Exploiting genomics to mitigate the public health impact of antimicrobial resistance

Mitigating Health Risks Through Environmental Tracking of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a prevalent nosocomial pathogen and a significant reservoir of antimicrobial resistance genes in residential and built environments. It is also widespread in various indoor and outdoor settings, including sewage, surface waters, soil, recreational waters (both treated and untreated), and industrial effluents. Surveillance efforts for P. aeruginosa are primarily focused on hospitals rather than built environments. However, evidence links multidrug-resistant P. aeruginosa of human origin with activity in built environments and hospital settings. Consequently, tracking this pathogen across all environments is crucial for understanding the mechanisms of reverse transmission from built environments to humans. This review explores public health hygiene by examining the prevalence of P. aeruginosa in various environments, its sequence types, the factors contributing to multidrug resistance, and the identification methods through global surveillance. Whole-genome sequencing with sequence typing and real-time quantitative PCR are widely used to identify and study antimicrobial-resistant strains worldwide. Additionally, advanced techniques such as functional metagenomics, next-generation sequencing, MALDI-TOF, and biosensors are being extensively employed to detect antimicrobial-resistant strains and mitigate the ongoing evolution of bacterial resistance to antibiotics. Our review strongly underscores the importance of environmental monitoring of P. aeruginosa in preventing human infections. Furthermore, strategic planning in built environments is essential for effective epidemiological surveillance of P. aeruginosa and the development of comprehensive risk assessment models.

Limited Evidence of Spillover of Antimicrobial-Resistant Klebsiella pneumoniae from Animal/Environmental Reservoirs to Humans in Vellore, India

BACKGROUND

Klebsiella pneumoniae is a common opportunistic pathogen in humans, often associated with both virulence and antimicrobial resistance (AMR) phenotypes. K. pneumoniae have a highly plastic genome and can act as a vehicle for disseminating genetic information. Aiming to assess the impact of the human-animal-environment interface on AMR dissemination in K. pneumoniae we sampled and genome sequenced organisms from a range of environments and compared their genetic composition.

METHODS

Representative K. pneumoniae isolated from clinical specimens (n = 59), livestock samples (n = 71), and hospital sewage samples (n = 16) during a two-year surveillance study were subjected to whole genome sequencing. We compared the taxonomic and genomic distribution of K. pneumoniae, AMR gene abundance, virulence gene composition, and mobile genetic elements between the three sources.

RESULTS

The K. pneumoniae isolates originating from livestock were clonally distinct from those derived from clinical/hospital effluent samples. Notably, the clinical and hospital sewage isolates typically possessed a greater number of resistance/virulence genes than those from animals. Overall, we observed a limited overlap of K. pneumoniae clones, AMR genes, virulence determinants, and plasmids between the different settings.

CONCLUSION

In this setting, the spread of XDR and hypervirulent clones of K. pneumoniae appears to be restricted to humans with no obvious association with non-clinical sources. Emergent clones of K. pneumoniae carrying both resistance and virulence determinants are likely to have emerged in hospital settings rather than in animal or natural environments. These data challenge the current view of AMR transmission in K. pneumoniae in a One-Health context.

Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR.

Antimicrobial susceptibility and resistome of Actinobacillus pleuropneumoniae in Taiwan: a next-generation sequencing analysis

Actinobacillus pleuropneumoniae infection causes a high mortality rate in porcine animals. Antimicrobial resistance poses global threats to public health. The current study aimed to determine the antimicrobial susceptibilities and probe the resistome of A. pleuropneumoniae in Taiwan. Herein, 133 isolates were retrospectively collected; upon initial screening, 38 samples were subjected to next-generation sequencing (NGS). Over the period 2017-2022, the lowest frequencies of resistant isolates were found for ceftiofur, cephalexin, cephalothin, and enrofloxacin, while the highest frequencies of resistant isolates were found for oxytetracycline, streptomycin, doxycycline, ampicillin, amoxicillin, kanamycin, and florfenicol. Furthermore, most isolates (71.4%) showed multiple drug resistance. NGS-based resistome analysis revealed aminoglycoside- and tetracycline-related genes at the highest prevalence, followed by genes related to beta-lactam, sulfamethoxazole, florphenicol, and macrolide. A plasmid replicon (repUS47) and insertion sequences (IS10R and ISVAp11) were identified in resistant isolates. Notably, the multiple resistance roles of the insertion sequence IS10R were widely proposed in human medicine; however, this is the first time IS10R has been reported in veterinary medicine. Concordance analysis revealed a high consistency of phenotypic and genotypic susceptibility to florphenicol, tilmicosin, doxycycline, and oxytetracycline. The current study reports the antimicrobial characterization of A. pleuropneumoniae for the first time in Taiwan using NGS.

Machine Learning Approaches for Microorganism Identification, Virulence Assessment, and Antimicrobial Susceptibility Evaluation Using DNA Sequencing Methods: A Systematic Review

Microbial infections pose a substantial global health challenge, particularly impacting immunocompromised individuals and exacerbating the issue of antimicrobial resistance (AMR). High virulence of pathogens can lead to severe infections and prolonged antimicrobial treatment, increasing the risk of developing resistant strains. Integrating machine-learning (ML) with DNA sequencing technologies offers potential solutions by enhancing microbial identification, virulence assessment, and antimicrobial susceptibility evaluation. This review explores recent advancements in these integrated approaches, addressing current limitations and identifying gaps in the literature. A comprehensive literature search was conducted across databases including PubMed, Scopus, Web of Science, and IEEE Xplore, covering publications from January 2014 to June 2024. Using a detailed Boolean search string, relevant studies focusing on ML applications in microorganism identification, antimicrobial susceptibility testing, and microbial virulence were included. The screening process involved a two-stage review of titles, abstracts, and full texts, with data extraction and critical appraisal performed using the QIAO tool. Data were analyzed through narrative synthesis to identify common themes and innovations. Out of 1,650 initially identified records, 19 studies met the inclusion criteria. These studies primarily focused on AMR, with additional research on microbial virulence and identification. Machine learning algorithms such as Random Forest, Support Vector Machines, and Convolutional Neural Networks, combined with DNA sequencing techniques like Whole Genome Sequencing and Metagenomic Sequencing, demonstrated significant advancements in predictive accuracy and efficiency. High-quality studies achieved impressive performance metrics, including F1-scores up to 0.88 and AUC scores up to 0.96. The integration of ML and DNA sequencing technologies has significantly enhanced microbial analysis, improving the identification of pathogens, assessment of virulence, and evaluation of antimicrobial susceptibility. Despite advancements, challenges such as data quality, high costs, and model interpretability persist. This review highlights the need for continued innovation and provides recommendations for future research to address these limitations and improve disease management and public health strategies. The systematic review is registered with PROSPERO (CRD42024571347).

Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects

The issue of antimicrobial resistance (AMR) in pathogenic microorganisms has emerged as a global public health crisis, posing a significant threat to the modern healthcare system. The advent of Artificial Intelligence (AI) and Machine Learning (ML) technologies has brought about revolutionary changes in this field. These advanced computational methods are capable of processing and analyzing large-scale biomedical data, thereby uncovering complex patterns and mechanisms behind the development of resistance. AI technologies are increasingly applied to predict the resistance of pathogens to various antibiotics based on gene content and genomic composition. This article reviews the latest advancements in AI and ML for predicting antimicrobial resistance in pathogenic microorganisms. We begin with an overview of the biological foundations of microbial resistance and its epidemiological research. Subsequently, we highlight the main AI and ML models used in resistance prediction, including but not limited to Support Vector Machines, Random Forests, and Deep Learning networks. Furthermore, we explore the major challenges in the field, such as data availability, model interpretability, and cross-species resistance prediction. Finally, we discuss new perspectives and solutions for research into microbial resistance through algorithm optimization, dataset expansion, and interdisciplinary collaboration. With the continuous advancement of AI technology, we will have the most powerful weapon in the fight against pathogenic microbial resistance in the future.

Identifying factors that influence the use of pathogen genomics in Australia and New Zealand: a protocol

Introduction

Pathogen genomics, where whole genome sequencing technologies are used to produce complete genomic sequences of pathogens, is being increasingly used for infectious disease surveillance and outbreak response. Although proof-of-concept studies have highlighted the viability of using pathogen genomics in public health, few studies have investigated how end-users utilize pathogen genomics in public health. We describe a protocol for a study that aims to identify key factors that influence the use of pathogen genomics to inform public health responses against infectious diseases in Australia and New Zealand.

Methods

We will use qualitative comparative analysis (QCA), a case-oriented methodology that systematically compares and analyses multiple cases (or 'units of analysis'), to identify multiple pathways leading to the use of pathogen genomics results in public health actions. As part of the process, we will develop a rubric to identify and define the use of pathogen genomics and individual factors affecting this process. Simultaneously, we will identify cases where pathogen genomics has been used in public health across Australia and New Zealand. Data for these cases will be collected from document review of publicly available and confidential documents and semi-structured interviews with technicians and end-users and summarized in a case report. These case reports will form the basis for scoring each case on the extent of the use of pathogen genomics data and the presence or absence of specific factors such as the ease of extracting essential information from pathogen genomics reports and perceptions toward pathogen genomics. Using the scores, cases will be analyzed using QCA techniques to identify pathways leading to the use of pathogen genomics data. These pathways will be interpreted alongside the cases to provide rich explanations of the use of pathogen genomics in public health.

Discussion

This study will improve our understanding of the key factors that facilitate or hinder the use of pathogen genomics to inform public health authorities and end-users. These findings may inform ways to enhance the use of pathogen genomics data in public health.

In-depth characterization of food and environmental microbiomes across different meat processing plants

BACKGROUND

Processing environments can be an important source of pathogenic and spoilage microorganisms that cross contaminate meat and meat products. The aim of this study was to characterize the microbiome of raw materials, processing environments and end products from 19 facilities producing different meat products.

RESULTS

The taxonomic profiles of the microbial communities evolved along processing, from raw materials to end products, suggesting that food contact (FC) surfaces play an important role in modulating the microbiome of final products. Some species persisted with the highest relative abundance in raw materials, food processing environments and/or in the final product, including species from the genera Pseudomonas, Staphylococcus, Brochothrix, Acinetobacter and Psychrobacter. Processing environments showed a very diverse core microbiota, partially shared with the products. Pseudomonas fragi and Pseudomonas sp. Lz4W (in all sample and facility types) and Brochothrix thermosphacta, Psychrobacter sp. and Psychrobacter sp. P11F6 (in raw materials, FC surfaces and end products) were prominent members of the core microbiota for all facilities, while Latilactobacillus sakei was found as a dominant species exclusively in end products from the facilities producing fermented sausages. Processing environments showed a higher amount of antimicrobial resistance genes and virulence factors than raw materials and end products. One thousand four hundred twenty-one medium/high-quality metagenome-assembled genomes (MAGs) were reconstructed. Of these, 274 high-quality MAGs (completeness > 90%) corresponded to 210 putative new species, mostly found in processing environments. For two relevant taxa in meat curing and fermentation processes (S. equorum and L. sakei, respectively), phylogenetic variation was observed associated with the specific processing facility under study, which suggests that specific strains of these taxa may be selected in different meat processing plants, likely contributing to the peculiar sensorial traits of the end products produced in them.

CONCLUSIONS

Overall, our findings provide the most detailed metagenomics-based perspective up to now of the microbes that thrive in meat, meat products and associated environments and open avenues for future research activities to better understand the microbiome functionality and potential contribution to meat quality and safety. Video Abstract.

