The Vibrio cholerae CBASS phage defence system modulates resistance and killing by antifolate antibiotics.

Toxic bacterial modules such as toxin-antitoxin systems hold antimicrobial potential, though successful applications are rare. Here we show that in Vibrio cholerae the cyclic-oligonucleotide-based anti-phage signalling system (CBASS), another example of a toxic module, increases sensitivity to antifolate antibiotics up to 10×, interferes with their synergy and ultimately enables bacterial lysis by these otherwise classic bacteriostatic antibiotics. Cyclic-oligonucleotide production by the CBASS nucleotidyltransferase DncV upon antifolate treatment confirms full CBASS activation under these conditions, and suggests that antifolates release DncV allosteric inhibition by folates. Consequently, the CBASS-antifolate interaction is specific to CBASS systems with closely related nucleotidyltransferases and similar folate-binding pockets. Last, antifolate resistance genes abolish the CBASS-antifolate interaction by bypassing the effects of on-target antifolate activity, thereby creating potential for their coevolution with CBASS. Altogether, our findings illustrate how toxic modules can impact antibiotic activity and ultimately confer bactericidal activity to classical bacteriostatic antibiotics.

Systematic analysis of drug combinations against Gram-positive bacteria.

Drug combinations can expand options for antibacterial therapies but have not been systematically tested in Gram-positive species. We profiled ~8,000 combinations of 65 antibacterial drugs against the model species Bacillus subtilis and two prominent pathogens, Staphylococcus aureus and Streptococcus pneumoniae. Thereby, we recapitulated previously known drug interactions, but also identified ten times more novel interactions in the pathogen S. aureus, including 150 synergies. We showed that two synergies were equally effective against multidrug-resistant S. aureus clinical isolates in vitro and in vivo. Interactions were largely species-specific and synergies were distinct from those of Gram-negative species, owing to cell surface and drug uptake differences. We also tested 2,728 combinations of 44 commonly prescribed non-antibiotic drugs with 62 drugs with antibacterial activity against S. aureus and identified numerous antagonisms that might compromise the efficacy of antimicrobial therapies. We identified even more synergies and showed that the anti-aggregant ticagrelor synergized with cationic antibiotics by modifying the surface charge of S. aureus. All data can be browsed in an interactive interface ( https://apps.embl.de/combact/ ).

Bioaccumulation of therapeutic drugs by human gut bacteria.

Bacteria in the gut can modulate the availability and efficacy of therapeutic drugs. However, the systematic mapping of the interactions between drugs and bacteria has only started recently1 and the main underlying mechanism proposed is the chemical transformation of drugs by microorganisms (biotransformation). Here we investigated the depletion of 15 structurally diverse drugs by 25 representative strains of gut bacteria. This revealed 70 bacteria-drug interactions, 29 of which had not to our knowledge been reported before. Over half of the new interactions can be ascribed to bioaccumulation; that is, bacteria storing the drug intracellularly without chemically modifying it, and in most cases without the growth of the bacteria being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using click chemistry, thermal proteome profiling and metabolomics. We find that duloxetine binds to several metabolic enzymes and changes the metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the composition of the community through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioural response of Caenorhabditis elegans to duloxetine. Together, our results show that bioaccumulation by gut bacteria may be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, probably in an individual manner.

Proton Motive Force Disruptors Block Bacterial Competence and Horizontal Gene Transfer.

Streptococcus pneumoniae is a commensal of the human nasopharynx that can also cause severe antibiotic-resistant infections. Antibiotics drive the spread of resistance by inducing S. pneumoniae competence, in which bacteria express the transformation machinery that facilitates uptake of exogenous DNA and horizontal gene transfer (HGT). We performed a high-throughput screen and identified potent inhibitors of S. pneumoniae competence, called COM-blockers. COM-blockers limit competence by inhibiting the proton motive force (PMF), thereby disrupting export of a quorum-sensing peptide that regulates the transformation machinery. Known chemical PMF disruptors and alterations in pH homeostasis similarly inhibit competence. COM-blockers limit transformation of clinical multi-drug-resistant strains and HGT in infected mice. At their active concentrations, COM-blockers do not affect growth, compromise antibiotic activity, or elicit detectable resistance. COM-blockers provide an experimental tool to inhibit competence and other PMF-involved processes and could help reduce the spread of virulence factors and antibiotic resistance in bacteria. VIDEO ABSTRACT.

Species-specific activity of antibacterial drug combinations.

The spread of antimicrobial resistance has become a serious public health concern, making once-treatable diseases deadly again and undermining the achievements of modern medicine1,2. Drug combinations can help to fight multi-drug-resistant bacterial infections, yet they are largely unexplored and rarely used in clinics. Here we profile almost 3,000 dose-resolved combinations of antibiotics, human-targeted drugs and food additives in six strains from three Gram-negative pathogens-Escherichia coli, Salmonella enterica serovar Typhimurium and Pseudomonas aeruginosa-to identify general principles for antibacterial drug combinations and understand their potential. Despite the phylogenetic relatedness of the three species, more than 70% of the drug-drug interactions that we detected are species-specific and 20% display strain specificity, revealing a large potential for narrow-spectrum therapies. Overall, antagonisms are more common than synergies and occur almost exclusively between drugs that target different cellular processes, whereas synergies are more conserved and are enriched in drugs that target the same process. We provide mechanistic insights into this dichotomy and further dissect the interactions of the food additive vanillin. Finally, we demonstrate that several synergies are effective against multi-drug-resistant clinical isolates in vitro and during infections of the larvae of the greater wax moth Galleria mellonella, with one reverting resistance to the last-resort antibiotic colistin.

