Synthetic Fatberg DNA Extraction

We began our strategy for fatberg degradation by contacting various wastewater treatment and management companies across the UK, with the hope of obtaining a fatberg sample. Eventually, we contacted United Utilities in Liverpool who offered to send us a sample. Whilst the sample was in transit we began to search for protocols concerning the extraction of DNA from samples with a high lipid content and were lucky enough to contact Qiagen who generously offered to sponsor us by sending a 'PowerFaecal pro DNA extraction tool kit'.

Following thorough advice concerning the difficulties surrounding DNA extraction from fatbergs from Dr. Justin Pachebat (University of Aberystwyth) and Dr. John Love (University of Exeter) we decided to construct a synthetic fatberg to test the proficiency of our Qiagen DNA extraction kit. After spiking 21 different synthetic fatberg aliquots with E. coli cells and extracting the DNA using the kit, we nano-dropped each aliquot to check the DNA concentration and ran a gel to ensure we had extracted DNA. Unfortunately, as the gel below shows, we were unable to isolate any DNA from the trial.

Fearing that the DNA samples were too low to be detected in our gels, we amplified the DNA using 16S RNA PCR and ran another gel on the products but were again, unsuccessful as seen by the gel below.

Sequencing The Largest Fatberg In The World

We prepared a DNA library from the sample of fatberg DNA provided by Dr. Pachebat for sequencing, which was loaded onto a flow cell comprising a MinION device, generously donated by another of our sponsors - Oxford Nanopore. Following base-calling, we obtained 12Gb of sequences from the fatberg metagenome - that's approximately four times the length of the human genome! we identified 22 predicted lipase coding sequences, 21 of which are completely novel. This information was obtained by checking these sequences against the BLAST protein database in NCBI (National Center for Biotechnology Information). These sequences were revealed to have between 55 - 84% identity to the nearest hits. Several bacterial and yeast species were also observed within the metagenome, some of which were the same species our candidate lipases were derived from, following their identification from various literature sources.

After contacting Dr. Chris Quince based at the University of Warwick we were provided with a CPU computer cluster for metagenomic assembly. Following assembly, we utilised the software 'kraken2' to assign the reads and the contigs from the assembly to compile a phylogenetic tree of all the species found within our raw sequence reads. Several species were identified, including Pseudomonas fluorescens, which Thermostable lipase A (TliA) - a previous iGEM part (BBa_K258006) - is derived from.

Over 12GB of sequence was found in the DNA sequence and further analysis revealed the presence of several bacterial species shown in the graph below

Despite the discovery of these novel lipases, candidate lipases were selected for cloning. This was done due to:

* A limited quantity of fatberg DNA

* The potential for the DNA to encode a pathogenic factor that we would be unaware of

* A serious lack of time

whilst we were waiting for both our fatberg sample from United Utilities and fatberg DNA provided by Dr. Pachebat, we searched various databases and literature sources for other candidate lipases to clone into our E. coli cells. Our starting point for this was the Thermostable lipase A (TliA) derived from the bacterial species Pseudomonas fluorescens - an iGEM part previously used by other teams such as Sheffield, Stuttgart and KAIST. BLAST searching TliA on the NCBI database revealed some similar lipases which were considered as potential candidates for our cloning strategy, including a lipase precursor from a compost metagenome. Other lipases and lipase-producing species were identified from research papers investigating industrial wastewater treatment plants, lipid-rich wastewater and restaurant wastewater. The complete list of selected lipases can be viewed in the table below.

A New Strategy

To combat this, we thought about using secretion tags to export the lipases from our cells and prevent their accumulation, as well as putting our lipases under the control of an inducible promoter. Consequently, our next strategy involved cloning a small selection of our candidate lipases (since at this point we were pressed for time) into a new vector: pET151/D-TOPO, as shown below. The lipases used included Lipase A, chain A from Candida antarctica (CALA), an alkaliphilic lipase from Bacillus subtilis (BSAL), our compost metagenome lipase precursor (CMLP) and the Thermostable lipase A (TliA) from Pseudomonas fluorescens (please see our design page for justifications on this selection of lipases). This new backbone included a T7 promoter, enabling the induction of lipase expression with Isopropyl-β-D-thiogalactoside (IPTG). We decided to use this backbone following discussions with Dr. Love from Exeter University, who advised we engineer our bacteria such that they secrete our lipases in a controlled manner. This would not be possible if the lipases were under the control of a constitutive promoter like 'glpT'.

The gel below reveals the products obtained following cloning, miniprepping of our clones and performing a PCR using primers specific to each of our chosen lipases. After obtaining Sanger sequencing data of each of our clones, we were really pleased to discover that we successfully cloned in three of our lipases, as well as a fragment of TliA. These results further supported the hypothesis that our bacterial cells were dying due to the constitutive expression and accumulation of our lipases. Our next step was to characterise the lipase activity of our engineered E. coli expression strains (BL21 star) using both a quantitative and qualitative assay of our own design.

Testing The New Lipases

In addition to cloning our lipases into our E.coli expression strain BL21 star and characterising the activity and efficiency of our lipases using a spectrophotometric assay to derive the kinetic parameters of Km, Vmax and kcat we also wanted to assess whether our engineered bacteria could survive and better yet, grow in oil. After all, these bacteria will need to survive within a fatberg! With this in mind, we carried out a series of oil media experiments, measuring the population growth of E. coli at different oil concentrations using CFU (colony forming units) counts. The graph obtained from this experiment can be visualised below.

From here, we used the logistics equation to produce our model depicting the growth of bacteria at varying oil concentrations (please see our model page). This model was used to help us determine the optimal oil concentration to grow our engineered bacteria in. After consulting our model and talking to Dr. Kalesh Sasidharan, we decided to use a MicrobeMeter from Humane Technologies, to measure the growth of our engineered bacteria at an oil concentration of 0.5%. This concentration was chosen after consulting the model and identifying that as an optimal oil concentration. Additionally, we decided to use a Microbemeter to measure bacterial growth as Dr. Sasidharan advised that using CFU counts would be too labour intensive and time-consuming, in addition to us not being able to obtain a complete data-set needed to construct a complete growth curve.

Using the MicrobeMeter we first measured the growth of each of our engineered cells containing a non-induced construct. The results of this experiment revealed that the engineered bacteria containing CMLP and CALA did not follow a traditional growth curve, unlike BSAL, but rather showed 'peaks of activity' before their absorbance declined, signifying that the cells had died. Despite the constructs remaining uninduced, the presence of each construct severely affected growth rate. This can be observed from the doubling time obtained from each construct.

We then decided to repeat the experiment and induce each of our cells, adding 10μl of 1mM IPTG five hours into growth. This was done to determine the effect of lipase expression and accumulation within our cells. Interestingly, we were surprised to find that the growth curves for CALA and CMLP were very similar to the growth curves obtained for the same, uninduced cells. The growth curves for BSAL differed but the cells containing the BSAL construct died as expected upon induction with IPTG, shown by the decrease in absorbance. There was no significant difference between the doubling times obtained for both the induced and non-induced cells, as shown in the table below. The limited difference in growth rates between our induced and non-induced cells, coupled to the remarkable similarity between both the induced and non-induced CMLP and CALA growth curves led us to theorize that our constructs were being transcribed without the use of IPTG.

Summary

Therefore, from sequencing fatberg DNA to find lipase activity to use, to successfully cloning our constructs despite failing the first time round, we managed to find conclusive data, both qualitative and quantitative to suggest that our transformed bacteria are able to help degrade fatberg deposits, and to provide data to be used in the future of synthetic biology.