Is antimicrobial resistance evolution accelerating?

The evolution of antibiotic resistance in Europe, 1998-2019

The evolutionary dynamics of antibiotic resistance are not well understood, particularly the long-term trajectories of resistance frequencies and their dependence on antibiotic consumption. Here, we systematically analyse resistance trajectories for 887 bug-drug-country combinations in Europe across 1998-2019, for eight bacterial species with a considerable resistance-associated public health burden. Our analyses support a model in which, after an initial increase, resistance frequencies reach a stable intermediate equilibrium. The plurality (37%) of analysed trajectories were best described as 'stable' (neither increasing nor decreasing). 21% of trajectories were best described as 'stabilising' - i.e. showing a transition from increasing frequency to a stable plateau; 21% as decreasing and 20% as increasing. The antibiotic consumption in a country predicts both the equilibrium frequency of the corresponding resistance and the speed at which this equilibrium is reached. Moreover, we find weak evidence that temporal fluctuations in resistance frequency are driven by temporal fluctuations in hospital antibiotic consumption. A large fraction of the variability in the speed of increase and the equilibrium level of resistance remains unexplained by antibiotic use, suggesting other factors may also drive resistance dynamics. Overall, our results indicate that ever increasing antibiotic resistance frequencies are not inevitable.

Similar antimicrobial resistance and virulence profiles among Aeromonas isolates from recreational beaches, post-chlorinated wastewater and clinical samples in Milwaukee, Wisconsin USA

The genus Aeromonas consists of Gram-negative facultative anaerobes that are ubiquitous in water and soil environments. Traditionally considered fish pathogens, members of the genus Aeromonas have received increasing attention over the years due to their association with human diseases. Furthermore, given their omnipresence and genetic flexibility, this genus is positioned at the intersection of One Health components and may disproportionately contribute to the dissemination of antimicrobial resistance (AMR) in the environment. To form a more complete picture of the relationship between Aeromonas and AMR dissemination, we assessed the prevalence, species composition, AMR and virulence profiles, and cytotoxicity of Aeromonas isolates from post-chlorinated wastewater effluents (WW), adjacent beach sands, and local clinics. Our data show that Aeromonas represents approximately 22-50 % of culturable bacteria across all three beaches. The species composition across beaches, WW, and the clinic were similar, and two of the four most notable pathogens, A. hydrophila and A. caviae, were present in all three sources. Conversely, AMR and multi-drug resistant (MDR) populations were significantly more prevalent in WW and the clinic compared to the beaches. Assessments of virulence genes and cytotoxic phenotypes revealed that while isolates carrying act were significantly associated with cytotoxic phenotypes, there were minimal differences between cytotoxicity and source, despite the relative abundance of act and hlyA in the clinic compared to the beach and WW. Our data suggests that environmental Aeromonas populations may be capable of higher AMR acquisition rates potentially causing infection in humans to a greater extent than is currently observed.

Identification of cell wall binding domains and repeats in Streptococcus pneumoniae phage endolysins: A molecular and diversity analysis

Streptococcus pneumoniae (pneumococcus) is a multidrug-resistant pathogen associated with pneumonia, otitis media, meningitis and other severe complications that are currently a global threat to human health. The World Health Organization listed Pneumococcus as the fourth of twelve globally prioritized pathogens. Identifying alternatives to antibiotic therapies is urgently needed to combat Pneumococcus. Bacteriophage-derived endolysins can be used as alternative therapeutics due to their bacterial cell wall hydrolyzing capability. In this study, S. pneumoniae phage genomes were screened to create a database of endolysins for molecular modelling and diversity analysis of these lytic proteins. A total of 89 lytic proteins were curated from 81 phage genomes and categorized into eight groups corresponding to their different enzymatically active (EAD) domains and cell wall binding (CBDs) domains. We then constructed three-dimensional structures that provided insights into these endolysins. Group I, II, III, V, and VI endolysins showed conserved catalytic and ion-binding residues similar to existing endolysins available in the Protein Data Bank. While performing structural and sequence analysis with template lysin, an additional cell wall binding repeat was observed in Group II lysin, which was not previously known. Molecular docking performed with choline confirmed the existence of this additional repeat. Group III endolysins showed 99.16 % similarity to LysME-EF1, a lysin derived from Enterococcus faecalis. Furthermore, the comparative computational analysis revealed the existence of CBDs in Group III lysin. This study provides the first insight into the molecular and diversity analysis of S. pneumoniae phage endolysins that could be valuable for developing novel lysin-based therapeutics.

Diagnosis of Human Endemic Mycoses Caused by Thermally Dimorphic Fungi: From Classical to Molecular Methods

Human endemic mycoses are potentially fatal diseases caused by a diverse group of fungi that can alter their morphology in response to an increase in temperature. These thermally dimorphic fungi affect both healthy and immunocompromised hosts, causing a substantial health and economic burden. Despite this, the diagnosis of endemic mycoses is still a formidable challenge for several reasons, including similar symptomatology, limited utility of classical diagnostic methods, inaccessibility to reliable molecular approaches in most endemic areas, and a lack of clinical suspicion out of these regions. This review summarizes essential knowledge on thermally dimorphic fungi and the life-threatening diseases they cause. The principle, advantages and limitations of the methods traditionally used for their diagnosis are also described, along with the application status and future directions for the development of alternative diagnostic strategies, which could help to reduce the disease burden in endemic areas.

