Development of Dicationic Bisguanidine-Arylfuran Derivatives as Potent Agents against Gram-Negative Bacteria.

Antibiotic resistance among bacteria is a growing global challenge. A major reason for this is the limited progress in developing new classes of antibiotics active against Gram-negative bacteria. Here, we investigate the antibacterial activity of a dicationic bisguanidine-arylfuran, originally developed as an antitrypanosomal agent, and new derivatives thereof. The compounds showed good activity (EC50 2-20 µM) against antibiotic-resistant isolates of the Gram-negative members of the ESKAPE group (Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp.) and Escherichia coli with different antibiotic susceptibility patterns, including ESBL isolates. Cytotoxicity was moderate, and several of the new derivatives were less cytotoxic than the lead molecule, offering better selectivity indices (40-80 for several ESKAPE isolates). The molecular mechanism for the antibacterial activity of these molecules is unknown, but sensitivity profiling against human ESKAPE isolates and E. coli collections with known susceptibility patterns against established antibiotics indicates that it is distinct from lactam and quinolone antibiotics.

The Effect of Heavy Metals on Conjugation Efficiency of an F-Plasmid in Escherichia coli.

Conjugation, the process by which conjugative plasmids are transferred between bacteria, is regarded as a major contributor to the spread of antibiotic resistance, in both environmental and clinical settings. Heavy metals are known to co-select for antibiotic resistance, but the impact of the presence of these metals on conjugation itself is not clear. Here, we systematically investigate the impact that five heavy metals (arsenic, cadmium, copper, manganese, and zinc) have on the transfer of an IncF conjugative plasmid in Escherichia coli. Our results show that two of the metals, cadmium and manganese, have no significant impact, while arsenic and zinc both reduce conjugation efficiency by approximately 2-fold. Copper showed the largest impact, with an almost 100-fold decrease in conjugation efficiency. This was not mediated by any change in transcription from the major Py promoter responsible for transcription of the conjugation machinery genes. Further, we show that in order to have this severe impact on the transfer of the plasmid, copper sulfate needs to be present during the mating process, and we suggest explanations for this.

Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes.

Escherichia coli is an important cause of bacterial infections worldwide, with multidrug-resistant strains incurring substantial costs on human lives. Besides therapeutic concentrations of antimicrobials in health care settings, the presence of subinhibitory antimicrobial residues in the environment and in clinics selects for antimicrobial resistance (AMR), but the underlying genetic repertoire is less well understood. Here, we used machine learning to predict the population doubling time and cell growth yield of 1,407 genetically diverse E. coli strains expanding under exposure to three subinhibitory concentrations of six classes of antimicrobials from single-nucleotide genetic variants, accessory gene variation, and the presence of known AMR genes. We predicted cell growth yields in the held-out test data with an average correlation (Spearman's ρ) of 0.63 (0.36 to 0.81 across concentrations) and cell doubling times with an average correlation of 0.59 (0.32 to 0.92 across concentrations), with moderate increases in sample size unlikely to improve predictions further. This finding points to the remaining missing heritability of growth under antimicrobial exposure being explained by effects that are too rare or weak to be captured unless sample size is dramatically increased, or by effects other than those conferred by the presence of individual single-nucleotide polymorphisms (SNPs) and genes. Predictions based on whole-genome information were generally superior to those based only on known AMR genes and were accurate for AMR resistance at therapeutic concentrations. We pinpointed genes and SNPs determining the predicted growth and thereby recapitulated many known AMR determinants. Finally, we estimated the effect sizes of resistance genes across the entire collection of strains, disclosing the growth effects for known resistance genes in each individual strain. Our results underscore the potential of predictive modeling of growth patterns from genomic data under subinhibitory concentrations of antimicrobials, although the remaining missing heritability poses a challenge for achieving the accuracy and precision required for clinical use. IMPORTANCE Predicting bacterial growth from genome sequences is important for a rapid characterization of strains in clinical diagnostics and to disclose candidate novel targets for anti-infective drugs. Previous studies have dissected the relationship between bacterial growth and genotype in mutant libraries for laboratory strains, yet no study so far has examined the predictive power of genome sequence in natural strains. In this study, we used a high-throughput phenotypic assay to measure the growth of a systematic collection of natural Escherichia coli strains and then employed machine learning models to predict bacterial growth from genomic data under nontherapeutic subinhibitory concentrations of antimicrobials that are common in nonclinical settings. We found a moderate to strong correlation between predicted and actual values for the different collected data sets. Moreover, we observed that the known resistance genes are still effective at sublethal concentrations, pointing to clinical implications of these concentrations.

Genomic Epidemiology and Evolution of Escherichia coli in Wild Animals in Mexico.

