Bacterial Genome Wide Association Studies (bGWAS) and Transcriptomics Identifies Cryptic Antimicrobial Resistance Mechanisms in Acinetobacter baumannii

Aminoglycoside resistance genes in early members of the Acinetobacter baumannii ST78A (SMAL, Italian clone) reside in an IS26-bounded island in the chromosome

BACKGROUND

The Acinetobacter baumannii isolate called SMAL, previously used to determine the structures of capsular polysaccharide and lipooligosaccharide, was recovered in Pavia, Italy in 2002 among the collection of aminoglycoside-resistant isolates designated as SMAL type. This type was later called the Italian clone, then ST78. ST78 isolates are now widely distributed.

OBJECTIVES

To establish the resistance gene complement and the location and structure of acquired resistance regions in early members of the Italian/ST78 clone.

METHODS

The draft genome of SMAL2002 was assembled from Illumina MiSeq reads. Contigs containing resistance genes were joined and located in the chromosome using PCR with custom primers. The resistance profile was determined using disc diffusion.

RESULTS

SMAL2002 is an ST78A isolate and includes three aminoglycoside resistance genes, aadB (gentamicin, kanamycin, tobramycin) aphA1 (kanamycin, neomycin) and aac(6')-Ian (amikacin, kanamycin, tobramycin). The aadB gene cassette is incorporated at a secondary site in a relative of the aphA1-containing, IS26-bounded pseudo-compound transposon, PTn6020. The aac(6')-Ian gene is in an adjacent IS26-bounded structure that includes sul2 (sulphonamide) and floR (florfenicol) resistance genes. The two pseudo-compound transposons overlap and are in the chromosomal hutU gene flanked by an 8 bp target site duplication. Although aac(6')-Ian was not noticed previously, the same genes and structures were found in several available draft genomes of early ST78A isolates.

CONCLUSIONS

This study highlights the importance of correlating resistance profiles with resistance gene content. The location of acquired resistance genes in the SMAL2002 chromosome represents the original location in the ST78A lineage of ST78.

Within-species variability of antibiotic interactions in Gram-negative bacteria

Treatments with antibiotic combinations are becoming increasingly important even though the supposed clinical benefits of combinations are, in many cases, unclear. Here, we systematically examined how several clinically used antibiotics interact and affect the antimicrobial efficacy against five especially problematic Gram-negative pathogens. A total of 232 bacterial isolates were tested against different pairwise antibiotic combinations spanning five classes, and the ability of all combinations in inhibiting growth was quantified. Descriptive statistics, principal component analysis (PCA), and Spearman's rank correlation matrix were used to determine the correlations between the different combinations on interaction outcome. Several important conclusions can be drawn from the 696 examined interactions. Firstly, within a species, the interactions are in general conserved but can be isolate-specific for a given antibiotic combination and can range from antagonistic to synergistic. Secondly, additive and antagonistic interactions are the most common observed across species and antibiotics, with 87.1% of isolate-antibiotic combinations being additive, 11.6% antagonistic, and only 0.3% showing synergy. These findings suggest that to achieve the highest precision and efficacy of combination therapy, not only isolate-specific interaction profiling ought to be routinely performed, in particular to avoid using drug combinations that show antagonistic interaction and an expected associated reduction in efficacy, but also discovering rare and potentially valuable synergistic interactions.IMPORTANCEAntibiotic combinations are often used to treat bacterial infections, which aim to increase treatment efficacy and reduce resistance evolution. Typically, it is assumed that one specific antibiotic combination has the same effect on different isolates of the same species, i.e., the interaction is conserved. Here, we tested this idea by examining how several clinically used antibiotics interact and affect the antimicrobial efficacy against several bacterial pathogens. Our results show that, even though within a species the interactions are often conserved, there are also isolate-specific differences for a given antibiotic combination that can range from antagonistic to synergistic. These findings suggest that isolate-specific interaction profiling ought to be performed in clinical microbiology routine to avoid using antagonistic drug combinations that might reduce treatment efficacy.

Genome-Wide Association Studies (GWAS) Approaches for the Detection of Genetic Variants Associated with Antibiotic Resistance: A Systematic Review

Antibiotic resistance is a significant threat to public health worldwide. Genome-wide association studies (GWAS) have emerged as a powerful tool to identify genetic variants associated with this antibiotic resistance. By analyzing large datasets of bacterial genomes, GWAS can provide valuable insights into the resistance mechanisms and facilitate the discovery of new drug targets. The present study aimed to undertake a systematic review of different GWAS approaches used for detecting genetic variants associated with antibiotic resistance. We comprehensively searched the PubMed and Scopus databases to identify relevant studies published from 2013 to February 2023. A total of 40 studies met our inclusion criteria. These studies explored a wide range of bacterial species, antibiotics, and study designs. Notably, most of the studies were centered around human pathogens such as Mycobacterium tuberculosis, Escherichia coli, Neisseria gonorrhoeae, and Staphylococcus aureus. The review seeks to explore the several GWAS approaches utilized to investigate the genetic mechanisms associated with antibiotic resistance. Furthermore, it examines the contributions of GWAS approaches in identifying resistance-associated genetic variants through binary and continuous phenotypes. Overall, GWAS holds great potential to enhance our understanding of bacterial resistance and improve strategies to combat infectious diseases.

