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Cyanobacteria engineering for restoring environments (CYBER)

Lead Research Organisation: University of Bristol
Department Name: Biological Sciences

Abstract

Pollution is one of the most pressing global challenges of today, threatening our ecosystems, health, and wellbeing. Traditional approaches to environmental clean-up often fall short due to the complexity of deploying them at scale into natural environments. There is an urgent need to rethink our strategy. This project aims to harness the potential of cyanobacteria - one of the oldest and most diverse organisms on Earth - to unlock new approaches. Cyanobacteria are ubiquitous in many environments and play a crucial role in basic ecosystem services due to their ability to fix nitrogen and perform photosynthesis. But despite a growing interest in the use of cyanobacteria for bioremediation, we currently lack reliable biological parts and experimental tools to safely reprogram cyanobacteria for this task. In this project we propose a multidisciplinary effort to overcome these hurdles and make engineered cyanobacteria a feasible platform for restoration of degraded environments.

To archive this, our project is built around four specific objectives. First, we will aim to construct what we term "ecological wind tunnels". These artificial ecosystems that we can build in the lab, better mimic the complex spatial-temporal organization and interactions found in the real world. By subjecting engineered organisms to these more realistic conditions, we can enhance their functionality, have greater confidence in their ability to perform in natural environments, and evaluate their ecological impact more accurately than in a typical lab setting. Second, we will use massively parallel assays and sequencing-based surveillance techniques, combined with rigorous measurements and advanced artificial intelligence, to facilitate the rapid development of biological tools for reprogramming cyanobacteria. Third, to ensure the traceability of our engineered cells in natural environments and facilitate their safe deployment, we will develop approaches to "barcode" our organisms. This will enable us to better track their dispersal and establish ownership of the organisms in commercial contexts. Finally, as a case study, we will engineer cyanobacteria that are able to naturally absorb various pollutants from the environment and alter their biology to simplify their physical removal from the environment. This ability to remove our engineered biology in a targeted way will help to reduce any long-term impact our cells have on a natural ecosystem by allowing them to only be present temporarily and not provide sufficient time for them to become embedded.

In addition to the science, we also recognise the crucial role of early engagement with society and policy makers around the acceptable use of this technology as it is developed. We have therefore partnered with a range of leading academics, companies, non-profit organisations, and funders to build a community and will hold inclusive events that connect our science to wider society and decision makers in government. We aim to use this point of interaction to understand concerns, communicate evidence-based risks and benefits regarding the science, and explore possible routes towards the acceptable use of engineered biology in environmental contexts.

Together, the science and engagement performed in this project will help revolutionize pollution control strategies and kick-start new sustainable bio-based solutions to environmental restoration. It will also develop the crucial foundational tools and methods needed to de-risk the deployment of engineered biology into real-world ecosystems and help to establish cyanobacteria as a versatile and safe platform for tackling diverse environmental challenges.

Technical Summary

Pollution is a major global challenge whose severe impact has been compounded by the current climate crisis. Standard approaches to clean-up are often ill-suited and ineffective given the complexity and difficulty of accessing the natural environments that are affected. Consequently, there is a critical need to rethink our approach.

We propose to harness nature itself by modifying the capabilities of cyanobacteria - some of the oldest and most diverse organisms on Earth - to unlock new solutions. Cyanobacteria are found in virtually every environment and often act as primary producers of organic compounds due to their ability to fix nitrogen and perform photosynthesis. These qualities make them ideally suited for wide-ranging environmental engineering and restoration applications. However, our ability to reprogram their internal biology and external interactions with wider ecosystems is currently lacking, hampering our ability to extend and employ their capabilities for tasks like bioremediation.

This project will directly address these issues by leveraging advances in high-throughput experimental assays, bio-focused metrology, machine learning and trustworthy strain engineering to enable the rapid and secure modification of cyanobacteria. We will build on recent developments in the creation of artificial ecosystems in the lab, to ensure the safe functioning and traceability of our engineered cells in complex environments that better mimic nature. As a case study, we will create engineered cyanobacteria that are able to absorb pollutants, be tracked as they populate an ecosystem, and have features to simplify their physical extraction using magnetic fields. The ability to combine advanced engineering biology tools and techniques with rigorous verification of safety and traceability will be key for future deployment of environmentally focused bioengineered products and ultimately realising the positive translational impact of engineering biology in this space.

Publications

10 25 50
 
Title Cas9-based enrichment for targeted long-read metabarcoding 
Description This includes: (1) a custom 18S NemaBase database for use with EPI2ME when processing metagenomic sequencing data including nematode species; and (2) nanopore sequencing data from Cas9-based enrichment experiments for several mixed species mock communities covering nematodes, yeast and bacteria. 
Type Of Material Database/Collection of data 
Year Produced 2025 
Provided To Others? Yes  
Impact This data is the first demonstration of the ability to use a mixture of probes for Cas9-enrichment experiments, enabling the simultaneous assessment of species composition across the tree of life. 
URL https://zenodo.org/doi/10.5281/zenodo.14250758
 