Artificial intelligence applications in the diagnosis and treatment of bacterial infections

The diagnosis and treatment of bacterial infections in the medical and public health field in the 21st century remain significantly challenging. Artificial Intelligence (AI) has emerged as a powerful new tool in diagnosing and treating bacterial infections. AI is rapidly revolutionizing epidemiological studies of infectious diseases, providing effective early warning, prevention, and control of outbreaks. Machine learning models provide a highly flexible way to simulate and predict the complex mechanisms of pathogen-host interactions, which is crucial for a comprehensive understanding of the nature of diseases. Machine learning-based pathogen identification technology and antimicrobial drug susceptibility testing break through the limitations of traditional methods, significantly shorten the time from sample collection to the determination of result, and greatly improve the speed and accuracy of laboratory testing. In addition, AI technology application in treating bacterial infections, particularly in the research and development of drugs and vaccines, and the application of innovative therapies such as bacteriophage, provides new strategies for improving therapy and curbing bacterial resistance. Although AI has a broad application prospect in diagnosing and treating bacterial infections, significant challenges remain in data quality and quantity, model interpretability, clinical integration, and patient privacy protection. To overcome these challenges and, realize widespread application in clinical practice, interdisciplinary cooperation, technology innovation, and policy support are essential components of the joint efforts required. In summary, with continuous advancements and in-depth application of AI technology, AI will enable doctors to more effectivelyaddress the challenge of bacterial infection, promoting the development of medical practice toward precision, efficiency, and personalization; optimizing the best nursing and treatment plans for patients; and providing strong support for public health safety.

Unveiling the genetic architecture and transmission dynamics of a novel multidrug-resistant plasmid harboring blaNDM-5 in E. Coli ST167: implications for antibiotic resistance management

BACKGROUND

The emergence of multidrug-resistant (MDR) Escherichia coli strains poses significant challenges in clinical settings, particularly when these strains harbor New Delhi metallo-ß-lactamase (NDM) gene, which confer resistance to carbapenems, a critical class of last-resort antibiotics. This study investigates the genetic characteristics and implications of a novel blaNDM-5-carrying plasmid pNDM-5-0083 isolated from an E. coli strain GZ04-0083 from clinical specimen in Zhongshan, China.

RESULTS

Phenotypic and genotypic evaluations confirmed that the E. coli ST167 strain GZ04-0083 is a multidrug-resistant organism, showing resistance to diverse classes of antibiotics including ß-lactams, carbapenems, fluoroquinolones, aminoglycosides, and sulfonamides, while maintaining susceptibility to monobactams. Investigations involving S1 pulsed-field gel electrophoresis, Southern blot analysis, and conjugation experiments, alongside genomic sequencing, confirmed the presence of the blaNDM-5 gene within a 146-kb IncFIB plasmid pNDM-5-0083. This evidence underscores a significant risk for the horizontal transfer of resistance genes among bacterial populations. Detailed annotations of genetic elements-such as resistance genes, transposons, and insertion sequences-and comparative BLAST analyses with other blaNDM-5-carrying plasmids, revealed a unique architectural configuration in the pNDM-5-0083. The MDR region of this plasmid shares a conserved gene arrangement (repA-IS15DIV-blaNDM-5-bleMBL-IS91-suI2-aadA2-dfrA12) with three previously reported plasmids, indicating a potential for dynamic genetic recombination and evolution within the MDR region. Additionally, the integration of virulence factors, including the iro and sit gene clusters and enolase, into its genetic architecture poses further therapeutic challenges by enhancing the strain's pathogenicity through improved host tissue colonization, immune evasion, and increased infection severity.

CONCLUSIONS

The detailed identification and characterization of pNDM-5-0083 enhance our understanding of the mechanisms facilitating the spread of carbapenem resistance. This study illuminates the intricate interplay among various genetic elements within the novel blaNDM-5-carrying plasmid, which are crucial for the stability and mobility of resistance genes across bacterial populations. These insights highlight the urgent need for ongoing surveillance and the development of effective strategies to curb the proliferation of antibiotic resistance.

Impact of template denaturation prior to whole genome amplification on gene detection in high GC-content species, Burkholderia mallei and B. pseudomallei

OBJECTIVE

In this study, we sought to determine the types and prevalence of antimicrobial resistance determinants (ARDs) in Burkholderia spp. strains using the Antimicrobial Resistance Determinant Microarray (ARDM).

RESULTS

Whole genome amplicons from 22 B. mallei (BM) and 37 B. pseudomallei (BP) isolates were tested for > 500 ARDs using ARDM v.3.1. ARDM detected the following Burkholderia spp.-derived genes, aac(6), blaBP/MBL-3, blaABPS, penA-BP, and qacE, in both BM and BP while blaBP/MBL-1, macB, blaOXA-42/43 and penA-BC were observed in BP only. The method of denaturing template for whole genome amplification greatly affected the numbers and types of genes detected by the ARDM. BlaTEM was detected in nearly a third of BM and BP amplicons derived from thermally, but not chemically denatured templates. BlaTEM results were confirmed by PCR, with 81% concordance between methods. Sequences from 414-nt PCR amplicons (13 preparations) were 100% identical to the Klebsiella pneumoniae reference gene. Although blaTEM sequences have been observed in B. glumae, B. cepacia, and other undefined Burkholderia strains, this is the first report of such sequences in BM/BP/B. thailandensis (BT) clade. These results highlight the importance of sample preparation in achieving adequate genome coverage in methods requiring untargeted amplification before analysis.

Pasa: leveraging population pangenome graph to scaffold prokaryote genome assemblies

Whole genome sequencing has increasingly become the essential method for studying the genetic mechanisms of antimicrobial resistance and for surveillance of drug-resistant bacterial pathogens. The majority of bacterial genomes sequenced to date have been sequenced with Illumina sequencing technology, owing to its high-throughput, excellent sequence accuracy, and low cost. However, because of the short-read nature of the technology, these assemblies are fragmented into large numbers of contigs, hindering the obtaining of full information of the genome. We develop Pasa, a graph-based algorithm that utilizes the pangenome graph and the assembly graph information to improve scaffolding quality. By leveraging the population information of the bacteria species, Pasa is able to utilize the linkage information of the gene families of the species to resolve the contig graph of the assembly. We show that our method outperforms the current state of the arts in terms of accuracy, and at the same time, is computationally efficient to be applied to a large number of existing draft assemblies.

The burden of bacterial antimicrobial resistance in the WHO African region in 2019: a cross-country systematic analysis

BACKGROUND

A critical and persistent challenge to global health and modern health care is the threat of antimicrobial resistance (AMR). Previous studies have reported a disproportionate burden of AMR in low-income and middle-income countries, but there remains an urgent need for more in-depth analyses across Africa. This study presents one of the most comprehensive sets of regional and country-level estimates of bacterial AMR burden in the WHO African region to date.

