Constitutive Stringent Response Restores Viability of Bacillus subtilis Lacking Structural Maintenance of Chromosome Protein

Diclazuril Inhibits Biofilm Formation and Hemolysis of Staphylococcus aureus

Biofilm formation and hemolysis induced by Staphylococcus aureus are closely related to pathogenicity. However, no drugs exist to inhibit biofilm formation or hemolysis induced by S. aureus in clinical practice. This study found diclazuril had antibacterial action against S. aureus with minimum inhibitory concentrations (MICs) at 50 μM for both methicillin-sensitive S. aureus (MSSA) and methicillin-resistant S. aureus (MRSA). Diclazuril (at 1/4× or 1/8× MICs) significantly inhibited biofilm formation of S. aureus under static or flow-based conditions and also inhibited hemolysis induced by S. aureus. The RNA levels of transcriptional regulatory genes (agrA, agrC, luxS, sarA, sigB, saeR, saeS), biofilm formation-related genes (aur, bap, ccpA, cidA, clfA, clfB, fnbA, fnbB, icaA, icaB, sasG), and virulence-related genes (hla, hlb, hld, hlg, lukDE, lukpvl-S, spa, sbi, alpha-3 PSM, beta PSM, coa) of S. aureus were decreased when treated by diclazuril (at 1/4× MIC) for 4 h. The diclazuril nonsensitive clones of S. aureus were selected in vitro by induction of wildtype strains for about 90 days under the pressure of diclazuril. Mutations in the possible target genes of diclazuril against S. aureus were detected by whole-genome sequencing. This study indicated that there were three amino acid mutations in the diclazuril nonsensitive clone of S. aureus, two of which were located in genes with known function (SMC-Scp complex subunit ScpB and glyceraldehyde-3-phosphate dehydrogenase 1, respectively) and one in a gene with unknown function (hypothetical protein). Diclazuril showed a strong inhibition effect on planktonic cells and biofilm formation of S. aureus with the overexpression of the scpB gene.

Computational Strategies for the Identification of a Transcriptional Biomarker Panel to Sense Cellular Growth States in Bacillus subtilis

A goal of the biotechnology industry is to be able to recognise detrimental cellular states that may lead to suboptimal or anomalous growth in a bacterial population. Our current knowledge of how different environmental treatments modulate gene regulation and bring about physiology adaptations is limited, and hence it is difficult to determine the mechanisms that lead to their effects. Patterns of gene expression, revealed using technologies such as microarrays or RNA-seq, can provide useful biomarkers of different gene regulatory states indicative of a bacterium's physiological status. It is desirable to have only a few key genes as the biomarkers to reduce the costs of determining the transcriptional state by opening the way for methods such as quantitative RT-PCR and amplicon panels. In this paper, we used unsupervised machine learning to construct a transcriptional landscape model from condition-dependent transcriptome data, from which we have identified 10 clusters of samples with differentiated gene expression profiles and linked to different cellular growth states. Using an iterative feature elimination strategy, we identified a minimal panel of 10 biomarker genes that achieved 100% cross-validation accuracy in predicting the cluster assignment. Moreover, we designed and evaluated a variety of data processing strategies to ensure our methods were able to generate meaningful transcriptional landscape models, capturing relevant biological processes. Overall, the computational strategies introduced in this study facilitate the identification of a detailed set of relevant cellular growth states, and how to sense them using a reduced biomarker panel.

Cell cycle control and environmental response by second messengers in Caulobacter crescentus

Background

Second messengers, c-di-GMP and (p)ppGpp, are vital regulatory molecules in bacteria, influencing cellular processes such as biofilm formation, transcription, virulence, quorum sensing, and proliferation. While c-di-GMP and (p)ppGpp are both synthesized from GTP molecules, they play antagonistic roles in regulating the cell cycle. In C. crescentus, c-di-GMP works as a major regulator of pole morphogenesis and cell development. It inhibits cell motility and promotes S-phase entry by inhibiting the activity of the master regulator, CtrA. Intracellular (p)ppGpp accumulates under starvation, which helps bacteria to survive under stressful conditions through regulating nucleotide levels and halting proliferation. (p)ppGpp responds to nitrogen levels through RelA-SpoT homolog enzymes, detecting glutamine concentration using a nitrogen phosphotransferase system (PTS ^Ntr). This work relates the guanine nucleotide-based second messenger regulatory network with the bacterial PTS ^Ntr system and investigates how bacteria respond to nutrient availability.

