Shaping human gut community assembly and butyrate production by controlling the arginine dihydrolase pathway.

The arginine dihydrolase pathway (arc operon) present in a subset of diverse human gut species enables arginine catabolism. This specialized metabolic pathway can alter environmental pH and nitrogen availability, which in turn could shape gut microbiota inter-species interactions. By exploiting synthetic control of gene expression, we investigated the role of the arc operon in probiotic Escherichia coli Nissle 1917 on human gut community assembly and health-relevant metabolite profiles in vitro and in the murine gut. By stabilizing environmental pH, the arc operon reduced variability in community composition across different initial pH perturbations. The abundance of butyrate producing bacteria were altered in response to arc operon activity and butyrate production was enhanced in a physiologically relevant pH range. While the presence of the arc operon altered community dynamics, it did not impact production of short chain fatty acids. Dynamic computational modeling of pH-mediated interactions reveals the quantitative contribution of this mechanism to community assembly. In sum, our framework to quantify the contribution of molecular pathways and mechanism modalities on microbial community dynamics and functions could be applied more broadly.

Inferring delays in partially observed gene regulation processes.

MOTIVATION: Cell function is regulated by gene regulatory networks (GRNs) defined by protein-mediated interaction between constituent genes. Despite advances in experimental techniques, we can still measure only a fraction of the processes that govern GRN dynamics. To infer the properties of GRNs using partial observation, unobserved sequential processes can be replaced with distributed time delays, yielding non-Markovian models. Inference methods based on the resulting model suffer from the curse of dimensionality. RESULTS: We develop a simulation-based Bayesian MCMC method employing an approximate likelihood for the efficient and accurate inference of GRN parameters when only some of their products are observed. We illustrate our approach using a two-step activation model: an activation signal leads to the accumulation of an unobserved regulatory protein, which triggers the expression of observed fluorescent proteins. With prior information about observed fluorescent protein synthesis, our method successfully infers the dynamics of the unobserved regulatory protein. We can estimate the delay and kinetic parameters characterizing target regulation including transcription, translation, and target searching of an unobserved protein from experimental measurements of the products of its target gene. Our method is scalable and can be used to analyze non-Markovian models with hidden components. AVAILABILITY AND IMPLEMENTATION: Our code is implemented in R and is freely available with a simple example data at https://github.com/Mathbiomed/SimMCMC.

Programming bacteria for multiplexed DNA detection.

DNA is a universal and programmable signal of living organisms. Here we develop cell-based DNA sensors by engineering the naturally competent bacterium Bacillus subtilis (B. subtilis) to detect specific DNA sequences in the environment. The DNA sensor strains can identify diverse bacterial species including major human pathogens with high specificity. Multiplexed detection of genomic DNA from different species in complex samples can be achieved by coupling the sensing mechanism to orthogonal fluorescent reporters. We also demonstrate that the DNA sensors can detect the presence of species in the complex samples without requiring DNA extraction. The modularity of the living cell-based DNA-sensing mechanism and simple detection procedure could enable programmable DNA sensing for a wide range of applications.

Efficient plasmid transfer via natural competence in a microbial co-culture.

The molecular and ecological factors shaping horizontal gene transfer (HGT) via natural transformation in microbial communities are largely unknown, which is critical for understanding the emergence of antibiotic-resistant pathogens. We investigate key factors shaping HGT in a microbial co-culture by quantifying extracellular DNA release, species growth, and HGT efficiency over time. In the co-culture, plasmid release and HGT efficiency are significantly enhanced than in the respective monocultures. The donor is a key determinant of HGT efficiency as plasmids induce the SOS response, enter a multimerized state, and are released in high concentrations, enabling efficient HGT. However, HGT is reduced in response to high donor lysis rates. HGT is independent of the donor viability state as both live and dead cells transfer the plasmid with high efficiency. In sum, plasmid HGT via natural transformation depends on the interplay of plasmid properties, donor stress responses and lysis rates, and interspecies interactions.

Bayesian inference of distributed time delay in transcriptional and translational regulation.

MOTIVATION: Advances in experimental and imaging techniques have allowed for unprecedented insights into the dynamical processes within individual cells. However, many facets of intracellular dynamics remain hidden, or can be measured only indirectly. This makes it challenging to reconstruct the regulatory networks that govern the biochemical processes underlying various cell functions. Current estimation techniques for inferring reaction rates frequently rely on marginalization over unobserved processes and states. Even in simple systems this approach can be computationally challenging, and can lead to large uncertainties and lack of robustness in parameter estimates. Therefore we will require alternative approaches to efficiently uncover the interactions in complex biochemical networks. RESULTS: We propose a Bayesian inference framework based on replacing uninteresting or unobserved reactions with time delays. Although the resulting models are non-Markovian, recent results on stochastic systems with random delays allow us to rigorously obtain expressions for the likelihoods of model parameters. In turn, this allows us to extend MCMC methods to efficiently estimate reaction rates, and delay distribution parameters, from single-cell assays. We illustrate the advantages, and potential pitfalls, of the approach using a birth-death model with both synthetic and experimental data, and show that we can robustly infer model parameters using a relatively small number of measurements. We demonstrate how to do so even when only the relative molecule count within the cell is measured, as in the case of fluorescence microscopy. AVAILABILITY AND IMPLEMENTATION: Accompanying code in R is available at https://github.com/cbskust/DDE_BD. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Timing of Transcriptional Regulation in Synthetic Gene Circuits.

Transcription factors and their target promoters are central to synthetic biology. By arranging these components into novel gene regulatory circuits, synthetic biologists have been able to create a wide variety of phenotypes, including bistable switches, oscillators, and logic gates. However, transcription factors (TFs) do not instantaneously regulate downstream targets. After the gene encoding a TF is turned on, the gene must first be transcribed, the transcripts must be translated, and sufficient TF must accumulate in order to bind operator sites of the target promoter. The time to complete this process, here called the "signaling time," is a critical aspect in the design of dynamic regulatory networks, yet it remains poorly characterized. In this work, we measured the signaling time of two TFs in Escherichia coli commonly used in synthetic biology: the activator AraC and the repressor LacI. We found that signaling times can range from a few to tens of minutes, and are affected by the expression rate of the TF. Our single-cell data also show that the variability of the signaling time increases with its mean. To validate these signaling time measurements, we constructed a two-step genetic cascade, and showed that the signaling time of the full cascade can be predicted from those of its constituent steps. These results provide concrete estimates for the time scales of transcriptional regulation in living cells, which are important for understanding the dynamics of synthetic transcriptional gene circuits.