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Frontiers in Cellular and Infection... 2023is a multidrug-resistant (MDR) opportunistic pathogen with high resistance to most clinically used antimicrobials. The dissemination of MDR and difficult treatment of...
BACKGROUND
is a multidrug-resistant (MDR) opportunistic pathogen with high resistance to most clinically used antimicrobials. The dissemination of MDR and difficult treatment of its infection in clinical settings are global issues.
METHODS
To provide more genetic information on and find a better treatment strategy, we isolated five , SMYN41-SMYN45, from a Chinese community that were subjected to antibiotic susceptibility testing, biofilm formation assay, and whole-genome sequencing. Whole-genome sequences were compared with other thirty-seven sequences.
RESULTS
The five strains had similar antibiotic resistance profiles and were resistant to β-lactams, aminoglycosides, and macrolides. They showed similar antimicrobial resistance (AMR) genes, including various efflux pumps, β-lactamase resistance genes (), aminoglycoside resistance genes [, ], and macrolide-resistant gene (). Genome sequencing analysis revealed that SMYN41-SMYN45 belonged to sequence type 925 (ST925), ST926, ST926, ST31, and ST928, respectively, and three new STs were identified (ST925, ST926, and ST928).
CONCLUSION
This study provides genetic information by comparing genome sequences of several isolates from a community of various origins, with the aim of optimizing empirical antibiotic medication and contributing to worldwide efforts to tackle antibiotic resistance.
Topics: Humans; Stenotrophomonas maltophilia; Anti-Bacterial Agents; Anti-Infective Agents; Drug Resistance, Microbial; Genomics; Gram-Negative Bacterial Infections; Microbial Sensitivity Tests
PubMed: 38089814
DOI: 10.3389/fcimb.2023.1266295 -
The ISME Journal Dec 2023Understanding how antibiotic resistance emerges and evolves in natural habitats is critical for predicting and mitigating antibiotic resistance in the context of global...
Understanding how antibiotic resistance emerges and evolves in natural habitats is critical for predicting and mitigating antibiotic resistance in the context of global change. Bacteria have evolved antibiotic production as a strategy to fight competitors, predators and other stressors, but how predation pressure of their most important consumers (i.e., protists) affects soil antibiotic resistance genes (ARGs) profiles is still poorly understood. To address this gap, we investigated responses of soil resistome to varying levels of protistan predation by inoculating low, medium and high concentrations of indigenous soil protist suspensions in soil microcosms. We found that an increase in protistan predation pressure was strongly associated with higher abundance and diversity of soil ARGs. High protist concentrations significantly enhanced the abundances of ARGs encoding multidrug (oprJ and ttgB genes) and tetracycline (tetV) efflux pump by 608%, 724% and 3052%, respectively. Additionally, we observed an increase in the abundance of numerous bacterial genera under high protistan pressure. Our findings provide empirical evidence that protistan predation significantly promotes antibiotic resistance in soil bacterial communities and advances our understanding of the biological driving forces behind the evolution and development of environmental antibiotic resistance.
Topics: Animals; Soil; Genes, Bacterial; Predatory Behavior; Soil Microbiology; Bacteria; Anti-Bacterial Agents; Drug Resistance, Microbial; Eukaryota
PubMed: 37794244
DOI: 10.1038/s41396-023-01524-8 -
The ISME Journal Sep 2023Some bacterial resistance mechanisms degrade antibiotics, potentially protecting neighbouring susceptible cells from antibiotic exposure. We do not yet understand how...
