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While global transcription factors (TFs) have been studied extensively in Escherichia coli model strains, conservation and diversity in TF regulation between strains is still unknown. Here we use a combination of ChIP-exo-to define ferric uptake regulator (Fur) binding sites-and differential gene expression-to define the Fur regulon in nine E. coli strains. We then define a pan-regulon consisting of 469 target genes that includes all Fur target genes in all nine strains. The pan-regulon is then divided into the core regulon (target genes found in all the strains, n = 36), the accessory regulon (target found in two to eight strains, n = 158) and the unique regulon (target genes found in one strain, n = 275). Thus, there is a small set of Fur regulated genes common to all nine strains, but a large number of regulatory targets unique to a particular strain. Many of the unique regulatory targets are genes unique to that strain. This first-established pan-regulon reveals a common core of conserved regulatory targets and significant diversity in transcriptional regulation amongst E. coli strains, reflecting diverse niche specification and strain history.

The massive sequencing of transposon insertion mutant libraries (Tn-Seq) represents a commonly used method to determine essential genes in bacteria. Using a hypersaturated transposon mutant library consisting of 400,096 unique Tn insertions, 523 genes were classified as essential in Escherichia coli K-12 MG1655. This provided a useful genome-wide gene essentiality landscape for rapidly identifying 233 of 301 essential genes previously validated by a knockout study. However, there was a discrepancy in essential gene sets determined by conventional gene deletion methods and Tn-Seq, although different Tn-Seq studies reported different extents of discrepancy. We have elucidated two causes of this discrepancy. First, 68 essential genes not detected by Tn-Seq contain nonessential subgenic domains that are tolerant to transposon insertion, which leads to the false assignment of an essential gene as a nonessential or dispensable gene. These genes exhibited a high level of transposon insertion in their subgenic nonessential domains. In contrast, 290 genes were additionally categorized as essential by Tn-Seq, although their knockout mutants were available. The comparative analysis of Tn-Seq and high-resolution footprinting of nucleoid-associated proteins (NAPs) revealed that a protein-DNA interaction hinders transposon insertion. We identified 213 false-positive genes caused by NAP-genome interactions. These two limitations have to be considered when addressing essential bacterial genes using Tn-Seq. Furthermore, a comparative analysis of high-resolution Tn-Seq with other data sets is required for a more accurate determination of essential genes in bacteria. IMPORTANCE Transposon mutagenesis is an efficient way to explore gene essentiality of a bacterial genome. However, there was a discrepancy between the essential gene set determined by transposon mutagenesis and that determined using single-gene knockout strains. In this study, we generated a hypersaturated Escherichia coli transposon mutant library comprising approximately 400,000 different mutants. Determination of transposon insertion sites using next-generation sequencing provided a high-resolution essentiality landscape of the E. coli genome. We identified false negatives of essential gene discovery due to the permissive insertion of transposons in the C-terminal region. Comparisons between the transposon insertion landscape with binding profiles of DNA-binding proteins revealed interference of nucleoid-associated proteins to transposon insertion, generating false positives of essential gene discovery. Consideration of these findings is required to avoid the misinterpretation of transposon mutagenesis results.

The transcriptional regulatory network in prokaryotes controls global gene expression mostly through transcription factors (TFs), which are DNA-binding proteins. Chromatin immunoprecipitation (ChIP) with DNA sequencing methods can identify TF binding sites across the genome, providing a bottom-up, mechanistic understanding of how gene expression is regulated. ChIP provides indispensable evidence toward the goal of acquiring a comprehensive understanding of cellular adaptation and regulation, including condition-specificity. ChIP-derived data's importance and labor-intensiveness motivate its broad dissemination and reuse, which is currently an unmet need in the prokaryotic domain. To fill this gap, we present proChIPdb (prochipdb.org), an information-rich, interactive web database. This website collects public ChIP-seq/-exo data across several prokaryotes and presents them in dashboards that include curated binding sites, nucleotide-resolution genome viewers, and summary plots such as motif enrichment sequence logos. Users can search for TFs of interest or their target genes, download all data, dashboards, and visuals, and follow external links to understand regulons through biological databases and the literature. This initial release of proChIPdb covers diverse organisms, including most major TFs of Escherichia coli, and can be expanded to support regulon discovery across the prokaryotic domain.

