Transcriptomic responses to antibiotic exposure in Mycobacterium tuberculosis

Transcriptional responses in bacteria following antibiotic exposure offer insights into antibiotic mechanism of action, bacterial responses, and characterization of antimicrobial resistance. We aimed to define the transcriptional antibiotic response (TAR) in Mycobacterium tuberculosis (Mtb) isolates for clinically relevant drugs by pooling and analyzing Mtb microarray and RNA-seq data sets. We generated 99 antibiotic transcription profiles across 17 antibiotics, with 76% of profiles generated using 3-24 hours of antibiotic exposure and 49% within one doubling of the WHO antibiotic critical concentration. TAR genes were time-dependent, and largely specific to the antibiotic mechanism of action. TAR signatures performed well at predicting antibiotic exposure, with the area under the receiver operating curve (AUC) ranging from 0.84-1.00 (TAR <6 hours of antibiotic exposure) and 0.76-1.00 (>6 hours of antibiotic exposure) for upregulated genes and 0.57-0.90 and 0.87-1.00, respectfully, for downregulated genes. This work desmonstrates that transcriptomics allows for the assessment of antibiotic activity in Mtb within 6 hours of exposure.

Roles of DJ41_1407 and DJ41_1408 in Acinetobacter baumannii ATCC19606 Virulence and Antibiotic Response

Acinetobacter baumannii is a major cause of nosocomial infections, and its highly adaptive nature and broad range of antibiotic resistance enable it to persist in hospital environments. A. baumannii often employs two-component systems (TCSs) to regulate adaptive responses and virulence-related traits. This study describes a previously uncharacterized TCS in the A. baumannii ATCC19606 strain, consisting of a transcriptional sensor, DJ41_1407, and its regulator, DJ41_1408, located adjacent to GacA of the GacSA TCS. Markerless mutagenesis was performed to construct DJ41_1407 and DJ41_1408 single and double mutants. DJ41_1408 was found to upregulate 49 genes and downregulate 43 genes, most of which were associated with carbon metabolism and other metabolic pathways, such as benzoate degradation. MEME analysis revealed a putative binding box for DJ41_1408, 5'TGTAAATRATTAYCAWTWAT3'. Colony size, motility, biofilm-forming ability, virulence, and antibiotic resistance of DJ41_1407 and DJ41_1408 single and double mutant strains were assessed against wild type. DJ41_1407 was found to enhance motility, while DJ41_1408 was found to upregulate biofilm-forming ability, and may also modulate antibiotic response. Both DJ41_1407 and DJ41_1408 suppressed virulence, based on results from a G. mellonella infection assay. These results showcase a novel A. baumannii TCS involved in metabolism, with effects on motility, biofilm-forming ability, virulence, and antibiotic response.

Mapping single‐cell responses to population‐level dynamics during antibiotic treatment

Treatment of sensitive bacteria with beta‐lactam antibiotics often leads to two salient population‐level features: a transient increase in total population biomass before a subsequent decline, and a linear correlation between growth and killing rates. However, it remains unclear how these population‐level responses emerge from collective single‐cell responses. During beta‐lactam treatment, it is well‐recognized that individual cells often exhibit varying degrees of filamentation before lysis. We show that the cumulative probability of cell lysis increases sigmoidally with the extent of filamentation and that this dependence is characterized by unique parameters that are specific to bacterial strain, antibiotic dose, and growth condition. Modeling demonstrates how the single‐cell lysis probabilities can give rise to population‐level biomass dynamics, which were experimentally validated. This mapping provides insights into how the population biomass time‐kill curve emerges from single cells and allows the representation of both single‐ and population‐level responses with universal parameters.

A Rapid Unraveling of the Activity and Antibiotic Susceptibility of Mycobacteria

The development of antibiotic-resistant bacteria is a worldwide health-related emergency that calls for new tools to study the bacterial metabolism and to obtain fast diagnoses. Indeed, the conventional analysis time scale is too long and affects our ability to fight infections. Slowly growing bacteria represent a bigger challenge, since their analysis may require up to months. Among these bacteria, Mycobacterium tuberculosis, the causative agent of tuberculosis, has caused more than 10 million new cases and 1.7 million deaths in 2016 only. We employed a particularly powerful nanomechanical oscillator, the nanomotion sensor, to characterize rapidly and in real time tuberculous and nontuberculous bacterial species, Mycobacterium bovis bacillus Calmette-Guérin and Mycobacterium abscessus, respectively, exposed to different antibiotics. Here, we show how high-speed and high-sensitivity detectors, the nanomotion sensors, can provide a rapid and reliable analysis of different mycobacterial species, obtaining qualitative and quantitative information on their responses to different drugs. This is the first application of the technique to tackle the urgent medical issue of mycobacterial infections, evaluating the dynamic response of bacteria to different antimicrobial families and the role of the replication rate in the resulting nanomotion pattern. In addition to a fast analysis, which could massively benefit patients and the overall health care system, we investigated the real-time responses of the bacteria to extract unique information on the bacterial mechanisms triggered in response to antibacterial pressure, with consequences both at the clinical level and at the microbiological level.

