Identification and understanding of critical transitions within mixed microbial communities

Susana Martinez Arbas, PhD supervisor: Paul Wilmes, with external partner Marten Scheffer

The goal of this project is to establish a solid description for population dynamics and system changes in metagenomics data based on the developed theoretical tools. PW’s group has developed a range of unprecedented wet- (Roume 2013) and dry- (Laczny 2014) lab methodologies that allow the systematic molecular characterisation of microbial consortia over space and time (Muller 2013). Furthermore, methods have been developed to integrate and analyse the multi-omic data at either the community- (Roume 2015) or population- (Muller, 2014) levels. Community-level analyses have involved the reconstruction of community-wide metabolic networks using the generated multi-omics data and the subsequent identification of key functional genes, which play a disproportionate role in governing community structure and function (Roume 2015). Conversely, population-level analyses have involved de novo reconstruction of genomic complements, which have facilitated functional insights into substrate utilisation and resulting lifestyle strategies (Muller 2014). The group has recently integrated the different steps and approaches into the integrated meta-omics pipeline (Narayanasamy et al., in preparation). The developed workflows have been applied to two distinct microbial consortia of biotechnological and biomedical research interest. These include lipid accumulating microbial consortia (LAMCs) from a biological wastewater treatment plant and human gastrointestinal tract (GIT) microbiota. In addition, extensive metadata on the two systems has been recorded that includes physicochemical parameters (e.g. temperature, pH, water chemistry), of the wastewater treatment plant as well as biomedical data of individuals undergoing sampling. Tipping elements have been reported in microbial communities, especially in the human gut microbiome, where robust bistable abundance distributions of certain bacterial taxa have been reported and the associated contrasting alternative states are associated with host characteristics such as age and body weight (Lahti 2014). However, a detailed understanding of which factors may drive such transitions so far remains elusive. Here, we propose to employ the unprecedented longitudinal datasets generated from two distinct communities with the aim of identifying drivers of critical transitions in such systems. More specifically, we aim to elucidate which biotic factors and environment/host factors may represent tipping elements governing overall microbial community structure and function. Consequently, this project’s approaches are tightly connected with the DTU and the group’s unprecedented high-resolution molecular datasets will provide an invaluable resource for comprehensive interrogation and analysis. Through the involvement of PW’s group, the DTU students will gain access to unique resources and confront research questions likely to have profound biotechnological or health implications.

PW’s group will provide access to time-resolved high-resolution molecular datasets from both a biological wastewater treatment plant and from 3 distinct human subjects spanning 1.5 and 1 year, respectively. The data have either already been obtained or are in the process of being generated using established methods. They comprise metagenomic, metatranscriptomic, metaproteomic and metabolomic data from each individual time point. The data will be subjected to our routine bioinformatic analyses resulting in integrated omic datasets for each time point. The data will be subsequently analysed to identify potential CTs in microbial community structure and function. For this, the project will draw upon the extensive theoretical and analytical expertise within the DTU consortium. Once identified, we aim to elucidate which are the major abiotic and biotic factors that precede these CTs. We will further focus on studying how community function is affected before, during and after such transitions. Furthermore, we will study whether the communities structurally or functionally converge following such an event. The subproject will interact with the theoretical developments by providing high-dimensional data as test cases for the suggested analytical approaches. The student will be also scientifically supervised by Dr. Emilie Muller, an expert in multi-omic analyses of microbial communities over space and time.