Novel Synergies and Isolate Specificities in the Drug Interaction Landscape of Mycobacterium abscessus.

Mycobacterium abscessus infections are difficult to treat and are often considered untreatable without tissue resection. Due to the intrinsic drug-resistant nature of the bacteria, combination therapy of three or more antibiotics is recommended. A major challenge in treating M. abscessus infections is the absence of a universal combination therapy with satisfying clinical success rates, leaving clinicians to treat infections using antibiotics lacking efficacy data. We systematically measured drug combinations in M. abscessus to establish a resource of drug interaction data and identify patterns of synergy to help design optimized combination therapies. We measured 191 pairwise drug combination effects among 22 antibacterials and identified 71 synergistic pairs, 54 antagonistic pairs, and 66 potentiator-antibiotic pairs. We found that commonly used drug combinations in the clinic, such as azithromycin and amikacin, are antagonistic in the lab reference strain ATCC 19977, whereas novel combinations, such as azithromycin and rifampicin, are synergistic. Another challenge in developing universally effective multidrug therapies for M. abscessus is the significant variation in drug response between isolates. We measured drug interactions in a focused set of 36 drug pairs across a small panel of clinical isolates with rough and smooth morphotypes. We observed strain-dependent drug interactions that cannot be predicted from single-drug susceptibility profiles or known drug mechanisms of action. Our study demonstrates the immense potential to identify synergistic drug combinations in the vast drug combination space and emphasizes the importance of strain-specific combination measurements for designing improved therapeutic interventions.

Advances in the design of combination therapies for the treatment of tuberculosis.

INTRODUCTION: Tuberculosis requires lengthy multi-drug therapy. Mycobacterium tuberculosis occupies different tissue compartments during infection, making drug access and susceptibility patterns variable. Antibiotic combinations are needed to ensure each compartment of infection is reached with effective drug treatment. Despite drug combinations' role in treating tuberculosis, the design of such combinations has been tackled relatively late in the drug development process, limiting the number of drug combinations tested. In recent years, there has been significant progress using in vitro, in vivo, and computational methodologies to interrogate combination drug effects. AREAS COVERED: This review discusses the advances in these methodologies and how they may be used in conjunction with new successful clinical trials of novel drug combinations to design optimized combination therapies for tuberculosis. Literature searches for approaches and experimental models used to evaluate drug combination effects were undertaken. EXPERT OPINION: We are entering an era richer in combination drug effect and pharmacokinetic/pharmacodynamic data, genetic tools, and outcome measurement types. Application of computational modeling approaches that integrate these data and produce predictive models of clinical outcomes may enable the field to generate novel, effective multidrug therapies using existing and new drug combination backbones.

Design principles to assemble drug combinations for effective tuberculosis therapy using interpretable pairwise drug response measurements.

A challenge in tuberculosis treatment regimen design is the necessity to combine three or more antibiotics. We narrow the prohibitively large search space by breaking down high-order drug combinations into drug pair units. Using pairwise in vitro measurements, we train machine learning models to predict higher-order combination treatment outcomes in the relapsing BALB/c mouse model. Classifiers perform well and predict many of the >500 possible combinations among 12 antibiotics to be improved over bedaquiline + pretomanid + linezolid, a treatment-shortening regimen compared with the standard of care in mice. We reformulate classifiers as simple rulesets to reveal guiding principles of constructing combination therapies for both preclinical and clinical outcomes. One example ruleset combines a drug pair that is synergistic in a dormancy model with a pair that is potent in a cholesterol-rich growth environment. These rulesets are predictive, intuitive, and practical, thus enabling rational construction of drug combinations.

Systematic measurement of combination-drug landscapes to predict in vivo treatment outcomes for tuberculosis.

Lengthy multidrug chemotherapy is required to achieve a durable cure in tuberculosis. However, we lack well-validated, high-throughput in vitro models that predict animal outcomes. Here, we provide an extensible approach to rationally prioritize combination therapies for testing in in vivo mouse models of tuberculosis. We systematically measured Mycobacterium tuberculosis response to all two- and three-drug combinations among ten antibiotics in eight conditions that reproduce lesion microenvironments, resulting in >500,000 measurements. Using these in vitro data, we developed classifiers predictive of multidrug treatment outcome in a mouse model of disease relapse and identified ensembles of in vitro models that best describe in vivo treatment outcomes. We identified signatures of potencies and drug interactions in specific in vitro models that distinguish whether drug combinations are better than the standard of care in two important preclinical mouse models. Our framework is generalizable to other difficult-to-treat diseases requiring combination therapies. A record of this paper's transparent peer review process is included in the supplemental information.

