Mathematical modelling of SigE regulatory network reveals new insights into bistability of mycobacterial stress response

Structure of the SigE regulatory network in Mycobacterium tuberculosis

SigE is one of the main regulators of mycobacterial stress response and is characterized by a complex regulatory network based on two pathways, which have been partially characterized in conditions of surface stress. The first pathway is based on the induction of sigE transcription by the two-component system MprAB, while the second is based on the degradation of SigE anti-sigma factor RseA by ClpC1P2, a protease whose structural genes are induced by ClgR. We characterized the dynamics of the SigE network activation in conditions of surface stress and low pH in Mycobacterium tuberculosis. Using a series of mutants in which the main regulatory nodes of the network have been inactivated, we could explore their hierarchy, and we determined that MprAB had a key role in the network activation in both stress conditions through the induction of sigE. However, while in conditions of surface stress the absence of MprAB totally abrogated sigE induction, under low pH conditions it only resulted in a small delay of the induction of sigE. In this case, sigE induction was due to SigH, which acted as a MprAB backup system. The ClgR pathway, leading to the degradation of the SigE anti-sigma factor RseA, was shown to be essential for the activation of the SigE network only following surface stress, where it showed an equal hierarchy with the MprAB pathway.

Reconstructing the transcriptional regulatory network of probiotic L. reuteri is enabled by transcriptomics and machine learning

Limosilactobacillus reuteri, a probiotic microbe instrumental to human health and sustainable food production, adapts to diverse environmental shifts via dynamic gene expression. We applied the independent component analysis (ICA) to 117 RNA-seq data sets to decode its transcriptional regulatory network (TRN), identifying 35 distinct signals that modulate specific gene sets. Our findings indicate that the ICA provides a qualitative advancement and captures nuanced relationships within gene clusters that other methods may miss. This study uncovers the fundamental properties of L. reuteri's TRN and deepens our understanding of its arginine metabolism and the co-regulation of riboflavin metabolism and fatty acid conversion. It also sheds light on conditions that regulate genes within a specific biosynthetic gene cluster and allows for the speculation of the potential role of isoprenoid biosynthesis in L. reuteri's adaptive response to environmental changes. By integrating transcriptomics and machine learning, we provide a system-level understanding of L. reuteri's response mechanism to environmental fluctuations, thus setting the stage for modeling the probiotic transcriptome for applications in microbial food production.

IMPORTANCE

We have studied Limosilactobacillus reuteri, a beneficial probiotic microbe that plays a significant role in our health and production of sustainable foods, a type of foods that are nutritionally dense and healthier and have low-carbon emissions compared to traditional foods. Similar to how humans adapt their lifestyles to different environments, this microbe adjusts its behavior by modulating the expression of genes. We applied machine learning to analyze large-scale data sets on how these genes behave across diverse conditions. From this, we identified 35 unique patterns demonstrating how L. reuteri adjusts its genes based on 50 unique environmental conditions (such as various sugars, salts, microbial cocultures, human milk, and fruit juice). This research helps us understand better how L. reuteri functions, especially in processes like breaking down certain nutrients and adapting to stressful changes. More importantly, with our findings, we become closer to using this knowledge to improve how we produce more sustainable and healthier foods with the help of microbes.

The σB alternative sigma factor circuit modulates noise to generate different types of pulsing dynamics

Single-cell approaches are revealing a high degree of heterogeneity, or noise, in gene expression in isogenic bacteria. How gene circuits modulate this noise in gene expression to generate robust output dynamics is unclear. Here we use the Bacillus subtilis alternative sigma factor σB as a model system for understanding the role of noise in generating circuit output dynamics. σB controls the general stress response in B. subtilis and is activated by a range of energy and environmental stresses. Recent single-cell studies have revealed that the circuit can generate two distinct outputs, stochastic pulsing and a single pulse response, but the conditions under which each response is generated are under debate. We implement a stochastic mathematical model of the σB circuit to investigate this and find that the system's core circuit can generate both response types. This is despite one response (stochastic pulsing) being stochastic in nature, and the other (single response pulse) being deterministic. We demonstrate that the main determinant for whichever response is generated is the degree with which the input pathway activates the core circuit, although the noise properties of the input pathway also biases the system towards one or the other type of output. Thus, our work shows how stochastic modelling can reveal the mechanisms behind non-intuitive gene circuit output dynamics.

