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Biomedical subjects

Michele Magrane

Publications and source records attributed to Michele Magrane.

5 recordsLinked to original sources

Expanding the human proteome with microproteins and peptideins.

A major scientific drive is to characterize the protein-coding genome, which is a primary basis for studying human health. But the fundamental question remains of what has been missed in previous analyses. Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states1-3, with major implications for biomedical science. However, a key gap in knowledge has been which ncORFs produce small microproteins or alternative protein molecules that contribute to the human proteome. Here we report the collaborative efforts of the TransCODE Consortium4 to produce a consensus landscape of protein-level evidence for ncORFs. We show that about 25% of a set of 7,264 ncORFs gives rise to detectable peptides in a large-scale analysis of 95,520 proteomics experiments. We develop an annotation framework for ncORF-encoded microproteins as human proteins and codify the new conceptual model of 'peptideins' as microproteins that have indeterminate potential as functional proteins. To probe the biological implications of peptideins, we create an evolutionary analysis approach, termed ORF relative branch length (ORBL), and determine that evolutionary constraint is common and associates with observation of ncORF-derived peptides. We then characterize a pan-essential cellular phenotype for one peptidein from the OLMALINC long non-coding RNA. Overall, we generate public research tools supported by GENCODE and PeptideAtlas and advance biomedical discovery for understudied components of the human proteome.

Humans↗

High-quality peptide evidence for annotating non-canonical open reading frames as human proteins.

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.

GENCODE↗

The Gene Ontology Annotation (GOA) project: implementation of GO in SWISS-PROT, TrEMBL, and InterPro.

Gene Ontology Annotation (GOA) is a project run by the European Bioinformatics Institute (EBI) that aims to provide assignments of terms from the Gene Ontology (GO) resource to gene products in a number of its databases (http://www.ebi.ac.uk/GOA). In the first stage of this project, GO assignments have been applied to a data set representing the complete human proteome by a combination of electronic mappings and manual curation. This vocabulary has also been applied to the nonredundant proteome sets for all other completely sequenced organisms as well as to proteins from a wide range of organisms where the proteome is not yet complete.

Computational Biology↗

The European Bioinformatics Institute's data resources.

As the amount of biological data grows, so does the need for biologists to store and access this information in central repositories in a free and unambiguous manner. The European Bioinformatics Institute (EBI) hosts six core databases, which store information on DNA sequences (EMBL-Bank), protein sequences (SWISS-PROT and TrEMBL), protein structure (MSD), whole genomes (Ensembl) and gene expression (ArrayExpress). But just as a cell would be useless if it couldn't transcribe DNA or translate RNA, our resources would be compromised if each existed in isolation. We have therefore developed a range of tools that not only facilitate the deposition and retrieval of biological information, but also allow users to carry out searches that reflect the interconnectedness of biological information. The EBI's databases and tools are all available on our website at www.ebi.ac.uk.

Animals↗

Mouse proteome analysis.

A general overview of the protein sequence set for the mouse transcriptome produced during the FANTOM2 sequencing project is presented here. We applied different algorithms to characterize protein sequences derived from a nonredundant representative protein set (RPS) and a variant protein set (VPS) of the mouse transcriptome. The functional characterization and assignment of Gene Ontology terms was done by analysis of the proteome using InterPro. The Superfamily database analyses gave a detailed structural classification according to SCOP and provide additional evidence for the functional characterization of the proteome data. The MDS database analysis revealed new domains which are not presented in existing protein domain databases. Thus the transcriptome gives us a unique source of data for the detection of new functional groups. The data obtained for the RPS and VPS sets facilitated the comparison of different patterns of protein expression. A comparison of other existing mouse and human protein sequence sets (e.g., the International Protein Index) demonstrates the common patterns in mammalian proteomes. The analysis of the membrane organization within the transcriptome of multiple eukaryotes provides valuable statistics about the distribution of secretory and transmembrane proteins

Animals↗