Cancer cell mechanotype changes are newly recognized cancer phenotypic events, whereas metastatic cancer cells show decreased cell stiffness and increased deformability relative to normal cells. To further examine how cell mechanotype changes in early stages of cancer transformation and progression, an in vitro multi-step human urothelial cell carcinogenic model was used to measure cellular Young's modulus, deformability, and transit time using single-cell atomic force microscopy, microfluidic-based deformability cytometry, and quantitative deformability cytometry, respectively. Measurable cell mechanotype changes of stiffness, deformability, and cell transit time occur early in the transformation process. As cells progress from normal, to preinvasive, to invasive cells, Young's modulus of stiffness decreases and deformability increases gradually. These changes were confirmed in three-dimensional cultured microtumor masses and urine exfoliated cells directly from patients. Using gene screening and proteomics approaches, we found that the main molecular pathway implicated in cell mechanotype changes appears to be epithelial to mesenchymal transition.

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome.

Catalysis using iron-sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can prevent cell growth and survival when unmanaged, thus eliciting an essential stress response that is universal and fundamental in biology. Here we develop a computable multiscale description of the ROS stress response in Escherichia coli, called OxidizeME. We use OxidizeME to explain four key responses to oxidative stress: 1) ROS-induced auxotrophy for branched-chain, aromatic, and sulfurous amino acids; 2) nutrient-dependent sensitivity of growth rate to ROS; 3) ROS-specific differential gene expression separate from global growth-associated differential expression; and 4) coordinated expression of iron-sulfur cluster (ISC) and sulfur assimilation (SUF) systems for iron-sulfur cluster biosynthesis. These results show that we can now develop fundamental and quantitative genotype-phenotype relationships for stress responses on a genome-wide basis.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications.

Microwave ablation (MWA) is based on the energy absorbed by biological tissue through microwave emission, which raises the temperature of the tumor to perform the treatment. MWA has the advantage of causing minimal bleeding and treating deep tumors. In this study, MWA for various slot design situations in a microwave coaxial antenna (MCA) was simulated through numerical analysis. For the slot design, the number of slots and the distance between slots were determined by the analysis parameters. A tumor developed inside the liver tissue was implemented, and the temperature distribution of the tumor and surrounding liver tissue was calculated for all cases selected for numerical analysis. The Helmholtz electromagnetic equation was used to calculate the electromagnetic field inside the tissue, and the modified Pennes bioheat equation was used to calculate the temperature change in the tissue due to the emitted microwave. Treatment effects were quantitatively analyzed for each slot design condition through an apoptotic variable based on the calculated temperature distribution. Lastly, conditions that produce optimal treatment effects were derived depending on the number of slots. The analysis showed that as the distance between the slots increased from 0.5 to 3.5 mm, the optimal treatment effect was obtained when the number of slots was 4, 3, 3, and 2, respectively, and the microwave input power at that time was 3.0, 2.8, 2.6, and 3.0 W, respectively. This is expected to allow for more rigorous and more therapeutically effective MWA. [ABSTRACT FROM AUTHOR]

Three RpoD-family sigma factors, RpoD, RpoS, and RpoH, play critical roles in transcriptional regulation in Salmonella enterica serovar Typhimurium under heat shock conditions. However, the genome-wide regulatory mechanisms of these sigma factors in response to heat stress have remained elusive. In this study, we comprehensively identified 2,319, 2,226, and 213 genome-wide binding sites for RpoD, RpoS, and RpoH, respectively, under sublethal heat shock conditions (42°C). Machine learning-based transcriptome analysis was employed to infer the relative activity of iModulons, providing valuable insights into the transcriptional impact of heat shock. Integrative data analysis enabled the reconstruction of the transcriptional regulatory network of sigma factors, revealing how they modulate gene expression to adapt to heat stress, including responses to anaerobic and oxidative stresses. Notably, we observed a significant expansion of the RpoS sigmulon from 97 to 301 genes in response to heat shock, underscoring the crucial role of RpoS in regulating various metabolic processes. Moreover, we uncovered a competition mechanism between RpoD and RpoS within RpoS sigmulons, where RpoS significantly increases its binding within promoter regions shared with RpoD under heat shock conditions. These findings illuminate how three RpoD-family sigma factors coordinate multiple cellular processes to orchestrate the overall response of S. Typhimurium to heat stress. Author summary: RpoD-family sigma factors in Salmonella enterica serovar Typhimurium orchestrate transcription initiation for diverse metabolic genes in response to environmental stimuli. Heat stress, a fundamental challenge for bacteria in both natural and industrial settings. Typically triggers the up-regulation of heat shock proteins that assist in protein folding and degradation of damaged proteins. However, the full scope of transcriptional changes induced by heat shock extends beyond this familiar response. In this study, we employed systems biology approaches to elucidate detailed alterations in transcriptional regulatory networks (TRNs) mediated by RpoD-family sigma factors in response to heat shock. Machine learning-based transcriptomic analysis broke down the components of heat stress, revealing its involvement in both anaerobic and oxidative stress responses. By integrating genome-scale data with this nuanced understanding of heat shock, we reconstructed the TRNs and uncovered several key findings: (i) A competition mechanism exists between RpoS and RpoD at overlapping binding sites upstream of promoter region in response to heat shock; (ii) Heat-induced anaerobic stress down-regulated the majority of electron transporter genes, leading to significant modifications in the TRN governing carbon metabolism; (iii) Under heat shock conditions, alterations in iron availability induced down-regulation of extracellular iron uptake and enterobactin systems, while up-regulating intracellular iron-utilizing genes. This regulatory response may reduce intracellular iron concentrations, potentially attenuating Fenton reaction-mediated oxidative stress. These comprehensive results illuminate how RpoD-family sigma factors in S. Typhimurium regulate diverse cellular processes to adapt to heat shock, encompassing responses to anaerobic and oxidative stress. Our findings provide a more holistic understanding of bacterial heat shock response, extending beyond the classical heat shock protein paradigm. [ABSTRACT FROM AUTHOR]

