What are the missing pieces needed to stop antibiotic resistance?

Unraveling the genomic diversity of the Pseudomonas putida group: exploring taxonomy, core pangenome, and antibiotic resistance mechanisms

The genus Pseudomonas is characterized by its rich genetic diversity, with over 300 species been validly recognized. This reflects significant progress made through sequencing and computational methods. Pseudomonas putida group comprises highly adaptable species that thrive in diverse environments and play various ecological roles, from promoting plant growth to being pathogenic in immunocompromised individuals. By leveraging the GRUMPS computational pipeline, we scrutinized 26 363 genomes labeled as Pseudomonas in the NCBI GenBank, categorizing all Pseudomonas spp. genomes into 435 distinct species-level clusters or cliques. We identified 224 strains deposited under the taxonomic identifier "Pseudomonas putida" distributed within 31 of these species-level clusters, challenging prior classifications. Nine of these 31 cliques contained at least six genomes labeled as "Pseudomonas putida" and were analysed in depth, particularly clique_1 (P. alloputida) and clique_2 (P. putida). Pangenomic analysis of a set of 413 P. putida group strains revealed over 2.2 million proteins and more than 77 000 distinct protein families. The core genome of these 413 strains includes 2226 protein families involved in essential biological processes. Intraspecific genetic homogeneity was observed within each clique, each possessing a distinct genomic identity. These cliques exhibit distinct core genes and diverse subgroups, reflecting adaptation to specific environments. Contrary to traditional views, nosocomial infections by P. alloputida, P. putida, and P. monteilii have been reported, with strains showing varied antibiotic resistance profiles due to diverse mechanisms. This review enhances the taxonomic understanding of key P. putida group species using advanced population genomics approaches and provides a comprehensive understanding of their genetic diversity, ecological roles, interactions, and potential applications.

Discovery of AMPs from random peptides via deep learning-based model and biological activity validation

The ample peptide field is the best source for discovering clinically available novel antimicrobial peptides (AMPs) to address emerging drug resistance. However, discovering novel AMPs is complex and expensive, representing a major challenge. Recent advances in artificial intelligence (AI) have significantly improved the efficiency of identifying antimicrobial peptides from large libraries, whereas using random peptides as negative data increases the difficulty of discovering antimicrobial peptides from random peptides using discriminative models. In this study, we constructed three multi-discriminator models using deep learning and successfully screened twelve AMPs from a library of 30,000 random peptides. three candidate peptides (P2, P11, and P12) were screened by antimicrobial experiments, and further experiments showed that they not only possessed excellent antimicrobial activity but also had extremely low hemolytic activity. Mechanistic studies showed that these peptides exerted their bactericidal effects through membrane disruption, thus reducing the possibility of bacterial resistance. Notably, peptide 12 (P12) showed significant efficacy in a mouse model of Staphylococcus aureus wound infection with low toxicity to major organs at the highest tested dose (400 mg/kg). These results suggest deep learning-based multi-discriminator models can identify AMPs from random peptides with potential clinical applications.

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.

Quantitative microbial risk assessment for ingestion of antibiotic resistance genes from private wells contaminated by human and livestock fecal sources

We used quantitative microbial risk assessment to estimate ingestion risk for intI1, erm(B), sul1, tet(A), tet(W), and tet(X) in private wells contaminated by human and/or livestock feces. Genes were quantified with five human-specific and six bovine-specific microbial source-tracking (MST) markers in 138 well-water samples from a rural Wisconsin county. Daily ingestion risk (probability of swallowing ≥1 gene) was based on daily water consumption and a Poisson exposure model. Calculations were stratified by MST source and soil depth over the aquifer where wells were drilled. Relative ingestion risk was estimated using wells with no MST detections and >6.1 m soil depth as a referent category. Daily ingestion risk varied from 0 to 8.8 × 10-1 by gene and fecal source (i.e., human or bovine). The estimated number of residents ingesting target genes from private wells varied from 910 (tet(A)) to 1,500 (intI1 and tet(X)) per day out of 12,000 total. Relative risk of tet(A) ingestion was significantly higher in wells with MST markers detected, including wells with ≤6.1 m soil depth contaminated by bovine markers (2.2 [90% CI: 1.1-4.7]), wells with >6.1 m soil depth contaminated by bovine markers (1.8 [1.002-3.9]), and wells with ≤6.1 m soil depth contaminated by bovine and human markers simultaneously (3.1 [1.7-6.5]). Antibiotic resistance genes (ARGs) were not necessarily present in viable microorganisms, and ingestion is not directly associated with infection. However, results illustrate relative contributions of human and livestock fecal sources to ARG exposure and highlight rural groundwater as a significant point of exposure.IMPORTANCEAntibiotic resistance is a global public health challenge with well-known environmental dimensions, but quantitative analyses of the roles played by various natural environments in transmission of antibiotic resistance are lacking, particularly for drinking water. This study assesses risk of ingestion for several antibiotic resistance genes (ARGs) and the class 1 integron gene (intI1) in drinking water from private wells in a rural area of northeast Wisconsin, United States. Results allow comparison of drinking water as an exposure route for antibiotic resistance relative to other routes like food and recreational water. They also enable a comparison of the importance of human versus livestock fecal sources in the study area. Our study demonstrates the previously unrecognized importance of untreated rural drinking water as an exposure route for antibiotic resistance and identifies bovine fecal material as an important exposure factor in the study setting.

Patent highlights August-September 2023

A snapshot of noteworthy recent developments in the patent literature of relevance to pharmaceutical and medical research and development.