Escherichia coli uses the ability of σ factors to recognize specific DNA sequences in order to quickly control large gene cohorts. While most genes respond to only one σ factor, approximately 5% have dual σ factor preference. The ones in significant numbers are ‘σ70+38 genes’, responsive to σ70, which controls housekeeping genes, as well as to σ38, which activates genes during stationary growth and stresses. We show that σ70+38 genes are almost as upregulated in stationary growth as genes responsive to σ38 alone. Also, their response strengths to σ38 are predictable from their promoter sequences. Next, we propose a sequence- and σ38 level-dependent, analytical model of σ70+38 genes applicable in the exponential, stationary, and in the transition period between the two growth phases. Finally, we propose a general model, applicable to other σ factors as well. This model can guide the design of synthetic circuits with sequence-dependent sensitivity and plasticity to transitions between the exponential and stationary growth phases.Author SummaryPresent challenges in Synthetic Biology include the design of genetic circuits that are robust to growth phase transitions and whose responsiveness is sequence-dependent, and, thus predictable prior to design. We present and validate an empirical-based, sequence-dependent analytical model of E. coli genes with dual responsiveness to the regulators σ70 and σ38. These genes, supported by our sequence-dependent model, could become building blocks for synthetic genetic circuits functional in both the exponential and the stationary growth phases.

Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For example, the species and its antibiotic susceptibility are essential to treating a bacterial infection. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to discriminate four species of bacteria relevant for human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We evaluate performance of convolutional neural networks and vision transformers, where the best models attain a class-average accuracy of 98%. Our successful proof of principle results suggests that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use.

Antimicrobial resistance is an increasing problem globally. Rapid antibiotic susceptibility testing (AST) is urgently needed in the clinic to enable personalized prescription in high-resistance environments and limit the use of broad-spectrum drugs. Previously we have described a 30 min AST method based on imaging of individual bacterial cells. However, current phenotypic AST methods do not include species identification (ID), leaving time-consuming plating or culturing as the only available option when ID is needed to make the sensitivity call. Here we describe a method to perform phenotypic AST at the single-cell level in a microfluidic chip that allows subsequent genotyping by in situ FISH. By stratifying the phenotypic AST response on the species of individual cells, it is possible to determine the susceptibility profile for each species in a mixed infection sample in 1.5 h. In this proof-of-principle study, we demonstrate the operation with four antibiotics and a mixed sample with four species.

Closely spaced promoters in tandem formation are abundant in bacteria. We investigated the evolutionary conservation, biological functions, and the RNA and single-cell protein expression of genes regulated by tandem promoters in E. coli. We also studied the sequence (distance between transcription start sites ‘dTSS’, pause sequences, and distances from oriC) and potential influence of the input transcription factors of these promoters. From this, we propose an analytical model of gene expression based on measured expression dynamics, where RNAP-promoter occupancy times and dTSS are the key regulators of transcription interference due to TSS occlusion by RNAP at one of the promoters (when dTSS ≤ 35 bp) and RNAP occupancy of the downstream promoter (when dTSS > 35 bp). Occlusion and downstream promoter occupancy are modeled as linear functions of occupancy time, while the influence of dTSS is implemented by a continuous step function, fit to in vivo data on mean single-cell protein numbers of 30 natural genes controlled by tandem promoters. The best-fitting step is at 35 bp, matching the length of DNA occupied by RNAP in the open complex formation. This model accurately predicts the squared coefficient of variation and skewness of the natural single-cell protein numbers as a function of dTSS. Additional predictions suggest that promoters in tandem formation can cover a wide range of transcription dynamics within realistic intervals of parameter values. By accurately capturing the dynamics of these promoters, this model can be helpful to predict the dynamics of new promoters and contribute to the expansion of the repertoire of expression dynamics available to synthetic genetic constructs.Author SummaryTandem promoters are common in nature, but investigations on their dynamics have so far largely relied on synthetic constructs. Thus, their regulation and potentially unique dynamics remain unexplored. We first performed a comprehensive exploration of the conservation of genes regulated by these promoters in E. coli and the properties of their input transcription factors. We then measured protein and RNA levels expressed by 30 Escherichia coli tandem promoters, to establish an analytical model of the expression dynamics of genes controlled by such promoters. We show that start site occlusion and downstream RNAP occupancy can be realistically captured by a model with RNAP binding affinity, the time length of open complex formation, and the nucleotide distance between transcription start sites. This study contributes to a better understanding of the unique dynamics tandem promoters can bring to the dynamics of gene networks and will assist in their use in synthetic genetic circuits.