Biology subjects

Paganini, J. A.

Publications and source records attributed to Paganini, J. A..

5 recordsLinked to original sources

bioRxiv2026

Exploring differences across pangenome-graph representations using Escherichia coli O157:H7 as a model

Pangenome graphs are increasingly used to represent population-scale bacterial diversity, yet construction methods span fundamentally different representation paradigms whose outputs and sensitivities to assembly quality remain poorly quantified. We systematically reviewed microbial pangenome graph tools and benchmarked six representative methods spanning gene-cluster, compacted coloured de Bruijn graph (ccDBG), multiple sequence alignment, and hybrid approaches. Using a repeat-rich Escherichia coli O157:H7 dataset with complete genomes and matched short-read data, we constructed graphs from identical inputs and observed orders-of-magnitude differences in graph size and fragmentation, indicating that global topology is driven by representation strategy. Varying completeness composition revealed that assembly fragmentation is a first-order determinant of graph structure: gene-cluster graphs contracted as draft assemblies replaced complete genomes, whereas unitig graphs expanded, with distinct degree-prevalence fingerprints across tools. Computational cost mirrored these shifts and depended strongly on completeness composition, including a pronounced runtime penalty for one ccDBG implementation on all-draft inputs. Finally, analysis of Shiga toxin loci showed that pangenome-level reconciliation does not reliably correct assembly artefacts at challenging multi-copy genes and that performance varies by locus. Together, these findings show that pangenome graphs are representation-dependent models of bacterial diversity, and that assembly completeness is a primary determinant of their topology, scalability, and locus-level accuracy. Authors SummaryBacterial populations are often described using "pangenome graphs," which aim to capture all genetic variation across many genomes in a single structure. However, different tools build these graphs in fundamentally different ways, and little is known about how those differences affect the results. In this study, we systematically compared several widely used approaches using a clinically important strain of Escherichia coli that is rich in repeated and mobile DNA. We found that the size, connectivity, and overall structure of the resulting graphs varied dramatically depending on the method used. Importantly, we also show that incomplete genome assemblies (common in large sequencing studies) strongly alter graph structure, and that different tools respond to incomplete data in different ways. In some cases, this affects the detection of medically relevant genes, including Shiga toxin genes linked to severe disease. Our results demonstrate that pangenome graphs are not interchangeable representations of bacterial diversity. Instead, their structure depends on both the method and the quality of the input data. We argue that researchers should choose graph-building tools carefully and report structural properties explicitly to ensure reproducible and interpretable results.

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bioRxiv2025

Impact of selective digestive decontamination on the pangenome composition of ESBL-E. coli

In Dutch ICUs patients receive selective digestive decontamination (SDD) as a prophylactic antimicrobial treatment to prevent colonisation with potentially pathogenic microorganisms and subsequent infections, with a beneficial effect on 28-day mortality. In the R-GNOSIS ICU study, conducted outside of The Netherlands, SDD consisted of a mix of an oropharyngeal paste and a gastric suspension containing colistin, tobramycin and nystatin. These topical antimicrobial agents aim to target aerobic Gram-negative bacteria, S. aureus and yeast. SDD improves patient outcome, but its effects on the resistome and pangenome of potentially pathongenic microorganisms have not been extensively studied. In this work, we compared 129 genomes of E. coli isolates from patients that received SDD and patients that did not receive SDD, but standard care only (baseline patients) in five ICUs located across three European countries (R-GNOSIS ICU study). We found that the overall pangenome compositions of E. coli recovered from both patient groups were highly similar. Variations in the accessory genome were strongly associated with the phylogeny of isolates but not with the use of SDD. Similarly, the plasmidome variations were not explained by treatment, but rather by the interaction between ICU location and phylogroup. One antibiotic resistance gene, which confers resistance to tobramycin, was found to be more prevalent in SDD patients. This gene was flanked by IS26 elements and significantly co-occurred with blaCTX-M-15 in various plasmid backbones and chromosomal contexts. Notably, no mcr genes coding for colistin resistance were detected.

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bioRxiv2024

gplasCC: classification and reconstruction of plasmids from short-read sequencing data for any bacterial species

Plasmids play a pivotal role in the spread of antibiotic resistance genes. Accurately reconstructing plasmids often requires long-read sequencing, but bacterial genomic data in publicly accessible repositories has historically been derived from short-read sequencing technology. We recently presented an approach for reconstructing Escherichia coli antimicrobial resistance plasmids using Illumina short reads. This method consisted of combining a robust binary classification tool named plasmidEC with gplas2, which is a tool that makes use of features of the assembly graph to bin predicted plasmid contigs into individual plasmids. Here, we developed gplasCC, a plasmidEC-simplification, capable of classifying plasmid contigs using Centrifuge databases. We have developed seven plasmidCC databases in addition to the database for E. coli: six species-specific models (Acinetobacter baumannii, Enterococcus faecium, Enterococcus faecalis, Klebsiella pneumoniae, Staphylococcus aureus and Salmonella enterica) and one species-independent model for less frequently studied bacterial species. We combined these models with gplas2 (now, gplasCC) to reconstruct plasmids from more than 100 bacterial species. This approach allows comprehensive analysis of the wealth of bacterial short-read sequencing data available in public repositories and advance our understanding of microbial plasmids.