Morphological profiling of tubercle bacilli identifies drug pathways of action.

Morphological profiling is a method to classify target pathways of antibacterials based on how bacteria respond to treatment through changes to cellular shape and spatial organization. Here we utilized the cell-to-cell variation in morphological features of Mycobacterium tuberculosis bacilli to develop a rapid profiling platform called Morphological Evaluation and Understanding of Stress (MorphEUS). MorphEUS classified 94% of tested drugs correctly into broad categories according to modes of action previously identified in the literature. In the other 6%, MorphEUS pointed to key off-target activities. We observed cell wall damage induced by bedaquiline and moxifloxacin through secondary effects downstream from their main target pathways. We implemented MorphEUS to correctly classify three compounds in a blinded study and identified an off-target effect for one compound that was not readily apparent in previous studies. We anticipate that the ability of MorphEUS to rapidly identify pathways of drug action and the proximal cause of cellular damage in tubercle bacilli will make it applicable to other pathogens and cell types where morphological responses are subtle and heterogeneous.

Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis.

The rapid spread of multidrug-resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen Mycobacterium tuberculosis (MTB), coupled with the large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico more than 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c upregulation reduces the antagonism of the bedaquiline-streptomycin combination. A retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (P value = 1 × 10-4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens.IMPORTANCE Multidrug combination therapy is an important strategy for treating tuberculosis, the world's deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB that identifies synergistic drug regimens from an immense set of possible drug combinations using the pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.

Efficient measurement and factorization of high-order drug interactions in Mycobacterium tuberculosis.

Combinations of three or more drugs are used to treat many diseases, including tuberculosis. Thus, it is important to understand how synergistic or antagonistic drug interactions affect the efficacy of combination therapies. However, our understanding of high-order drug interactions is limited because of the lack of both efficient measurement methods and theoretical framework for analysis and interpretation. We developed an efficient experimental sampling and scoring method [diagonal measurement of n-way drug interactions (DiaMOND)] to measure drug interactions for combinations of any number of drugs. DiaMOND provides an efficient alternative to checkerboard assays, which are commonly used to measure drug interactions. We established a geometric framework to factorize high-order drug interactions into lower-order components, thereby establishing a road map of how to use lower-order measurements to predict high-order interactions. Our framework is a generalized Loewe additivity model for high-order drug interactions. Using DiaMOND, we identified and analyzed synergistic and antagonistic antibiotic combinations against Mycobacteriumtuberculosis. Efficient measurement and factorization of high-order drug interactions by DiaMOND are broadly applicable to other cell types and disease models.

Selective Degradation of Host RNA Polymerase II Transcripts by Influenza A Virus PA-X Host Shutoff Protein.

Influenza A viruses (IAVs) inhibit host gene expression by a process known as host shutoff. Host shutoff limits host innate immune responses and may also redirect the translation apparatus to the production of viral proteins. Multiple IAV proteins regulate host shutoff, including PA-X, a ribonuclease that remains incompletely characterized. We report that PA-X selectively targets host RNA polymerase II (Pol II) transcribed mRNAs, while sparing products of Pol I and Pol III. Interestingly, we show that PA-X can also target Pol II-transcribed RNAs in the nucleus, including non-coding RNAs that are not destined to be translated, and reporter transcripts with RNA hairpin structures that block ribosome loading. Transcript degradation likely occurs in the nucleus, as PA-X is enriched in the nucleus and its nuclear localization correlates with reduction in target RNA levels. Complete degradation of host mRNAs following PA-X-mediated endonucleolytic cleavage is dependent on the host 5'->3'-exonuclease Xrn1. IAV mRNAs are structurally similar to host mRNAs, but are synthesized and modified at the 3' end by the action of the viral RNA-dependent RNA polymerase complex. Infection of cells with wild-type IAV or a recombinant PA-X-deficient virus revealed that IAV mRNAs resist PA-X-mediated degradation during infection. At the same time, loss of PA-X resulted in changes in the synthesis of select viral mRNAs and a decrease in viral protein accumulation. Collectively, these results significantly advance our understanding of IAV host shutoff, and suggest that the PA-X causes selective degradation of host mRNAs by discriminating some aspect of Pol II-dependent RNA biogenesis in the nucleus.

Lipid signalling couples translational surveillance to systemic detoxification in Caenorhabditis elegans.