Making sense of drug-efflux transporters in the physiological environment

Bacterial drug-efflux transporters act synergistically with diffusion barriers of cellular membranes and other resistance mechanisms to protect cells from antibiotics and toxic metabolites. Their critical roles in clinical antibiotic and multidrug resistance are well established. In addition, a large body of evidence has been accumulated in support of their important contributions to bacterial growth and proliferation during infections. However, how these diverse functions of drug transporters are integrated at the level of bacterial cell physiology remains unclear. This opinion briefly summarizes the current understanding of substrate specificities and physiological roles of drug-efflux pumps from Resistance-Nodulation-Division (RND) superfamily of proteins in two ESKAPE pathogens Pseudomonas aeruginosa and Acinetobacter baumannii. Based on the analysis of phenotypic and transcriptomic studies in vitro and in vivo, we propose that RND pumps of Gram-negative bacteria fall into three categories: constitutively expressed, regulated, and silent. These three categories of efflux pumps participate in different physiological programs, which are not involved in the central metabolism and bacterial growth.

Phenotype-Based Threat Assessment

Assessing the threat posed by bacterial samples is fundamentally important to safeguarding human health. Whole-genome sequence analysis of bacteria provides a route to achieving this goal. However, this approach is fundamentally constrained by the scope, the diversity, and our understanding of the bacterial genome sequences that are available for devising threat assessment schemes. For example, genome-based strategies offer limited utility for assessing the threat associated with pathogens that exploit novel virulence mechanisms or are recently emergent. To address these limitations, we developed PathEngine, a machine learning strategy that features the use of phenotypic hallmarks of pathogenesis to assess pathogenic threat. PathEngine successfully classified potential pathogenic threats with high accuracy and thereby establishes a phenotype-based, sequence-independent pipeline for threat assessment.

Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pathogen identification. However, relying solely on genetic information to identify emerging or new pathogens is fundamentally constrained, especially if novel virulence factors exist. In addition, even WGSs with ML pipelines are unable to discern phenotypes associated with cryptic genetic loci linked to virulence. Here, we set out to determine if ML using phenotypic hallmarks of pathogenesis could assess potential pathogenic threat without using any sequence-based analysis. This approach successfully classified potential pathogenetic threat associated with previously machine-observed and unobserved bacteria with 99% and 85% accuracy, respectively. This work establishes a phenotype-based pipeline for potential pathogenic threat assessment, which we term PathEngine, and offers strategies for the identification of bacterial pathogens.

Towards risk assessment for antibiotic resistant pathogens in recycled water: a systematic review and summary of research needs

Risk assessment is critical for identifying target concentrations of antibiotic resistant pathogens necessary for mitigating potential harmful exposures associated with water reuse. However, there is currently limited available data characterizing the concentrations of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in recycled water to support robust efforts at risk assessment. The objective of this systematic review was to identify and synthesize the existing literature documenting the presence and abundance of ARB and ARGs in recycled water. In addition, this review identifies best practices and explores monitoring targets for studying ARB and ARGs in recycled water to guide future work and identifies key research needs aimed at better supporting quantitative microbial risk assessment focused on recycled water and antibiotic resistance. Future efforts to collect data about ARB and ARG prevalence in recycled water should report concentration data per unit volume. Sample metadata should also be provided, including a description of treatment approach, a description of planned water uses (e.g., potable, irrigation), methods for conveyance to the point of use, and available physicochemical water quality data. Additional research is needed aimed at identifying recommended ARB and ARG monitoring targets and for developing approaches to incorporate metagenomic data into risk assessment.

Update on Multidrug Resistance Efflux Pumps in Acinetobacter spp

Acinetobacter spp. have become of increased clinical importance as studies have shown the antimicrobial resistant potential of these species. Efflux pumps can lead to reduced susceptibility to a variety of antibiotics and are present in large number across Acinetobacter spp. There are six families of efflux pumps that have been shown to be of clinical relevance: the major facilitator superfamily (MFS), small multidrug resistance (SMR) family, ATP-binding cassette (ABC) family, multidrug and toxic compound extrusion (MATE) family, proteobacterial antimicrobial compound efflux (PACE) family, and the resistance-nodulation-division (RND) family. Much work has been done for understanding and characterizing the roles these efflux pumps play in relation to antimicrobial resistance and the physiology of these bacteria. RND efflux pumps, with their expansive substrate profiles, are a major component of Acinetobacter spp. antimicrobial resistance. New discoveries over the last decade have shed light on the complex regulation of these efflux pumps, leading to greater understanding and the potential of slowing the reduced susceptibility seen in these bacterial species.

Genetic variation and function: revealing potential factors associated with microbial phenotypes

Innovations in sequencing technology have generated voluminous microbial and host genomic data, making it possible to detect these genetic variations and analyze the function influenced by them. Recently, many studies have linked such genetic variations to phenotypes through association or comparative analysis, which have further advanced our understanding of multiple microbial functions. In this review, we summarized the application of association analysis in microbes like Mycobacterium tuberculosis, focusing on screening of microbial genetic variants potentially associated with phenotypes such as drug resistance, pathogenesis and novel drug targets etc.; reviewed the application of additional comparative genomic or transcriptomic methods to identify genetic factors associated with functions in microbes; expanded the scope of our study to focus on host genetic factors associated with certain microbes or microbiome and summarized the recent host genetic variations associated with microbial phenotypes, including susceptibility and load after infection of HIV, presence/absence of different taxa, and quantitative traits of microbiome, and lastly, discussed the challenges that may be encountered and the apparent or potential viable solutions. Gene-function analysis of microbe and microbiome is still in its infancy, and in order to unleash its full potential, it is necessary to understand its history, current status, and the challenges hindering its development.