METHODS

We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with AMR for 23 bacterial pathogens and 88 pathogen-drug combinations for countries in the WHO African region in 2019. Our methodological approach consisted of five broad components: the number of deaths in which infection had a role, the proportion of infectious deaths attributable to a given infectious syndrome, the proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antimicrobial drug of interest, and the excess risk of mortality (or duration of an infection) associated with this resistance. These components were then used to estimate the disease burden by using two counterfactual scenarios: deaths attributable to AMR (considering an alternative scenario where infections with resistant pathogens are replaced with susceptible ones) and deaths associated with AMR (considering an alternative scenario where drug-resistant infections would not occur at all). We obtained data from research hospitals, surveillance networks, and infection databases maintained by private laboratories and medical technology companies. We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity.

FINDINGS

In the WHO African region in 2019, there were an estimated 1·05 million deaths (95% UI 829 000-1 316 000) associated with bacterial AMR and 250 000 deaths (192 000-325 000) attributable to bacterial AMR. The largest fatal AMR burden was attributed to lower respiratory and thorax infections (119 000 deaths [92 000-151 000], or 48% of all estimated bacterial pathogen AMR deaths), bloodstream infections (56 000 deaths [37 000-82 000], or 22%), intra-abdominal infections (26 000 deaths [17 000-39 000], or 10%), and tuberculosis (18 000 deaths [3850-39 000], or 7%). Seven leading pathogens were collectively responsible for 821 000 deaths (636 000-1 051 000) associated with resistance in this region, with four pathogens exceeding 100 000 deaths each: Streptococcus pneumoniae, Klebsiella pneumoniae, Escherichia coli, and Staphylococcus aureus. Third-generation cephalosporin-resistant K pneumoniae and meticillin-resistant S aureus were shown to be the leading pathogen-drug combinations in 25 and 16 countries, respectively (53% and 34% of the whole region, comprising 47 countries) for deaths attributable to AMR.

INTERPRETATION

This study reveals a high level of AMR burden for several bacterial pathogens and pathogen-drug combinations in the WHO African region. The high mortality rates associated with these pathogens demonstrate an urgent need to address the burden of AMR in Africa. These estimates also show that quality and access to health care and safe water and sanitation are correlated with AMR mortality, with a higher fatal burden found in lower resource settings. Our cross-country analyses within this region can help local governments to leverage domestic and global funding to create stewardship policies that target the leading pathogen-drug combinations.

FUNDING

Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.

Active surveillance of carbapenemase-producing Enterobacterales using genomic sequencing for hospital-based infection control interventions

BACKGROUND

Whole-genome sequencing (WGS) is increasingly used to characterize hospital outbreaks of carbapenemase-producing Enterobacterales (CPE). However, access to WGS is variable and testing is often centralized, leading to delays in reporting of results.

OBJECTIVE

We describe the utility of a local sequencing service to promptly respond to facility needs over an 8-year period.

METHODS

The study was conducted at Royal Prince Alfred Hospital in Sydney, Australia. All CPE isolated from patient (screening and clinical) and environmental samples from 2015 onward underwent prospective WGS. Results were notified to the infection control unit in real time. When outbreaks were identified, WGS reports were also provided to senior clinicians and the hospital executive administration. Enhanced infection control interventions were refined based on the genomic data.

RESULTS

In total, 141 CPE isolates were detected from 123 patients and 5 environmental samples. We identified 9 outbreaks, 4 of which occurred in high-risk wards (intensive care unit and/or solid-organ transplant ward). The largest outbreak involved Enterobacterales containing an NDM gene. WGS detected unexpected links among patients, which led to further investigation of epidemiological data that uncovered the outpatient setting and contaminated equipment as reservoirs for ongoing transmission. Targeted interventions as part of outbreak management halted further transmission.

CONCLUSIONS

WGS has transitioned from an emerging technology to an integral part of local CPE control strategies. Our results show the value of embedding this technology in routine surveillance, with timely reports generated in clinically relevant timeframes to inform and optimize local control measures for greatest impact.

An insights into emerging trends to control the threats of antimicrobial resistance (AMR): an address to public health risks

Antimicrobial agents are used to treat microbial ailments, but increased use of antibiotics and exposure to infections in healthcare facilities and hospitals as well as the excessive and inappropriate use of antibiotics at the society level lead to the emergence of multidrug-resistant (MDR) bacteria. Antimicrobial resistance (AMR) is considered a public health concern and has rendered the treatment of different infections more challenging. The bacterial strains develop resistance against antimicrobial agents by limiting intracellular drug accumulation (increasing efflux or decreasing influx of antibiotics), modification and inactivation of drugs and its targets, enzymatic inhibition, and biofilm formation. However, the driving factors of AMR include the sociocultural and economic circumstances of a country, the use of falsified and substandard medicines, the use of antibiotics in farm animals, and food processing technologies. These factors make AMR one of the major menaces faced by mankind. In order to promote reciprocal learning, this article summarizes the current AMR situation in Pakistan and how it interacts with the health issues related to the COVID-19 pandemic. The COVID-19 pandemic aids in illuminating the possible long-term impacts of AMR, which are less immediate but not less severe since their measures and effects are equivalent. Impact on other sectors, including the health industry, the economy, and trade are also discussed. We conclude by summarizing the several approaches that could be used to address this issue.

Postbiotic as Novel Alternative Agent or Adjuvant for the Common Antibiotic Utilized in the Food Industry

BACKGROUND

Antibiotic resistance is a serious public health problem as it causes previously manageable diseases to become deadly infections that can cause serious disability or even death. Scientists are creating novel approaches and procedures that are essential for the treatment of infections and limiting the improper use of antibiotics in an effort to counter this rising risk.

OBJECTIVES

With a focus on the numerous postbiotic metabolites formed from the beneficial gut microorganisms, their potential antimicrobial actions, and recent associated advancements in the food and medical areas, this review presents an overview of the emerging ways to prevent antibiotic resistance.

