Dynamics of Bacterial Gene Regulatory Networks

Regulation of microbial gene expression: the key to understanding our gut microbiome

During the past two decades, gut microbiome studies have established the significant impact of the gut microbiota and its metabolites on host health. However, the molecular mechanisms governing the production of microbial metabolites in the gut environment remain insufficiently investigated and thus are poorly understood. Here, we propose that an enhanced understanding of gut microbial gene regulation, which is responsive to dietary components and gut environmental conditions, is needed in the research field and essential for our ability to effectively promote host health and prevent diseases through interventions targeting the gut microbiome.

Targeting bacterial transcription factors for infection control: opportunities and challenges

The rising threat of antibiotic resistance in pathogenic bacteria emphasizes the need for new therapeutic strategies. This review focuses on bacterial transcription factors (TFs), which play crucial roles in bacterial pathogenesis. We discuss the regulatory roles of these factors through examples, and we outline potential therapeutic strategies targeting bacterial TFs. Specifically, we discuss the use of small molecules to interfere with TF function and the development of transcription factor decoys, oligonucleotides that compete with promoters for TF binding. We also cover peptides that target the interaction between the bacterial TF and other factors, such as RNA polymerase, and the targeting of sigma factors. These strategies, while promising, come with challenges, from identifying targets to designing interventions, managing side effects, and accounting for changing bacterial resistance patterns. We also delve into how Artificial Intelligence contributes to these efforts and how it may be exploited in the future, and we touch on the roles of multidisciplinary collaboration and policy to advance this research domain.Abbreviations: AI, artificial intelligence; CNN, convolutional neural networks; DTI: drug-target interaction; HTH, helix-turn-helix; IHF, integration host factor; LTTRs, LysR-type transcriptional regulators; MarR, multiple antibiotic resistance regulator; MRSA, methicillin resistant Staphylococcus aureus; MSA: multiple sequence alignment; NAP, nucleoid-associated protein; PROTACs, proteolysis targeting chimeras; RNAP, RNA polymerase; TF, transcription factor; TFD, transcription factor decoying; TFTRs, TetR-family transcriptional regulators; wHTH, winged helix-turn-helix.

Leveraging independent component analysis to unravel transcriptional regulatory networks: A critical review and future directions

Transcriptional regulatory networks (TRNs) play a crucial role in exploring microbial life activities and complex regulatory mechanisms. The comprehensive reconstruction of TRNs requires the integration of large-scale experimental data, which poses significant challenges due to the complexity of regulatory relationships. The application of machine learning tools, such as clustering analysis, has been employed to investigate TRNs, but these methods have limitations in capturing both global and local co-expression effects. In contrast, Independent Component Analysis (ICA) has emerged as a powerful analysis algorithm for modularizing independently regulated gene sets in TRNs, allowing it to account for both global and local co-expression effects. In this review, we comprehensively summarize the application of ICA in unraveling TRNs and highlight the research progress in three key aspects: (1) extending TRNs with iModulon analysis; (2) elucidating the regulatory mechanisms triggered by environmental perturbation; and (3) exploring the mechanisms of transcriptional regulation triggered by changes in microbial physiological state. At the end of this review, we also address the challenges facing ICA in TRN analysis and outline future research directions to promote the advancement of ICA-based transcriptomics analysis in biotechnology and related fields.

Tracing the Evolutionary Pathways of Serogroup O78 Avian Pathogenic Escherichia coli

Avian pathogenic E. coli (APEC) causes severe economic losses in the poultry industry, and O78 serogroup APEC strains are prevalent in chickens. In this study, we aimed to understand the evolutionary pathways and relationships between O78 APEC and other E. coli strains. To trace these evolutionary pathways, we classified 3101 E. coli strains into 306 subgenotypes according to the numbers and types of single nucleotide polymorphisms (RST0 to RST63-1) relative to the consensus sequence (RST0) of the RNA polymerase beta subunit gene and performed network analysis. The E. coli strains showed four apparently different evolutionary pathways (I-1, I-2, I-3, and II). The thirty-two Korean O78 APEC strains tested in this study were classified into RST4-4 (45.2%), RST3-1 (32.3%), RST21-1 (12.9%), RST4-5 (3.2%), RST5-1 (3.2%), and RST12-6 (3.2%), and all RSTs except RST21-1 (I-2) may have evolved through the same evolutionary pathway (I-1). A comparative genomic study revealed the highest relatedness between O78 strains of the same RST in terms of genome sequence coverage/identity and the spacer sequences of CRISPRs. The early-appearing RST3-1 and RST4-4 prevalence among O78 APEC strains may reflect the early settlement of O78 E. coli in chickens, after which these bacteria accumulated virulence and antibiotic resistance genes to become APEC strains. The zoonotic risk of the conventional O78 APEC strains is low at present, but the appearance of genetically distinct and multiple virulence gene-bearing RST21-1 O78 APEC strains may alert us to a need to evaluate their virulence in chickens as well as their zoonotic risk.

