| 1. |
- Inda Diaz, Juan Salvador, 1984, et al.
(författare)
-
Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes
- 2023
-
Ingår i: Microbiome. - : Springer Science and Business Media LLC. - 2049-2618. ; 11:1, s. 44-
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Bacterial communities in humans, animals, and the external environment maintain a large collection of antibiotic resistance genes (ARGs). However, few of these ARGs are well-characterized and thus established in existing resistance gene databases. In contrast, the remaining latent ARGs are typically unknown and overlooked in most sequencing-based studies. Our view of the resistome and its diversity is therefore incomplete, which hampers our ability to assess risk for promotion and spread of yet undiscovered resistance determinants. RESULTS: A reference database consisting of both established and latent ARGs (ARGs not present in current resistance gene repositories) was created. By analyzing more than 10,000 metagenomic samples, we showed that latent ARGs were more abundant and diverse than established ARGs in all studied environments, including the human- and animal-associated microbiomes. The pan-resistomes, i.e., all ARGs present in an environment, were heavily dominated by latent ARGs. In comparison, the core-resistome, i.e., ARGs that were commonly encountered, comprised both latent and established ARGs. We identified several latent ARGs shared between environments and/or present in human pathogens. Context analysis of these genes showed that they were located on mobile genetic elements, including conjugative elements. We, furthermore, identified that wastewater microbiomes had a surprisingly large pan- and core-resistome, which makes it a potentially high-risk environment for the mobilization and promotion of latent ARGs. CONCLUSIONS: Our results show that latent ARGs are ubiquitously present in all environments and constitute a diverse reservoir from which new resistance determinants can be recruited to pathogens. Several latent ARGs already had high mobile potential and were present in human pathogens, suggesting that they may constitute emerging threats to human health. We conclude that the full resistome-including both latent and established ARGs-needs to be considered to properly assess the risks associated with antibiotic selection pressures. Video Abstract.
|
|
| 2. |
- Lund, David, 1994
(författare)
-
Computational discovery of antibiotic resistance genes and their horizontal transfer
- 2022
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Antibiotic resistance is increasing among clinical infections and represents one of the most serious threats to public health. Pathogens often become resistant by acquiring mobile antibiotic resistance genes (ARGs) via horizontal gene transfer (HGT). To limit the spread of new ARGs, it is important that we identify emerging threats early, and that we improve our understanding of what drives the HGT of ARGs. The three papers encompassing this thesis aim to increase our knowledge about ARGs and their mobility. In paper I, computational screening of large genomic datasets was used to identify new resistance genes for macrolide antibiotics, and to clarify their evolution. A large diversity of new erm and mph genes was identified, including six new families of mobile ARGs carried by pathogens, that showed varied phylogenetic origins. Of the tested genes, 70% induced resistance in Escherichia coli . In paper II, we identified previously undiscovered mobile genes giving resistance to aminoglycoside antibiotics in pathogens, further demonstrating how computational methods can discover potential emerging ARGs. Close to one million bacterial genomes were screened for aac and aph genes, and the mobility of each predicted gene was evaluated. A total of 50 families of new mobile ARGs were identified in pathogens. When new ARGs were tested in E. coli . 86% were functional, with 39% giving clinical resistance. In paper III, the factors influencing the HGT of ARGs were investigated. Phylogenetic analysis was used to identify HGT events from a large set of ARGs. For each event, the genetic compatibility of the involved gene(s) and genomes, as well as the co-occurrence of donor and recipient in different environments, were computed and used as input to train random forest classifiers. The resulting models suggested that the most important factor for determining if a mobile ARG successfully undergoes horizontal transfer is the genetic compatibility between the gene and the recipient genome. The findings presented in this thesis increase our knowledge about new genes giving resistance to two important classes of antibiotics. Furthermore, the results provide new insights into the horizontal transfer of resistance genes.
|
|
| 3. |
- Lund, David, 1994, et al.
(författare)
-
Extensive screening reveals previously undiscovered aminoglycoside resistance genes in human pathogens
- 2023
-
Ingår i: Communications Biology. - 2399-3642. ; 6:1
-
Tidskriftsartikel (refereegranskat)abstract
- Antibiotic resistance is a growing threat to human health, caused in part by pathogens accumulating antibiotic resistance genes (ARGs) through horizontal gene transfer. New ARGs are typically not recognized until they have become widely disseminated, which limits our ability to reduce their spread. In this study, we use large-scale computational screening of bacterial genomes to identify previously undiscovered mobile ARGs in pathogens. From ~1 million genomes, we predict 1,071,815 genes encoding 34,053 unique aminoglycoside-modifying enzymes (AMEs). These cluster into 7,612 families (<70% amino acid identity) of which 88 are previously described. Fifty new AME families are associated with mobile genetic elements and pathogenic hosts. From these, 24 of 28 experimentally tested AMEs confer resistance to aminoglycoside(s) in Escherichia coli, with 17 providing resistance above clinical breakpoints. This study greatly expands the range of clinically relevant aminoglycoside resistance determinants and demonstrates that computational methods enable early discovery of potentially emerging ARGs.
|
|
| 4. |
- Lund, David, 1994, et al.
(författare)
-
Large-scale characterization of the macrolide resistome reveals high diversity and several new pathogen-associated genes
- 2022
-
Ingår i: Microbial Genomics. - : Microbiology Society. - 2057-5858. ; 8:1
-
Tidskriftsartikel (refereegranskat)abstract
- Macrolides are broad-spectrum antibiotics used to treat a range of infections. Resistance to macrolides is often conferred by mobile resistance genes encoding Erm methyltransferases or Mph phosphotransferases. New erm and mph genes keep being discovered in clinical settings but their origins remain unknown, as is the type of macrolide resistance genes that will appear in the future. In this study, we used optimized hidden Markov models to characterize the macrolide resistome. Over 16 terabases of genomic and metagenomic data, representing a large taxonomic diversity (11 030 species) and diverse environments (1944 metagenomic samples), were searched for the presence of erm and mph genes. From this data, we predicted 28 340 macrolide resistance genes encoding 2892 unique protein sequences, which were clustered into 663 gene families (<70 % amino acid identity), of which 619 (94 %) were previously uncharacterized. This included six new resistance gene families, which were located on mobile genetic elements in pathogens. The function of ten predicted new resistance genes were experimentally validated in Escherichia coli using a growth assay. Among the ten tested genes, seven conferred increased resistance to erythromycin, with five genes additionally conferring increased resistance to azithromycin, showing that our models can be used to predict new functional resistance genes. Our analysis also showed that macrolide resistance genes have diverse origins and have transferred horizontally over large phylogenetic distances into human pathogens. This study expands the known macrolide resistome more than ten-fold, provides insights into its evolution, and demonstrates how computational screening can identify new resistance genes before they become a significant clinical problem.
|
|