Some bacterial resistance mechanisms degrade antibiotics, potentially protecting neighbouring susceptible cells from antibiotic exposure. We do not yet understand how such effects influence bacterial communities of more than two species, which are typical in nature. Here, we used experimental multispecies communities to test the effects of clinically important pOXA-48-plasmid-encoded resistance on community-level responses to antibiotics. We found that resistance in one community member reduced antibiotic inhibition of other species, but some benefitted more than others. Further experiments with supernatants and pure-culture growth assays showed the susceptible species profiting most from detoxification were those that grew best at degraded antibiotic concentrations (greater than zero, but lower than the starting concentration). This pattern was also observed on agar surfaces, and the same species also showed relatively high survival compared to most other species during the initial high-antibiotic phase. By contrast, we found no evidence of a role for higher-order interactions or horizontal plasmid transfer in community-level responses to detoxification in our experimental communities. Our findings suggest carriage of an antibiotic-degrading resistance mechanism by one species can drastically alter community-level responses to antibiotics, and the identities of the species that profit most from antibiotic detoxification are predicted by their intrinsic ability to survive and grow at changing antibiotic concentrations.
Topics: Humans; Anti-Bacterial Agents; Bacterial Infections; Drug Resistance, Microbial; Bacteria; Plasmids; Drug Resistance, Bacterial
PubMed: 37380830
DOI: 10.1038/s41396-023-01465-2 -
Current Opinion in Microbiology Dec 2023Scientists now have access to millions of accurate three-dimensional (3D) models of protein structures. How do we leverage 3D structural models to learn about microbial... (Review)
Review
Scientists now have access to millions of accurate three-dimensional (3D) models of protein structures. How do we leverage 3D structural models to learn about microbial functions encoded in metagenomes? Here, we review recent developments using protein structural features to mine metagenomes from diverse environments ranging from the human gut to soil and ocean viromes. We compare 3D protein structural methods to characterize antibiotic resistance phenotypes, nutrient cycling, and host-drug-microbe interactions. Broadly, we encourage the scientific community to look beyond global sequence and structure alignments by considering fine-grained descriptors such as distance to ligand, active site, and tertiary interactions between amino acid residues scaling to microbiomes. Finally, we highlight structure-inspired approaches to chart new areas of microbial protein-coding sequence space.
Topics: Humans; Metagenome; Microbiota; Soil Microbiology; Phenotype; Drug Resistance, Microbial
PubMed: 37741262
DOI: 10.1016/j.mib.2023.102382 -
Journal of Hazardous Materials Aug 2023The presence of ARGs (antibiotic resistance genes) in the aquatic environment is a serious threat to public health especially in environmental biofilms as natural...
Seasonal and spatial variations of antibiotic resistance genes and bacterial biodiversity in biofilms covering the equipment at successive stages of drinking water purification.
The presence of ARGs (antibiotic resistance genes) in the aquatic environment is a serious threat to public health especially in environmental biofilms as natural reservoirs of ARGs in water treatment plants (WTP). It has been shown that the treatment technology and source of water have a significant impact on the abundance and type of genes determining antibiotic resistance. The following indicator genes were proposed that should absolutely be controlled in environmental biofilms: intl1, sul2, sul1, tetA, blaOXA, and blaTEM. In both studied WTPs, the highest number of copies was determined for the intI1 gene. Among the tested ARGs, the highest values were obtained for genes sul1 and tetA. The qPCR analysis also revealed that the amounts of determined ARGs decreased in the following order: sulphonamides>carbapenems >tetracyclines > β-lactams >macrolides. The dominant bacterial types in all analysed samples were Proteobacteria and Bacteroidetes. Both ARGs and bacterial biodiversity was determined rather by sampling site (spatial variation) than seasonality. The obtained results show that biofilms are reservoirs of ARGs. This may affect the microbiological quality of water entering the water system. It is therefore necessary to include their analysis in the classical studies of water quality.
