AbstractEvery year an increasing number of people are diagnosed with metabolic diseases such as obesity, type 2 diabetes (T2D) and atherosclerosis (ACVD). These three diseases have high mortality rates, accounting for a significant proportion of global death rates and are often diagnosed as comorbidities. They have been closely linked to lifestyle choices and diet. An increasingly common treatment for obesity and T2D is bariatric surgery. This is usually a permanent procedure which involves altering the digestive tract to reduce hunger and, in some cases, reduce nutrient uptake ability. Many studies have reported the association of the gut microbiome in various pathologies, including metabolic diseases and have fuelled interest in the microbiome as a potential new target for treating disease.
In this study, using high throughput sequencing data sets such as metagenomics alongside a systems biology approach, links were drawn between the microbiome, metabolic diseases and the effects of bariatric surgery. The aim was to understand better the dysbiosis seen in the gut microbiome of metabolic disease patients and to determine the mechanisms driving this dysbiosis.
Metagenomic analysis of obesity, T2D and ACVD patient samples revealed the composition of the microbiome in relation to disease. Differences were seen in the richness, diversity and enterotype profile of the microbiome when comparing diseased cohorts to controls or surgical states in three different bariatric surgery cohorts. A lower richness and diversity in the microbiome is symptomatic of poor health and these findings confirm this. Statistical analysis of microbial species abundance uncovered species which showed significant differences in abundance between healthy- and diseased cohorts or between pre- and post-surgery. These species were then looked at further as they are key players in the dysbiosis in the microbiome of diseased patients.
Following on from this metagenomic analysis, genome scale metabolic models (GEMs) were used to look at these species more closely and predict the behavior of the bacteria within the biological system. With the use of GEMs, the metabolism of key bacterial species could be analyzed and the differences in flux noted. Integrating GEMs to construct personalized community models to better represent the ecosystem within the gut microbiome showed further differences between the flux of metabolites within diseased patients against controls or pre- and post-surgery. This was done among important metabolites such as short chain fatty acids and branch chain amino acids.
Personalized reaction abundance highlighted reactions and pathways which are significantly different between cohorts. In metabolic disease, there was a consistent increase in the abundance of the glyoxylate and dicarboxylate pathway in diseased patients, showing this pathway is significantly increased in metabolic disease which can then lead to exacerbating disease or inflammation. Whilst in bariatric surgery patients, BCAA degradation and biosynthesis showed significant difference between surgical state.
Finally, metabolomic analysis was performed to observe the molecular phenotype in the different cohorts and to help further evaluate and interpret the findings from the genome scale model work. This work highlighted the link between the glyoxylate and dicarboxylate pathway in the microbiome and plasma levels in obese but otherwise healthy individuals. Metabolomic analysis on bariatric surgery patients also highlighted the upregulation of BCAAs before surgery and decreased levels of BCAAs after surgery. These results helped further interpret the modelling and reaction abundance results to show the importance of the glyoxylate and dicarboxylate pathway in disease and BCAA
metabolism in obesity and T2D.
This work helps to elucidate the role of gut bacteria in metabolic diseases, in particular in obesity and T2D. It has uncovered potential new biomarkers of disease, and new possible links of how the microbiome impacts on disease via metabolite production and increased pathways. These novel findings have highlighted areas of research which need to be investigated further in the future.
|Date of Award||1 Aug 2022|
|Supervisor||Dave Moyes (Supervisor) & Saeed Shoaie (Supervisor)|