Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance

in Nature Communications (2020)

The development of reliable, mixed-culture biotechnological processes hinges on understanding how microbial ecosystems respond to disturbances. Here we reveal extensive phenotypic plasticity and niche ... [more ▼]

The development of reliable, mixed-culture biotechnological processes hinges on understanding how microbial ecosystems respond to disturbances. Here we reveal extensive phenotypic plasticity and niche complementarity in oleaginous microbial populations from a biological wastewater treatment plant. We perform meta-omics analyses (metagenomics, metatranscriptomics, metaproteomics and metabolomics) on in situ samples over 14 months at weekly intervals. Based on 1,364 de novo metagenome-assembled genomes, we uncover four distinct fundamental niche types. Throughout the time-series, we observe a major, transient shift in community structure, coinciding with substrate availability changes. Functional omics data reveals extensive variation in gene expression and substrate usage amongst community members. Ex situ bioreactor experiments confirm that responses occur within five hours of a pulse disturbance, demonstrating rapid adaptation by specific populations. Our results show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity, and set the foundation for future ecological engineering efforts. [less ▲]

A multi-omic view of invasive genetic elements and their linked prokaryotic population dynamics within a mixed microbial community

Poster (2018, September 11)

Integrated time-resolved multi-omics for understanding microbial niche ecology

Poster (2018, August)

Microbial communities are strongly shaped by the niche breadths of their constituent populations. However, a detailed understanding of microbial niche ecology is typically lacking. Integrated multi-omic ... [more ▼]

Microbial communities are strongly shaped by the niche breadths of their constituent populations. However, a detailed understanding of microbial niche ecology is typically lacking. Integrated multi-omic analyses of host- or environment-derived samples offer the prospect of resolving fundamental and realised niches in situ. In turn, this is considered a prerequisite for niche engineering in order to drive an individual population or a community towards a specific phenotype, e.g., improvement of a biotechnological process. Here, we sampled floating islets on the surface of an activated sludge tank in a time-series spanning 51 time-points over 14 months. Multi-omics datasets (metagenomics, metatranscriptomics, metaproteomics, and (meta-)metabolomics) were generated for all time-points. Leveraging nucleotide sequencing data, we analyzed the community structure and reconstructed genomes for specific populations of interest. Moreover, based on their metabolic potential, three major groups emerged, serving as proxies for their respective fundamental niches . Time-resolved linkage of the proteomic and transcriptomic data to the reconstructed genomes revealed a fine-grained picture of niche realization. In particular, environmental factors (temperature, metabolites, oxygen) were significantly associated with gene expression of individual populations. Furthermore, we subjected the community to controlled oxygen conditions (stable or dynamic) in a bioreactor experiment and measured the transcriptomic response. Our results suggest short-term adaptations of populations of interest with respect to lipid metabolism, among other pathways. In conclusion, our work demonstrates how longitudinal multi-omic datasets can be integrated in order to further our understanding of microbial niche ecology within a biotechnological process with potential applications beyond waste water treatment. [less ▲]

Using metabolic networks to resolve ecological properties of microbiomes

in Current Opinion in Systems Biology (2018)

The systematic collection, integration and modelling of high-throughput molecular data (multi-omics) allows the detailed characterisation of microbiomes in situ. Through metabolic trait inference ... [more ▼]

The systematic collection, integration and modelling of high-throughput molecular data (multi-omics) allows the detailed characterisation of microbiomes in situ. Through metabolic trait inference, metabolic network reconstruction and modelling, we are now able to define ecological interactions based on metabolic exchanges, identify keystone genes, functions and species, and resolve ecological niches of constituent microbial populations. The resulting knowledge provides detailed information on ecosystem functioning. However, as microbial communities are dynamic in nature the field needs to move towards the integration of time- and space-resolved multi-omic data along with detailed environmental information to fully harness the power of community- and population-level metabolic network modelling. Such approaches will be fundamental for future targeted management strategies with wide-ranging applications in biotechnology and biomedicine. [less ▲]

Comparative integrated-omic analyses of phage-host interactions within natural and engineered microbial communities

Poster (2017, July 21)