Systems-level analysis of NalD mutation, a recurrent driver of rapid drug resistance in acute Pseudomonas aeruginosa infection

Applications of genome-scale metabolic models to the study of human diseases: A systematic review

BACKGROUND AND OBJECTIVES

Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined.

RESULTS

The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models.

CONCLUSIONS

The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.

Keeping up with the pathogens: improved antimicrobial resistance detection and prediction from Pseudomonas aeruginosa genomes

BACKGROUND

Antimicrobial resistance (AMR) is an intensifying threat that requires urgent mitigation to avoid a post-antibiotic era. Pseudomonas aeruginosa represents one of the greatest AMR concerns due to increasing multi- and pan-drug resistance rates. Shotgun sequencing is gaining traction for in silico AMR profiling due to its unambiguity and transferability; however, accurate and comprehensive AMR prediction from P. aeruginosa genomes remains an unsolved problem.

METHODS

We first curated the most comprehensive database yet of known P. aeruginosa AMR variants. Next, we performed comparative genomics and microbial genome-wide association study analysis across a Global isolate Dataset (n = 1877) with paired antimicrobial phenotype and genomic data to identify novel AMR variants. Finally, the performance of our P. aeruginosa AMR database, implemented in our AMR detection and prediction tool, ARDaP, was compared with three previously published in silico AMR gene detection or phenotype prediction tools-abritAMR, AMRFinderPlus, ResFinder-across both the Global Dataset and an analysis-naïve Validation Dataset (n = 102).

RESULTS

Our AMR database comprises 3639 mobile AMR genes and 728 chromosomal variants, including 75 previously unreported chromosomal AMR variants, 10 variants associated with unusual antimicrobial susceptibility, and 281 chromosomal variants that we show are unlikely to confer AMR. Our pipeline achieved a genotype-phenotype balanced accuracy (bACC) of 85% and 81% across 10 clinically relevant antibiotics when tested against the Global and Validation Datasets, respectively, vs. just 56% and 54% with abritAMR, 58% and 54% with AMRFinderPlus, and 60% and 53% with ResFinder. ARDaP's superior performance was predominantly due to the inclusion of chromosomal AMR variants, which are generally not identified with most AMR identification tools.

CONCLUSIONS

Our ARDaP software and associated AMR variant database provides an accurate tool for predicting AMR phenotypes in P. aeruginosa, far surpassing the performance of current tools. Implementation of ARDaP for routine AMR prediction from P. aeruginosa genomes and metagenomes will improve AMR identification, addressing a critical facet in combatting this treatment-refractory pathogen. However, knowledge gaps remain in our understanding of the P. aeruginosa resistome, particularly the basis of colistin AMR.

Antibiotic influx and efflux in Pseudomonas aeruginosa: Regulation and therapeutic implications

Pseudomonas aeruginosa is a notorious multidrug-resistant pathogen that poses a serious and growing threat to the worldwide public health. The expression of resistance determinants is exquisitely modulated by the abundant regulatory proteins and the intricate signal sensing and transduction systems in this pathogen. Downregulation of antibiotic influx porin proteins and upregulation of antibiotic efflux pump systems owing to mutational changes in their regulators or the presence of distinct inducing molecular signals represent two of the most efficient mechanisms that restrict intracellular antibiotic accumulation and enable P. aeruginosa to resist multiple antibiotics. Treatment of P. aeruginosa infections is extremely challenging due to the highly inducible mechanism of antibiotic resistance. This review comprehensively summarizes the regulatory networks of the major porin proteins (OprD and OprH) and efflux pumps (MexAB-OprM, MexCD-OprJ, MexEF-OprN, and MexXY) that play critical roles in antibiotic influx and efflux in P. aeruginosa. It also discusses promising therapeutic approaches using safe and efficient adjuvants to enhance the efficacy of conventional antibiotics to combat multidrug-resistant P. aeruginosa by controlling the expression levels of porins and efflux pumps. This review not only highlights the complexity of the regulatory network that induces antibiotic resistance in P. aeruginosa but also provides important therapeutic implications in targeting the inducible mechanism of resistance.

Comparative molecular profiling of multidrug-resistant Pseudomonas aeruginosa identifies novel mutations in regional clinical isolates from South India

Objectives

We sought to analyse the antibiotic susceptibility profiles and molecular epidemiology of MDR clinical Pseudomonas aeruginosa isolates from South India using non-MDR isolates as a reference.

