation ◽

Large Scale ◽

Phosphorylation Sites ◽

Murine Brain

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Polymyositis, Topological Proteomics Technology and Paradigm for Cell Invasion Dynamics

Journal of Theoretical Medicine ◽

10.1080/10273660290015224 ◽

2002 ◽

Vol 4 (1) ◽

pp. 75-84 ◽

Cited By ~ 8

Author(s):

Walter Schubert

Keyword(s):

T Cells ◽

T Cell ◽

Cell Surface ◽

Cell Invasion ◽

Large Scale ◽

Expression Patterns ◽

Inflammatory Myopathy ◽

Protein Profiling ◽

Cell Surface Receptors ◽

Surface Receptors

Polymyositis is an inflammatory myopathy characterized by muscle invasion of T-cells penetrating the basal lamina and displacing the plasma membrane of normal muscle fibers. This investigation presents a technology for the direct mapping of protein networks involved in T-cell invasionin situ. Simultaneous localization of 17 adhesive cell surface receptors reveals 18 different combinatorial expression patterns (CEP), which are unique for the T-cell invasion process in muscle tissue. Each invasion step can be assigned to specific CEP on the surface of individual T-cells. This indicates, that the T-cell invasion is enciphered combinatorially in the T-cells' adhesive cell surface proteome fraction. Given 217possible combinations, the T-cell appears to have at its disposal a highly non-random restricted repertoire to specify migratory pathways at the cell surface. These higher-level order functions in the cellular proteome cannot be detected by large-scale protein profiling techniques from tissue homogenates. High-throughput whole cell mapping machines working on structurally intact tissues, as shown here, will allow to measure how cells of different origin (immune cells, tumor cells) combine cell surface receptors to encipher specificity and selectivity for interactions.

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Large-Scale Identification of Phosphorylation Sites for Profiling Protein Kinase Selectivity

Journal of Proteome Research ◽

10.1021/pr500319y ◽

2014 ◽

Vol 13 (7) ◽

pp. 3410-3419 ◽

Cited By ~ 27

Author(s):

Haruna Imamura ◽

Naoyuki Sugiyama ◽

Masaki Wakabayashi ◽

Yasushi Ishihama

Keyword(s):

Protein Kinase ◽

Large Scale ◽

Phosphorylation Sites

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Large-Scale Comparative Phosphoproteomics Identifies Conserved Phosphorylation Sites in Plants

PLANT PHYSIOLOGY ◽

10.1104/pp.110.157347 ◽

2010 ◽

Vol 153 (3) ◽

pp. 1161-1174 ◽

Cited By ~ 256

Author(s):

Hirofumi Nakagami ◽

Naoyuki Sugiyama ◽

Keiichi Mochida ◽

Arsalan Daudi ◽

Yuko Yoshida ◽

...

Keyword(s):

Large Scale ◽

Phosphorylation Sites

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Assembling a phosphoproteomic knowledge base using ProtMapper to normalize phosphosite information from databases and text mining

10.1101/822668 ◽

2019 ◽

Author(s):

John A. Bachman ◽

Benjamin M. Gyori ◽

Peter K. Sorger

Keyword(s):

Text Mining ◽

Open Source ◽

Large Scale ◽

Large Body ◽

Fold Increase ◽

Human Protein ◽

Phosphorylation Sites ◽

Scientific Publications ◽

Link Type ◽

Aggregate Information

AbstractA major challenge in analyzing large phosphoproteomic datasets is that information on phosphorylating kinases and other upstream regulators is limited to a small fraction of phosphosites. One approach to addressing this problem is to aggregate and normalize information from all available information sources, including both curated databases and large-scale text mining. However, when we attempted to aggregate information on post-translational modifications (PTMs) from six databases and three text mining systems, we found that a substantial proportion of phosphosites were positioned on non-canonical residue positions. These errors were attributable to the use of residue numbers from non-canonical isoforms, mouse or rat proteins, post-translationally processed proteins and also from errors in curation and text mining. Published mass spectrometry datasets from large-scale efforts such as the Clinical Proteomic Tumor Analysis Consortium (CPTAC) also localize many PTMs to non-canonical sequences, precluding their accurate annotation. To address these problems, we developed ProtMapper, an open-source Python tool that automatically normalizes site positions to human protein reference sequences using data from PhosphoSitePlus and Uniprot. ProtMapper identifies valid reference positions with high precision and reasonable recall, making it possible to filter out machine reading errors from text mining and thereby assemble a corpus of 29,400 regulatory annotations for 13,668 sites, a 2.8-fold increase over PhosphoSitePlus, the current gold standard. To our knowledge this corpus represents the most comprehensive source of literature-derived information about phosphosite regulation currently available and its assembly illustrates the importance of sequence normalization. Combining the expanded corpus of annotations with normalization of CPTAC data nearly doubled the number of CPTAC annotated sites and the mean number of annotations per site. ProtMapper is available under an open source BSD 2-clause license at https://github.com/indralab/protmapper, and the corpus of phosphosite annotations is available as Supplementary Data with this paper under a CC-BY-NC-SA license. All results from the paper are reproducible from code available at https://github.com/johnbachman/protmapper_paper.Author SummaryPhosphorylation is a type of chemical modification that can affect the activity, interactions, or cellular location of proteins. Experimentally measured patterns of protein phosphorylation can be used to infer the mechanisms of cell behavior and disease, but this type of analysis depends on the availability of functional information about the regulation and effects of individual phosphorylation sites. In this study we show that inconsistent descriptions of the physical locations of phosphorylation sites on proteins present a barrier to the functional analysis of phosphorylation data. These inconsistencies are found in both pathway databases and text mining results and often come from the underlying scientific publications. We describe a method to normalize phosphosite locations to standard human protein sequences and use this method to robustly aggregate information from many sources. The result is a large body of functional annotations that increases the proportion of phosphosites with known regulators in two large experimental surveys of phosphorylation in cancer.