Regulation by cyclic di-GMP attenuates dynamics and enhances robustness of bimodal curli gene activation in Escherichia coli

Curli amyloid fibers are a major constituent of the extracellular biofilm matrix formed by bacteria of the Enterobacteriaceae family. Within Escherichia coli biofilms, curli gene expression is limited to a subpopulation of bacteria, leading to heterogeneity of extracellular matrix synthesis. Here we show that bimodal activation of curli gene expression also occurs in well-mixed planktonic cultures of E. coli, resulting in all-or-none stochastic differentiation into distinct subpopulations of curli-positive and curli-negative cells at the entry into the stationary phase of growth. Stochastic curli activation in individual E. coli cells could further be observed during continuous growth in a conditioned medium in a microfluidic device, which further revealed that the curli-positive state is only metastable. In agreement with previous reports, regulation of curli gene expression by the second messenger c-di-GMP via two pairs of diguanylate cyclase and phosphodiesterase enzymes, DgcE/PdeH and DgcM/PdeR, modulates the fraction of curli-positive cells. Unexpectedly, removal of this regulatory network does not abolish the bimodality of curli gene expression, although it affects dynamics of activation and increases heterogeneity of expression levels among individual cells. Moreover, the fraction of curli-positive cells within an E. coli population shows stronger dependence on growth conditions in the absence of regulation by DgcE/PdeH and DgcM/PdeR pairs. We thus conclude that, while not required for the emergence of bimodal curli gene expression in E. coli, this c-di-GMP regulatory network attenuates the frequency and dynamics of gene activation and increases its robustness to cellular heterogeneity and environmental variation.

GReNaDIne: A Data-Driven Python Library to Infer Gene Regulatory Networks from Gene Expression Data

Context: Inferring gene regulatory networks (GRN) from high-throughput gene expression data is a challenging task for which different strategies have been developed. Nevertheless, no ever-winning method exists, and each method has its advantages, intrinsic biases, and application domains. Thus, in order to analyze a dataset, users should be able to test different techniques and choose the most appropriate one. This step can be particularly difficult and time consuming, since most methods' implementations are made available independently, possibly in different programming languages. The implementation of an open-source library containing different inference methods within a common framework is expected to be a valuable toolkit for the systems biology community. Results: In this work, we introduce GReNaDIne (Gene Regulatory Network Data-driven Inference), a Python package that implements 18 machine learning data-driven gene regulatory network inference methods. It also includes eight generalist preprocessing techniques, suitable for both RNA-seq and microarray dataset analysis, as well as four normalization techniques dedicated to RNA-seq. In addition, this package implements the possibility to combine the results of different inference tools to form robust and efficient ensembles. This package has been successfully assessed under the DREAM5 challenge benchmark dataset. The open-source GReNaDIne Python package is made freely available in a dedicated GitLab repository, as well as in the official third-party software repository PyPI Python Package Index. The latest documentation on the GReNaDIne library is also available at Read the Docs, an open-source software documentation hosting platform. Contribution: The GReNaDIne tool represents a technological contribution to the field of systems biology. This package can be used to infer gene regulatory networks from high-throughput gene expression data using different algorithms within the same framework. In order to analyze their datasets, users can apply a battery of preprocessing and postprocessing tools and choose the most adapted inference method from the GReNaDIne library and even combine the output of different methods to obtain more robust results. The results format provided by GReNaDIne is compatible with well-known complementary refinement tools such as PYSCENIC.

RNase E and HupB dynamics foster mycobacterial cell homeostasis and fitness

RNA turnover is a primary source of gene expression variation, in turn promoting cellular adaptation. Mycobacteria leverage reversible mRNA stabilization to endure hostile conditions. Although RNase E is essential for RNA turnover in several species, its role in mycobacterial single-cell physiology and functional phenotypic diversification remains unexplored. Here, by integrating live-single-cell and quantitative-mass-spectrometry approaches, we show that RNase E forms dynamic foci, which are associated with cellular homeostasis and fate, and we discover a versatile molecular interactome. We show a likely interaction between RNase E and the nucleoid-associated protein HupB, which is particularly pronounced during drug treatment and infection, where phenotypic diversity increases. Disruption of RNase E expression affects HupB levels, impairing Mycobacterium tuberculosis growth homeostasis during treatment, intracellular replication, and host spread. Our work lays the foundation for targeting the RNase E and its partner HupB, aiming to undermine M. tuberculosis cellular balance, diversification capacity, and persistence.