Results

We propose a mathematical model for the dynamics of c-di-GMP and (p)ppGpp in C. crescentus and analyze how the guanine nucleotide-based second messenger system responds to certain environmental changes communicated through the PTS ^Ntr system. Our mathematical model consists of seven ODEs describing the dynamics of nucleotides and PTS ^Ntr enzymes. Our simulations are consistent with experimental observations and suggest, among other predictions, that SpoT can effectively decrease c-di-GMP levels in response to nitrogen starvation just as well as it increases (p)ppGpp levels. Thus, the activity of SpoT (or its homologues in other bacterial species) can likely influence the cell cycle by influencing both c-di-GMP and (p)ppGpp.

Conclusions

In this work, we integrate current knowledge and experimental observations from the literature to formulate a novel mathematical model. We analyze the model and demonstrate how the PTS ^Ntr system influences (p)ppGpp, c-di-GMP, GMP and GTP concentrations. While this model does not consider all aspects of PTS ^Ntr signaling, such as cross-talk with the carbon PTS system, here we present our first effort to develop a model of nutrient signaling in C. crescentus.

Bacterial Chromosome Replication and DNA Repair During the Stringent Response

The stringent response regulates bacterial growth rate and is important for cell survival under changing environmental conditions. The effect of the stringent response is pleiotropic, affecting almost all biological processes in the cell including transcriptional downregulation of genes involved in stable RNA synthesis, DNA replication, and metabolic pathways, as well as the upregulation of stress-related genes. In this Review, we discuss how the stringent response affects chromosome replication and DNA repair activities in bacteria. Importantly, we address how accumulation of (p)ppGpp during the stringent response shuts down chromosome replication using highly different strategies in the evolutionary distant Gram-negative Escherichia coli and Gram-positive Bacillus subtilis. Interestingly, (p)ppGpp-mediated replication inhibition occurs downstream of the origin in B. subtilis, whereas replication inhibition in E. coli takes place at the initiation level, suggesting that stringent cell cycle arrest acts at different phases of the replication cycle between E. coli and B. subtilis. Furthermore, we address the role of (p)ppGpp in facilitating DNA repair activities and cell survival during exposure to UV and other DNA damaging agents. In particular, (p)ppGpp seems to stimulate the efficiency of nucleotide excision repair (NER)-dependent repair of DNA lesions. Finally, we discuss whether (p)ppGpp-mediated cell survival during DNA damage is related to the ability of (p)ppGpp accumulation to inhibit chromosome replication.

Functions of Bacterial tRNA Modifications: From Ubiquity to Diversity

Modified nucleotides in tRNA are critical components of the translation apparatus, but their importance in the process of translational regulation had until recently been greatly overlooked. Two breakthroughs have recently allowed a fuller understanding of the importance of tRNA modifications in bacterial physiology. One is the identification of the full set of tRNA modification genes in model organisms such as Escherichia coli K12. The second is the improvement of available analytical tools to monitor tRNA modification patterns. The role of tRNA modifications varies greatly with the specific modification within a given tRNA and with the organism studied. The absence of these modifications or reductions can lead to cell death or pleiotropic phenotypes or may have no apparent visible effect. By linking translation through their decoding functions to metabolism through their biosynthetic pathways, tRNA modifications are emerging as important components of the bacterial regulatory toolbox.

Contact-dependent growth inhibition induces high levels of antibiotic-tolerant persister cells in clonal bacterial populations

Bacterial populations can use bet-hedging strategies to cope with rapidly changing environments. One example is non-growing cells in clonal bacterial populations that are able to persist antibiotic treatment. Previous studies suggest that persisters arise in bacterial populations either stochastically through variation in levels of global signalling molecules between individual cells, or in response to various stresses. Here, we show that toxins used in contact-dependent growth inhibition (CDI) create persisters upon direct contact with cells lacking sufficient levels of CdiI immunity protein, which would otherwise bind to and neutralize toxin activity. CDI-mediated persisters form through a feedforward cycle where the toxic activity of the CdiA toxin increases cellular (p)ppGpp levels, which results in Lon-mediated degradation of the immunity protein and more free toxin. Thus, CDI systems mediate a population density-dependent bet-hedging strategy, where the fraction of non-growing cells is increased only when there are many cells of the same genotype. This may be one of the mechanisms of how CDI systems increase the fitness of their hosts.