Transcriptional regulation enables cells to respond to environmental changes. Of the estimated 304 candidate transcription factors (TFs) in Escherichia coli K-12 MG1655, 185 have been experimentally identified, but ChIP methods have been used to fully characterize only a few dozen. Identifying these remaining TFs is key to improving our knowledge of the E. coli transcriptional regulatory network (TRN). Here, we developed an integrated workflow for the computational prediction and comprehensive experimental validation of TFs using a suite of genome-wide experiments. We applied this workflow to (i) identify 16 candidate TFs from over a hundred uncharacterized genes; (ii) capture a total of 255 DNA binding peaks for ten candidate TFs resulting in six high-confidence binding motifs; (iii) reconstruct the regulons of these ten TFs by determining gene expression changes upon deletion of each TF and (iv) identify the regulatory roles of three TFs (YiaJ, YdcI, and YeiE) as regulators of l-ascorbate utilization, proton transfer and acetate metabolism, and iron homeostasis under iron-limited conditions, respectively. Together, these results demonstrate how this workflow can be used to discover, characterize, and elucidate regulatory functions of uncharacterized TFs in parallel.

Understanding the complex interactions of protein posttranslational modifications (PTMs) represents a major challenge in metabolic engineering, synthetic biology, and the biomedical sciences. Here, we present a workflow that integrates multiplex automated genome editing (MAGE), genome-scale metabolic modeling, and atomistic molecular dynamics to study the effects of PTMs on metabolic enzymes and microbial fitness. This workflow incorporates complementary approaches across scientific disciplines; provides molecular insight into how PTMs influence cellular fitness during nutrient shifts; and demonstrates how mechanistic details of PTMs can be explored at different biological scales. As a proof of concept, we present a global analysis of PTMs on enzymes in the metabolic network of Escherichia coli Based on our workflow results, we conduct a more detailed, mechanistic analysis of the PTMs in three proteins: enolase, serine hydroxymethyltransferase, and transaldolase. Application of this workflow identified the roles of specific PTMs in observed experimental phenomena and demonstrated how individual PTMs regulate enzymes, pathways, and, ultimately, cell phenotypes.

Two major transcriptional regulators of carbon metabolism in bacteria are Cra and CRP. CRP is considered to be the main mediator of catabolite repression. Unlike for CRP, in vivo DNA binding information of Cra is scarce. Here we generate and integrate ChIP-exo and RNA-seq data to identify 39 binding sites for Cra and 97 regulon genes that are regulated by Cra in Escherichia coli. An integrated metabolic-regulatory network was formed by including experimentally-derived regulatory information and a genome-scale metabolic network reconstruction. Applying analysis methods of systems biology to this integrated network showed that Cra enables optimal bacterial growth on poor carbon sources by redirecting and repressing glycolysis flux, by activating the glyoxylate shunt pathway, and by activating the respiratory pathway. In these regulatory mechanisms, the overriding regulatory activity of Cra over CRP is fundamental. Thus, elucidation of interacting transcriptional regulation of core carbon metabolism in bacteria by two key transcription factors was possible by combining genome-wide experimental measurement and simulation with a genome-scale metabolic model.