Comparative study of sequence aligners for detecting antibiotic resistance in bacterial metagenomes

The metabolic, virulence and antimicrobial resistance profiles of colonising Streptococcus pneumoniae shift after PCV13 introduction in urban Malawi

Streptococcus pneumoniae causes substantial mortality among children under 5-years-old worldwide. Polysaccharide conjugate vaccines (PCVs) are highly effective at reducing vaccine serotype disease, but emergence of non-vaccine serotypes and persistent nasopharyngeal carriage threaten this success. We investigated the hypothesis that following vaccine, adapted pneumococcal genotypes emerge with the potential for vaccine escape. We genome sequenced 2804 penumococcal isolates, collected 4-8 years after introduction of PCV13 in Blantyre, Malawi. We developed a pipeline to cluster the pneumococcal population based on metabolic core genes into "Metabolic genotypes" (MTs). We show that S. pneumoniae population genetics are characterised by emergence of MTs with distinct virulence and antimicrobial resistance (AMR) profiles. Preliminary in vitro and murine experiments revealed that representative isolates from emerging MTs differed in growth, haemolytic, epithelial infection, and murine colonisation characteristics. Our results suggest that in the context of PCV13 introduction, pneumococcal population dynamics had shifted, a phenomenon that could further undermine vaccine control and promote spread of AMR.

Occurrence and distribution of antimicrobial resistance genes in the soil of an industrial park in China: A metagenomics survey

As zoned areas of industries, industrial parks have great impacts on the environment. Several studies have demonstrated that chemical compounds and heavy metals released from industrial parks can contaminate soil, water, and air. However, as an emerging pollutant, antimicrobial resistance genes (ARGs) in industrial parks have not yet been investigated. Here, we collected soil samples from 35 sites in an industrial park in China and applied a metagenomics strategy to profile the ARGs and virulence factors (VFs). We further compared the relative abundance of ARGs between the sites (TZ_31-35) located in a beta-lactam antimicrobial-producing factory and other sites (TZ_1-30) in this industrial park. Metagenomic sequencing and assembly generated 14, 383, 065 contigs and 17, 631, 051 open reading frames (ORFs). Taxonomy annotation revealed Proteobacteria and Actinobacteria as the most abundant phylum and class, respectively. The 32 pathogenic bacterial genera listed in the virulence factor database (VFDB) were all identified from the soil metagenomes in this industrial park. In total, 685,354 ARGs (3.89% of the ORFs) and 272,694 virulence factors (VFs) (1.55% of the ORFs) were annotated. These ARGs exhibited resistance to several critically important antimicrobials, such as rifampins, fluroquinolones, and beta-lactams. In addition, no significant difference in the relative abundance of ARGs was observed between sites TZ_31-35 and TZ_1-30, indicating that ARGs have already disseminated widely in this industrial park. The present study gave us a better understanding of the whole picture of the resistome and virulome in the soil of the industrial park and suggested that we should treat the industrial park as a whole in the surveillance and maintenance of ARGs.

Mobilization of Antibiotic Resistance: Are Current Approaches for Colocalizing Resistomes and Mobilomes Useful?

