Shotgun-metagenomics based prediction of antibiotic resistance and virulence determinants in Staphylococcus aureus from periprosthetic tissue on blood culture bottles

Metagenomics insights into microbiome and antibiotic resistance genes from free living amoeba in chlorinated wastewater effluents

Free living amoeba (FLA) are among the organisms commonly found in wastewater and are well-established hosts for diverse microbial communities. Despite its clinical significance, there is little knowledge on the FLA microbiome and resistome, with previous studies relying mostly on conventional approaches. In this study we comprehensively analyzed the microbiome, antibiotic resistome and virulence factors (VFs) within FLA isolated from final treated effluents of two wastewater treatment plants (WWTPs) using shotgun metagenomics. Acanthamoeba has been identified as the most common FLA, followed by Entamoeba. The bacterial diversity showed no significant difference (p > 0.05) in FLA microbiomes obtained from the two WWTPs. At phylum level, the most dominant taxa were Proteobacteria, followed by Firmicutes and Actinobacteria. The most abundant genera identified were Enterobacter followed by Citrobacter, Paenibacillus, and Cupriavidus. The latter three genera are reported here for the first time in Acanthamoeba. In total, we identified 43 types of ARG conferring resistance to cephalosporins, phenicol, streptomycin, trimethoprim, quinolones, cephalosporins, tigecycline, rifamycin, and kanamycin. Similarly, a variety of VFs in FLA metagenomes were detected which included flagellar proteins, Type IV pili twitching motility proteins (pilH and rpoN), alginate biosynthesis genes AlgI, AlgG, AlgD and AlgW and Type VI secretion system proteins and general secretion pathway proteins (tssM, tssA, tssL, tssK, tssJ, fha, tssG, tssF, tssC and tssB, gspC, gspE, gspD, gspF, gspG, gspH, gspI, gspJ, gspK, and gspM). To the best of our knowledge, this is the first study of its kind to examine both the microbiomes and resistome in FLA, as well as their potential pathogenicity in treated effluents. Additionally, this study showed that FLA can host a variety of potentially pathogenic bacteria including Paenibacillus, and Cupriavidus that had not previously been reported, indicating that their relationship may play a role in the spread and persistence of antibiotic resistant bacteria (ARBs) and antibiotic resistance genes (ARGs) as well as the evolution of novel pathogens.

Emerging strategies and therapeutic innovations for combating drug resistance in Staphylococcus aureus strains: A comprehensive review

In recent years, antibiotic therapy has encountered significant challenges due to the rapid emergence of multidrug resistance among bacteria responsible for life-threatening illnesses, creating uncertainty about the future management of infectious diseases. The escalation of antimicrobial resistance in the post-COVID era compared to the pre-COVID era has raised global concern. The prevalence of nosocomial-related infections, especially outbreaks of drug-resistant strains of Staphylococcus aureus, have been reported worldwide, with India being a notable hotspot for such occurrences. Various virulence factors and mutations characterize nosocomial infections involving S. aureus. The lack of proper alternative treatments leading to increased drug resistance emphasizes the need to investigate and examine recent research to combat future pandemics. In the current genomics era, the application of advanced technologies such as next-generation sequencing (NGS), machine learning (ML), and quantum computing (QC) for genomic analysis and resistance prediction has significantly increased the pace of diagnosing drug-resistant pathogens and insights into genetic intricacies. Despite prompt diagnosis, the elimination of drug-resistant infections remains unattainable in the absence of effective alternative therapies. Researchers are exploring various alternative therapeutic approaches, including phage therapy, antimicrobial peptides, photodynamic therapy, vaccines, host-directed therapies, and more. The proposed review mainly focuses on the resistance journey of S. aureus over the past decade, detailing its resistance mechanisms, prevalence in the subcontinent, innovations in rapid diagnosis of the drug-resistant strains, including the applicants of NGS and ML application along with QC, it helps to design alternative novel therapeutics approaches against S. aureus infection.

