Can Metagenomic and Metadata be Combined Using Bioinformatics and Computational Biology Methods to Personalise Patient Treatment.

Background Mitochondrial diseases are an important group of inherited neurometabolic disorders that invariably exhibit multi-organ involvement, are relentlessly progressive, and result in significant morbidity and mortality. They may manifest as discrete clinical syndromes such as mitochondrial encephalomyopathy, lactic acidosis, and stroke like episodes (MELAS), chronic and progressive external ophthalmoplegia (CPEO), and maternally inherited deafness and diabetes (MIDD), or more commonly with a wide overlapping spectrum of clinical features, including hypertrophic cardiomyopathy and gastrointestinal (GI) dysmotility. Symptoms arising from gastrointestinal (GI) dysmotility in patients with mitochondrial disease are increasingly recognized and often include dysphagia, abdominal pain, abdominal distention, bacterial overgrowth, constipation, and in severe cases, intestinal pseudo obstruction mimicking an acute surgical abdomen. The high incidence of this was recently confirmed when we surveyed 86 mitochondrial patients about GI symptoms. Sixty five percent of patients experienced GI dysmotility symptoms, including constipation, early satiety, abdominal pain and abdominal distension (under preparation for publication). Although the proposed pathological mechanisms underlying the development of GI dysmotility remain diverse, potential mechanisms include mitochondrial dysfunction of smooth muscle within the GI tract and visceral myopathy. Moreover, bacteria within the GI tract, termed 'gut microbiota' has also been identified as a key contributor towards GI dysmotility.

Rationale To date, there are few effective treatments for patients with mitochondrial disease and those available are predominantly supportive in nature with no proven treatment efficacy, and poor understanding of the links between the gut microbiota, mitochondrial disease, GI dysmotility and patient health and quality of life. Treatments include various antibiotics and laxatives which are generic and not disease specific. Long term effects of drugs are unknown and the impact these have on the GI tract and gut microbiota in mitochondrial disorders are currently unknown. It is essential to optimise supportive therapeutic strategies and design novel modalities to improve clinical management. Although advances in technology now provide more biological information than ever before, the complexity and volume of data generated exceeds the ability to analyse, interpret and translate this information back into the clinical management, highlighting the need to increase clinical analytical capabilities. The use of bioinformatics and computational biology to combine metagenomics data relating to the gut microbiota and metadata (patient characteristics; phenotype/genotype) is one approach to identify and predict what factors, such as drugs, phenotype and genotype, induce gut microbiota dysbiosis.

Elucidating the complexity and workings of the gut microbiota in mitochondrial disease provides a unique approach and deeper understanding of the biology in general, which is currently lacking in primary mitochondrial disorders. This research will contribute to the gut microbiome field and provide a novel insight into the complex microbe:microbe and microbiota-host interactions. The new insights generated here will provide the foundation for interventional studies aimed at manipulating the gut microbiome and relieving disease burden in patients with mitochondrial disease and potentially diseases associated with mitochondrial dysfunction, such as obesity, diabetes and neuro-degenerative disorders such as dementia and Parkinson's disease.

Objectives

The working hypotheses is that patients with mitochondrial disease experience GI dysmotility, and that the gut microbiota accentuates clinical disease severity. This study aims to provide novel information relating to: