Antibiotic resistance genes are differentially mobilized according to resistance mechanism

Bacterial Diversity and Antimicrobial Resistance of Microorganisms Isolated from Teat Cup Liners in Dairy Farms in Shandong Province, China

Global milk consumption exceeds 800 million tons a year and is still growing. Milk quality and its products are critical to human health. A teat cup makes direct contact with the cow's teats during milking and its cleanliness is very important for the quality of raw milk. In this study, the microorganism from post-milking teat cup liners were collected from six dairy farms in Shandong Province of China, the bacterial species were identified using microbial mass spectrometry, the minimum inhibitory concentrations of the isolated strains against ten antimicrobial agents were determined using the broth microdilution method, and the antimicrobial resistance genes were detected by PCR. The results indicated that the most frequently isolated bacteria in this study were Bacillus licheniformis (39/276, 14.13%), followed by Bacillus pumilus (20/276, 7.25%), Bacillus cereus (17/276, 6.16%), and Bacillus subtili (16/276, 5.80%). The isolates exhibited the highest average resistance to lincomycin (87.37%), followed by sulfadiazine (61.05%) and streptomycin (42.63%); the highest detection rate of resistance genes was Sul1 (55.43%), followed by ant(4') (51.09%), tet(M) (25.36%), blaKPC (3.62%) and qnrS (3.62%). These findings imply the necessity for enhanced measures in disinfecting cow udders and milking equipment, highlighting the persistently challenging issue of antimicrobial resistance in Shandong Province.

Influence of helicobacter pylori on composition and function of gastric microbiota in patients with chronic non-atrophic gastritis

Objective

Helicobacter pylori (H. pylori) plays a major role in causing and advancing gastrointestinal illnesses. Our aim is to analyze the unique makeup and functional changes in the gastric microbiota of patients with chronic non-atrophic gastritis (CNAG), regardless of the presence of H. pylori, and to determine the potential signaling pathways.

Methods

We performed metagenomic sequencing on gastric mucosa samples collected from 17 individuals with non-atrophic gastritis, comprising 6 cases were infected with H. pylori (H. pylori-infected case group) and 11 cases without (control group). The species composition was evaluated with DIAMOND software, and functional enrichment was assessed utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We analyzed antibiotic resistance patterns using the Comprehensive Antibiotic Resistance Database as a reference (CARD).

Results

The presence of H. pylori colonization in CNAG patients was associated with increased diversity in the gastric microbiota. The Phylum Firmicutes was found to be less prevalent, while the Phylum Proteobacteria showed an increase. Functionally, pathways associated with metabolic pathways, including vitamins, auxiliaries, amino acid residue, carbon hydrate, and metabolic energy pathways, were enriched in CNAG patients with H. pylori infection. Additionally, antibiotic resistance genes correlated with antibiotic efflux pump were enriched.

Conclusions

From a holistic genomic perspective, our findings offer fresh perspectives into the gastric microbiome among CNAG patients carrying H. pylori, which is valuable for future research on CNAG.

Machine learning reveals the selection pressure exerted by nonantibiotic pharmaceuticals at environmentally relevant concentrations on antibiotic resistance genotypes

The emergence and increasing prevalence of antibiotic resistance pose a global public risk for human health, and nonantimicrobial pharmaceuticals play an important role in this process. Herein, five nonantimicrobial pharmaceuticals, including acetaminophen (ACT), clofibric acid (CA), carbamazepine (CBZ), caffeine (CF) and nicotine (NCT), tetracycline-resistant strains, five ARGs (sul1, sul2, tetG, tetM and tetW) and one integrase gene (intI1), were detected in 101 wastewater samples during two typical sewage treatment processes including anaerobic-oxic (A/O) and biological aerated filter (BAF) in Harbin, China. The impact of nonantibiotic pharmaceuticals at environmentally relevant concentrations on both the resistance genotypes and resistance phenotypes were explored. The results showed that a significant impact of nonantibiotic pharmaceuticals at environmentally relevant concentrations on tetracycline resistance genes encoding ribosomal protection proteins (RPPs) was found, while no changes in antibiotic phenotypes, such as minimal inhibitory concentrations (MICs), were observed. Machine learning was applied to further sort out the contribution of nonantibiotic pharmaceuticals at environmentally relevant concentrations to different ARG subtypes. The highest contribution and correlation were found at concentrations of 1400-1800 ng/L for NCT, 900-1500 ng/L for ACT and 7000-10,000 ng/L for CF for tetracycline resistance genes encoding RPPs, while no significant correlation was found between the target compounds and ARGs when their concentrations were lower than 500 ng/L for NCT, 100 ng/L for ACT and 1000 ng/L for CF, which were higher than the concentrations detected in effluent samples. Therefore, the removal of nonantibiotic pharmaceuticals in WWTPs can reduce their selection pressure for resistance genes in wastewater.

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.

Interplay Between Antimicrobial Resistance and Global Environmental Change

Antibiotic resistance genes predate the therapeutic uses of antibiotics. However, the current antimicrobial resistance crisis stems from our extensive use of antibiotics and the generation of environmental stressors that impose new selective pressure on microbes and drive the evolution of resistant pathogens that now threaten human health. Similar to climate change, this global threat results from human activities that change habitats and natural microbiomes, which in turn interact with human-associated ecosystems and lead to adverse impacts on human health. Human activities that alter our planet at global scales exacerbate the current resistance crisis and exemplify our central role in large-scale changes in which we are both protagonists and architects of our success but also casualties of unanticipated collateral outcomes. As cognizant participants in this ongoing planetary experiment, we are driven to understand and find strategies to curb the ongoing crises of resistance and climate change.

Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide

The acquisition of antimicrobial resistance (AR) genes has rendered important pathogens nearly or fully unresponsive to antibiotics. It has been suggested that pathogens acquire AR traits from the gut microbiota, which collectively serve as a global reservoir for AR genes conferring resistance to all classes of antibiotics. However, only a subset of AR genes confers resistance to clinically relevant antibiotics, and, although these AR gene profiles are well-characterized for common pathogens, less is known about their taxonomic associations and transfer potential within diverse members of the gut microbiota. We examined a collection of 14,850 human metagenomes and 1666 environmental metagenomes from 33 countries, in addition to nearly 600,000 isolate genomes, to gain insight into the global prevalence and taxonomic range of clinically relevant AR genes. We find that several of the most concerning AR genes, such as those encoding the cephalosporinase CTX-M and carbapenemases KPC, IMP, NDM, and VIM, remain taxonomically restricted to Proteobacteria. Even cfiA, the most common carbapenemase gene within the human gut microbiome, remains tightly restricted to Bacteroides, despite being found on a mobilizable plasmid. We confirmed these findings in gut microbiome samples from India, Honduras, Pakistan, and Vietnam, using a high-sensitivity single-cell fusion PCR approach. Focusing on a set of genes encoding carbapenemases and cephalosporinases, thus far restricted to Bacteroides species, we find that few mutations are required for efficacy in a different phylum, raising the question of why these genes have not spread more widely. Overall, these data suggest that globally prevalent, clinically relevant AR genes have not yet established themselves across diverse commensal gut microbiota.

Population-level impacts of antibiotic usage on the human gut microbiome

The widespread usage of antimicrobials has driven the evolution of resistance in pathogenic microbes, both increased prevalence of antimicrobial resistance genes (ARGs) and their spread across species by horizontal gene transfer (HGT). However, the impact on the wider community of commensal microbes associated with the human body, the microbiome, is less well understood. Small-scale studies have determined the transient impacts of antibiotic consumption but we conduct an extensive survey of ARGs in 8972 metagenomes to determine the population-level impacts. Focusing on 3096 gut microbiomes from healthy individuals not taking antibiotics we demonstrate highly significant correlations between both the total ARG abundance and diversity and per capita antibiotic usage rates across ten countries spanning three continents. Samples from China were notable outliers. We use a collection of 154,723 human-associated metagenome assembled genomes (MAGs) to link these ARGs to taxa and detect HGT. This reveals that the correlations in ARG abundance are driven by multi-species mobile ARGs shared between pathogens and commensals, within a highly connected central component of the network of MAGs and ARGs. We also observe that individual human gut ARG profiles cluster into two types or resistotypes. The less frequent resistotype has higher overall ARG abundance, is associated with certain classes of resistance, and is linked to species-specific genes in the Proteobacteria on the periphery of the ARG network.

Metagenomic Analysis of the Abundance and Composition of Antibiotic Resistance Genes in Hospital Wastewater in Benin, Burkina Faso, and Finland

Antibiotic resistance is a global threat to human health, with the most severe effect in low- and middle-income countries. We explored the presence of antibiotic resistance genes (ARGs) in the hospital wastewater (HWW) of nine hospitals in Benin and Burkina Faso, two low-income countries in West Africa, with shotgun metagenomic sequencing. For comparison, we also studied six hospitals in Finland. The highest sum of the relative abundance of ARGs in the 68 HWW samples was detected in Benin and the lowest in Finland. HWW resistomes and mobilomes in Benin and Burkina Faso resembled each other more than those in Finland. Many carbapenemase genes were detected at various abundances, especially in HWW from Burkina Faso and Finland. The bla_GES genes, the most widespread carbapenemase gene in the Beninese HWW, were also found in water intended for hand washing and in a puddle at a hospital yard in Benin. mcr genes were detected in the HWW of all three countries, with mcr-5 being the most common mcr gene. These and other mcr genes were observed in very high relative abundances, even in treated wastewater in Burkina Faso and a street gutter in Benin. The results highlight the importance of wastewater treatment, with particular attention to HWW. IMPORTANCE The global emergence and increased spread of antibiotic resistance threaten the effectiveness of antibiotics and, thus, the health of the entire population. Therefore, understanding the resistomes in different geographical locations is crucial in the global fight against the antibiotic resistance crisis. However, this information is scarce in many low- and middle-income countries (LMICs), such as those in West Africa. In this study, we describe the resistomes of hospital wastewater in Benin and Burkina Faso and, as a comparison, Finland. Our results help to understand the hitherto unrevealed resistance in Beninese and Burkinabe hospitals. Furthermore, the results emphasize the importance of wastewater management infrastructure design to minimize exposure events between humans, HWW, and the environment, preventing the circulation of resistant bacteria and ARGs between humans (hospitals and community) and the environment.

Systematic In Silico Assessment of Antimicrobial Resistance Dissemination across the Global Plasmidome

The emergence of pathogenic strains resistant to multiple antimicrobials is a pressing problem in modern healthcare. Antimicrobial resistance is mediated primarily by dissemination of resistance determinants via horizontal gene transfer. The dissemination of some resistance genes has been well documented, but few studies have analyzed the patterns underpinning the dissemination of antimicrobial resistance genes. Analyzing the %GC content of plasmid-borne antimicrobial resistance genes relative to their host genome %GC content provides a means to efficiently detect and quantify dissemination of antimicrobial resistance genes. In this work we automate %GC content analysis to perform a comprehensive analysis of known antimicrobial resistance genes in publicly available plasmid sequences. We find that the degree to which antimicrobial resistance genes are disseminated depends primarily on the resistance mechanism. Our analysis identifies conjugative plasmids as primary dissemination vectors and indicates that most broadly disseminated genes have spread from single genomic backgrounds. We show that resistance dissemination profiles vary greatly among antimicrobials, oftentimes reflecting stewardship measures. Our findings establish %GC content analysis as a powerful, intuitive and scalable method to monitor the dissemination of resistance determinants using publicly available sequence data.

Antibiotic resistance genes are differentially mobilized according to resistance mechanism

BACKGROUND

Screening for antibiotic resistance genes (ARGs) in especially environmental samples with (meta)genomic sequencing is associated with false-positive predictions of phenotypic resistance. This stems from the fact that most acquired ARGs require being overexpressed before conferring resistance, which is often caused by decontextualization of putative ARGs by mobile genetic elements (MGEs). Consequent overexpression of ARGs can be caused by strong promoters often present in insertion sequence (IS) elements and integrons and the copy number effect of plasmids, which may contribute to high expression of accessory genes.

RESULTS

Here, we screen all complete bacterial RefSeq genomes for ARGs. The genetic contexts of detected ARGs are investigated for IS elements, integrons, plasmids, and phylogenetic dispersion. The ARG-MOB scale is proposed, which indicates how mobilized detected ARGs are in bacterial genomes. It is concluded that antibiotic efflux genes are rarely mobilized and even 80% of β-lactamases have never, or very rarely, been mobilized in the 15,790 studied genomes. However, some ARGs are indeed mobilized and co-occur with IS elements, plasmids, and integrons.

CONCLUSIONS

In this study, ARGs in all complete bacterial genomes are classified by their association with MGEs, using the proposed ARG-MOB scale. These results have consequences for the design and interpretation of studies screening for resistance determinants, as mobilized ARGs pose a more concrete risk to human health. An interactive table of all results is provided for future studies targeting highly mobilized ARGs.