Mycobacterial Mutagenesis and Drug Resistance Are Controlled by Phosphorylation- and Cardiolipin-Mediated Inhibition of the RecA Coprotease

Infection with Mycobacterium tuberculosis continues to cause substantial human mortality, in part because of the emergence of antimicrobial resistance. Antimicrobial resistance in tuberculosis is solely the result of chromosomal mutations that modify drug activators or targets, yet the mechanisms controlling the mycobacterial DNA-damage response (DDR) remain incompletely defined. Here, we identify RecA serine 207 as a multifunctional signaling hub that controls the DDR in mycobacteria. RecA S207 is phosphorylated after DNA damage, which suppresses the emergence of antibiotic resistance by selectively inhibiting the LexA coprotease function of RecA without affecting its ATPase or strand exchange functions. Additionally, RecA associates with the cytoplasmic membrane during the mycobacterial DDR, where cardiolipin can specifically inhibit the LexA coprotease function of unmodified, but not S207 phosphorylated, RecA. These findings reveal that RecA S207 controls mutagenesis and antibiotic resistance in mycobacteria through phosphorylation and cardiolipin-mediated inhibition of RecA coprotease function.

Bacterial Adaptation to Antibiotics through Regulatory RNAs

The extensive use of antibiotics has resulted in a situation where multidrug-resistant pathogens have become a severe menace to human health worldwide. A deeper understanding of the principles used by pathogens to adapt to, respond to, and resist antibiotics would pave the road to the discovery of drugs with novel mechanisms. For bacteria, antibiotics represent clinically relevant stresses that induce protective responses. The recent implication of regulatory RNAs (small RNAs [sRNAs]) in antibiotic response and resistance in several bacterial pathogens suggests that they should be considered innovative drug targets. This minireview discusses sRNA-mediated mechanisms exploited by bacterial pathogens to fight against antibiotics. A critical discussion of the newest findings in the field is provided, with emphasis on the implication of sRNAs in major mechanisms leading to antibiotic resistance, including drug uptake, active drug efflux, drug target modifications, biofilms, cell walls, and lipopolysaccharide (LPS) biosynthesis. Of interest is the lack of knowledge about sRNAs implicated in Gram-positive compared to Gram-negative bacterial resistance.

Nontargeted Metabolomics Reveals the Multilevel Response to Antibiotic Perturbations

Microbes have shown a remarkable ability in evading the killing actions of antimicrobial agents, such that treatment of bacterial infections represents once more an urgent global challenge. Understanding the initial bacterial response to antimicrobials may reveal intrinsic tolerance mechanisms to antibiotics and suggest alternative and less conventional therapeutic strategies. Here, we used mass spectrometry-based metabolomics to monitor the immediate metabolic response of Escherichia coli to a variety of antibiotic perturbations. We show that rapid metabolic changes can reflect drug mechanisms of action and reveal the active role of metabolism in mediating the first stress response to antimicrobials. We uncovered a role for ammonium imbalance in aggravating chloramphenicol toxicity and the essential function of deoxythymidine 5'-diphosphate (dTDP)-rhamnose synthesis for the immediate transcriptional upregulation of GyrA in response to quinolone antibiotics. Our results suggest bacterial metabolism as an attractive target to interfere with the early bacterial response to antibiotic treatments and reduce the probability for survival and eventual evolution of antibiotic resistance.

Programming stress-induced altruistic death in engineered bacteria

Programmed death is often associated with a bacterial stress response. This behavior appears paradoxical, as it offers no benefit to the individual. This paradox can be explained if the death is 'altruistic': the killing of some cells can benefit the survivors through release of 'public goods'. However, the conditions where bacterial programmed death becomes advantageous have not been unambiguously demonstrated experimentally. Here, we determined such conditions by engineering tunable, stress-induced altruistic death in the bacterium Escherichia coli. Using a mathematical model, we predicted the existence of an optimal programmed death rate that maximizes population growth under stress. We further predicted that altruistic death could generate the 'Eagle effect', a counter-intuitive phenomenon where bacteria appear to grow better when treated with higher antibiotic concentrations. In support of these modeling insights, we experimentally demonstrated both the optimality in programmed death rate and the Eagle effect using our engineered system. Our findings fill a critical conceptual gap in the analysis of the evolution of bacterial programmed death, and have implications for a design of antibiotic treatment.