Forecasting antimicrobial resistance evolution

Antimicrobial resistance (AMR) is a major global health issue. Current measures for tackling it comprise mainly the prudent use of drugs, the development of new drugs, and rapid diagnostics. Relatively little attention has been given to forecasting the evolution of resistance. Here, we argue that forecasting has the potential to be a great asset in our arsenal of measures to tackle AMR. We argue that, if successfully implemented, forecasting resistance will help to resolve the antibiotic crisis in three ways: it will (i) guide a more sustainable use (and therefore lifespan) of antibiotics and incentivize investment in drug development, (ii) reduce the spread of AMR genes and pathogenic microbes in the environment and between patients, and (iii) allow more efficient treatment of persistent infections, reducing the continued evolution of resistance. We identify two important challenges that need to be addressed for the successful establishment of forecasting: (i) the development of bespoke technology that allows stakeholders to empirically assess the risks of resistance evolving during the process of drug development and therapeutic/preventive use, and (ii) the transformative shift in mindset from the current praxis of mostly addressing the problem of antibiotic resistance a posteriori to a concept of a priori estimating, and acting on, the risks of resistance.

Non-Canonical Aspects of Antibiotics and Antibiotic Resistance

The understanding of antibiotic resistance, one of the major health threats of our time, is mostly based on dated and incomplete notions, especially in clinical contexts. The "canonical" mechanisms of action and pharmacodynamics of antibiotics, as well as the methods used to assess their activity upon bacteria, have not changed in decades; the same applies to the definition, acquisition, selective pressures, and drivers of resistance. As a consequence, the strategies to improve antibiotic usage and overcome resistance have ultimately failed. This review gathers most of the "non-canonical" notions on antibiotics and resistance: from the alternative mechanisms of action of antibiotics and the limitations of susceptibility testing to the wide variety of selective pressures, lateral gene transfer mechanisms, ubiquity, and societal factors maintaining resistance. Only by having a "big picture" view of the problem can adequate strategies to harness resistance be devised. These strategies must be global, addressing the many aspects that drive the increasing prevalence of resistant bacteria aside from the clinical use of antibiotics.

The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution

Pharmacokinetic-pharmacodynamic (PKPD) models, which describe how drug concentrations change over time and how that affects pathogen growth, have proven highly valuable in designing optimal drug treatments aimed at bacterial eradication. However, the fast rise of antimicrobial resistance calls for increased focus on an additional treatment optimization criterion: avoidance of resistance evolution. We demonstrate here how coupling PKPD and population genetics models can be used to determine treatment regimens that minimize the potential for antimicrobial resistance evolution. Importantly, the resulting modelling framework enables the assessment of resistance evolution in response to dynamic selection pressures, including changes in antimicrobial concentration and the emergence of adaptive phenotypes. Using antibiotics and antimicrobial peptides as an example, we discuss the empirical evidence and intuition behind individual model parameters. We further suggest several extensions of this framework that allow a more comprehensive and realistic prediction of bacterial escape from antimicrobials through various phenotypic and genetic mechanisms.

Ecology, more than antibiotics consumption, is the major predictor for the global distribution of aminoglycoside-modifying enzymes

Antibiotic consumption and its abuses have been historically and repeatedly pointed out as the major driver of antibiotic resistance emergence and propagation. However, several examples show that resistance may persist despite substantial reductions in antibiotic use, and that other factors are at stake. Here, we study the temporal, spatial, and ecological distribution patterns of aminoglycoside resistance, by screening more than 160,000 publicly available genomes for 27 clusters of genes encoding aminoglycoside-modifying enzymes (AME genes). We find that AME genes display a very ubiquitous pattern: about 25% of sequenced bacteria carry AME genes. These bacteria were sequenced from all the continents (except Antarctica) and terrestrial biomes, and belong to a wide number of phyla. By focusing on European countries between 1997 and 2018, we show that aminoglycoside consumption has little impact on the prevalence of AME-gene-carrying bacteria, whereas most variation in prevalence is observed among biomes. We further analyze the resemblance of resistome compositions across biomes: soil, wildlife, and human samples appear to be central to understand the exchanges of AME genes between different ecological contexts. Together, these results support the idea that interventional strategies based on reducing antibiotic use should be complemented by a stronger control of exchanges, especially between ecosystems.