Title Photomovement of Euglena and Volvox in the DOME 
Description Photomovement of Microorganisms in the DOME Intro This dataset presents experimental data on the movement and light response of two species of microorganisms, specifically Euglena gracilis and Volvox aureus. Experiments were performed using the experimental platform DOME. A water sample containing multiple specimens of microorganisms was imaged with a microscopic camera. Simultaneously, light inputs, either time-varying or spatially defined, were projected onto the sample. Automatic object detection and tracking were employed to capture the trajectories and spatial distribution of the microorganisms. This data, together with the known light inputs, enables the study of the microorganisms' light responses, detailing both individual behaviors (e.g., photokinesis) and collective behaviors (e.g., photoaccumulation). The related article on modelling the photomovement of microorganisms, with more details on data acquisition and elaboration, is currently under review, and will be linked here once published. The software used to collect and analyse the data will soon be published and linked here. Content description Each zip file contains data from a specific experimental scenario, including multiple replicates and/or aggregated data. In the following M represents the number of sampling instants occurred during an experiment, and N the number of agents observed. Scenarios with time-varying inputs Files from scenarios with time-varying inputs have the following structure: SPECIES_SCENARIO.zip|- DATE_SPECIES_1                            Folder of a single experiment (i.e. replicate).|  |- data.npz                                   Experimental data and parameters. Compressed numpy file with the following fields:|  |  |  |> activation_times    Sampling time instants. Size M. Units [s].|  |  |  |> totalT                          Total duration of the experiment. Units [s].|  |  |  |> deltaT                          Nominal sampling time step. Units [s].|  |  |  |> commands                     Input commands applied during the experiment. List of dictionaries.|  |  |  |> camera2projector   Linear transformation matrix between camera and projector frames. Size 3x3.|  |  |  |> off_light                   Light value correspondig to zero input. BGR values.|  |  |  |> on_light                     Light value correspondig to full input. BGR values.|  |- experiment_log.txt            Experimental parameters and log. Human readable.|  |- images                                       Folder of pictures acquired during the experiment.|  |- patterns                                   Folder of input patterns as projected by the projector.|  |- patterns_cam                          Folder of input patterns as seen by the camera.|  |- tracking_DATE                        Folder of data generated during tracking and data processing.|  |  |- analysis_data.npz        Data from tracking procedure. Compressed numpy file with the following fields:|  |  |  |> positions                   Position of agents in the camera frame at each time instant. Size MxNx2. Units [px].|  |  |  |> inactivity                 Inactivity counter of agents at each time instant. Size MxN.|  |  |  |> total_cost                 Total tracking cost.|  |  |  |> parameters                 Tracking parameters.|  |  |- analysed_data.npz        Processed trajectories data. Compressed numpy file with the following fields:|  |  |  |> time_steps                 Duration of the sampling time steps. Size M-1. Units [s].|  |  |  |> interp_positions    Smoothed and interpolated positions of the agents. Size MxNx2. Units [px].|  |  |  |> speeds_smooth           Smoothed longitudinal speeds of the agents. Size MxN. Units [um/s].|  |  |  |> ang_vel_smooth         Smoothed angular speeds of the agents. Size MxN. Units [rad/s].|  |  |  |> inputs                          Sequence of light inputs (averaged over space). Size Mx3. BGR values.|  |  |- inputs.txt                        Light inputs. Tab-separeted file. BGR format with 0-255 range.|  |  |- ang_vel_smooth.txt      Recorded angular velocity of the microorganisms. Tab-separeted file. Units [rad/s].|  |  |- speeds_smooth.txt        Recorded speed of the microorganisms. Tab-separeted file. Units [um/s].|  |  |- plots                                   Folder of images from data analysis.|  |  |- tracking.mp4                   Video with microorgnisms tracking.|  |  |- trk_00.0.jpeg                 Image of microorgnisms tracking at time instant 0s.|  |  |...|  |  |- trk_180.0.jpeg              Image of microorgnisms tracking at time instant 180s.|... |- DATE_SPECIES_N                            Folder of a single experiment (i.e. replicate).|- combo                                                Folder of combined data from multiple replicates. |  |- combo_info.txt                     Description of combined data from multiple replicates. Human readable.|  |- inputs.txt                              Light inputs. Tab-separeted file. BGR format with 0-255 range.|  |- ang_vel_smooth.txt            Recorded angular velocity of the microorganisms. Tab-separeted file. Units [rad/s].|  |- speeds_smooth.txt              Recorded speed of the microorganisms. Tab-separeted file. Units [um/s].|  |- plots                                         Folder of images from data analysis. Scenarios with spatial inputs Files from scenarios with spatial inputs have the following structure: SPECIES_SCENARIO.zip|- DATE_SPECIES_1                            Folder of a single experiment (i.e. replicate).|  |- data.npz                                   Experimental data and parameters. Compressed numpy file. See above for information about the fields.|  |- experiment_log.txt            Experimental parameters and log. Human readable.|  |- images                                       Folder of pictures acquired during the experiment.|  |- patterns                                   Folder of input patterns as projected by the projector.|  |- patterns_cam                          Folder of input patterns as seen by the camera.|  |- background.jpeg                   Background image generated by the tracking software.|... |- DATE_SPECIES_N                            Folder of a single experiment (i.e. replicate). 
Type Of Material Database/Collection of data 
Year Produced 2024 
Provided To Others? Yes  
Impact This dataset allows for a demonstration of organism tracking, which will be crucial to future detailed analysis we would like to perform as part of the grant. 
URL https://zenodo.org/doi/10.5281/zenodo.13683455