SigE: A master regulator of Mycobacterium tuberculosis

The Extracellular function (ECF) sigma factor SigE is one of the best characterized out of the 13 sigma factors encoded in the Mycobacterium tuberculosis chromosome. SigE is required for blocking phagosome maturation and full virulence in both mice and guinea pigs. Moreover, it is involved in the response to several environmental stresses as surface stress, oxidative stress, acidic pH, and phosphate starvation. Underscoring its importance in M. tuberculosis physiology, SigE is subjected to a very complex regulatory system: depending on the environmental conditions, its expression is regulated by three different sigma factors (SigA, SigE, and SigH) and a two-component system (MprAB). SigE is also regulated at the post-translational level by an anti-sigma factor (RseA) which is regulated by the intracellular redox potential and by proteolysis following phosphorylation from PknB upon surface stress. The set of genes under its direct control includes other regulators, as SigB, ClgR, and MprAB, and genes involved in surface remodeling and stabilization. Recently SigE has been shown to interact with PhoP to activate a subset of genes in conditions of acidic pH. The complex structure of its regulatory network has been suggested to result in a bistable switch leading to the development of heterogeneous bacterial populations. This hypothesis has been recently reinforced by the finding of its involvement in the development of persister cells able to survive to the killing activity of several drugs.

Bacterial survivors: evaluating the mechanisms of antibiotic persistence

Bacteria withstand antibiotic onslaughts by employing a variety of strategies, one of which is persistence. Persistence occurs in a bacterial population where a subpopulation of cells (persisters) survives antibiotic treatment and can regrow in a drug-free environment. Persisters may cause the recalcitrance of infectious diseases and can be a stepping stone to antibiotic resistance, so understanding persistence mechanisms is critical for therapeutic applications. However, current understanding of persistence is pervaded by paradoxes that stymie research progress, and many aspects of this cellular state remain elusive. In this review, we summarize the putative persister mechanisms, including toxin-antitoxin modules, quorum sensing, indole signalling and epigenetics, as well as the reasons behind the inconsistent body of evidence. We highlight present limitations in the field and underscore a clinical context that is frequently neglected, in the hope of supporting future researchers in examining clinically important persister mechanisms.

Delineating transcriptional crosstalk between Mycobacterium avium subsp. paratuberculosis and human THP-1 cells at the early stage of infection via dual RNA-seq analysis

Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of Johne’s disease, a chronic debilitating disease in ruminants. To control this disease, it is crucial to understand immune evasion and the mechanism of persistence by analyzing the early phase interplays of the intracellular pathogens and their hosts. In the present study, host–pathogen interactions at the transcriptomic level were investigated in an in vitro macrophage infection model. When differentiated human THP-1 cells were infected with MAP, the expression of various genes associated with stress responses and metabolism was altered in both host and MAP at 3 h post-infection. MAP upregulates stress-responsive global gene regulators, such as two-component systems and sigma factors, in response to oxidative and cell wall stress. Downstream genes involved in type VII secretion systems, cell wall synthesis (polyketide biosynthesis proteins), and iron uptake were changed in response to the intracellular environment of macrophages. On the host side, upregulation of inflammatory cytokine genes was observed along with pattern recognition receptor genes. Notably, alterations in gene sets involved in arginine metabolism were observed in both the host and MAP, along with significant downregulation of NOS2 expression. These observations suggest that the utilization of metabolites such as arginine by intracellular MAP might affect host NO production. Our dual RNA-seq data can provide novel insights by capturing the global transcriptome with higher resolution, especially in MAP, thus enabling a more systematic understanding of host–pathogen interactions.

Transcriptional regulation and drug resistance in Mycobacterium tuberculosis

Bacterial drug resistance is one of the major challenges to present and future human health, as the continuous selection of multidrug resistant bacteria poses at serious risk the possibility to treat infectious diseases in the near future. One of the infection at higher risk to become incurable is tuberculosis, due to the few drugs available in the market against Mycobacterium tuberculosis. Drug resistance in this species is usually due to point mutations in the drug target or in proteins required to activate prodrugs. However, another interesting and underexplored aspect of bacterial physiology with important impact on drug susceptibility is represented by the changes in transcriptional regulation following drug exposure. The main regulators involved in this phenomenon in M. tuberculosis are the sigma factors, and regulators belonging to the WhiB, GntR, XRE, Mar and TetR families. Better understanding the impact of these regulators in survival to drug treatment might contribute to identify new drug targets and/or to design new strategies of intervention.