Topics: Anti-Bacterial Agents; Genes, Bacterial; Seasons; Bacteria; Drug Resistance, Microbial; Water Purification; Drinking Water
PubMed: 37210784
DOI: 10.1016/j.jhazmat.2023.131660 -
Microbial Biotechnology Oct 2023As recognized by several international agencies, antibiotic resistance is nowadays one of the most relevant problems for human health. While this problem was alleviated... (Review)
Review
As recognized by several international agencies, antibiotic resistance is nowadays one of the most relevant problems for human health. While this problem was alleviated with the introduction of new antibiotics into the market in the golden age of antimicrobial discovery, nowadays few antibiotics are in the pipeline. Under these circumstances, a deep understanding on the mechanisms of emergence, evolution and transmission of antibiotic resistance, as well as on the consequences for the bacterial physiology of acquiring resistance is needed to implement novel strategies, beyond the development of new antibiotics or the restriction in the use of current ones, to more efficiently treat infections. There are still several aspects in the field of antibiotic resistance that are not fully understood. In the current article, we make a non-exhaustive critical review of some of them that we consider of special relevance, in the aim of presenting a snapshot of the studies that still need to be done to tackle antibiotic resistance.
Topics: Humans; Drug Resistance, Bacterial; Anti-Bacterial Agents; Anti-Infective Agents
PubMed: 37417823
DOI: 10.1111/1751-7915.14310 -
Bioinformatics (Oxford, England) Nov 2023Antibiotic resistance presents a formidable global challenge to public health and the environment. While considerable endeavors have been dedicated to identify...
MOTIVATION
Antibiotic resistance presents a formidable global challenge to public health and the environment. While considerable endeavors have been dedicated to identify antibiotic resistance genes (ARGs) for assessing the threat of antibiotic resistance, recent extensive investigations using metagenomic and metatranscriptomic approaches have unveiled a noteworthy concern. A significant fraction of proteins defies annotation through conventional sequence similarity-based methods, an issue that extends to ARGs, potentially leading to their under-recognition due to dissimilarities at the sequence level.
RESULTS
Herein, we proposed an Artificial Intelligence-powered ARG identification framework using a pretrained large protein language model, enabling ARG identification and resistance category classification simultaneously. The proposed PLM-ARG was developed based on the most comprehensive ARG and related resistance category information (>28K ARGs and associated 29 resistance categories), yielding Matthew's correlation coefficients (MCCs) of 0.983 ± 0.001 by using a 5-fold cross-validation strategy. Furthermore, the PLM-ARG model was verified using an independent validation set and achieved an MCC of 0.838, outperforming other publicly available ARG prediction tools with an improvement range of 51.8%-107.9%. Moreover, the utility of the proposed PLM-ARG model was demonstrated by annotating resistance in the UniProt database and evaluating the impact of ARGs on the Earth's environmental microbiota.
AVAILABILITY AND IMPLEMENTATION
PLM-ARG is available for academic purposes at https://github.com/Junwu302/PLM-ARG, and a user-friendly webserver (http://www.unimd.org/PLM-ARG) is also provided.
Topics: Anti-Bacterial Agents; Artificial Intelligence; Drug Resistance, Microbial; Genes, Bacterial; Metagenome
PubMed: 37995287
DOI: 10.1093/bioinformatics/btad690 -
Frontiers in Cellular and Infection... 2023Antibiotic resistance represents one of the greatest threats to global health. The spread of antibiotic resistance genes among bacteria occurs mostly through horizontal...
Antibiotic resistance represents one of the greatest threats to global health. The spread of antibiotic resistance genes among bacteria occurs mostly through horizontal gene transfer via conjugation mediated by plasmids. This process implies a direct contact between a donor and a recipient bacterium which acquires the antibiotic resistance genes encoded by the plasmid and, concomitantly, the capacity to transfer the acquired plasmid to a new recipient. Classical assays for the measurement of plasmid transfer frequency (i.e., conjugation frequency) are often characterized by a high variability and, hence, they require many biological and technical replicates to reduce such variability and the accompanying uncertainty. In addition, classical conjugation assays are commonly tedious and time-consuming because they typically involve counting colonies on a large number of plates for the quantification of donors, recipients, and transconjugants (i.e., the bacteria that have received the genetic material by conjugation). Due to the magnitude of the antibiotic resistance problem, it is critical to develop reliable and rapid methods for the quantification of plasmid transfer frequency that allow the simultaneous analysis of many samples. Here, we present the development of a high-throughput, reliable, quick, easy, and cost-effective method to simultaneously accomplish and measure multiple conjugation events in 96-well plates, in which the quantification of donors, recipients, and transconjugants is estimated from the time required to reach a specific threshold value (OD value) in the bacterial growth curves. Our method successfully discriminates different plasmid transfer frequencies, yielding results that are equivalent to those obtained by a classical conjugation assay.
