Engineered Bacteria Sense and Record Environmental Signals
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Scientists from Columbia University have engineered bacteria to record signals from the external environment in their swarming patterns. The team successfully decoded these signals using artificial intelligence (AI). The research, published in Nature Chemical Biology, provides a framework for building a “natural” environment recording system.
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Bacteria create unique patterns through swarming
Many species of bacteria are motile, a characteristic that can support their survival if environmental conditions are unfavorable. Several types of bacterial motility have been classified, such as twitching, gliding, swimming and swarming. The latter is a coordinated movement of bacterial cells mediated by the flagella – a hairlike structure that can be likened to a “tail”. In response to specific environmental cues the flagella create a whip-like motion that enables bacterial cells to swarm collectively, sometimes producing patterns that are visible to the naked eye. Scientists have even been known to “accidentally” produce works of art in the lab while studying bacterial swarm patterns; Balagam et al engineered mutant strains of Myxococcus xanthus – predatory bacteria that form cooperate swarms – which resemble Van Gogh’s “Starry Night” when swarming.
Researchers in the laboratory of Dr. Tal Danino, associate professor in the department of biomedical engineering at Columbia University, had been contemplating how bacteria that naturally form patterns – such as Proteus mirabilis – could be engineered to create recording systems. Their inspiration came from other sources of patterns in nature that document useful information, such as tree rings, which visibly document a tree’s age and climate history. Could bacteria be engineered to possess a similar function? “This seemed to us to be an untapped opportunity to create a natural recording system for specific cues,” says Danino, who is also a member of Columbia’s Data Science Institute (DSI).
Engineering P. mirabilis to record its environment
The researchers’ decision to focus on P. mirabilis was twofold: its native patterns can be easily seen by the naked eye and it forms on solid agar media. Combined, these factors reduce the cost of a hypothetical natural recording system. The bacteria – which form bullseye-like colony patterns – alternate between phases of growth, where dense circles form, and phases of swarming, where the colony expands outwards.
Danino and colleagues engineered P. mirabilis by adding genetic circuits that enabled the bacteria to “write” external inputs – specifically chosen by the researchers – into a visible record. Such inputs included a change in temperature or adding sugar molecules or heavy metals to the medium. In response to these inputs, the engineered bacteria changed their swarming ability, which produced a visible change in the output – a different pattern.
Dr. Andrew Laine, Percy K. and Vida L. W. Hudson Professor of biomedical engineering and a DSI member, and Dr. Jia Guo, assistant professor of neurobiology at the Columbia University Irving Medical Center, joined the study to apply deep learning to decode the input from the pattern. Using this approach, the team could predict the sugar concentration in a sample, or how many times the temperature changed while the colony grew.
What is deep learning?
Deep learning is a type of machine learning that teaches computers to replicate the human brain and learn by example.
Deep learning – a sophisticated tool in the AI toolbox – could be harnessed to extract information from incredibly complex patterns, the researchers suggest. “Our goal is to develop this system as a low-cost detection and recording system for conditions such as pollutants and toxic compounds in the environment,” says Dr. Anjali Doshi, the study’s lead author and a recent graduate from Danino’s lab. “To our knowledge, this work is the first study where a naturally pattern-forming bacterial species has been engineered by synthetic biologists to modify its native swarming ability and function as a sensor.”
The ultimate goal of the Danino lab is to develop a device that can take the recording system out of the lab. Their next steps towards this ambition will involve engineering bacteria to detect a wider range of inputs and to move the system into probiotic bacteria. Beyond creating natural recording devices, the team also have their sights set on engineering bacterial behaviors in the human body, which could enable better control of bacteria beyond the use of antibiotics.
“This work creates an approach for building macroscale bacterial recorders, expanding the framework for engineering emergent microbial behaviors,” the researchers write in the Nature paper.
Reference: Doshi A, Shaw M, Tonea R, et al. Engineered bacterial swarm patterns as spatial records of environmental inputs. Nat Chem Bio. 2023. doi: 10.1038/s41589-023-01325-2
This article is a rework of a press release issued by the University of Columbia. Material has been edited for length and content.