Antimicrobial resistance (AMR) poses a global human and animal health threat, and predicting AMR persistence and transmission remains an intractable challenge. Shotgun metagenomic sequencing can help overcome this by enabling characterization of AMR genes within all bacterial taxa, most of which are uncultivatable in laboratory settings. Shotgun sequencing, therefore, provides a more comprehensive glance at AMR "potential" within samples, i.e., the "resistome." However, the risk inherent within a given resistome is predicated on the genomic context of various AMR genes, including their presence within mobile genetic elements (MGEs). Therefore, resistome risk stratification can be advanced if AMR profiles are considered in light of the flanking mobilizable genomic milieu (e.g., plasmids, integrative conjugative elements (ICEs), phages, and other MGEs). Because such mediators of horizontal gene transfer (HGT) are involved in uptake by pathogens, investigators are increasingly interested in characterizing that resistome fraction in genomic proximity to HGT mediators, i.e., the "mobilome"; we term this "colocalization." We explored the utility of common colocalization approaches using alignment- and assembly-based techniques, on clinical (human) and agricultural (cattle) fecal metagenomes, obtained from antimicrobial use trials. Ordination revealed that tulathromycin-treated cattle experienced a shift in ICE and plasmid composition versus untreated animals, though the resistome was unaffected during the monitoring period. Contrarily, the human resistome and mobilome composition both shifted shortly after antimicrobial administration, though this rebounded to pre-treatment status. Bayesian networks revealed statistical AMR-MGE co-occurrence in 19 and 2% of edges from the cattle and human networks, respectively, suggesting a putatively greater mobility potential of AMR in cattle feces. Conversely, using Mobility Index (MI) and overlap analysis, abundance of de novo-assembled contigs supporting resistomes flanked by MGE increased shortly post-exposure within human metagenomes, though > 40 days after peak dose such contigs were rare (∼2%). MI was not substantially altered by antimicrobial exposure across all cattle metagenomes, ranging 0.5-4.0%. We highlight that current alignment- and assembly-based methods estimating resistome mobility yield contradictory and incomplete results, likely constrained by approach-specific data inputs, and bioinformatic limitations. We discuss recent laboratory and computational advancements that may enhance resistome risk analysis in clinical, regulatory, and commercial applications.

A Modular Metagenomics Pipeline Allowing for the Inclusion of Prior Knowledge Using the Example of Anaerobic Digestion

Metagenomics analysis revealing the composition and functional repertoire of complex microbial communities typically relies on large amounts of sequence data. Numerous analysis strategies and computational tools are available for their analysis. Fully integrated automated analysis pipelines such as MG-RAST or MEGAN6 are user-friendly but not designed for integrating specific knowledge on the biological system under study. In order to facilitate the consideration of such knowledge, we introduce a modular, adaptable analysis pipeline combining existing tools. We applied the novel pipeline to simulated mock data sets focusing on anaerobic digestion microbiomes and compare results to those obtained with established automated analysis pipelines. We find that the analysis strategy and choice of tools and parameters have a strong effect on the inferred taxonomic community composition, but not on the inferred functional profile. By including prior knowledge, computational costs can be decreased while improving result accuracy. While automated off-the-shelf analysis pipelines are easy to apply and require no knowledge on the microbial system under study, custom-made pipelines require more preparation time and bioinformatics expertise. This extra effort is minimized by our modular, flexible, custom-made pipeline, which can be adapted to different scenarios and can take available knowledge on the microbial system under study into account.

Deciphering extracellular antibiotic resistance genes (eARGs) in activated sludge by metagenome

Antibiotic resistance genes (ARGs) including extracellular ARGs (eARGs) and intracellular ARGs (iARGs), are recognized as emerging environmental contaminants. Despite extensive efforts to profile ARGs in their "hotspots" wastewater treatment plants (WWTPs), the contribution of eARGs to antibiotic resistance spread remains unclear. Here, we applied metagenomic sequencing to investigate the distribution, mobility and microbial hosts of eARGs in activated sludge from five WWTPs. The total relative abundance eARGs ranged from 9.5 × 10^-6 to 1.3 × 10^-4, mainly encompassing elfamycin, dual drug, and aminoglycoside resistance genes. Multiple eARGs (e.g., EF-Tu, ropB, and rpsL mutants) were shared among the five WWTPs, and some clustered in the same genetic element (e.g., EF-Tu-rpsJ). eARGs were found to frequently co-localize with the eMGEs (e.g., sul1-3'CS-TnAs3, sul2-intI1-ISVsa3, and tetX-p63039), which may facilitate the mobilization of eARGs. Most eARGs likely originated from the genera Mycobacterium (6.7%), Nitrosomonas (5.3%), Steroidobacter (5.3%), Nitrospira (5.2%) and Pseudomonas (5.1%). No significant difference in the diversity, abundance, and mobility was observed between eARGs and iARGs. The host composition of eARGs and iARGs from municipal WWTPs are consistently dominated by Nitrosomonas, Steroidobacter, Nitrospira and Pseudomonas, while some differentially enriched genera (especially Nitrospira) in the hosts of iARGs compared with those of eARGs from the swine WWTP were identified. Our findings corroborate the mobile eARGs reservoir in WWTPs, thereby laying foundation for mitigating widespread antibiotic resistance.