Translation in eukaryotes is followed to detect toxins and virulence factors and coupled to the induction of defence pathways. Caenorhabditis elegans germline-specific mutations in translation components are detected by this system to induce detoxification and immune responses in distinct somatic cells. An RNA interference screen revealed gene inactivations that act at multiple steps in lipid biosynthetic and kinase pathways upstream of MAP kinase to mediate the systemic communication of translation defects to induce detoxification genes. Mammalian bile acids can rescue the defect in detoxification gene induction caused by C. elegans lipid biosynthetic gene inactivations. Extracts prepared from C. elegans with translation deficits but not from the wild type can also rescue detoxification gene induction in lipid-biosynthesis-defective strains. These eukaryotic antibacterial countermeasures are not ignored by bacteria: particular bacterial species suppress normal C. elegans detoxification responses to mutations in translation factors.

A new antibiotic with potent activity targets MscL.

The growing problem of antibiotic-resistant bacteria is a major threat to human health. Paradoxically, new antibiotic discovery is declining, with most of the recently approved antibiotics corresponding to new uses for old antibiotics or structurally similar derivatives of known antibiotics. We used an in silico approach to design a new class of nontoxic antimicrobials for the bacteria-specific mechanosensitive ion channel of large conductance, MscL. One antimicrobial of this class, compound 10, is effective against methicillin-resistant Staphylococcus aureus with no cytotoxicity in human cell lines at the therapeutic concentrations. As predicted from in silico modeling, we show that the mechanism of action of compound 10 is at least partly dependent on interactions with MscL. Moreover we show that compound 10 cured a methicillin-resistant S. aureus infection in the model nematode Caenorhabditis elegans. Our work shows that compound 10, and other drugs that target MscL, are potentially important therapeutics against antibiotic-resistant bacterial infections.

Insect-derived cecropins display activity against Acinetobacter baumannii in a whole-animal high-throughput Caenorhabditis elegans model.

The rise of multidrug-resistant Acinetobacter baumannii and a concomitant decrease in antibiotic treatment options warrants a search for new classes of antibacterial agents. We have found that A. baumannii is pathogenic and lethal to the model host organism Caenorhabditis elegans and have exploited this phenomenon to develop an automated, high-throughput, high-content screening assay in liquid culture that can be used to identify novel antibiotics effective against A. baumannii. The screening assay involves coincubating C. elegans with A. baumannii in 384-well plates containing potential antibacterial compounds. At the end of the incubation period, worms are stained with a dye that stains only dead animals, and images are acquired using automated microscopy and then analyzed using an automated image analysis program. This robust assay yields a Z' factor consistently greater than 0.7. In a pilot experiment to test the efficacy of the assay, we screened a small custom library of synthetic antimicrobial peptides (AMPs) that were synthesized using publicly available sequence data and/or transcriptomic data from immune-challenged insects. We identified cecropin A and 14 other cecropin or cecropin-like peptides that were able to enhance C. elegans survival in the presence of A. baumannii. Interestingly, one particular hit, BR003-cecropin A, a cationic peptide synthesized by the mosquito Aedes aegypti, showed antibiotic activity against a panel of Gram-negative bacteria and exhibited a low MIC (5 µg/ml) against A. baumannii. BR003-cecropin A causes membrane permeability in A. baumannii, which could be the underlying mechanism of its lethality.

On the Mechanism of Berberine-INF55 (5-Nitro-2-phenylindole) Hybrid Antibacterials.

Berberine-INF55 hybrids are a promising class of antibacterials that combine berberine and the NorA multidrug resistance pump inhibitor INF55 (5-nitro-2-phenylindole) together in one molecule via a chemically stable linkage. Previous studies demonstrated the potential of these compounds for countering efflux-mediated antibacterial drug resistance but they didn't establish whether the compounds function as originally intended, i.e. with the berberine moiety providing antibacterial activity and the attached INF55 component independently blocking multidrug resistance pumps, thereby enhancing the activity of berberine by reducing its efflux. We hypothesised that if the proposed mechanism is correct, then hybrids carrying more potent INF55 pump inhibitor structures should show enhanced antibacterial effects relative to those bearing weaker inhibitors. Two INF55 analogues showing graded reductions in NorA inhibitory activity compared with INF55 were identified and their corresponding berberine-INF55 hybrids carrying equivalent INF55 inhibitor structures synthesised. Multiple assays comparing the antibacterial effects of the hybrids and their corresponding berberine-INF55 analogue combinations showed that the three hybrids all show very similar activities, leading us to conclude that the antibacterial mechanism(s) of berberine-INF55 hybrids is different from berberine-INF55 combinations.

