PlasmidHostFinder: Prediction of Plasmid Hosts Using Random Forest

Microbial evolution through horizontal gene transfer by mobile genetic elements

Mobile genetic elements (MGEs) are crucial for horizontal gene transfer (HGT) in bacteria and facilitate their rapid evolution and adaptation. MGEs include plasmids, integrative and conjugative elements, transposons, insertion sequences and bacteriophages. Notably, the spread of antimicrobial resistance genes (ARGs), which poses a serious threat to public health, is primarily attributable to HGT through MGEs. This mini-review aims to provide an overview of the mechanisms by which MGEs mediate HGT in microbes. Specifically, the behaviour of conjugative plasmids in different environments and conditions was discussed, and recent methodologies for tracing the dynamics of MGEs were summarised. A comprehensive understanding of the mechanisms underlying HGT and the role of MGEs in bacterial evolution and adaptation is important to develop strategies to combat the spread of ARGs.

Machine Learning Suggests That Small Size Helps Broaden Plasmid Host Range

Plasmids mediate gene exchange across taxonomic barriers through conjugation, shaping bacterial evolution for billions of years. While plasmid mobility can be harnessed for genetic engineering and drug-delivery applications, rapid plasmid-mediated spread of resistance genes has rendered most clinical antibiotics useless. To solve this urgent and growing problem, we must understand how plasmids spread across bacterial communities. Here, we applied machine-learning models to identify features that are important for extending the plasmid host range. We assembled an up-to-date dataset of more than thirty thousand bacterial plasmids, separated them into 1125 clusters, and assigned each cluster a distribution possibility score, taking into account the host distribution of each taxonomic rank and the sampling bias of the existing sequencing data. Using this score and an optimized plasmid feature pool, we built a model stack consisting of DecisionTreeRegressor, EvoTreeRegressor, and LGBMRegressor as base models and LinearRegressor as a meta-learner. Our mathematical modeling revealed that sequence brevity is the most important determinant for plasmid spread, followed by P-loop NTPases, mobility factors, and β-lactamases. Ours and other recent results suggest that small plasmids may broaden their range by evading host defenses and using alternative modes of transfer instead of autonomous conjugation.

Plasmid permissiveness of wastewater microbiomes can be predicted from 16S rRNA sequences by machine learning

MOTIVATION

Wastewater treatment plants (WWTPs) harbor a dense and diverse microbial community. They constantly receive antimicrobial residues and resistant strains, and therefore provide conditions for horizontal gene transfer (HGT) of antimicrobial resistance (AMR) determinants. This facilitates the transmission of clinically important genes between, e.g. enteric and environmental bacteria, and vice versa. Despite the clinical importance, tools for predicting HGT remain underdeveloped.

RESULTS

In this study, we examined to which extent water cycle microbial community composition, as inferred by partial 16S rRNA gene sequences, can predict plasmid permissiveness, i.e. the ability of cells to receive a plasmid through conjugation, based on data from standardized filter mating assays using fluorescent bio-reporter plasmids. We leveraged a range of machine learning models for predicting the permissiveness for each taxon in the community, representing the range of hosts a plasmid is able to transfer to, for three broad host-range resistance IncP plasmids (pKJK5, pB10, and RP4). Our results indicate that the predicted permissiveness from the best performing model (random forest) showed a moderate-to-strong average correlation of 0.49 for pB10 [95% confidence interval (CI): 0.44-0.55], 0.43 for pKJK5 (0.95% CI: 0.41-0.49), and 0.53 for RP4 (0.95% CI: 0.48-0.57) with the experimental permissiveness in the unseen test dataset. Predictive phylogenetic signals occurred despite the broad host-range nature of these plasmids. Our results provide a framework that contributes to the assessment of the risk of AMR pollution in wastewater systems.

AVAILABILITY AND IMPLEMENTATION

The predictive tool is available as an application at https://github.com/DaneshMoradigaravand/PlasmidPerm.

Inference of the Life Cycle of Environmental Phages from Genomic Signature Distances to Their Hosts

The environmental impact of uncultured phages is shaped by their preferred life cycle (lytic or lysogenic). However, our ability to predict it is very limited. We aimed to discriminate between lytic and lysogenic phages by comparing the similarity of their genomic signatures to those of their hosts, reflecting their co-evolution. We tested two approaches: (1) similarities of tetramer relative frequencies, (2) alignment-free comparisons based on exact k = 14 oligonucleotide matches. First, we explored 5126 reference bacterial host strains and 284 associated phages and found an approximate threshold for distinguishing lysogenic and lytic phages using both oligonucleotide-based methods. The analysis of 6482 plasmids revealed the potential for horizontal gene transfer between different host genera and, in some cases, distant bacterial taxa. Subsequently, we experimentally analyzed combinations of 138 Klebsiella pneumoniae strains and their 41 phages and found that the phages with the largest number of interactions with these strains in the laboratory had the shortest genomic distances to K. pneumoniae. We then applied our methods to 24 single-cells from a hot spring biofilm containing 41 uncultured phage-host pairs, and the results were compatible with the lysogenic life cycle of phages detected in this environment. In conclusion, oligonucleotide-based genome analysis methods can be used for predictions of (1) life cycles of environmental phages, (2) phages with the broadest host range in culture collections, and (3) potential horizontal gene transfer by plasmids.

Mobilome-driven segregation of the resistome in biological wastewater treatment

Biological wastewater treatment plants (BWWTP) are considered to be hotspots for the evolution and subsequent spread of antimicrobial resistance (AMR). Mobile genetic elements (MGEs) promote the mobilization and dissemination of antimicrobial resistance genes (ARGs) and are thereby critical mediators of AMR within the BWWTP microbial community. At present, it is unclear whether specific AMR categories are differentially disseminated via bacteriophages (phages) or plasmids. To understand the segregation of AMR in relation to MGEs, we analyzed meta-omic (metagenomic, metatranscriptomic and metaproteomic) data systematically collected over 1.5 years from a BWWTP. Our results showed a core group of 15 AMR categories which were found across all timepoints. Some of these AMR categories were disseminated exclusively (bacitracin) or primarily (aminoglycoside, MLS and sulfonamide) via plasmids or phages (fosfomycin and peptide), whereas others were disseminated equally by both. Combined and timepoint-specific analyses of gene, transcript and protein abundances further demonstrated that aminoglycoside, bacitracin and sulfonamide resistance genes were expressed more by plasmids, in contrast to fosfomycin and peptide AMR expression by phages, thereby validating our genomic findings. In the analyzed communities, the dominant taxon Candidatus Microthrix parvicella was a major contributor to several AMR categories whereby its plasmids primarily mediated aminoglycoside resistance. Importantly, we also found AMR associated with ESKAPEE pathogens within the BWWTP, and here MGEs also contributed differentially to the dissemination of the corresponding ARGs. Collectively our findings pave the way toward understanding the segmentation of AMR within MGEs, thereby shedding new light on resistome populations and their mediators, essential elements that are of immediate relevance to human health.