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Microorganisms colonizing the body of mammals are not only responsible for infectious diseases, they are also a very important factor for maintaining health. Commensal bacteria have a direct influence on health, as they compete with pathogens and can prevent them from colonizing. Additionally microorganisms have a more indirect influence: The gut microbiota is involved in development and shaping of the immune system, therefore dysbiosis may result in disease development. Within the last decade, many different diseases have been associated with a shift in the gut microbiota. Most studies however do not answer the question whether changes of the intestinal microbiome are cause or result of disease. As a mouse model, we used Rag1-/- mice, lacking B- and T-cells. An immune reaction, and therefor colitis development, is only possible after T-cell transfer. Depending on the composition of the gut microbiome at the time of T-cell transfer, the mice subsequently remain healthy or develop colitis. Changing the microbiome of the mice prior to reconstitution of the immune system influences disease development. Analysis of the intestinal microbiome can be done using different methods, such as PCR, FISH or by culture. However, these methods are limited to microorganisms, of which the genome sequence is known or that can be grown in the lab. These limitations can be overcome by using Next-Generation-Sequencing (NGS) to identify the members of the gut microbiome. Therefore, here we developed protocols for analyzing the intestinal microbiome from fecal samples. Our results show, that environmental factors influence the composition of the intestinal microbiome severely. Differences between single experiments were often greater than the differences between different treatments making it impossible to link a single or combination of bacterial species to induction of colitis or maintenance of health. For identifying microbiota compositions that are causative for health or disease, these environmental influences have to be eliminated or at least reduced. Otherwise the strong variations between animals will cover up causative alterations. |