High- and low-throughput scoring of fat mass and body fat distribution in C. elegans.

Fat accumulation is a complex phenotype affected by factors such as neuroendocrine signaling, feeding, activity, and reproductive output. Accordingly, the most informative screens for genes and compounds affecting fat accumulation would be those carried out in whole living animals. Caenorhabditis elegans is a well-established and effective model organism, especially for biological processes that involve organ systems and multicellular interactions, such as metabolism. Every cell in the transparent body of C. elegans is visible under a light microscope. Consequently, an accessible and reliable method to visualize worm lipid-droplet fat depots would make C. elegans the only metazoan in which genes affecting not only fat mass but also body fat distribution could be assessed at a genome-wide scale. Here we present a radical improvement in oil red O worm staining together with high-throughput image-based phenotyping. The three-step sample preparation method is robust, formaldehyde-free, and inexpensive, and requires only 15min of hands-on time to process a 96-well plate. Together with our free and user-friendly automated image analysis package, this method enables C. elegans sample preparation and phenotype scoring at a scale that is compatible with genome-wide screens. Thus we present a feasible approach to small-scale phenotyping and large-scale screening for genetic and/or chemical perturbations that lead to alterations in fat quantity and distribution in whole animals.

Whole animal automated platform for drug discovery against multi-drug resistant Staphylococcus aureus.

Staphylococcus aureus, the leading cause of hospital-acquired infections in the United States, is also pathogenic to the model nematode Caenorhabditis elegans. The C. elegans-S. aureus infection model was previously carried out on solid agar plates where the bacteriovorous C. elegans feeds on a lawn of S. aureus. However, agar-based assays are not amenable to large scale screens for antibacterial compounds. We have developed a high throughput liquid screening assay that uses robotic instrumentation to dispense a precise amount of methicillin resistant S. aureus (MRSA) and worms in 384-well assay plates, followed by automated microscopy and image analysis. In validation of the liquid assay, an MRSA cell wall defective mutant, MW2ΔtarO, which is attenuated for killing in the agar-based assay, was found to be less virulent in the liquid assay. This robust assay with a Z'-factor consistently greater than 0.5 was utilized to screen the Biomol 4 compound library consisting of 640 small molecules with well characterized bioactivities. As proof of principle, 27 of the 30 clinically used antibiotics present in the library conferred increased C. elegans survival and were identified as hits in the screen. Surprisingly, the antihelminthic drug closantel was also identified as a hit in the screen. In further studies, we confirmed the anti-staphylococcal activity of closantel against vancomycin-resistant S. aureus isolates and other Gram-positive bacteria. The liquid C. elegans-S. aureus assay described here allows screening for anti-staphylococcal compounds that are not toxic to the host.

High-throughput screening for novel anti-infectives using a C. elegans pathogenesis model.

In recent history, the nematode Caenorhabditis elegans has provided a compelling platform for the discovery of novel antimicrobial drugs. In this protocol, we present an automated, high-throughput C. elegans pathogenesis assay, which can be used to screen for anti-infective compounds that prevent nematodes from dying due to Pseudomonas aeruginosa. New antibiotics identified from such screens would be promising candidates for treatment of human infections, and also can be used as probe compounds to identify novel targets in microbial pathogenesis or host immunity.

Pseudomonas aeruginosa disrupts Caenorhabditis elegans iron homeostasis, causing a hypoxic response and death.

The opportunistic pathogen Pseudomonas aeruginosa causes serious human infections, but effective treatments and the mechanisms mediating pathogenesis remain elusive. Caenorhabditis elegans shares innate immune pathways with humans, making it invaluable to investigate infection. To determine how P. aeruginosa disrupts host biology, we studied how P. aeruginosa kills C. elegans in a liquid-based pathogenesis model. We found that P. aeruginosa-mediated killing does not require quorum-sensing pathways or host colonization. A chemical genetic screen revealed that iron chelators alleviate P. aeruginosa-mediated killing. Consistent with a role for iron in P. aeruginosa pathogenesis, the bacterial siderophore pyoverdin was required for virulence and was sufficient to induce a hypoxic response and death in the absence of bacteria. Loss of the C. elegans hypoxia-inducing factor HIF-1, which regulates iron homeostasis, exacerbated P. aeruginosa pathogenesis, further linking hypoxia and killing. As pyoverdin is indispensable for virulence in mice, pyoverdin-mediated hypoxia is likely to be relevant in human pathogenesis.

