Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases

Abstract

Primary mitochondrial diseases form one of the most common and severe groups of genetic disease, with a birth prevalence of at least 1 in 5000. These disorders are multi-genic and multi-phenotypic (even within the same gene defect) and span the entire age range from prenatal to late adult onset. Mitochondrial disease typically affects one or multiple high-energy demanding organs, and is frequently fatal in early life. Unfortunately, to date there are no known curative therapies, mostly owing to the rarity and heterogeneity of individual mitochondrial diseases, leading to diagnostic odysseys and difficulties in clinical trial design. This review aims to discuss recent advances and challenges of systems approaches for the study of primary mitochondrial diseases. Although there has been an explosion in the generation of omics data, few studies have progressed toward the integration of multiple levels of omics. It is evident that the integration of different types of data to create a more complete representation of biology remains challenging, perhaps due to the scarcity of available integrative tools and the complexity inherent in their use. In addition, "bottom-up" systems approaches have been adopted for use in the iterative cycle of systems biology: from data generation to model prediction and validation. Primary mitochondrial diseases, owing to their complex nature, will most likely benefit from a multidisciplinary approach encompassing clinical, molecular and computational studies integrated together by systems biology to elucidate underlying pathomechanisms for better diagnostics and therapeutic discovery. Just as next generation sequencing has rapidly increased diagnostic rates from approximately 5% up to 60% over two decades, more recent advancing technologies are encouraging; the generation of multi-omics, the integration of multiple types of data, and the ability to predict perturbations will, ultimately, be translated into improved patient care.

Keywords: biomarkers; constraint based modelling; diagnostics; genome scale models; integrative omics; mitochondrial disease; network biology; novel therapy development.

FIGURE 1

An overview of systems approaches applied for mitochondrial research. To simplify systems-based approaches, they can be categorised into two main approaches, top-down and bottom-up. The top-down workflow can simplistically be described as samples that have been collected, processed by high throughput methods, and analysed by bioinformatics, e.g., protein network analysis, to gain a better understanding of function. On the other side of the spectrum, the bottom-up workflow can be described as identifying molecular data, formatting this information into a genome scale metabolic model (GEM), and utilising constraint-based modelling (CBM) to predict solutions and gain a better understanding of mechanisms. However, in practice the researcher must use whatever data is sufficiently available at any level of organisation, and build up/down/across to other levels, known as middle-out. Together, these systems approaches can aid in mitochondrial research by providing further insight into mitochondrial diseases, therapeutic approaches, and ultimately improving patient health care.

FIGURE 2

Schematic representation of the utilisation of genome scale metabolic models (GEMs) for constraint-based modelling (CBM). Mitochondrial metabolic pathways can be represented as a list of stoichiometric formulas and converted into a large stoichiometric matrix (S). In this format, the model does not have any constraints, thus the solution space may even include biologically irrelevant solutions. Constraints are then applied to utilise constraint-based modelling (CBM); such as (i) mass balance so that energy is conserved and the net flux is zero; (ii) flux bounds so that each flux (v) has a lower (a_n) and upper (b_n) flux rate, and others such as directionality and nutrient availability. This creates a constrained solution space that represents predictable solutions that are more biologically feasible. One example of CBM used in this illustration is flux balance analysis, where an objective function (Z, ATP utilisation) is defined and maximised for the optimal solution of Z, which can be identified within the solution space.

FIGURE 3

Schematic representation of possible future biological networks for mitochondrial systems biology. Different omics datasets can be generated from high throughput mitochondrial studies. To date, these are performed for single data types, for example using proteomic data to generate protein-protein interaction networks. However, future advances in computational tools could allow high-throughput omics data to be transformed into biological networks using mathematical algorithms, in order to find interactions and connections between biological moieties. Ultimately, the aim is to be able to integrate all relevant data types together to study the mitochondria within a whole system to improve patient care.