MitoMiner, an integrated database for the storage and analysis of mitochondrial proteomics data

Abstract

Mitochondria are a vital component of eukaryotic cells with functions that extend beyond energy production to include metabolism, signaling, cell growth, and apoptosis. Their dysfunction is implicated in a large number of metabolic, degenerative, and age-related human diseases. Therefore, it is important to characterize and understand the mitochondrion. Many experiments have attempted to define the mitochondrial proteome, resulting in large and complex data sets that are difficult to analyze. To address this, we developed a new public resource for the storage and investigation of this mitochondrial proteomics data, called MitoMiner, that uses a model to describe the proteomics data and associated biological information. The proteomics data of 33 publications from both mass spectrometry and green fluorescent protein tagging experiments were imported and integrated with protein annotation from UniProt and genome projects, metabolic pathway data from Kyoto Encyclopedia of Genes and Genomes, homology relationships from HomoloGene, and disease information from Online Mendelian Inheritance in Man. We demonstrate the strengths of MitoMiner by investigating these data sets and show that the number of different mitochondrial proteins that have been reported is about 3700, although the number of proteins common to both animals and yeast is about 1400, and membrane proteins appear to be underrepresented. Furthermore analysis indicated that enzymes of some cytosolic metabolic pathways are regularly detected in mitochondrial proteomics experiments, suggesting that they are associated with the outside of the outer mitochondrial membrane. The data and advanced capabilities of MitoMiner provide a framework for further mitochondrial analysis and future systems level modeling of mitochondrial physiology.

Fig. 1.

The process of extracting data from external public resources for loading and integration into MitoMiner. Data were downloaded from public sources, and the identifiers among the different sources were mapped by using either the MGI or PIR identifier mapping tools. XML-formatted data and configuration files were created from the data sources by using Perl scripts. The XML files were loaded into an underlying PostgreSQL database by the InterMine system. InterMine gives access to the data through a configurable Web application (Webapp) by using Apache Tomcat. GO, Gene Ontology.

Fig. 2.

The Web page of the data category for proteins. This page describes the data (top left) and provides bulk download options (top right) and template queries that perform common searches (bottom).

Fig. 3.

The protein report page for the oxysterol-binding protein homolog 7 (Osh7p) of S. cerevisiae (UniProt accession number P38755). This page shows properties of the protein, cross-references to other data including the UniProt entry, and the evidence for its mitochondrial localization from GFP tagging and mass spectrometry. Osh7p is not annotated as mitochondrial in UniProt, the Saccharomyces Genome Database, or the Gene Ontology.

Fig. 4.

The Web page of the Query Builder for building bespoke queries. The Web page has three components: the model browser (top left) from which data classes and attributes of the object model are selected for inclusion in the query; the data classes included in the query, the constraints on their attributes, and the Boolean logic used to combine them (top right); and the data columns to display and sort the output of the results (bottom). The query displayed is to “show all proteins that are present in KEGG pathway 00071 (fatty acid metabolism) in H. sapiens and have evidence of mitochondrial localization by either mass spectrometry or GFP experiments.”

Fig. 5.

An example of a Web page for the results for a query. The query was “show all proteins that are present in KEGG pathway 00071 (fatty acid metabolism) in H. sapiens and have evidence of mitochondrial localization by either mass spectrometry or GFP tagging experiments.” Data in columns can be sorted or summarized, the columns can be moved or hidden, identifiers can be saved to a list, and the results can be exported in different formats.

Fig. 6.

A Web page for a predefined template query. The query is show all proteins that are present in KEGG pathway 00071 (fatty acid metabolism) in H. sapiens and have evidence of mitochondrial localization by either mass spectrometry or GFP experiments. Before the query is run, the user specifies the KEGG pathway by its identifier and picks the desired organism from a drop-down box.

Fig. 7.

The cumulative increase in the number of (i) proteins reported as mitochondrial (gray bars) and (ii) publications (black line) over the last 10 years using the 33 publications on mitochondrial localization included in MitoMiner. Protein redundancy was removed by using HomoloGene to merge orthologs and duplicate proteins. The number of transmembrane proteins (dark gray) as annotated in UniProt that have been found is about 20% of the total (light gray).

Fig. 8.

The frequency distribution (gray bars) and cumulative frequency distribution (black line) for the number of publications reporting a protein as mitochondrial by mass spectrometry. Protein redundancy was removed by using HomoloGene to combine orthologs and duplicate proteins. Proteins that have been identified in 10 or more studies are grouped as “10+.”

Fig. 9.

The numbers of orthologous proteins among three mitochondrial proteomes. A protein was assigned as mitochondrial by either experimental evidence (mass spectrometry or GFP tagging) or annotation or by the mitochondrial localization of an ortholog. Main numbers were calculated by using HomoloGene to determine redundancy and orthology among proteins. Numbers in parentheses were calculated by using BLAST (with a threshold of 10⁻³⁵ for the expect score) to define orthologs.