Programmable bacteria detect and record an environmental signal in the mammalian gutJonathan W. Kotula, S. Jordan Kerns, Lev Shaket et al.|Proceedings of the National Academy of Sciences|2014 Significance The human microbiota represents the trillions of bacteria that live on the skin, in the oral, nasal, and aural cavities, and throughout the gastrointestinal tract. The species that live in the gastrointestinal tract, the gut microbiota, closely interact with host cells and have a profound impact on health. To develop tools to effectively monitor the gut microbiota and ultimately help in disease diagnosis, we have engineered Escherichia coli to sense and record environmental stimuli, and demonstrated that E. coli with such memory systems can survive and function in the mammalian gut. This work demonstrates that E. coli can be engineered into living diagnostics capable of nondestructively probing the mammalian gut.
Inferring gene regulation from stochastic transcriptional variation across single cells at steady stateAnika Gupta, Jorge D. Martin-Rufino, Thouis R. Jones et al.|Proceedings of the National Academy of Sciences|2022 Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.