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Single mycobacterial cells exhibit phenotypic variation in RNase E expression

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RNase E is implicated in the maintenance of mycobacterial cell growth homeostasis

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RNase E and HupB show a functional interplay in single mycobacterial cells

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RNase E-HupB disruption impairs Mycobacterium tuberculosis fate under drug and in macrophages

In-Cell Structural Biology by NMR: The Benefits of the Atomic Scale

In-cell structural biology aims at extracting structural information about proteins or nucleic acids in their native, cellular environment. This emerging field holds great promise and is already providing new facts and outlooks of interest at both fundamental and applied levels. NMR spectroscopy has important contributions on this stage: It brings information on a broad variety of nuclei at the atomic scale, which ensures its great versatility and uniqueness. Here, we detail the methods, the fundamental knowledge, and the applications in biomedical engineering related to in-cell structural biology by NMR. We finally propose a brief overview of the main other techniques in the field (EPR, smFRET, cryo-ET, etc.) to draw some advisable developments for in-cell NMR. In the era of large-scale screenings and deep learning, both accurate and qualitative experimental evidence are as essential as ever to understand the interior life of cells. In-cell structural biology by NMR spectroscopy can generate such a knowledge, and it does so at the atomic scale. This review is meant to deliver comprehensive but accessible information, with advanced technical details and reflections on the methods, the nature of the results, and the future of the field.

Signal metrics analysis of oscillatory patterns in bacterial multi-omic networks

MOTIVATION

One of the branches of Systems Biology is focused on a deep understanding of underlying regulatory networks through the analysis of the biomolecules oscillations and their interplay. Synthetic Biology exploits gene or/and protein regulatory networks towards the design of oscillatory networks for producing useful compounds. Therefore, at different levels of application and for different purposes, the study of biomolecular oscillations can lead to different clues about the mechanisms underlying living cells. It is known that network-level interactions involve more than one type of biomolecule as well as biological processes operating at multiple omic levels. Combining network/pathway-level information with genetic information it is possible to describe well-understood or unknown bacterial mechanisms and organism-specific dynamics.

RESULTS

Following the methodologies used in signal processing and communication engineering, a methodology is introduced to identify and quantify the extent of multi-omic oscillations. These are due to the process of multi-omic integration and depend on the gene positions on the chromosome. Ad hoc signal metrics are designed to allow further biotechnological explanations and provide important clues about the oscillatory nature of the pathways and their regulatory circuits. Our algorithms designed for the analysis of multi-omic signals are tested and validated on 11 different bacteria for thousands of multi-omic signals perturbed at the network level by different experimental conditions. Information on the order of genes, codon usage, gene expression and protein molecular weight is integrated at three different functional levels. Oscillations show interesting evidence that network-level multi-omic signals present a synchronized response to perturbations and evolutionary relations along taxa.

AVAILABILITY AND IMPLEMENTATION

The algorithms, the code (in language R), the tool, the pipeline and the whole dataset of multi-omic signal metrics are available at: https://github.com/lodeguns/Multi-omicSignals.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genome-Wide Analysis of Targets for Post-Transcriptional Regulation by Rsm Proteins in Pseudomonas putida

Post-transcriptional regulation is an important step in the control of bacterial gene expression in response to environmental and cellular signals. Pseudomonas putida KT2440 harbors three known members of the CsrA/RsmA family of post-transcriptional regulators: RsmA, RsmE and RsmI. We have carried out a global analysis to identify RNA sequences bound in vivo by each of these proteins. Affinity purification and sequencing of RNA molecules associated with Rsm proteins were used to discover direct binding targets, corresponding to 437 unique RNA molecules, 75 of them being common to the three proteins. Relevant targets include genes encoding proteins involved in signal transduction and regulation, metabolism, transport and secretion, stress responses, and the turnover of the intracellular second messenger c-di-GMP. To our knowledge, this is the first combined global analysis in a bacterium harboring three Rsm homologs. It offers a broad overview of the network of processes subjected to this type of regulation and opens the way to define what are the sequence and structure determinants that define common or differential recognition of specific RNA molecules by these proteins.