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Large-scale Proteomic and Phosphoproteomic Analysis of Maize Seedling Leaves During De-etiolation

10.1101/2020.03.13.977843 ◽

2020 ◽

Author(s):

Zhi-Fang Gao ◽

Zhuo Shen ◽

Qing Chao ◽

Zhen Yan ◽

Xuan-Liang Ge ◽

...

Keyword(s):

Large Scale ◽

Zea Mays L ◽

Maize Seedling ◽

Protein Abundance ◽

Phosphorylation Sites ◽

Physiological Changes ◽

Protein Coding ◽

Changing Environments ◽

Mesocotyl Elongation ◽

Rate Limiting

AbstractDe-etiolation consists of a series of developmental and physiological changes that a plant undergoes in response to light. During this process light, an important environmental signal, triggers the inhibition of mesocotyl elongation and the production of photosynthetically active chloroplasts, and etiolated leaves transition from the “sink” stage to the “source” stage. De-etiolation has been extensively studied in maize (Zea mays L). However, little is known about how this transition is regulated. In this study, we describe a quantitative proteomic and phosphoproteomic atlas of the de-etiolation process in maize. We identified 16,420 proteins and quantified 14,168. In addition, 8,746 phosphorylation sites within 3,110 proteins were identified. From the proteomic and phosphoproteomic data combined, we identified a total of 17,436 proteins, 27.6% of which are annotated protein coding genes in the Zea_mays AGPv3.28 database. Only 6% of proteins significantly changed in abundance during de-etiolation. In contrast, the phosphorylation levels of more than 25% of phosphoproteins significantly changed; these included proteins involved in gene expression and homeostatic pathways and rate-limiting enzymes involved in photosynthesis light and carbon reactions. Based on phosphoproteomic analysis, 34% (1,057) of all phosphoproteins identified in this study contained more than three phosphorylation sites, and 37 proteins contained more than 16 phosphorylation sites, which shows that multi-phosphorylation is ubiquitous during the de-etiolation process. Our results suggest that plants might preferentially regulate the level of PTMs rather than protein abundance for adapting to changing environments. The study of PTMs could thus better reveal the regulation of de-etiolation.

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Cooperativity within proximal phosphorylation sites is revealed from large-scale proteomics data

Biology Direct ◽

10.1186/1745-6150-5-6 ◽

2010 ◽

Vol 5 (1) ◽

pp. 6 ◽

Cited By ~ 64

Author(s):

Regev Schweiger ◽

Michal Linial

Keyword(s):

Large Scale ◽

Phosphorylation Sites ◽

Proteomics Data

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Large‐scale identification of phosphorylation sites in renal cortical transporters

The FASEB Journal ◽

10.1096/fasebj.24.1_supplement.1002.2 ◽

2010 ◽

Vol 24 (S1) ◽

Author(s):

Marina Feric ◽

J D Hoffert ◽

T Pisitkun ◽

M A Knepper

Keyword(s):

Large Scale ◽

Phosphorylation Sites

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Spatially Interacting Phosphorylation Sites and Mutations in Cancer

10.1101/2021.02.14.431184 ◽

2021 ◽

Author(s):

Kuan-lin Huang ◽

Adam D. Scott ◽

Daniel Cui Zhou ◽

Liang-Bo Wang ◽

Amila Weerasinghe ◽

...

Keyword(s):

Mass Spectrometry ◽

Clustering Analysis ◽

Clinical Relevance ◽

Large Scale ◽

Protein Structures ◽

Phosphorylation Sites ◽

Bioinformatics Tool ◽

Recurrent Mutations ◽

Cancer Mutations ◽

Cancer Types

ABSTRACTAdvances in mass-spectrometry have generated increasingly large-scale proteomics datasets containing tens of thousands of phosphorylation sites (phosphosites) that require prioritization. We develop a bioinformatics tool called HotPho and systematically discover 3D co-clustering of phosphosites and cancer mutations on protein structures. HotPho identifies 474 such hybrid clusters containing 1,255 co-clustering phosphosites, including RET p.S904/Y928, the conserved HRAS/KRAS p.Y96, and IDH1 p.Y139/IDH2 p.Y179 that are adjacent to recurrent mutations on protein structures not found by linear proximity approaches. Hybrid clusters, enriched in histone and kinase domains, frequently include expression-associated mutations experimentally shown as activating and conferring genetic dependency. Approximately 300 co-clustering phosphosites are verified in patient samples of 5 cancer types or previously implicated in cancer, including CTNNB1 p.S29/Y30, EGFR p.S720, MAPK1 p.S142, and PTPN12 p.S275. In summary, systematic 3D clustering analysis highlights nearly 3,000 likely functional mutations and over 1,000 cancer phosphosites for downstream investigation and evaluation of potential clinical relevance.

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