Multi-omics analyses

We use vast volumes of functional information we generate through big data approaches such as metagenomics and untargeted metabolomics and combine them with industrially-relevant process data in order to make informed decisions based on microbial interactions.

Whole genome sequencing (WGS) approaches have highlighted that AD communities are highly complex, often consisting of hundreds of organisms present at differing abundances. These communities also contain high numbers of organisms which are not readily manipulated, poorly characterised and may be low abundance but contribute significantly to the functionality of the community. We use data and statistics driven analytical methods in order to identify and dissect the functional abilities of these organisms and the community as a whole.

Novel long-read sequencing technologies such as Oxford Nanopore Technology (ONT) have transformed our ability to understand the microbial interactions underpinning these communities and we have a wealth of expertise in the analysis of complex metagenomics analysis using ONT data.

We understand encoded functional capacity does not fully represent the underlying biology and so we also use proteomic and untargeted metabolomic approaches to identify the activity of individual members within these communities. We have found that the combination of these omic approaches alongside the integration of process data has enabled us to better understand community structure both in steady states and in response to environmental changes.

“At CEAD we utilise access to cutting edge genomics and metabolomics facilities and use our expertise to gain mechanistic understanding into these poorly characterised microbial communities.”

Dr Sarah Forrester, Research lead.

Activities and Partnerships

Pre treatment and process impact

In collaboration with Anglian Water, we sampled from nine sludge treatment centres over an eight-week time course and investigated the impact of sludge pre-treatment on microbial communities at each stage of the AD process. We used metagenomics and the proteomic labelling approach BONCAT.

We combined this with operational data from these sights which enabled us to understand how changes to operational process impact these microbial communities.

Metabolomics for biogas prediction

We collaborated with the University of Exeter and used multi-omic approaches to look at the comparative predictive power of genomic and small molecular signatures in defined mixed microbial communities. We wanted to understand which approach was better for determining the productiveness (biogas generating potential) of the community.

Feedstock impact on activity

We used a protein labelling method BONCAT and proteomics in combination with metagenomics to identify how feeding communities' different carbon sources can influence both the VFA profile of communities and also the microbial community composition.