Photochemical control of bacterial gene expression based on trans encoded genetic switches

Controlling gene expression by light with fine spatiotemporal resolution not only allows understanding and manipulating fundamental biological processes but also fuels the development of novel therapeutic strategies. In complement to exploiting optogenetic tools, photochemical strategies mostly rely on the incorporation of photo-responsive small molecules into the corresponding biomacromolecular scaffolds. Therefore, generally large synthetic effort is required and the switching of gene expression in both directions within a single system remains a challenge. Here, we report a trans encoded ribo-switch, which consists of an engineered tRNA mimicking structure (TMS), under control of small photo-switchable signalling molecules. The signalling molecules consist of two amino glycoside molecules that are connected via an azobenzene unit. The light responsiveness of our system originates from the photo-switchable noncovalent interactions between the signalling molecule and the TMS switch, leading to the demonstration of photochemically controlled expression of two different genes. We believe that this modular design will provide a powerful platform for controlling the expression of other functional proteins with high spatiotemporal resolution employing light as a stimulus.

Misuse of the Michaelis-Menten rate law for protein interaction networks and its remedy

For over a century, the Michaelis-Menten (MM) rate law has been used to describe the rates of enzyme-catalyzed reactions and gene expression. Despite the ubiquity of the MM rate law, it accurately captures the dynamics of underlying biochemical reactions only so long as it is applied under the right condition, namely, that the substrate is in large excess over the enzyme-substrate complex. Unfortunately, in circumstances where its validity condition is not satisfied, especially so in protein interaction networks, the MM rate law has frequently been misused. In this review, we illustrate how inappropriate use of the MM rate law distorts the dynamics of the system, provides mistaken estimates of parameter values, and makes false predictions of dynamical features such as ultrasensitivity, bistability, and oscillations. We describe how these problems can be resolved with a slightly modified form of the MM rate law, based on the total quasi-steady state approximation (tQSSA). Furthermore, we show that the tQSSA can be used for accurate stochastic simulations at a lower computational cost than using the full set of mass-action rate laws. This review describes how to use quasi-steady state approximations in the right context, to prevent drawing erroneous conclusions from in silico simulations.

Genome-scale transcriptional dynamics and environmental biosensing

Genome-scale technologies have enabled mapping of the complex molecular networks that govern cellular behavior. An emerging theme in the analyses of these networks is that cells use many layers of regulatory feedback to constantly assess and precisely react to their environment. The importance of complex feedback in controlling the real-time response to external stimuli has led to a need for the next generation of cell-based technologies that enable both the collection and analysis of high-throughput temporal data. Toward this end, we have developed a microfluidic platform capable of monitoring temporal gene expression from over 2,000 promoters. By coupling the "Dynomics" platform with deep neural network (DNN) and associated explainable artificial intelligence (XAI) algorithms, we show how machine learning can be harnessed to assess patterns in transcriptional data on a genome scale and identify which genes contribute to these patterns. Furthermore, we demonstrate the utility of the Dynomics platform as a field-deployable real-time biosensor through prediction of the presence of heavy metals in urban water and mine spill samples, based on the the dynamic transcription profiles of 1,807 unique Escherichia coli promoters.

The Physics of Cellular Decision Making During Epithelial-Mesenchymal Transition

The epithelial-mesenchymal transition (EMT) is a process by which cells lose epithelial traits, such as cell-cell adhesion and apico-basal polarity, and acquire migratory and invasive traits. EMT is crucial to embryonic development and wound healing. Misregulated EMT has been implicated in processes associated with cancer aggressiveness, including metastasis. Recent experimental advances such as single-cell analysis and temporal phenotypic characterization have established that EMT is a multistable process wherein cells exhibit and switch among multiple phenotypic states. This is in contrast to the classical perception of EMT as leading to a binary choice. Mathematical modeling has been at the forefront of this transformation for the field, not only providing a conceptual framework to integrate and analyze experimental data, but also making testable predictions. In this article, we review the key features and characteristics of EMT dynamics, with a focus on the mathematical modeling approaches that have been instrumental to obtaining various useful insights.

On the importance of reaction networks for synthetic living systems

The goal of creating a synthetic cell necessitates the development of reaction networks which will underlie all of its behaviours. Recent developments in in vitro systems, based upon both DNA and enzymes, have created networks capable of a range of behaviours e.g. information processing, adaptation and diffusive signalling. These networks are based upon reaction motifs that when combined together produce more complex behaviour. We highlight why it is inevitable that networks, based on enzymes or enzyme-like catalysts, will be required for the construction of a synthetic cell. We outline several of the challenges, including (a) timing, (b) regulation and (c) energy distribution, that must be overcome in order to transition from the simple networks we have today to much more complex networks capable of a variety of behaviours and which could find application one day within a synthetic cell.