RESULTS

Presently, scientific literature confirms that plant-derived antimicrobials, RNA therapy, fecal microbiota transplantation, vaccines, nanoantibiotics, haemofiltration, predatory bacteria, immunotherapeutics, quorum-sensing inhibitors, phage therapies, and probiotics can be considered natural and efficient antibiotic alternative candidates. The investigations on appropriate probiotic strains have led to the characterization of specific metabolic byproducts of probiotics named postbiotics. Based on preclinical and clinical studies, postbiotics with their unique characteristics in terms of clinical (safe origin, without the potential spread of antibiotic resistance genes, unique and multiple antimicrobial action mechanisms), technological (stability and feasibility of largescale production), and economic (low production costs) aspects can be used as a novel alternative agent or adjuvant for the common antibiotics utilized in the production of animal-based foods.

CONCLUSION

Postbiotic constituents may be a new approach for utilization in the pharmaceutical and food sectors for developing therapeutic treatments. Further metabolomics investigations are required to describe novel postbiotics and clinical trials are also required to define the sufficient dose and optimum administration frequency of postbiotics.

Genomic analysis of almost 8,000 Salmonella genomes reveals drivers and landscape of antimicrobial resistance in China

IMPORTANCE

We established the largest Salmonella genome database from China and presented the landscape and spatiotemporal dynamics of antimicrobial resistance genes. We also found that economic, climatic, and social factors can drive the rise of antimicrobial resistance. The Chinese local Salmonella genome database version 2 was released as an open-access database (https://nmdc.cn/clsgdbv2) and thus can assist surveillance studies across the globe. This database will help inform interventions for AMR, food safety, and public health.

Enterococcus species: insights into antimicrobial resistance and whole-genome features of isolates recovered from livestock and raw meat in Ghana

Introduction

Enterococcus spp. have gradually evolved from commensals to causing life-threatening hospital-acquired infections globally due to their inherent antimicrobial resistance ability and virulence potential. Enterococcus spp. recovered from livestock and raw meat samples were characterized using antimicrobial susceptibility testing and whole-genome sequencing.

Materials and methods

Isolates were confirmed using the MALDI-ToF mass spectrometer, and antimicrobial susceptibility was determined using the Kirby-Bauer disk diffusion method. Whole genome sequencing was performed on isolates resistant to two or more antibiotics. Bioinformatics analysis was performed to determine sequence types, resistance and virulence gene content and evolutionary relationships between isolates from meat and livestock samples, and other enterococci genomes curated by PATRIC. eBURST analysis was used to assign genomes to clonal complexes.

Results

Enterococcus spp. were predominantly E. faecalis (96/236; 41%) and E. faecium (89/236; 38%). Overall, isolates showed resistance to erythromycin (78/236; 33%), tetracycline (71/236; 30%), ciprofloxacin (20/236; 8%), chloramphenicol (12/236; 5%), linezolid (7/236; 3%), ampicillin (4/236; 2%) and vancomycin (1/236, 0.4%). Resistance to two or more antimicrobial agents was detected among 17% (n = 40) Enterococcus spp. Resistance genes for streptogramins [lsa(A), lsa(E), msr(C)], aminoglycosides [aac(6')-Ii, aph(3')-III, ant(6)-Ia, aac(6')-aph(2″), str], amphenicol [cat], macrolides [erm(B), erm(T), msr(C)], tetracyclines [tet(M), tet(L), tet(S)] and lincosamides [lsa(A), lsa(E), lnu(B)] were detected among the isolates. Genes for biofilm formation, adhesins, sex pheromones, cytolysins, hyaluronidase, oxidative stress resistance, quorum-sensing and anti-phagocytic activity were also identified. Potential plasmids with replicon sequences (rep1, rep2, repUS43, repUS47, rep9a, rep9b) and other mobile genetic elements (Tn917, cn_5536_ISEnfa1, Tn6009, ISEnfa1, ISEfa10) were detected. Clinically relevant E. faecium ST32 and ST416 clones were identified in meat samples.

Conclusion

The occurrence of antimicrobial-resistant Enterococcus spp. in livestock and raw meat samples, carrying multiple resistance and virulence genes, including known clones associated with hospital-acquired infections, underscores the critical need for employing robust tools like whole genome sequencing. Such tools provide detailed data essential for ongoing surveillance efforts aimed at addressing the challenge of antimicrobial resistance with a focus on one health.

Innovations in genomic antimicrobial resistance surveillance

Whole-genome sequencing of antimicrobial-resistant pathogens is increasingly being used for antimicrobial resistance (AMR) surveillance, particularly in high-income countries. Innovations in genome sequencing and analysis technologies promise to revolutionise AMR surveillance and epidemiology; however, routine adoption of these technologies is challenging, particularly in low-income and middle-income countries. As part of a wider series of workshops and online consultations, a group of experts in AMR pathogen genomics and computational tool development conducted a situational analysis, identifying the following under-used innovations in genomic AMR surveillance: clinical metagenomics, environmental metagenomics, gene or plasmid tracking, and machine learning. The group recommended developing cost-effective use cases for each approach and mapping data outputs to clinical outcomes of interest to justify additional investment in capacity, training, and staff required to implement these technologies. Harmonisation and standardisation of methods, and the creation of equitable data sharing and governance frameworks, will facilitate successful implementation of these innovations.

Global pathogenomic analysis identifies known and candidate genetic antimicrobial resistance determinants in twelve species

Surveillance programs for managing antimicrobial resistance (AMR) have yielded thousands of genomes suited for data-driven mechanism discovery. We present a workflow integrating pangenomics, gene annotation, and machine learning to identify AMR genes at scale. When applied to 12 species, 27,155 genomes, and 69 drugs, we 1) find AMR gene transfer mostly confined within related species, with 925 genes in multiple species but just eight in multiple phylogenetic classes, 2) demonstrate that discovery-oriented support vector machines outperform contemporary methods at recovering known AMR genes, recovering 263 genes compared to 145 by Pyseer, and 3) identify 142 AMR gene candidates. Validation of two candidates in E. coli BW25113 reveals cases of conditional resistance: ΔcycA confers ciprofloxacin resistance in minimal media with D-serine, and frdD V111D confers ampicillin resistance in the presence of ampC by modifying the overlapping promoter. We expect this approach to be adaptable to other species and phenotypes.

