Systematic approach for dissecting the molecular mechanisms of transcriptional regulation in bacteria

Abstract

SignificanceOrganisms must constantly make regulatory decisions in response to a change in cellular state or environment. However, while the catalog of genomes expands rapidly, we remain ignorant about how the genes in these genomes are regulated. Here, we show how a massively parallel reporter assay, Sort-Seq, and information-theoretic modeling can be used to identify regulatory sequences. We then use chromatography and mass spectrometry to identify the regulatory proteins that bind these sequences. The approach results in quantitative base pair-resolution models of promoter mechanism and was shown in both well-characterized and unannotated promoters in Escherichia coli. Given the generality of the approach, it opens up the possibility of quantitatively dissecting the mechanisms of promoter function in a wide range of bacteria.

Multiplex transcriptional characterizations across diverse and hybrid bacterial cell-free expression systems

“…These transcriptional models were able to explain up to 50% of the variance in some species (Figure 4b,c). Our findings highlight the utility of applying cell-free expression approaches to diverse bacteria for gene regulation characterization and modeling to deepen mechanistic studies 28,[45][46][47] .…”

“…In these cases, additional regulatory proteins could be co-expressed with a library of regulatory DNA variants in the same in vitro reaction and their ligand-response dynamics could be monitored over time. Such approaches could also expedite the characterization of promoters controlled by multiple regulators 47 and facilitate their integration into complex regulatory networks 51,55 .…”

MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect

“…While there are many approaches for estimating mutual information, these methods are relatively slow and IM inference requires doing this after each update of the model parameters. Moreover, Kinney et al [17] and subsequent work [33,34,35] have performed IM inference using custom Metropolis Monte Carlo implementations that are slow and which have not been deployed as robust general-use software.…”

“…This approach focuses on the use of mutual information as an objective function. It arose from techniques in sensory neuroscience [57,58] that were elaborated and adapted for the analysis of microarray data [59,56], then applied to MPRAs [33,34,17,18]. However, due to difficulties in estimating mutual information, such analyses of MAVE data have relied on Metropolis Monte Carlo, which (in our experience) is too slow to support widespread adoption.…”

Dynamic control of pathway expression with riboregulated switchable feedback promoters

“…This is a result of the fact that microbial cells persist and thrive in changing environments due to their ability to sense and respond to stresses and environmental conditions 14 . Much of this ability is encoded within regulatory elements called stress-response promoters that integrate signals from complex and interconnected transcriptional networks to modulate mRNA synthesis in response to specific cellular stresses 15 . This creates the possibility of using stressresponse promoters to regulate heterologous pathway expression as a means to implement genetic feedback networks that lead to improvements in productivity and yield.…”