Methods

We established a comprehensive clinical strain library consisting of 58 isolates collected from patients across the South Indian state of Kerala from March 2017 to July 2019. The strains were subject to antibiotic susceptibility testing, modified carbapenem inactivation method assay for carbapenemase production, PCR sequencing, comparative sequence analysis and quantitative PCR of MDR determinants associated with antibiotic efflux pump systems, fluoroquinolone resistance and carbapenem resistance. We performed in silico modelling of MDR-specific SNPs.

Results

Of our collection of South Indian P. aeruginosa clinical isolates, 74.1% were MDR and 55.8% were resistant to the entire panel of antibiotics tested. All MDR isolates were resistant to levofloxacin and 93% were resistant to meropenem. We identified seven distinct, MDR-specific mutations in nalD, three of which are novel. mexA was significantly overexpressed in strains that were resistant to the entire test antibiotic panel while gyrA and gyrB were overexpressed in MDR isolates. Mutations in fluoroquinolone determinants were significantly associated with MDR phenotype and a novel GyrA Y100C substitution was observed. Carbapenem resistance in MDR isolates was associated with loss-of-function mutations in oprD and high prevalence of NDM (blaNDM-1) within our sample.

Conclusions

This study provides insight into MDR mechanisms adopted by P. aeruginosa clinical isolates, which may guide the potential development of therapeutic regimens to improve clinical outcomes.

Whole-Genome Sequencing Reveals Diversity of Carbapenem-Resistant Pseudomonas aeruginosa Collected through CDC's Emerging Infections Program, United States, 2016-2018

The CDC's Emerging Infections Program (EIP) conducted population- and laboratory-based surveillance of US carbapenem-resistant Pseudomonas aeruginosa (CRPA) from 2016 through 2018. To characterize the pathotype, 1,019 isolates collected through this project underwent antimicrobial susceptibility testing and whole-genome sequencing. Sequenced genomes were classified using the seven-gene multilocus sequence typing (MLST) scheme and a core genome (cg)MLST scheme was used to determine phylogeny. Both chromosomal and horizontally transmitted mechanisms of carbapenem resistance were assessed. There were 336 sequence types (STs) among the 1,019 sequenced genomes, and the genomes varied by an average of 84.7% of the cgMLST alleles used. Mutations associated with dysfunction of the porin OprD were found in 888 (87.1%) of the genomes and were correlated with carbapenem resistance, and a machine learning model incorporating hundreds of genetic variations among the chromosomal mechanisms of resistance was able to classify resistant genomes. While only 7 (0.1%) isolates harbored carbapenemase genes, 66 (6.5%) had acquired non-carbapenemase β-lactamase genes, and these were more likely to have OprD dysfunction and be resistant to all carbapenems tested. The genetic diversity demonstrates that the pathotype includes a variety of strains, and clones previously identified as high-risk make up only a minority of CRPA strains in the United States. The increased carbapenem resistance in isolates with acquired non-carbapenemase β-lactamase genes suggests that horizontally transmitted mechanisms aside from carbapenemases themselves may be important drivers of the spread of carbapenem resistance in P. aeruginosa.

Genomic characterization of denitrifying methylotrophic Pseudomonas aeruginosa strain AAK/M5 isolated from municipal solid waste landfill soil

Municipal landfills are known for methane production and a source of nitrate pollution leading to various environmental issues. Therefore, this niche was selected for the isolation of one-carbon (C1) utilizing bacteria with denitrifying capacities using anaerobic enrichment on nitrate mineral salt medium supplemented with methanol as carbon source. Eight axenic cultures were isolated of which, isolate AAK/M5 demonstrated the highest methanol removal (73.28%) in terms of soluble chemical oxygen demand and methane removal (41.27%) at the expense of total nitrate removal of 100% and 33% respectively. The whole genome characterization with phylogenomic approach suggested that the strain AAK/M5 could be assigned to Pseudomonas aeruginosa with close neighbours as type strains DVT779, AES1M, W60856, and LES400. The circular genome annotation showed the presence of complete set of genes essential for methanol utilization and complete denitrification process. The study demonstrates the potential of P. aeruginosa strain AAK/M5 in catalysing methane oxidation thus serving as a methane sink vis-à-vis utilization of nitrate. Considering the existence of such bacteria at landfill site, the study highlights the need to develop strategies for their enrichment and designing of efficient catabolic activity for such environments.