Integration of phenotypic, qPCR and genome sequencing methodologies for the detection of antimicrobial resistance and virulence in clinical isolates of a tertiary hospital, India

The emergence of antimicrobial resistance (AMR) and virulence in clinical isolates is a significant public health concern. The rapid and accurate detection of these traits in clinical isolates is essential for effective infection control and treatment. We demonstrated the integration of multiple detection methodologies, including phenotypic testing, quantitative polymerase chain reaction (qPCR), and genome sequencing, to detect AMR and virulence in clinical isolates. One hundred sixty-two gram-negative bacterial clinical isolates were selected for this study from the Shri Vinoba Bhave Civil Hospital, Silvassa, a tertiary government hospital. Antimicrobial susceptibility was detected by determining the Minimum Inhibitory Concentration (MIC) using Vitek-2, whereas the combined disk (CD) method was used for phenotypic detection of carbapenemase activity. The highest sensitivity rates were obtained for antibiotics colistin 87.93%, amikacin 67.52%, tigecycline 63.39%, nitrofurantoin 60.87%, and gentamycin 56.08%. The most resistant antibiotics were ceftazidime (71.93%), ciprofloxacin (67.95%) and trimethoprim/sulfamethoxazole (65.56%). Approximately 46.91% (76) of all the isolates were MBL isolates. The qPCR results confirmed the presence of blaNDM-1 in 29.01% of the isolates. The blaNDM-1 harbouring isolates in descending order, were Acinetobacter, Enterobacter cloacae, and Klebsiella pneumoniae. Klebsiella and Acinetobacter isolates were extensively drug-resistant. Whole genome sequencing performed on one of the Klebsiella pneumoniae isolates revealed the presence of many virulence factors, which increased the pathogenicity of the clinical isolates. The results showed that antimicrobial resistance, including carbapenem resistance, blaNDM-1, and virulence factors, was highly prevalent among isolates from tertiary clinical hospitals. The integration of multiple detection methodologies can potentially improve the detection of AMR and virulence in clinical isolates, leading to better patient outcomes and a reduced spread of these essential traits.

Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology

Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.

Antimicrobial Stewardship in the Hospital Setting: A Narrative Review

The increasing global threat of antibiotic resistance, which has resulted in countless fatalities due to untreatable infections, underscores the urgent need for a strategic action plan. The acknowledgment that humanity is perilously approaching the "End of the Miracle Drugs" due to the unjustifiable overuse and misuse of antibiotics has prompted a critical reassessment of their usage. In response, numerous relevant medical societies have initiated a concerted effort to combat resistance by implementing antibiotic stewardship programs within healthcare institutions, grounded in evidence-based guidelines and designed to guide antibiotic utilization. Crucial to this initiative is the establishment of multidisciplinary teams within each hospital, led by a dedicated Infectious Diseases physician. This team includes clinical pharmacists, clinical microbiologists, hospital epidemiologists, infection control experts, and specialized nurses who receive intensive training in the field. These teams have evidence-supported strategies aiming to mitigate resistance, such as conducting prospective audits and providing feedback, including the innovative 'Handshake Stewardship' approach, implementing formulary restrictions and preauthorization protocols, disseminating educational materials, promoting antibiotic de-escalation practices, employing rapid diagnostic techniques, and enhancing infection prevention and control measures. While initial outcomes have demonstrated success in reducing resistance rates, ongoing research is imperative to explore novel stewardship interventions.

Extended Spectrum β-Lactamase-Producing Enterobacterales of Shrimp and Salmon Available for Purchase by Consumers in Canada-A Risk Profile Using the Codex Framework

The extended-spectrum β-lactamase (ESBL)-producing Enterobacterales (ESBL-EB) encompass several important human pathogens and are found on the World Health Organization (WHO) priority pathogens list of antibiotic-resistant bacteria. They are a group of organisms which demonstrate resistance to third-generation cephalosporins (3GC) and their presence has been documented worldwide, including in aquaculture and the aquatic environment. This risk profile was developed following the Codex Guidelines for Risk Analysis of Foodborne Antimicrobial Resistance with the objectives of describing the current state of knowledge of ESBL-EB in relation to retail shrimp and salmon available to consumers in Canada, the primary aquacultured species consumed in Canada. The risk profile found that Enterobacterales and ESBL-EB have been found in multiple aquatic environments, as well as multiple host species and production levels. Although the information available did not permit the conclusion as to whether there is a human health risk related to ESBLs in Enterobacterales in salmon and shrimp available for consumption by Canadians, ESBL-EB in imported seafood available at the retail level in Canada have been found. Surveillance activities to detect ESBL-EB in seafood are needed; salmon and shrimp could be used in initial surveillance activities, representing domestic and imported products.

Associations between Nutrigenomic Effects and Incidences of Microbial Resistance against Novel Antibiotics

Nutrigenomics is the study of the impact of diets or nutrients on gene expression and phenotypes using high-throughput technologies such as transcriptomics, proteomics, metabolomics, etc. The bioactive components of diets and nutrients, as an environmental factor, transmit information through altered gene expression and hence the overall function and traits of the organism. Dietary components and nutrients not only serve as a source of energy but also, through their interactions with genes, regulate gut microbiome composition, the production of metabolites, various biological processes, and finally, health and disease. Antimicrobial resistance in pathogenic and probiotic microorganisms has emerged as a major public health concern due to the presence of antimicrobial resistance genes in various food products. Recent evidence suggests a correlation between the regulation of genes and two-component and other signaling systems that drive antibiotic resistance in response to diets and nutrients. Therefore, diets and nutrients may be alternatively used to overcome antibiotic resistance against novel antibiotics. However, little progress has been made in this direction. In this review, we discuss the possible implementations of nutrigenomics in antibiotic resistance against novel antibiotics.