Topics: Plasmids; Anti-Bacterial Agents; Drug Resistance, Microbial; Conjugation, Genetic; Gene Transfer, Horizontal
PubMed: 37886666
DOI: 10.3389/fcimb.2023.1269732 -
Nature Food Aug 2023China is the largest global consumer of antimicrobials and improving surveillance methods could help to reduce antimicrobial resistance (AMR) spread. Here we report the...
China is the largest global consumer of antimicrobials and improving surveillance methods could help to reduce antimicrobial resistance (AMR) spread. Here we report the surveillance of ten large-scale chicken farms and four connected abattoirs in three Chinese provinces over 2.5 years. Using a data mining approach based on machine learning, we analysed 461 microbiomes from birds, carcasses and environments, identifying 145 potentially mobile antibiotic resistance genes (ARGs) shared between chickens and environments across all farms. A core set of 233 ARGs and 186 microbial species extracted from the chicken gut microbiome correlated with the AMR profiles of Escherichia coli colonizing the same gut, including Arcobacter, Acinetobacter and Sphingobacterium, clinically relevant for humans, and 38 clinically relevant ARGs. Temperature and humidity in the barns were also correlated with ARG presence. We reveal an intricate network of correlations between environments, microbial communities and AMR, suggesting multiple routes to improving AMR surveillance in livestock production.
Topics: Animals; Humans; Anti-Bacterial Agents; Chickens; Drug Resistance, Bacterial; Farms; Metagenomics; Abattoirs; Escherichia coli; Machine Learning
PubMed: 37563495
DOI: 10.1038/s43016-023-00814-w -
PloS One 2023This study aimed to investigate the antimicrobial resistance (AMR), antibiotic resistance genes (ARGs) and integrons in 157 Escherichia coli (E. coli) strains isolated...
This study aimed to investigate the antimicrobial resistance (AMR), antibiotic resistance genes (ARGs) and integrons in 157 Escherichia coli (E. coli) strains isolated from feces of captive musk deer from 2 farms (Dujiang Yan and Barkam) in Sichuan province. Result showed that 91.72% (144/157) strains were resistant to at least one antimicrobial and 24.20% (38/157) strains were multi-drug resistant (MDR). The antibiotics that most E. coli strains were resistant to was sulfamethoxazole (85.99%), followed by ampicillin (26.11%) and tetracycline (24.84%). We further detected 13 ARGs in the 157 E. coli strains, of which blaTEM had the highest occurrence (91.72%), followed by aac(3')-Iid (60.51%) and blaCTX-M (16.56%). Doxycycline, chloramphenicol, and ceftriaxone resistance were strongly correlated with the presence of tetB, floR and blaCTX-M, respectively. The strongest positive association among AMR phenotypes was ampicillin/cefuroxime sodium (OR, 828.000). The strongest positive association among 16 pairs of ARGs was sul1/floR (OR, 21.667). Nine pairs positive associations were observed between AMR phenotypes and corresponding resistance genes and the strongest association was observed for CHL/floR (OR, 301.167). Investigation of integrons revealed intl1 and intl2 genes were detected in 10.19% (16/157) and 1.27% (2/157) E. coli strains, respectively. Only one type of gene cassettes (drA17-aadA5) was detected in class 1 integron positive strains. Our data implied musk deer is a reservoir of ARGs and positive associations were common observed among E. coli strains carrying AMRs and ARGs.
Topics: Animals; Anti-Bacterial Agents; Escherichia coli; Escherichia coli Infections; Drug Resistance, Bacterial; Deer; Ampicillin; China; Anti-Infective Agents; Ruminants; Integrons; Microbial Sensitivity Tests
PubMed: 38011149
DOI: 10.1371/journal.pone.0289028