Systems Biochemistry Approaches to Defining Mitochondrial Protein Function

Abstract

Defining functions for the full complement of proteins is a grand challenge in the post-genomic era and is essential for our understanding of basic biology and disease pathogenesis. In recent times, this endeavor has benefitted from a combination of modern large-scale and classical reductionist approaches-a process we refer to as "systems biochemistry"-that helps surmount traditional barriers to the characterization of poorly understood proteins. This strategy is proving to be particularly effective for mitochondria, whose well-defined proteome has enabled comprehensive analyses of the full mitochondrial system that can position understudied proteins for fruitful mechanistic investigations. Recent systems biochemistry approaches have accelerated the identification of new disease-related mitochondrial proteins and of long-sought "missing" proteins that fulfill key functions. Collectively, these studies are moving us toward a more complete understanding of mitochondrial activities and providing a molecular framework for the investigation of mitochondrial pathogenesis.

Keywords: mitochondria; multi-omics; orphan proteins; rare disease; systems biochemistry.

Figure 1.

Understanding of the mammalian mitochondrial proteome has increased rapidly since 2000, yet ~20% of its proteins remain uncharacterized. The current list stands at 1,158 proteins, close to the early estimates of 1,300–1,500.

Figure 2.

A defined system catalyzes the assignment of orphan protein functions.

Figure 3.

Defining a biological system helps reverse the over focus on proteins of known function. A, There is a striking disproportionality in citations across the mitochondrial proteome. B, After defining a high-confidence mitochondrial proteome in 2009, the distribution in citations in the top decile and bottom half have begun to shift towards a more balanced distribution. C, While this trend is true for the mitochondrial genes, research on the rest of the genome continues to be skewed towards the more ‘popular’ proteins.

Figure 4.

Multi-omics analyses of well-defined, contrasting biological states are a powerful approach for leveraging large-scale data to generate mechanistic hypotheses. A, These large datasets can be mined in many ways. B, Straightforward analyses include, 1) KO vs. KO regression analyses, 2) outlier analyses, and 3) molecular covariance network analyses.

Figure 5.

The identification of mitochondrial disease genes has increased markedly since the 1980s when the first mitochondrial disease was characterized. However, ~40% of diagnosed mitochondrial disorders have no identified genetic cause, and overall diagnostic success rates remain surprisingly low. Data from OMIM (Amberger et al. 2015) and (Frazier, Thorburn, and Compton 2019).