Genomic footprinting uncovers global transcription factor responses to amino acids in Escherichia coli

Low leucine levels in the blood enhance the pathogenicity of neonatal meningitis-causing Escherichia coli

Neonatal bacterial meningitis is associated with substantial mortality and morbidity worldwide. Neonatal meningitis-causing Escherichia coli (NMEC) is the most common gram-negative bacteria responsible for this disease. However, the interactions of NMEC with its environment within the host are poorly understood. Here, we showed that a low level of leucine, a niche-specific signal in the blood, promotes NMEC pathogenicity by enhancing bacterial survival and replication in the blood. A low leucine level downregulates the expression of NsrP, a small RNA (sRNA) identified in this study, in NMEC in an Lrp-dependent manner. NsrP destabilizes the mRNA of the purine biosynthesis-related gene purD by direct base pairing. Decreased NsrP expression in response to low leucine levels in the blood, which is a purine-limiting environment, activates the bacterial de novo purine biosynthesis pathway, thereby enhancing bacterial pathogenicity in the host. Deletion of NsrP or purD significantly increases or decreases the development of E. coli bacteremia and meningitis in animal models, respectively. Furthermore, we showed that intravenous administration of leucine effectively reduces the development of bacteremia and meningitis caused by NMEC by blocking the Lrp-NsrP-PurD signal transduction pathway. This study provides a potential strategy for the prevention and treatment of E. coli-induced meningitis.

Deciphering regulatory architectures of bacterial promoters from synthetic expression patterns

For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRAs, we need a "theory of the experiment" to help us better understand the impact of various biological and experimental parameters on the interpretation of experimental data. These parameters include binding site copy number, where a large number of specific binding sites may titrate away transcription factors, as well as the presence of overlapping binding sites, which may affect analysis of the degree of mutual dependence between mutations in the regulatory region and expression levels. To that end, in this paper we create tens of thousands of synthetic gene expression outputs for bacterial promoters using both equilibrium and out-of-equilibrium models. These models make it possible to imitate the summary statistics (information footprints and expression shift matrices) used to characterize the output of MPRAs and thus to infer the underlying regulatory architecture. Specifically, we use a more refined implementation of the so-called thermodynamic models in which the binding energies of each sequence variant are derived from energy matrices. Our simulations reveal important effects of the parameters on MPRA data and we demonstrate our ability to optimize MPRA experimental designs with the goal of generating thermodynamic models of the transcriptome with base-pair specificity. Further, this approach makes it possible to carefully examine the mapping between mutations in binding sites and their corresponding expression profiles, a tool useful not only for developing a theory of transcription, but also for exploring regulatory evolution.

Deciphering regulatory architectures from synthetic single-cell expression patterns

For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRA pipelines, we need a "theory of the experiment" to help us better understand the impact of various biological and experimental parameters on the interpretation of experimental data. These parameters include binding site copy number, where a large number of specific binding sites may titrate away transcription factors, as well as the presence of overlapping binding sites, which may affect analysis of the degree of mutual dependence between mutations in the regulatory region and expression levels. To that end, in this paper we create tens of thousands of synthetic single-cell gene expression outputs using both equilibrium and out-of-equilibrium models. These models make it possible to imitate the summary statistics (information footprints and expression shift matrices) used to characterize the output of MPRAs and from this summary statistic to infer the underlying regulatory architecture. Specifically, we use a more refined implementation of the so-called thermodynamic models in which the binding energies of each sequence variant are derived from energy matrices. Our simulations reveal important effects of the parameters on MPRA data and we demonstrate our ability to optimize MPRA experimental designs with the goal of generating thermodynamic models of the transcriptome with base-pair specificity. Further, this approach makes it possible to carefully examine the mapping between mutations in binding sites and their corresponding expression profiles, a tool useful not only for better designing MPRAs, but also for exploring regulatory evolution.

Predictive Biophysical Neural Network Modeling of a Compendium of in vivo Transcription Factor DNA Binding Profiles for Escherichia coli

The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs using ChIP-Seq. We used these data to train BoltzNet, a novel neural network that predicts TF binding energy from DNA sequence. BoltzNet mirrors a quantitative biophysical model and provides directly interpretable predictions genome-wide at nucleotide resolution. We used BoltzNet to quantitatively design novel binding sites, which we validated with biophysical experiments on purified protein. We have generated models for 125 TFs that provide insight into global features of TF binding, including clustering of sites, the role of accessory bases, the relevance of weak sites, and the background affinity of the genome. Our paper provides new paradigms for studying TF-DNA binding and for the development of biophysically motivated neural networks.

Multilayered regulation of amino acid metabolism in Escherichia coli

Amino acid metabolism in Escherichia coli has long been studied and has established the basis for regulatory mechanisms at the transcriptional, posttranscriptional, and posttranslational levels. In addition to the classical signal transduction cascade involving posttranslational modifications (PTMs), novel PTMs in the two primary nitrogen assimilation pathways have recently been uncovered. The regulon of the master transcriptional regulator NtrC is further expanded by a small RNA derived from the 3´UTR of glutamine synthetase mRNA, which coordinates central carbon and nitrogen metabolism. Furthermore, recent advances in sequencing technologies have revealed the global regulatory networks of transcriptional and posttranscriptional regulators, Lrp and GcvB. This review provides an update of the multilayered and interconnected regulatory networks governing amino acid metabolism in E. coli.