Understanding how and why the Gene Ontology and its annotations evolve: the GO within UniProt

Pitfalls in the application of gene-set analysis to genetics studies

Gene-set analysis (GSA) (‘enrichment’) is a popular approach for the interpretation of genome-wide association studies (GWASs). GSA is most commonly applied to the analysis of transcriptomes, but from the outset it has been considered useful for any study that provides rankings or ‘hit lists’ of genes. The recent review by Mooney et al. [1] is a valuable resource for geneticists wishing to apply GSA to the output of GWASs. Here we describe some additional points of practical importance if the methods are to be applied and interpreted soundly.

The GOA database: gene Ontology annotation updates for 2015

The Gene Ontology Annotation (GOA) resource (http://www.ebi.ac.uk/GOA) provides evidence-based Gene Ontology (GO) annotations to proteins in the UniProt Knowledgebase (UniProtKB). Manual annotations provided by UniProt curators are supplemented by manual and automatic annotations from model organism databases and specialist annotation groups. GOA currently supplies 368 million GO annotations to almost 54 million proteins in more than 480 000 taxonomic groups. The resource now provides annotations to five times the number of proteins it did 4 years ago. As a member of the GO Consortium, we adhere to the most up-to-date Consortium-agreed annotation guidelines via the use of quality control checks that ensures that the GOA resource supplies high-quality functional information to proteins from a wide range of species. Annotations from GOA are freely available and are accessible through a powerful web browser as well as a variety of annotation file formats.

Proteomics-Based Identification of Differentially Abundant Proteins from Human Keratinocytes Exposed to Arsenic Trioxide

INTRODUCTION

Arsenic is a widely distributed environmental toxicant that can cause multi-tissue pathologies. Proteomic assays allow for the identification of biological processes modulated by arsenic in diverse tissue types.

METHOD

The altered abundance of proteins from HaCaT human keratinocyte cell line exposed to arsenic was quantified using a label-free LC-MS/MS mass spectrometry workflow. Selected proteomics results were validated using western blot and RT-PCR. A functional annotation analytics strategy that included visual analytical integration of heterogeneous data sets was developed to elucidate functional categories. The annotations integrated were mainly tissue localization, biological process and gene family.

RESULT

The abundance of 173 proteins was altered in keratinocytes exposed to arsenic; in which 96 proteins had increased abundance while 77 proteins had decreased abundance. These proteins were also classified into 69 Gene Ontology biological process terms. The increased abundance of transferrin receptor protein (TFRC) was validated and also annotated to participate in response to hypoxia. A total of 33 proteins (11 increased abundance and 22 decreased abundance) were associated with 18 metabolic process terms. The Glutamate--cysteine ligase catalytic subunit (GCLC), the only protein annotated with the term sulfur amino acid metabolism process, had increased abundance while succinate dehydrogenase [ubiquinone] iron-sulfur subunit, mitochondrial precursor (SDHB), a tumor suppressor, had decreased abundance.

CONCLUSION

A list of 173 differentially abundant proteins in response to arsenic trioxide was grouped using three major functional annotations covering tissue localization, biological process and protein families. A possible explanation for hyperpigmentation pathologies observed in arsenic toxicity is that arsenic exposure leads to increased iron uptake in the normally hypoxic human skin. The proteins mapped to metabolic process terms and differentially abundant are candidates for evaluating metabolic pathways perturbed by arsenicals.