Extensive impact of non-antibiotic drugs on human gut bacteria.

A few commonly used non-antibiotic drugs have recently been associated with changes in gut microbiome composition, but the extent of this phenomenon is unknown. Here, we screened more than 1,000 marketed drugs against 40 representative gut bacterial strains, and found that 24% of the drugs with human targets, including members of all therapeutic classes, inhibited the growth of at least one strain in vitro. Particular classes, such as the chemically diverse antipsychotics, were overrepresented in this group. The effects of human-targeted drugs on gut bacteria are reflected on their antibiotic-like side effects in humans and are concordant with existing human cohort studies. Susceptibility to antibiotics and human-targeted drugs correlates across bacterial species, suggesting common resistance mechanisms, which we verified for some drugs. The potential risk of non-antibiotics promoting antibiotic resistance warrants further exploration. Our results provide a resource for future research on drug-microbiome interactions, opening new paths for side effect control and drug repurposing, and broadening our view of antibiotic resistance.

Model-guided identification of gene deletion targets for metabolic engineering in Saccharomyces cerevisiae.

Identification of metabolic engineering strategies for rerouting intracellular fluxes towards a desired product is often a challenging task owing to the topological and regulatory complexity of metabolic networks. Genome-scale metabolic models help tackling this complexity through systematic consideration of mass balance and reaction directionality constraints over the entire network. Here, we describe how genome-scale metabolic models can be used for identifying gene deletion targets leading to increased production of the desired product. Vanillin production in Saccharomyces cerevisiae is used as a case study throughout this chapter.

Fe-S cluster biosynthesis controls uptake of aminoglycosides in a ROS-less death pathway.

All bactericidal antibiotics were recently proposed to kill by inducing reactive oxygen species (ROS) production, causing destabilization of iron-sulfur (Fe-S) clusters and generating Fenton chemistry. We find that the ROS response is dispensable upon treatment with bactericidal antibiotics. Furthermore, we demonstrate that Fe-S clusters are required for killing only by aminoglycosides. In contrast to cells, using the major Fe-S cluster biosynthesis machinery, ISC, cells using the alternative machinery, SUF, cannot efficiently mature respiratory complexes I and II, resulting in impendence of the proton motive force (PMF), which is required for bactericidal aminoglycoside uptake. Similarly, during iron limitation, cells become intrinsically resistant to aminoglycosides by switching from ISC to SUF and down-regulating both respiratory complexes. We conclude that Fe-S proteins promote aminoglycoside killing by enabling their uptake.

High-throughput approaches to understanding gene function and mapping network architecture in bacteria.

Advances in sequencing technology have provided an unprecedented view of bacterial diversity, along with a daunting number of novel genes. Within this new reality lies the challenge of developing large-scale approaches to assign function to the new genes and place them in pathways. Here, we highlight recent advances on this front, focusing on how high-throughput gene-gene, gene-drug and drug-drug interactions can yield functional and mechanistic inferences in bacteria.

Overexpression of O-methyltransferase leads to improved vanillin production in baker's yeast only when complemented with model-guided network engineering.

Overproduction of a desired metabolite is often achieved via manipulation of the pathway directly leading to the product or through engineering of distant nodes within the metabolic network. Empirical examples illustrating the combined effect of these local and global strategies have been so far limited in eukaryotic systems. In this study, we compared the effects of overexpressing a key gene in de novo vanillin biosynthesis (coding for O-methyltransferase, hsOMT) in two yeast strains, with and without model-guided global network modifications. Overexpression of hsOMT resulted in increased vanillin production only in the strain with model-guided modifications, exemplifying advantage of using a global strategy prior to local pathway manipulation.

Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks.

Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in Saccharomyces cerevisiae.

Improved vanillin production in baker's yeast through in silico design.

BACKGROUND: Vanillin is one of the most widely used flavouring agents, originally obtained from cured seed pods of the vanilla orchid Vanilla planifolia. Currently vanillin is mostly produced via chemical synthesis. A de novo synthetic pathway for heterologous vanillin production from glucose has recently been implemented in baker's yeast, Saccharamyces cerevisiae. In this study we aimed at engineering this vanillin cell factory towards improved productivity and thereby at developing an attractive alternative to chemical synthesis. RESULTS: Expression of a glycosyltransferase from Arabidopsis thaliana in the vanillin producing S. cerevisiae strain served to decrease product toxicity. An in silico metabolic engineering strategy of this vanillin glucoside producing strain was designed using a set of stoichiometric modelling tools applied to the yeast genome-scale metabolic network. Two targets (PDC1 and GDH1) were selected for experimental verification resulting in four engineered strains. Three of the mutants showed up to 1.5 fold higher vanillin ß-D-glucoside yield in batch mode, while continuous culture of the Δpdc1 mutant showed a 2-fold productivity improvement. This mutant presented a 5-fold improvement in free vanillin production compared to the previous work on de novo vanillin biosynthesis in baker's yeast. CONCLUSION: Use of constraints corresponding to different physiological states was found to greatly influence the target predictions given minimization of metabolic adjustment (MOMA) as biological objective function. In vivo verification of the targets, selected based on their predicted metabolic adjustment, successfully led to overproducing strains. Overall, we propose and demonstrate a framework for in silico design and target selection for improving microbial cell factories.