A collaborative approach to improving representation in viral genomic surveillance

The lack of routine viral genomic surveillance delayed the initial detection of SARS-CoV-2, allowing the virus to spread unfettered at the outset of the U.S. epidemic. Over subsequent months, poor surveillance enabled variants to emerge unnoticed. Against this backdrop, long-standing social and racial inequities have contributed to a greater burden of cases and deaths among minority groups. To begin to address these problems, we developed a new variant surveillance model geared toward building 'next generation' genome sequencing capacity at universities in or near rural areas and engaging the participation of their local communities. The resulting genomic surveillance network has generated more than 1,000 SARS-CoV-2 genomes to date, including the first confirmed case in northeast Louisiana of Omicron, and the first and sixth confirmed cases in Georgia of the emergent BA.2.75 and BQ.1.1 variants, respectively. In agreement with other studies, significantly higher viral gene copy numbers were observed in Delta variant samples compared to those from Omicron BA.1 variant infections, and lower copy numbers were seen in asymptomatic infections relative to symptomatic ones. Collectively, the results and outcomes from our collaborative work demonstrate that establishing genomic surveillance capacity at smaller academic institutions in rural areas and fostering relationships between academic teams and local health clinics represent a robust pathway to improve pandemic readiness.

The role of plasmids in carbapenem resistant E. coli in Alameda County, California

BACKGROUND

Antimicrobial resistant infections continue to be a leading global public health crisis. Mobile genetic elements, such as plasmids, have been shown to play a major role in the dissemination of antimicrobial resistance (AMR) genes. Despite its ongoing threat to human health, surveillance of AMR in the United States is often limited to phenotypic resistance. Genomic analyses are important to better understand the underlying resistance mechanisms, assess risk, and implement appropriate prevention strategies. This study aimed to investigate the extent of plasmid mediated antimicrobial resistance that can be inferred from short read sequences of carbapenem resistant E. coli (CR-Ec) in Alameda County, California. E. coli isolates from healthcare locations in Alameda County were sequenced using an Illumina MiSeq and assembled with Unicycler. Genomes were categorized according to predefined multilocus sequence typing (MLST) and core genome multilocus sequence typing (cgMLST) schemes. Resistance genes were identified and corresponding contigs were predicted to be plasmid-borne or chromosome-borne using two bioinformatic tools (MOB-suite and mlplasmids).

RESULTS

Among 82 of CR-Ec identified between 2017 and 2019, twenty-five sequence types (STs) were detected. ST131 was the most prominent (n = 17) followed closely by ST405 (n = 12). bla_CTX-M were the most common ESBL genes and just over half (18/30) of these genes were predicted to be plasmid-borne by both MOB-suite and mlplasmids. Three genetically related groups of E. coli isolates were identified with cgMLST. One of the groups contained an isolate with a chromosome-borne bla_CTX-M-15 gene and an isolate with a plasmid-borne bla_CTX-M-15 gene.

CONCLUSIONS

This study provides insights into the dominant clonal groups driving carbapenem resistant E. coli infections in Alameda County, CA, USA clinical sites and highlights the relevance of whole-genome sequencing in routine local genomic surveillance. The finding of multi-drug resistant plasmids harboring high-risk resistance genes is of concern as it indicates a risk of dissemination to previously susceptible clonal groups, potentially complicating clinical and public health intervention.

Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review

Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician's point of view.

Factors associated with plasmid antibiotic resistance gene carriage revealed using large-scale multivariable analysis

Plasmids are major vectors of bacterial antibiotic resistance, but understanding of factors associated with plasmid antibiotic resistance gene (ARG) carriage is limited. We curated > 14,000 publicly available plasmid genomes and associated metadata. Duplicate and replicate plasmids were excluded; where possible, sample metadata was validated externally (BacDive database). Using Generalised Additive Models (GAMs) we assessed the influence of 12 biotic/abiotic factors (e.g. plasmid genetic factors, isolation source, collection date) on ARG carriage, modelled as a binary outcome. Separate GAMs were built for 10 major ARG types. Multivariable analysis indicated that plasmid ARG carriage patterns across time (collection years), isolation sources (human/livestock) and host bacterial taxa were consistent with antibiotic selection pressure as a driver of plasmid-mediated antibiotic resistance. Only 0.42% livestock plasmids carried carbapenem resistance (compared with 12% human plasmids); conversely, tetracycline resistance was enriched in livestock vs human plasmids, reflecting known prescribing practices. Interpreting results using a timeline of ARG type acquisition (determined by literature review) yielded additional novel insights. More recently acquired ARG types (e.g. colistin and carbapenem) showed increases in plasmid carriage during the date range analysed (1994-2019), potentially reflecting recent onset of selection pressure; they also co-occurred less commonly with ARGs of other types, and virulence genes. Overall, this suggests that following acquisition, plasmid ARGs tend to accumulate under antibiotic selection pressure and co-associate with other adaptive genes (other ARG types, virulence genes), potentially re-enforcing plasmid ARG carriage through co-selection.

Man vs Microbes - The Race of the Century

The complexity of the antimicrobial resistance (AMR) crisis and its global impact on healthcare invokes an urgent need to understand the underlying forces and to conceive and implement innovative solutions. Beyond focusing on a traditional pathogen-centric approach to antibiotic discovery yielding diminishing returns, future therapeutic interventions can expand to focus more comprehensively on host-pathogen interactions. In this manner, increasing the resiliency of our innate immune system or attenuating the virulence mechanisms of the pathogens can be explored to improve therapeutic outcomes. Key pathogen survival strategies such as tolerance, persistence, aggregation, and biofilm formation can be considered and interrupted to sensitize pathogens for more efficient immune clearance. Understanding the evolution and emergence of so-called 'super clones' that drive AMR spread with rapid clonotyping assays may guide more precise antibiotic regimens. Innovative alternatives to classical antibiotics such as bacteriophage therapy, novel engineered peptide antibiotics, ionophores, nanomedicines, and repurposing drugs from other domains of medicine to boost innate immunity are beginning to be successfully implemented to combat AMR. Policy changes supporting shorter durations of antibiotic treatment, greater antibiotic stewardship, and increased surveillance measures can enhance patient safety and enable implementation of the next generation of targeted prevention and control programmes at a global level.