Escherichia coli is a common bacterial species in the gastrointestinal tracts of warm-blooded animals and humans. Pathogenicity and antimicrobial resistance in E. coli may emerge via host switching from animal reservoirs. Despite its potential clinical importance, knowledge of the population structure of commensal E. coli within wild hosts and the epidemiological links between E. coli in nonhuman hosts and E. coli in humans is still scarce. In this study, we analyzed the whole-genome sequencing data of a collection of 119 commensal E. coli strains recovered from the guts of 55 mammal and bird species in Mexico and Venezuela in the 1990s. We observed low concordance between the population structures of E. coli isolates colonizing wild animals and the phylogeny, taxonomy, and ecological and physiological attributes of the host species, with distantly related E. coli strains often colonizing the same or similar host species and distantly related host species often hosting closely related E. coli strains. We found no evidence for recent transmission of E. coli genomes from wild animals to either domesticated animals or humans. However, multiple livestock- and human-related virulence factor genes were present in E. coli of wild animals, including virulence factors characteristic of Shiga toxin-producing E. coli (STEC) and atypical enteropathogenic E. coli (aEPEC), where several isolates from wild hosts harbored the locus of enterocyte effacement (LEE) pathogenicity island. Moreover, E. coli isolates from wild animal hosts often harbored known antibiotic resistance determinants, including those against ciprofloxacin, aminoglycosides, tetracyclines, and beta-lactams, with some determinants present in multiple, distantly related E. coli lineages colonizing very different host animals. We conclude that genome pools of E. coli colonizing the guts of wild animals and humans share virulence and antibiotic resistance genes, underscoring the idea that wild animals could serve as reservoirs for E. coli pathogenicity in human and livestock infections.IMPORTANCEEscherichia coli is a clinically important bacterial species implicated in human- and livestock-associated infections worldwide. The bacterium is known to reside in the guts of humans, livestock, and wild animals. Although wild animals are recognized as potential reservoirs for pathogenic E. coli strains, the knowledge of the population structure of E. coli in wild hosts is still scarce. In this study, we used fine resolution of whole-genome sequencing to provide novel insights into the evolution of E. coli genomes from a small yet diverse collection of strains recovered within a broad range of wild animal species (including mammals and birds), the coevolution of E. coli strains with their hosts, and the genetics of pathogenicity of E. coli strains in wild hosts in Mexico. Our results provide evidence for the clinical importance of wild animals as reservoirs for pathogenic strains and highlight the need to include nonhuman hosts in the surveillance programs for E. coli infections.

A High-Throughput Method for Screening for Genes Controlling Bacterial Conjugation of Antibiotic Resistance.

The rapid horizontal transmission of antibiotic resistance genes on conjugative plasmids between bacterial host cells is a major cause of the accelerating antibiotic resistance crisis. There are currently no experimental platforms for fast and cost-efficient screening of genetic effects on antibiotic resistance transmission by conjugation, which prevents understanding and targeting conjugation. We introduce a novel experimental framework to screen for conjugation-based horizontal transmission of antibiotic resistance between >60,000 pairs of cell populations in parallel. Plasmid-carrying donor strains are constructed in high-throughput. We then mix the resistance plasmid-carrying donors with recipients in a design where only transconjugants can reproduce, measure growth in dense intervals, and extract transmission times as the growth lag. As proof-of-principle, we exhaustively explore chromosomal genes controlling F-plasmid donation within Escherichia coli populations, by screening the Keio deletion collection in high replication. We recover all seven known chromosomal gene mutants affecting conjugation as donors and identify many novel mutants, all of which diminish antibiotic resistance transmission. We validate nine of the novel genes' effects in liquid mating assays and complement one of the novel genes' effect on conjugation (rseA). The new framework holds great potential for exhaustive disclosing of candidate targets for helper drugs that delay resistance development in patients and societies and improve the longevity of current and future antibiotics. Further, the platform can easily be adapted to explore interspecies conjugation, plasmid-borne factors, and experimental evolution and be used for rapid construction of strains.IMPORTANCE The rapid transmission of antibiotic resistance genes on conjugative plasmids between bacterial host cells is a major cause of the accelerating antibiotic resistance crisis. There are currently no experimental platforms for fast and cost-efficient screening of genetic effects on antibiotic resistance transmission by conjugation, which prevents understanding and targeting conjugation. We introduce a novel experimental framework to screen for conjugation-based horizontal transmission of antibiotic resistance between >60,000 pairs of cell populations in parallel. As proof-of-principle, we exhaustively explore chromosomal genes controlling F-plasmid donation within E. coli populations. We recover all previously known and many novel chromosomal gene mutants that affect conjugation efficiency. The new framework holds great potential for rapid screening of compounds that decrease transmission. Further, the platform can easily be adapted to explore interspecies conjugation, plasmid-borne factors, and experimental evolution and be used for rapid construction of strains.

Teaching about antibiotic resistance to a broad audience: a multidisciplinary approach.

