During our early ideation stages, we first chose the Clostridium genus after a conversation with an ISTAART journal club (Quoilin et. al, 20230 and a conversation with Bloom Sciences’ Dr.Meano. Though harder to engineer, Clostridium has proven to have an important link in neurodegenerative disorders.
First, We immediately eliminated certain Clostridia from our list, although quite suited to life in the gut, their pathogenicity made us eliminate them; C. difficile, C. tetani, C. perfringens and C. botulinum. We eventually arrived at C. acetobutylicum and C. butyricum. C. acetobutylicum has not been used outside of its industrial setting, along with the possible complications of solvent production in the human gut, we decided not to go with this organism.
Our team attended and volunteered at the 2023 Synbiobeta conference in Oakland, California.We did so, hoping to gain more insight into the design of our project and biosafety measures by talking to professionals in the space. At this time, we hoped to engineer a kill switch as a measure to curb the engineered probiotic upon implementation.
From a panel at the conference, we were involved in a robust discussion of why kill switches are not foolproof-
Kill switches are difficult to maintain because they create strong selection pressure. This can lead to the evolution of escape mutant populations.
Kill switches are also prone to inactivating point mutations, especially when constitutively expressed (Rottinghaus et. al, 2022)
More easily overridden than other genetic controls currently available.
Gallagher et. al, 2015 demonstrated that engineered probiotics need at least two layers of protection to be implemented effectively. Our first layer of control is the
Toxin-antitoxin auxotrophy for biocontainment, second layer of control:
We were unable to create this layer of biosafety due to a lack of time in this competition cycle. We hope that the design we propose helps new iGEM teams work with non model gut commensals
This second layer upon the riboswitch is to prevent horizontal gene transfer and colonizing the gut. This can be determined after simulated studies.
Adapting proposed system to Clostridium butyricum:
▪ An auxotrophic Clostridium butyricum strain is to be engineered using two plasmids.
▪ One plasmid has the toxin gene and antidote gene.
▪ The second plasmid encodes the machinery for inserting the unnatural amino acid 3-iodo-L-tyrosine (IY) at amber stops (Kato et. al, 2015).
▪ In the presence of IY, it is incorporated into the first plasmid, enabling production of the antidote.
▪ This allows the engineered strain, C. butyricum (IY, immC), to survive toxicity when IY is provided.
▪ Without IY, translation is stopped, leading to toxicity and death of the strain.
▪ Therefore, the strain is auxotrophic for the unnatural amino acid IY for survival.
Design considerations for when implemented in the gut-
▪ Encapsulating IY in a gut-stable formulation could allow targeted release in specific regions of the GI tract.
▪ Using inert, non-toxic materials like alginate or cellulose for encapsulation should improve safety.
▪ Testing capsule stability and release kinetics in gut-mimicking conditions would be critical.
Dry Lab
The rapid advances in artificial intelligence (AI) and its integration with biotechnology present exciting opportunities for innovation but also raise biosecurity concerns. Recent work by Urbina et al. demonstrated how AI models for drug discovery could easily be repurposed to design lethal chemical weapons, highlighting the dual use risks (Urbina et. al, 2022). Federated learning (FL) works by preventing central aggregation of sensitive biological information, thus dual use concerns posed by Urbina et. al can be overcome.
In our own project, we have created a hybrid Quantum Machine learning drug discovery tool to uncover Acetylcholinesterase (AChE) inhibitors as drug targets. To mitigate these risks, we used a FL framework (named Flower, created by the University of Cambridge). We were able to conclusively demonstrate that FL is just as equally accurate and efficient as our Classical ML model. These findings have been compiled in the below report.
FL can also be extended to our caregiver resource (Integrated Human practices), to protect patient data privacy.
NCSU iGEM with collaborators, Raina Talwar Bhatia and Anguzu Simon created a comprehensive report that provides actionable recommendations across governance layers for AI in biology. We have interviewed some of the foremost professionals in this space.
Finally, we proposed policies that involve organizational controls like ethics reviews and misuse testing, to federated learning's preservation of sensitive data privacy, to international dialogues for aligning biosecurity protocol. Our solutions pave an equitably shared path to progress. With robust oversight and collective wisdom, the incredible potential of AI convergence with synthetic biology can be harnessed to uplift humanity.