Comparative genomics of drug-resistant strains of Mycobacterium tuberculosis in Ecuador

BACKGROUND

Tuberculosis is a serious infectious disease affecting millions of people. In spite of efforts to reduce the disease, increasing antibiotic resistance has contributed to persist in the top 10 causes of death worldwide. In fact, the increased cases of multi (MDR) and extreme drug resistance (XDR) worldwide remains the main challenge for tuberculosis control. Whole genome sequencing is a powerful tool for predicting drug resistance-related variants, studying lineages, tracking transmission, and defining outbreaks. This study presents the identification and characterization of resistant clinical isolates of Mycobacterium tuberculosis including a phylogenetic and molecular resistance profile study by sequencing the complete genome of 24 strains from different provinces of Ecuador.

RESULTS

Genomic sequencing was used to identify the variants causing resistance. A total of 15/21 isolates were identified as MDR, 4/21 as pre-XDR and 2/21 as XDR, with three isolates discarded due to low quality; the main sub-lineage was LAM (61.9%) and Haarlem (19%) but clades X, T and S were identified. Of the six pre-XDR and XDR strains, it is noteworthy that five come from females; four come from the LAM sub-lineage and two correspond to the X-class sub-lineage. A core genome of 3,750 genes, distributed in 295 subsystems, was determined. Among these, 64 proteins related to virulence and implicated in the pathogenicity of M. tuberculosis and 66 possible pharmacological targets stand out. Most variants result in nonsynonymous amino acid changes and the most frequent genotypes were identified as conferring resistance to rifampicin, isoniazid, ethambutol, para-aminosalicylic acid and streptomycin. However, an increase in the resistance to fluoroquinolones was detected.

CONCLUSION

This work shows for the first time the variability of circulating resistant strains between men and women in Ecuador, highlighting the usefulness of genomic sequencing for the identification of emerging resistance. In this regard, we found an increase in fluoroquinolone resistance. Further sampling effort is needed to determine the total variability and associations with the metadata obtained to generate better health policies.

BacARscan: an in silico resource to discern diversity in antibiotic resistance genes

Antibiotic resistance has escalated as a significant problem of broad public health significance. Regular surveillance of antibiotic resistance genes (ARGs) in microbes and metagenomes from human, animal and environmental sources is vital to understanding ARGs' epidemiology and foreseeing the emergence of new antibiotic resistance determinants. Whole-genome sequencing (WGS)-based identification of the microbial ARGs using antibiotic resistance databases and in silico prediction tools can significantly expedite the monitoring and characterization of ARGs in various niches. The major hindrance to the annotation of ARGs from WGS data is that most genome databases contain fragmented genes/genomes (due to incomplete assembly). Herein, we describe an insilicoBacterial Antibiotic Resistance scan (BacARscan) (http://proteininformatics.org/mkumar/bacarscan/) that can detect, predict and characterize ARGs in -omics datasets, including short sequencing, reads, and fragmented contigs. Benchmarking on an independent non-redundant dataset revealed that the performance of BacARscan was better than other existing methods, with nearly 92% Precision and 95% F-measure on a combined dataset of ARG and non-ARG proteins. One of the most notable improvements of BacARscan over other ARG annotation methods is its ability to work on genomes and short-reads sequence libraries with equal efficiency and without any requirement for assembly of short reads. Thus, BacARscan can help monitor the prevalence and diversity of ARGs in microbial populations and metagenomic samples from animal, human, and environmental settings. The authors intend to constantly update the current version of BacARscan as and when new ARGs are discovered. Executable versions, source codes, sequences used for development and usage instructions are available at (http://www.proteininformatics.org/mkumar/bacarscan/downloads.html) and GitHub repository (https://github.com/mkubiophysics/BacARscan).

Next-generation sequencing and PCR technologies in monitoring the hospital microbiome and its drug resistance

The hospital environment significantly contributes to the onset of healthcare-associated infections (HAIs), which represent one of the most frequent complications occurring in healthcare facilities worldwide. Moreover, the increased antimicrobial resistance (AMR) characterizing HAI-associated microbes is one of the human health’s main concerns, requiring the characterization of the contaminating microbial population in the hospital environment. The monitoring of surface microbiota in hospitals is generally addressed by microbial cultural isolation. However, this has some important limitations mainly relating to the inability to define the whole drug-resistance profile of the contaminating microbiota and to the long time period required to obtain the results. Hence, there is an urgent need to implement environmental surveillance systems using more effective methods. Molecular approaches, including next-generation sequencing and PCR assays, may be useful and effective tools to monitor microbial contamination, especially the growing AMR of HAI-associated pathogens. Herein, we summarize the results of our recent studies using culture-based and molecular analyses in 12 hospitals for adults and children over a 5-year period, highlighting the advantages and disadvantages of the techniques used.

A comprehensive review on genomics, systems biology and structural biology approaches for combating antimicrobial resistance in ESKAPE pathogens: computational tools and recent advancements

In recent decades, antimicrobial resistance has been augmented as a global concern to public health owing to the global spread of multidrug-resistant strains from different ESKAPE pathogens. This alarming trend and the lack of new antibiotics with novel modes of action in the pipeline necessitate the development of non-antibiotic ways to treat illnesses caused by these isolates. In molecular biology, computational approaches have become crucial tools, particularly in one of the most challenging areas of multidrug resistance. The rapid advancements in bioinformatics have led to a plethora of computational approaches involving genomics, systems biology, and structural biology currently gaining momentum among molecular biologists since they can be useful and provide valuable information on the complex mechanisms of AMR research in ESKAPE pathogens. These computational approaches would be helpful in elucidating the AMR mechanisms, identifying important hub genes/proteins, and their promising targets together with their interactions with important drug targets, which is a crucial step in drug discovery. Therefore, the present review aims to provide holistic information on currently employed bioinformatic tools and their application in the discovery of multifunctional novel therapeutic drugs to combat the current problem of AMR in ESKAPE pathogens. The review also summarizes the recent advancement in the AMR research in ESKAPE pathogens utilizing the in silico approaches.