Genome-scale metabolic modeling in antimicrobial pharmacology

The increasing antimicrobial resistance has seriously threatened human health worldwide over the last three decades. This severe medical crisis and the dwindling antibiotic discovery pipeline require the development of novel antimicrobial treatments to combat life-threatening infections caused by multidrug-resistant microbial pathogens. However, the detailed mechanisms of action, resistance, and toxicity of many antimicrobials remain uncertain, significantly hampering the development of novel antimicrobials. Genome-scale metabolic model (GSMM) has been increasingly employed to investigate microbial metabolism. In this review, we discuss the latest progress of GSMM in antimicrobial pharmacology, particularly in elucidating the complex interplays of multiple metabolic pathways involved in antimicrobial activity, resistance, and toxicity. We also highlight the emerging areas of GSMM applications in modeling non-metabolic cellular activities (e.g., gene expression), identification of potential drug targets, and integration with machine learning and pharmacokinetic/pharmacodynamic modeling. Overall, GSMM has significant potential in elucidating the critical role of metabolic changes in antimicrobial pharmacology, providing mechanistic insights that will guide the optimization of dosing regimens for the treatment of antimicrobial-resistant infections.

Coming from the Wild: Multidrug Resistant Opportunistic Pathogens Presenting a Primary, Not Human-Linked, Environmental Habitat

The use and misuse of antibiotics have made antibiotic-resistant bacteria widespread nowadays, constituting one of the most relevant challenges for human health at present. Among these bacteria, opportunistic pathogens with an environmental, non-clinical, primary habitat stand as an increasing matter of concern at hospitals. These organisms usually present low susceptibility to antibiotics currently used for therapy. They are also proficient in acquiring increased resistance levels, a situation that limits the therapeutic options for treating the infections they cause. In this article, we analyse the most predominant opportunistic pathogens with an environmental origin, focusing on the mechanisms of antibiotic resistance they present. Further, we discuss the functions, beyond antibiotic resistance, that these determinants may have in the natural ecosystems that these bacteria usually colonize. Given the capacity of these organisms for colonizing different habitats, from clinical settings to natural environments, and for infecting different hosts, from plants to humans, deciphering their population structure, their mechanisms of resistance and the role that these mechanisms may play in natural ecosystems is of relevance for understanding the dissemination of antibiotic resistance under a One-Health point of view.

An atlas of the binding specificities of transcription factors in Pseudomonas aeruginosa directs prediction of novel regulators in virulence

A high-throughput systematic evolution of ligands by exponential enrichment assay was applied to 371 putative TFs in Pseudomonas aeruginosa, which resulted in the robust enrichment of 199 unique sequence motifs describing the binding specificities of 182 TFs. By scanning the genome, we predicted in total 33,709 significant interactions between TFs and their target loci, which were more than 11-fold enriched in the intergenic regions but depleted in the gene body regions. To further explore and delineate the physiological and pathogenic roles of TFs in P. aeruginosa, we constructed regulatory networks for nine major virulence-associated pathways and found that 51 TFs were potentially significantly associated with these virulence pathways, 32 of which had not been characterized before, and some were even involved in multiple pathways. These results will significantly facilitate future studies on transcriptional regulation in P. aeruginosa and other relevant pathogens, and accelerate to discover effective treatment and prevention strategies for the associated infectious diseases.

Compilation of longitudinal microbiota data and hospitalome from hematopoietic cell transplantation patients

The impact of the gut microbiota in human health is affected by several factors including its composition, drug administrations, therapeutic interventions and underlying diseases. Unfortunately, many human microbiota datasets available publicly were collected to study the impact of single variables, and typically consist of outpatients in cross-sectional studies, have small sample numbers and/or lack metadata to account for confounders. These limitations can complicate reusing the data for questions outside their original focus. Here, we provide comprehensive longitudinal patient dataset that overcomes those limitations: a collection of fecal microbiota compositions (>10,000 microbiota samples from >1,000 patients) and a rich description of the "hospitalome" experienced by the hosts, i.e., their drug exposures and other metadata from patients with cancer, hospitalized to receive allogeneic hematopoietic cell transplantation (allo-HCT) at a large cancer center in the United States. We present five examples of how to apply these data to address clinical and scientific questions on host-associated microbial communities.

Leveraging genome-scale metabolic models for human health applications

Genome-scale metabolic modeling is a scalable and extensible computational method for analyzing and predicting biological function. With the ongoing improvements in computational methods and experimental capabilities, genome-scale metabolic models (GEMs) are demonstrating utility in addressing human health applications. The initial areas of highest impact are likely to be health applications where disease states involve metabolic changes. In this review, we focus on recent application of GEMs to studying cancer and the human microbiome by describing the enabling methodologies and outcomes of these studies. We conclude with proposing some areas of research that are likely to arise as a result of recent methodological advances.