Trait-Based Method of Quantitative Assessment of Ecological Functional Groups in the Human Intestinal Microbiome

SIMPLE SUMMARY: Understanding what functions can be performed by members of the human gut microbial community and how they are interconnected can be very useful for the comprehension of this ecosystem’s function in human health and disease. Here, we propose an original approach to derive this information from metatranscriptomes and test it on publicly available data. The main idea is to develop an ecosystem-centric method aimed at quantitatively assessing the activity of particular groups of microorganisms associated with crucial functions performed by the human gut microbiota, such as the production of butyrate and acetate, the reduction of sulfate, and the decomposition of mucin—the key component of the intestinal mucus layer. The proposed method provides more information about the structure and properties of the analyzed ecosystem than other similar methods. We believe that such a strategy has great potential for biomedical research and potential applications in clinical medicine. ABSTRACT: We propose the trait-based method for quantifying the activity of functional groups in the human gut microbiome based on metatranscriptomic data. It allows one to assess structural changes in the microbial community comprised of the following functional groups: butyrate-producers, acetogens, sulfate-reducers, and mucin-decomposing bacteria. It is another way to perform a functional analysis of metatranscriptomic data by focusing on the ecological level of the community under study. To develop the method, we used published data obtained in a carefully controlled environment and from a synthetic microbial community, where the problem of ambiguity between functionality and taxonomy is absent. The developed method was validated using RNA-seq data and sequencing data of the 16S rRNA amplicon on a simplified community. Consequently, the successful verification provides prospects for the application of this method for analyzing natural communities of the human intestinal microbiota.