Mitochondria are multifaceted organelles with key roles in anabolic and catabolic metabolism, bioenergetics, cellular signalling and nutrient sensing, and programmed cell death processes. Their diverse functions are enabled by a sophisticated set of protein components encoded by the nuclear and mitochondrial genomes. The extent and complexity of the mitochondrial proteome remained unclear for decades. This began to change 20 years ago when, driven by the emergence of mass spectrometry-based proteomics, the first draft mitochondrial proteomes were established. In the ensuing decades, further technological and computational advances helped to refine these ‘maps’, with current estimates of the core mammalian mitochondrial proteome ranging from 1,000 to 1,500 proteins. The creation of these compendia provided a systemic view of an organelle previously studied primarily in a reductionist fashion and has accelerated both basic scientific discovery and the diagnosis and treatment of human disease. Yet numerous challenges remain in understanding mitochondrial biology and translating this knowledge into the medical context. In this Roadmap, we propose a path forward for refining the mitochondrial protein map to enhance its discovery and therapeutic potential. We discuss how emerging technologies can assist the detection of new mitochondrial proteins, reveal their patterns of expression across diverse tissues and cell types, and provide key information on proteoforms. We highlight the power of an enhanced map for systematically defining the functions of its members. Finally, we examine the utility of an expanded, functionally annotated mitochondrial proteome in a translational setting for aiding both diagnosis of mitochondrial disease and targeting of mitochondria for treatment.
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The authors thank R. Guerra for critical feedback on this manuscript. This work was funded by National Institutes of Health (NIH) grants R35GM131795 and R01DK098672 and funds from the BJC Investigator Program (to D.J.P.), and fellowships from the European Molecular Biology Organization (ALTF 263-2022) and the Swiss National Science Foundation (P500PB_211038) (to P.F.).
The authors declare no competing interests.
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Cancer Therapeutics Response Portal: https://portals.broadinstitute.org/ctrp.v2.1/
Human Phenotype Ontology: https://hpo.jax.org/
protein–protein BLAST: https://blast.ncbi.nlm.nih.gov/
Saccharomyces Genome Database: https://www.yeastgenome.org/
- Activity-based protein profiling
A proteomic method that uses enzyme-specific chemical probes to elucidate protein–small molecule interactions. Probes can be fluorescent species, biotin, alkynes or other small molecules that bind to their target proteins and enable enrichment for downstream mass spectrometry analyses.
- Affinity-enrichment mass spectrometry
A technique that couples affinity purification of a protein or protein complex with mass spectrometry analysis to identify protein–protein interactions (PPIs).
- Crosslinking mass spectrometry
A common technique for identifying protein–protein interactions (PPIs). Small molecules with reactive head groups interact with proteins in close proximity and ‘crosslink’ them together. The crosslinked proteins, and the sites of crosslinking, can then be identified using liquid chromatography–mass spectrometry.
- Discovery proteomics
An open-ended proteomics analysis that aims to identify as many proteins in a sample as possible without directly targeting any particular protein species. Often used as the first step in defining a particular proteome.
- False discovery rate
(FDR). A value that estimates the proportion of false positives among all positive findings in a statistical analysis where FDR = false positives / (false positives + true positives). This value can be cross-validated by withholding a portion of a training set and measuring the rate at which the algorithm can correctly assign values.
A method used in the mitochondrial high-confidence proteome (MitoCoP) study to identify proteins that are typically imported into mitochondria by quantifying their degradation following disruption of the mitochondrial import machinery.
- Inborn errors of metabolism
A group of monogenic diseases caused by pathogenic variants in genes that code for proteins involved in metabolism.
- Iterative support vector machine learning
Iterative support vector machine is a supervised machine learning algorithm that uses a set of training data to build a model that can then be used to classify new data. The algorithm seeks to define a boundary within dimensional space that is used to separate and define classes. This boundary is defined by maximizing the distance between it and the closest individual data points (support vectors) within the training set and is then used by the support vector machine model to make further classification decisions.
- Mass spectrometry
An analytical method capable of identifying and quantifying diverse chemical species, including proteins and peptides, by virtue of their mass and related properties.
- Monogenic pathogenic variants
Genetic lesions that can cause or increase the risk of developing an inherited disease.
- Naive Bayesian classifier
Naive Bayes is a probabilistic machine learning algorithm that uses Bayes’ theorem to classify data points. Bayes theorem defines the probability of an outcome or class (for example, ‘mitochondrial’) being true based on prior knowledge of information related to the outcome. The naive Bayes model will assume that the predictors of the model are independent of one another and assign weights to each predictor.
- Nonsense-mediated mRNA decay
A cellular process that degrades mRNAs containing premature stop codons, thereby helping to prevent the production of truncated proteins.
- Primary mitochondrial diseases
A group of genetic disorders that are caused by pathogenic mutations in genes that code for proteins that affect mitochondrial function.
- Proximity labelling approaches
A class of techniques used to analyse macromolecular complexes, protein interaction networks and subcellular protein localization. These methods employ a promiscuous labelling enzyme that is targeted to a specific cellular location through its genetic fusion to a protein of interest. Addition of a small molecule substrate then enables the promiscuous enzyme to covalently tag other proteins within its vicinity, allowing those proteins to be enriched and identified. Commonly used proximity labelling methods include APEX2 (based on an engineered ascorbate peroxidase) and BioID/TurboID (based on a mutant biotin ligase).
- Small open reading frame
(smORF). A compact DNA sequence with fewer than 100 codons that can be translated into one or more proteins.
- Spatial proteomics
In the Mitochondrial high Confidence Proteome (MitoCoP) manuscript, spatial proteomics is defined as a technique whereby tissues or subcellular fractions are isolated and then analysed using liquid chromatography–mass spectrometry proteomics. These data can then be used to establish sub-tissue or subcellular protein mapping.
- Subtractive proteomics
A technique that helps to assign a protein’s subcellular localization by quantifying its enrichment during purification (for example, quantifying the enrichment of a protein from crude to pure mitochondria).
- Tandem mass spectrometry
(MS/MS). A technique that relies on multiple mass analysers assembled in a single mass spectrometer. Following ionization and separation by the first mass analyser, often a quadrupole, ions are fragmented and then analysed in the second mass analyser. This allows for the detection of unique molecules with the same parent (unfragmented) mass.
- Top-down and bottom-up approaches
A top-down approach aims to uncover missing elements in a well-defined process (known unknowns), whereas a bottom-up approach aims to assign functions to poorly understood genes or proteins without a predefined notion of what those functions might be (unknown unknowns).
- Whole genome or exome sequencing
Techniques used to identify causal genetic variants in patients by sequencing the entire genome or only its protein-coding (exome) portion.
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Baker, Z.N., Forny, P. & Pagliarini, D.J. Mitochondrial proteome research: the road ahead. Nat Rev Mol Cell Biol (2023). https://doi.org/10.1038/s41580-023-00650-7