Is antimicrobial resistance evolution accelerating?

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.