Stimulation of host immune defenses by a small molecule protects C. elegans from bacterial infection.

The nematode Caenorhabditis elegans offers currently untapped potential for carrying out high-throughput, live-animal screens of low molecular weight compound libraries to identify molecules that target a variety of cellular processes. We previously used a bacterial infection assay in C. elegans to identify 119 compounds that affect host-microbe interactions among 37,214 tested. Here we show that one of these small molecules, RPW-24, protects C. elegans from bacterial infection by stimulating the host immune response of the nematode. Using transcriptome profiling, epistasis pathway analyses with C. elegans mutants, and an RNAi screen, we show that RPW-24 promotes resistance to Pseudomonas aeruginosa infection by inducing the transcription of a remarkably small number of C. elegans genes (∼1.3% of all genes) in a manner that partially depends on the evolutionarily-conserved p38 MAP kinase pathway and the transcription factor ATF-7. These data show that the immunostimulatory activity of RPW-24 is required for its efficacy and define a novel C. elegans-based strategy to identify compounds with activity against antibiotic-resistant bacterial pathogens.

C. elegans Notch signaling regulates adult chemosensory response and larval molting quiescence.

BACKGROUND: The conserved DOS-motif proteins OSM-7 and OSM-11 function as coligands with canonical DSL (Delta, Serrate, and LAG-2) ligands to activate C. elegans Notch receptors during development. We report here that Notch ligands, coligands, and the receptors LIN-12 and GLP-1 regulate two C. elegans behaviors: chemosensory avoidance of octanol and quiescence during molting lethargus. RESULTS: C. elegans lacking osm-7 or osm-11 are defective in their response to octanol. We find that OSM-11 is secreted from hypodermal seam cells into the pseudocoelomic body cavity and acts non-cell autonomously as a diffusible factor. OSM-11 acts with the DSL ligand LAG-2 to activate LIN-12 and GLP-1 Notch receptors in the neurons of adult animals, thereby regulating octanol avoidance response. In adult animals, overexpression of osm-11 and consequent Notch receptor activation induces anachronistic sleep-like quiescence. Perturbation of Notch signaling alters basal activity in adults as well as arousal thresholds and quiescence during molting lethargus. Genetic epistasis studies reveal that Notch signaling regulates quiescence via previously identified circuits and genetic pathways including the egl-4 cGMP-dependent kinase. CONCLUSIONS: Our findings indicate that the conserved Notch pathway modulates behavior in adult C. elegans in response to environmental stress. Additionally, Notch signaling regulates sleep-like quiescence in C. elegans, suggesting that Notch may regulate sleep in other species.

Berberine-INF55 (5-nitro-2-phenylindole) hybrid antimicrobials: effects of varying the relative orientation of the berberine and INF55 components.

Hybrid antimicrobials containing an antibacterial linked to a multidrug resistance (MDR) pump inhibitor make up a promising new class of agents for countering efflux-mediated bacterial drug resistance. This study explores the effects of varying the relative orientation of the antibacterial and efflux pump inhibitor components in three isomeric hybrids (SS14, SS14-M, and SS14-P) which link the antibacterial alkaloid and known substrate for the NorA MDR pump berberine to different positions on INF55 (5-nitro-2-phenylindole), an inhibitor of NorA. The MICs for all three hybrids against wild-type, NorA-knockout, and NorA-overexpressing Staphylococcus aureus cells were found to be similar (9.4 to 40.2 microM), indicating that these compounds are not effectively effluxed by NorA. The three hybrids were also found to have similar curing effects in a Caenorhabditis elegans live infection model. Each hybrid was shown to accumulate in S. aureus cells to a greater extent than either berberine or berberine in the presence of INF55, and the uptake kinetics of SS14 were found to differ from those of SS14-M and SS14-P. The effects on the uptake and efflux of the NorA substrate ethidium bromide into S. aureus cells in the presence or absence of the hybrids were used to confirm MDR inhibition by the hybrids. MDR-inhibitory activity was confirmed for SS14-M and SS14-P but not for SS14. Molecular dynamics simulations revealed that SS14 prefers to adopt a conformation that is not prevalent in either SS14-M or SS14-P, which may explain why some properties of SS14 diverge from those of its two isomers. In summary, subtle repositioning of the pump-blocking INF55 moiety in berberine-INF55 hybrids was found to have a minimal effect on their antibacterial activities but to significantly alter their effects on MDR pumps.