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bioRxiv2023

PlasmidEC and gplas2: An optimised short-read approach to predict and reconstruct antibiotic resistance plasmids in Escherichia coli

Accurate reconstruction of Escherichia coli antibiotic resistance gene (ARG) plasmids from Illumina sequencing data has proven to be a challenge with current bioinformatic tools. In this work, we present an improved method to reconstruct E. coli plasmids using short reads. We developed plasmidEC, an ensemble classifier that identifies plasmid-derived contigs by combining the output of three different binary classification tools. We showed that plasmidEC is especially suited to classify contigs derived from ARG plasmids with a high recall of 0.941. Additionally, we optimised gplas, a graph-based tool that bins plasmid-predicted contigs into distinct plasmid predictions. Gplas2 is more effective at recovering plasmids with large sequencing coverage variations and can be combined with the output of any binary classifier. The combination of plasmidEC with gplas2 showed a high completeness (median=0.818) and F1-score (median=0.812) when reconstructing ARG plasmids and exceeded the binning capacity of the reference-based method MOB-suite. In the absence of long read data, our method offers an excellent alternative to reconstruct ARG plasmids in E. coli. Data SummaryNo new sequencing data have been generated in this study. All genomes used in this research are publicly available at the GenBank and Sequence Read Archive of the National Center for Biotechnology Information. Accession numbers are specified in Supplementary Materials. Scripts to reproduce the results reported in this manuscript can be accessed at https://gitlab.com/jpaganini/ecoli-binary-classifier. The ensemble classifier, plasmidEC, is publicly available at https://gitlab.com/mmb-umcu/plasmidEC (release 1.3.1), and gplas2 (release 1.0.0) can be found at https://gitlab.com/mmb-umcu/gplas2. Impact StatementEscherichia coli has emerged as a highly pervasive multidrug resistant pathogen on a global scale. The dissemination of resistance is significantly influenced by plasmids, mobile genetic elements that facilitate the transfer of antimicrobial resistance genes within and between diverse bacterial species. Consequently, precise and high-throughput identification of plasmids is imperative for effective genomic surveillance of resistance. However, accurate plasmid reconstruction remains challenging with the use of affordable short-read sequencing data. In this work, we present a novel method to accurately predict and reconstruct E. coli plasmids based on Illumina data. Additionally, we demonstrate that our approach outperforms the reference-based method MOB-suite, especially when reconstructing plasmids carrying antimicrobial resistance genes.

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bioRxiv2021

Recovering Escherichia coli plasmids in the absence of long-read sequencing data

The incidence of infections caused by multidrug-resistant E. coli strains has risen in the past years. Antibiotic resistance in E. coli is often mediated by acquisition and maintenance of plasmids. The study of E. coli plasmid epidemiology and genomics often requires long-read sequencing information, but recently a number of tools that allow plasmid prediction from short-read data have been developed. Here, we reviewed 25 available plasmid prediction tools and categorized them into binary plasmid/chromosome classification tools and plasmid reconstruction tools. We benchmarked six tools that aim to reliably reconstruct distinct plasmids, with a special focus on plasmids carrying antibiotic resistance genes (ARGs) such as extended-spectrum beta-lactamase genes. They use either assembly graph information (plasmidSPAdes, gplas), reference databases (MOB-Suite, FishingForPlasmids) or both (HyAsP and SCAPP) to produce plasmid predictions. The benchmark data set consisted of 240 E. coli strains, harboring 631 plasmids, which were representative for the diversity of E. coli in public databases. Notably, these strains were not used for training any of the tools. We found that two thirds (n=425, 66.3.%) of all plasmids were correctly reconstructed by at least one of the six tools, with a range of 92 (14.58%) to 317 (50.23%) correctly predicted plasmids. However, the majority of plasmids that carried antibiotic resistance genes (n=85, 57.8%) could not be completely recovered as distinct plasmids by any of the tools. MOB-suite was the only tool that was able to correctly reconstruct the majority of plasmids (n=317, 50.23%), and performed best at reconstructing large plasmids (n=166, 46.37%) and ARG-plasmids (n=41, 27.9%), but predictions frequently contained chromosome contamination (40%). In contrast, plasmidSPAdes reconstructed the highest fraction of plasmids smaller than 18 kbp (n=168, 61.54%). Large ARG-plasmids, however, were recovered with small precision values (median=0.47, IQR=0.61), indicating that plasmidSPAdes frequently merged sequences derived from distinct replicons. Additionally, only 63% of all plasmid-borne ARGs were correctly predicted by plasmidSPAdes. The remaining four tools (FishingForPlasmids, HyAsP, SCAPP and gplas) were able to correctly reconstruct a combined total of 18 plasmids that were missed by MOB-suite and plasmidSPAdes. Available bioinformatic tools can provide valuable insight into E. coli plasmids, but also have important limitations. This work will serve as a guideline for selecting the most appropriate plasmid reconstruction tool for studies focusing on E. coli plasmids in the absence of long-read sequencing data.

bioinformatics↗