Large-scale inference of gene function through phylogenetic annotation of Gene Ontology terms: case study of the apoptosis and autophagy cellular processes

Feuermann, Marc; Gaudet, Pascale; Mi, Huaiyu; Lewis, Suzanna; Thomas, Paul D.

doi:10.1093/database/baw155

Cited by 14 publications

(13 citation statements)

References 39 publications

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“…Bioinformatics analysis. Classification of identified PIAS1 substrates was performed using PANTHER (Protein Analysis Through Evolutionary Relationships) (http://www.pantherdb.org), which classifies genes and proteins by their functions 58,59 . The identified PIAS1 substrates were grouped into the biological process, molecular function, and cellular component classes against the background of quantified SUMOylome using DAVID Bioinformatics Resources 6.7 60 .…”

Section: Methodsmentioning

confidence: 99%

Quantitative SUMO proteomics identifies PIAS1 substrates involved in cell migration and motility

McManus

Plutoni

et al. 2020

Nat Commun

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The protein inhibitor of activated STAT1 (PIAS1) is an E3 SUMO ligase that plays important roles in various cellular pathways. Increasing evidence shows that PIAS1 is overexpressed in various human malignancies, including prostate and lung cancers. Here we used quantitative SUMO proteomics to identify potential substrates of PIAS1 in a system-wide manner. We identified 983 SUMO sites on 544 proteins, of which 62 proteins were assigned as putative PIAS1 substrates. In particular, vimentin (VIM), a type III intermediate filament protein involved in cytoskeleton organization and cell motility, was SUMOylated by PIAS1 at Lys-439 and Lys-445 residues. VIM SUMOylation was necessary for its dynamic disassembly and cells expressing a non-SUMOylatable VIM mutant showed a reduced level of migration. Our approach not only enables the identification of E3 SUMO ligase substrates but also yields valuable biological insights into the unsuspected role of PIAS1 and VIM SUMOylation on cell motility.

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Section: Methodsmentioning

confidence: 99%

Quantitative SUMO proteomics identifies PIAS1 substrates involved in cell migration and motility

McManus

Plutoni

et al. 2020

Nat Commun

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“…Macroautophagy occurs through the formation of a specific organelle called the autophagosome [ 9 ] (GO:0005776, Figure 2 ). The steps of the autophagosome assembly, as well as the different cellular components involved, have been described in Feuermann et al [ 32 ]. We have improved the definitions of autophagy-related terms by adding appropriate relationships between some processes and the relevant cellular component terms.…”

Section: Resultsmentioning

confidence: 99%

“…As an example, the function of S. cerevisiae Atg2 (UniProt identifier: P53855) has been extensively studied, but experimental data are rather poor for the corresponding human homologs ATG2A and ATG2B (Q2TAZ0 and Q96BY7). Propagation of annotations from yeast to human via the Phylogenetic Annotation and INference Tool (PAINT) pipeline developed by the GOC [ 32 , 42 , 43 ] enabled the association of ‘autophagosome assembly’ (GO:0000045) and ‘autophagy of mitochondrion’ (GO:0000422) with the 2 human homologs (see Materials and Methods for details).…”

Section: Resultsmentioning

confidence: 99%

Exploring autophagy with Gene Ontology

et al. 2018

Self Cite

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Autophagy is a fundamental cellular process that is well conserved among eukaryotes. It is one of the strategies that cells use to catabolize substances in a controlled way. Autophagy is used for recycling cellular components, responding to cellular stresses and ridding cells of foreign material. Perturbations in autophagy have been implicated in a number of pathological conditions such as neurodegeneration, cardiac disease and cancer. The growing knowledge about autophagic mechanisms needs to be collected in a computable and shareable format to allow its use in data representation and interpretation. The Gene Ontology (GO) is a freely available resource that describes how and where gene products function in biological systems. It consists of 3 interrelated structured vocabularies that outline what gene products do at the biochemical level, where they act in a cell and the overall biological objectives to which their actions contribute. It also consists of ‘annotations’ that associate gene products with the terms. Here we describe how we represent autophagy in GO, how we create and define terms relevant to autophagy researchers and how we interrelate those terms to generate a coherent view of the process, therefore allowing an interoperable description of its biological aspects. We also describe how annotation of gene products with GO terms improves data analysis and interpretation, hence bringing a significant benefit to this field of study.

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“…Classification schemes for BLs are of utmost importance due to the diversity of these enzymes and their importance in the scenario of bacterial resistance to antibiotics. 1 , 4 , 5 , 16 In general, the identification of new sequences is most often done by sequence comparison methods, 48 such as the BLASTP program. 34 Profiles HMM and other profile-sequence comparison methods led to a significant improvement in sensitivity over sequence comparison approaches and are already used in the identification of antibiotic resistance proteins.…”

Section: Discussionmentioning

confidence: 99%

Systematic Identification and Classification of β-Lactamases Based on Sequence Similarity Criteria: β-Lactamase Annotation

Silveira

Silva

Mota

et al. 2018

Evol Bioinform Online

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β-lactamases, the enzymes responsible for resistance to β-lactam antibiotics, are widespread among prokaryotic genera. However, current β-lactamase classification schemes do not represent their present diversity. Here, we propose a workflow to identify and classify β-lactamases. Initially, a set of curated sequences was used as a model for the construction of profiles Hidden Markov Models (HMM), specific for each β-lactamase class. An extensive, nonredundant set of β-lactamase sequences was constructed from 7 different resistance proteins databases to test the methodology. The profiles HMM were improved for their specificity and sensitivity and then applied to fully assembled genomes. Five hierarchical classification levels are described, and a new class of β-lactamases with fused domains is proposed. Our profiles HMM provide a better annotation of β-lactamases, with classes and subclasses defined by objective criteria such as sequence similarity. This classification offers a solid base to the elaboration of studies on the diversity, dispersion, prevalence, and evolution of the different classes and subclasses of this critical enzymatic activity.

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Large-scale inference of gene function through phylogenetic annotation of Gene Ontology terms: case study of the apoptosis and autophagy cellular processes

Cited by 14 publications

References 39 publications

Quantitative SUMO proteomics identifies PIAS1 substrates involved in cell migration and motility

Quantitative SUMO proteomics identifies PIAS1 substrates involved in cell migration and motility

Exploring autophagy with Gene Ontology

Systematic Identification and Classification of β-Lactamases Based on Sequence Similarity Criteria: β-Lactamase Annotation

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