Read the full report to gain key insights into governing emerging technologies at the critical nexus of artificial intelligence and biology.
While setting up our iGEM team, we realized that there was no standardized safety course or resource on campus for student lead projects.There is a lack of comprehensive, and individual-laboratory-centric training and education for early researchers. We worked with the professors and lab technicians at the Biotechnology department to create this quick video summarizing biosafety 101, which was used in our Education drive as well. We stuck religiously to these policies and practices throughout the course of this competition cycle.
Below are the guidelines our team followed:
▪ Conduct thorough risk assessments for all experiments and laboratory procedures to identify potential hazards and determine appropriate biosafety levels, equipment, and protocols. Update assessments regularly.
▪ Use proper personal protective equipment (PPE) including lab coats, gloves, eye protection, face masks, shoe coverings, etc. based on biosafety level. Inspect PPE regularly for defects. Have designated areas for putting on and removing PPE.
▪ Clearly label all biological materials, reagents, specimens, etc. Follow storage protocols for different biohazards. Use secondary containment.
▪ Follow protocols for safe handling, transport, and shipping of biological materials. Only move what is essential and use sealed, sturdy secondary containers.
▪ Clean spills immediately following established protocols based on type of biohazard. Use appropriate disinfectants and containment techniques.
▪ Decontaminate all contaminated materials and equipment before washing, reuse or disposal. Follow protocols like autoclaving, chemical disinfection, etc.
▪ Wash hands frequently and thoroughly especially after handling biohazards, before leaving the lab, and before eating or other activities. Never touch your face while in the lab.
▪ Follow established laboratory safety procedures and biosafety manuals exactly. Never take shortcuts or rush protocols. Seek help if unsure.
▪ Use Biological Safety Cabinets for procedures that may disseminate hazardous material. Verify cabinets are operating correctly.
▪ Sterilize media, glassware, supplies and all waste. Autoclave equipment and materials properly before reuse, washing or disposal.
▪ Report any laboratory accidents, exposures, or breaches immediately per institutional policies and procedures. Seek medical attention if exposed.
▪ Maintain clean, uncluttered spaces. Stow bags and coats away from work areas. Know locations of emergency equipment like eyewashes, showers, spill kits and fire extinguishers.
▪ Restrict access to the laboratory only to trained, authorized personnel.
To complement the safety measures in the lab, we also created this Cybersecurity in biology material linked below:
References
Urbina, F., Lentzos, F., Invernizzi, C. et al. Dual use of artificial-intelligence-powered drug discovery. Nat Mach Intell 4, 189–191 (2022). https://doi.org/10.1038/s42256-022-00465-9
Caroline Quoilin, Camille Amadieu, Fanny Fievez, Nathalie M. Delzenne, Philippe de Timary, Julie Duque, Sophie Leclercq, Exploring the links between gut microbiota and excitatory and inhibitory brain processes in alcohol use disorder: A TMS study,Neuropharmacology, 10.1016/j.neuropharm.2022.109384, 225, (109384), (2023)
Gallagher RR, Patel JR, Interiano AL, Rovner AJ, Isaacs FJ. Multilayered genetic safeguards limit growth of microorganisms to defined environments. Nucleic Acids Res. 2015 Feb 18;43(3):1945-54. doi: 10.1093/nar/gku1378. Epub 2015 Jan 7. PMID: 25567985; PMCID: PMC4330353.
p>Rottinghaus, A.G., Ferreiro, A., Fishbein, S.R.S. et al. Genetically stable CRISPR-based kill switches for engineered microbes. Nat Commun 13, 672 (2022). https://doi.org/10.1038/s41467-022-28163-5
Inés C. Cañadas, Daphne Groothuis, Maria Zygouropoulou, Raquel Rodrigues, and Nigel P. Minton, ACS Synthetic Biology 2019 8 (6), 1379-1390, DOI: 10.1021/acssynbio.9b00075
Kato Y. An engineered bacterium auxotrophic for an unnatural amino acid: a novel biological containment system. PeerJ. 2015 Sep 15;3:e1247. doi: 10.7717/peerj.1247. PMID: 26401457; PMCID: PMC4579030.