Application of next-generation sequencing to identify different pathogens

Early and precise detection and identification of various pathogens are essential for epidemiological monitoring, disease management, and reducing the prevalence of clinical infectious diseases. Traditional pathogen detection techniques, which include mass spectrometry, biochemical tests, molecular testing, and culture-based methods, are limited in application and are time-consuming. Next generation sequencing (NGS) has emerged as an essential technology for identifying pathogens. NGS is a cutting-edge sequencing method with high throughput that can create massive volumes of sequences with a broad application prospects in the field of pathogen identification and diagnosis. In this review, we introduce NGS technology in detail, summarizes the application of NGS in that identification of different pathogens, including bacteria, fungi, and viruses, and analyze the challenges and outlook for using NGS to identify clinical pathogens. Thus, this work provides a theoretical basis for NGS studies and provides evidence to support the application of NGS in distinguishing various clinical pathogens.

Metagenomic next-generation sequencing for the identification of infections caused by Gram-negative pathogens and the prediction of antimicrobial resistance

OBJECTIVE

The aim of this study was to evaluate the efficacy of metagenomic next-generation sequencing (mNGS) for the identification of Gram-negative bacteria (GNB) infections and the prediction of antimicrobial resistance.

METHODS

A retrospective analysis was conducted on 182 patients with diagnosis of GNB infections who underwent mNGS and conventional microbiological tests (CMTs).

RESULTS

The detection rate of mNGS was 96.15%, higher than CMTs (45.05%) with a significant difference (χ 2 = 114.46, P < .01). The pathogen spectrum identified by mNGS was significantly wider than CMTs. Interestingly, the detection rate of mNGS was substantially higher than that of CMTs (70.33% vs 23.08%, P < .01) in patients with but not without antibiotic exposure. There was a significant positive correlation between mapped reads and pro-inflammatory cytokines (interleukin-6 and interleukin-8). However, mNGS failed to predict antimicrobial resistance in 5 of 12 patients compared to phenotype antimicrobial susceptibility testing results.

CONCLUSIONS

Metagenomic next-generation sequencing has a higher detection rate, a wider pathogen spectrum, and is less affected by prior antibiotic exposure than CMTs in identifying Gram-negative pathogens. The mapped reads may reflect a pro-inflammatory state in GNB-infected patients. Inferring actual resistance phenotypes from metagenomic data remains a great challenge.

Dissecting microbial communities and resistomes for interconnected humans, soil, and livestock

A debate is currently ongoing as to whether intensive livestock farms may constitute reservoirs of clinically relevant antimicrobial resistance (AMR), thus posing a threat to surrounding communities. Here, combining shotgun metagenome sequencing, machine learning (ML), and culture-based methods, we focused on a poultry farm and connected slaughterhouse in China, investigating the gut microbiome of livestock, workers and their households, and microbial communities in carcasses and soil. For both the microbiome and resistomes in this study, differences are observed across environments and hosts. However, at a finer scale, several similar clinically relevant antimicrobial resistance genes (ARGs) and similar associated mobile genetic elements were found in both human and broiler chicken samples. Next, we focused on Escherichia coli, an important indicator for the surveillance of AMR on the farm. Strains of E. coli were found intermixed between humans and chickens. We observed that several ARGs present in the chicken faecal resistome showed correlation to resistance/susceptibility profiles of E. coli isolates cultured from the same samples. Finally, by using environmental sensing these ARGs were found to be correlated to variations in environmental temperature and humidity. Our results show the importance of adopting a multi-domain and multi-scale approach when studying microbial communities and AMR in complex, interconnected environments.

Molecular Approach for the Laboratory Diagnosis of Periprosthetic Joint Infections

The incidence of total joint arthroplasty is increasing over time since the last decade and expected to be more than 4 million by 2030. As a consequence, the detection of infections associated with surgical interventions is increasing and prosthetic joint infections are representing both a clinically and economically challenging problem. Many pathogens, from bacteria to fungi, elicit the immune system response and produce a polymeric matrix, the biofilm, that serves as their protection. In the last years, the implementation of diagnostic methodologies reduced the error rate and the turn-around time: polymerase chain reaction, targeted or broad-spectrum, and next-generation sequencing have been introduced and they represent a robust approach nowadays that frees laboratories from the unique approach based on culture-based techniques.