The Age of Next-Generation Therapeutic-Microbe Discovery: Exploiting Microbe-Microbe and Host-Microbe Interactions for Disease Prevention

Humans are considered "superorganisms," harboring a diverse microbial collective that outnumbers human cells 10 to 1. Complex and gravely understudied host- and microbe-microbe interactions-the product of millions of years of host-microbe coevolution-govern the superorganism in almost every aspect of life functions and overall well-being. Abruptly disrupting these interactions via extrinsic factors has undesirable consequences for the host. On the other hand, supplementing commensal or beneficial microbes may mitigate perturbed interactions or enhance the interactive relationships that ultimately benefit all parties. Hence, immense efforts have focused on dissecting the innumerable host- and microbe-microbe relationships to characterize if a "positive" or "negative" interaction is at play and to exploit such behavior for broader implications. For example, microbiome research has worked to identify and isolate naturally antipathogenic microbes that may offer therapeutic potential either in a direct, one-on-one application or by leveraging its unique metabolic properties. However, the discovery and isolation of such desired therapeutic microbes from complex microbiota have proven challenging. Currently, there is no conventional technique to universally and functionally screen for these microbes. With this said, we first describe in this review the historical (probiotics) and current (fecal microbiota or defined consortia) perspectives on therapeutic microbes, present the discoveries of therapeutic microbes through exploiting microbe-microbe and host-microbe interactions, and detail our team's efforts in discovering therapeutic microbes via our novel microbiome screening platform. We conclude this minireview by briefly discussing challenges and possible solutions with therapeutic microbes' applications and paths ahead for discovery.

Phage Therapies: Lessons (Not) Learned from the "Antibiotic Era"

The use of phages as therapeutic or prophylactic approaches is gaining increased interest amid the growing menace of antibiotic resistance. Phages, along with other new anti-infective strategies, are certainly welcome as much needed additions to the medicinal arsenal. However, we can easily make with phages the same mistakes we made with antibiotics, which caused the current resistance crisis. The oversimplification of the ecological role of antibiotics, neglecting ancient resistance and the role of horizontal gene transfer; the active search for wide spectrum, and the massive agricultural abuse; and, most importantly, the financial greed behind the development and use of antibiotics; these are all trends that are now visible in phage research. Should we bring phages to the same track that wasted antibiotics, we could be looking at a "postphage era" in our near future.

Predicting drug targets by homology modelling of Pseudomonas aeruginosa proteins of unknown function

The efficacy of antibiotics to treat bacterial infections declines rapidly due to antibiotic resistance. This problem has stimulated the development of novel antibiotics, but most attempts have failed. Consequently, the idea of mining uncharacterized genes of pathogens to identify potential targets for entirely new classes of antibiotics was proposed. Without knowing the biochemical function of a protein, it is difficult to validate its potential for drug targeting; therefore, the functional characterization of bacterial proteins of unknown function must be accelerated. Here, we present a paradigm for comprehensively predicting the biochemical functions of a large set of proteins encoded by hypothetical genes in human pathogens to identify candidate drug targets. A high-throughput approach based on homology modelling with ten templates per target protein was applied to the set of 2103 P. aeruginosa proteins encoded by hypothetical genes. The >21000 homology modelling results obtained and available biological and biochemical information about several thousand templates were scrutinized to predict the function of reliably modelled proteins of unknown function. This approach resulted in assigning one or often multiple putative functions to hundreds of enzymes, ligand-binding proteins and transporters. New biochemical functions were predicted for 41 proteins whose essential or virulence-related roles in P. aeruginosa were already experimentally demonstrated. Eleven of them were shortlisted as promising drug targets that participate in essential pathways (maintaining genome and cell wall integrity), virulence-related processes (adhesion, cell motility, host recognition) or antibiotic resistance, which are general drug targets. These proteins are conserved in other WHO priority pathogens but not in humans; therefore, they represent high-potential targets for preclinical studies. These and many more biochemical functions assigned to uncharacterized proteins of P. aeruginosa, made available as PaPUF database, may guide the design of experimental screening of inhibitors, which is a crucial step towards the validation of the highest-potential targets for the development of novel drugs against P. aeruginosa and other high-priority pathogens.

Modeling transmission of pathogens in healthcare settings

PURPOSE OF REVIEW

Mathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings.

RECENT FINDINGS

The COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts.

SUMMARY

As the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.

Microfluidic Tools for Enhanced Characterization of Therapeutic Stem Cells and Prediction of Their Potential Antimicrobial Secretome

Antibiotic resistance is creating enormous attention on the development of new antibiotic-free therapy strategies for bacterial diseases. Mesenchymal stromal stem cells (MSCs) are the most promising candidates in current clinical trials and included in several cell-therapy protocols. Together with the well-known immunomodulatory and regenerative potential of the MSC secretome, these cells have shown direct and indirect anti-bacterial effects. However, the low reproducibility and standardization of MSCs from different sources are the current limitations prior to the purification of cell-free secreted antimicrobial peptides and exosomes. In order to improve MSC characterization, novel label-free functional tests, evaluating the biophysical properties of the cells, will be advantageous for their cell profiling, population sorting, and quality control. We discuss the potential of emerging microfluidic technologies providing new insights into density, shape, and size of live cells, starting from heterogeneous or 3D cultured samples. The prospective application of these technologies to studying MSC populations may contribute to developing new biopharmaceutical strategies with a view to naturally overcoming bacterial defense mechanisms.