Education for the general public about antibiotic resistance is advocated as a key component of our response to this crisis. Since this is a multidisciplinary problem encompassing natural, medical and social sciences, it is an educational challenge as both students and lecturers will have vastly different backgrounds in the topics. Here we describe an online multidisciplinary course on antibiotic resistance spanning topics as diverse as chemistry and practical philosophy. The target group was any post-secondary school student and the participating students had different occupations and educational experience. Although as many as 38% of the students were currently studying natural sciences at university, the course included a diverse group with medical professionals (16%) and teachers (6%) making up a significant fraction of the class. The outcomes based on examination and the course evaluations were very positive and we have indications that the information students gained from this course has been spread to others. Unlike other online courses addressing antibiotic resistance, this course is both accessible to a wide range of students and covers a broad range of topics. We advocate courses like ours as an effective tool in educating the public about this crisis.

Chemical Changes On, and Through, The Bacterial Envelope in Escherichia coli Mutants Exhibiting Impaired Plasmid Transfer Identified Using Time-of-Flight Secondary Ion Mass Spectrometry.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) using a (CO2)6k+ gas cluster ion beam (GCIB) was used to analyze Escherichia coli mutants previously identified as having impaired plasmid transfer capability related to the spread of antibiotic resistance. The subset of mutants selected were expected to result in changes in the bacterial envelope composition through the deletion of genes encoding for FabF, DapF, and Lpp, where the surface sensitivity of ToF-SIMS can be most useful. Analysis of arrays of spotted bacteria allowed changes in the lipid composition of the bacteria to be elucidated using multivariate analysis and confirmed through imaging of individual ion signals. Significant changes in chemical composition were observed, including a surprising loss of cyclopropanated fatty acids in the fabF mutant where FabF is associated with the elongation of FA(16:1) to FA(18:1) and not cyclopropane formation. The ability of the GCIB to generate increased higher mass signals from biological samples allowed intact lipid A (m/z 1796) to be detected on the bacteria and, despite a 40 keV impact energy, depth profiled through the bacterial envelope along with other high mass ions including species at m/z 1820 and 2428, attributed to ECACYC, that were only observed below the surface of the bacteria and were notably absent in the depth profile of the lpp mutant. The analysis provides new insights into the action of the specific pathways targeted in this study and paves the way for whole new avenues for the characterization of intact molecules within the bacterial envelope.

Inhibiting conjugation as a tool in the fight against antibiotic resistance.

Antibiotic resistance, especially in gram-negative bacteria, is spreading globally and rapidly. Development of new antibiotics lags behind; therefore, novel approaches to the problem of antibiotic resistance are sorely needed and this commentary highlights one relatively unexplored target for drug development: conjugation. Conjugation is a common mechanism of horizontal gene transfer in bacteria that is instrumental in the spread of antibiotic resistance among bacteria. Most resistance genes are found on mobile genetic elements and primarily spread by conjugation. Furthermore, conjugative elements can act as a reservoir to maintain antibiotic resistance in the bacterial population even in the absence of antibiotic selection. Thus, conjugation can spread antibiotic resistance quickly between bacteria of the microbiome and pathogens when selective pressure (antibiotics) is introduced. Potential drug targets include the plasmid-encoded conjugation system and the host-encoded proteins important for conjugation. Ideally, a conjugation inhibitor will be used alongside antibiotics to prevent the spread of resistance to or within pathogens while not acting as a growth inhibitor itself. Inhibiting conjugation will be an important addition to our arsenal of strategies to combat the antibiotic resistance crisis, allowing us to extend the usefulness of antibiotics.

Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data.

The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The detection of antibiotic resistance is typically made by measuring a few known determinants previously identified from genome sequencing, and thus requires the prior knowledge of its biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average accuracy of 0.91 on held-out data (range 0.81-0.97). While the best models most frequently employed gene content, an average accuracy score of 0.79 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and significantly improved prediction only for two antibiotics, including ciprofloxacin. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data can be informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic.

Origin of the Low Magnetic Moment in Fe2AlTi: An Ab Initio Study.

The intermetallic compound Fe 2 AlTi (alternatively Fe 2 TiAl) is an important phase in the ternary Fe-Al-Ti phase diagram. Previous theoretical studies showed a large discrepancy of approximately an order of magnitude between the ab initio computed magnetic moments and the experimentally measured ones. To unravel the source of this discrepancy, we analyze how various mechanisms present in realistic materials such as residual strain effects or deviations from stoichiometry affect magnetism. Since in spin-unconstrained calculations the system always evolves to the spin configuration which represents a local or global minimum in the total energy surface, finite temperature spin effects are not well described. We therefore turn the investigation around and use constrained spin calculations, fixing the global magnetic moment. This approach provides direct insight into local and global energy minima (reflecting metastable and stable spin phases) as well as the curvature of the energy surface, which correlates with the magnetic entropy and thus the magnetic configuration space accessible at finite temperatures. Based on this approach, we show that deviations from stoichiometry have a huge impact on the local magnetic moment and can explain the experimentally observed low magnetic moments.