Biology subjects

Willems, R. J.

Publications and source records attributed to Willems, R. J..

7 recordsLinked to original sources

bioRxiv2020

Genomic rearrangements uncovered by genome-wide co-evolution analysis of a major nosocomial pathogen Enterococcus faecium

Enterococcus faecium is a gut commensal of the gastro-digestive tract, but also known as nosocomial pathogen among hospitalized patients. Population genetics based on whole-genome sequencing has revealed that E. faecium strains from hospitalized patients form a distinct clade, designated as clade A1 and that plasmids are major contributors to the emergence of nosocomial E. faecium. Here we further explored the adaptive evolution of E. faecium using a genome-wide co-evolution study (GWES) to identify co-evolving SNPs. We identified three genomic regions harboring large numbers of SNPs in tight linkage which are not proximal to each other based on the completely assembled chromosome of clade A1 reference hospital isolate AUS0004. Close examination of these regions revealed that they are located at the borders of four different types of large-scale genomic rearrangements, insertion sites of two different genomic islands and an IS30-like transposon. In non-clade A1 isolates, these regions are adjacent to each other and they lack the insertions of the genomic islands and IS30-like transposon. Additionally, among the clade A1 isolates there is one group of pet isolates lacking the genomic rearrangement and insertion of the genomic islands, suggesting a distinct evolutionary trajectory. In silico analysis of the biological functions of the genes encoded in three regions revealed a common link to a stress response. This suggests that these rearrangements may reflect adaptation to the stringent conditions in the hospital environment, such as antibiotics and detergents, to which bacteria are exposed. In conclusion, to our knowledge, this is the first study using GWES to identify genomic rearrangements, suggesting that there is considerable untapped potential to unravel hidden evolutionary signals from population genomic data. Impact statementEnterococcus faecium has emerged as an important nosocomial pathogen around the world. Population genetics revealed that clinical E. faecium strains form a distinct clade, designated as clade A1 and that plasmids are major contributors to the emergence of nosocomial E. faecium. Here, the adaptive evolution of E. faecium was further explored using an unsupervised machine learning method (SuperDCA) to identify genome-wide co-evolving SNPs. We identified three genomic regions harboring large numbers of SNPs in tight linkage which are separated by a large chromosomal distance in a clinical clade A1 reference isolate, but appeared adjacent to each other in non-clade A1 isolates. We identified four different types of large-scale genomic rearrangements and in all cases, we found insertion of two different genomic islands and an insertion element at the border. In contrast, no genomic rearrangement and insertions were identified among a group of clade A1 pet isolates, suggesting a distinct evolutionary trajectory. Based on the in silico predicted biological functions, we found a common link to a stress response for the genes encoded in three regions. This suggests that these rearrangements may reflect adaptation to the stringent conditions in the hospital environment, such as antibiotics and detergents, to which bacteria are exposed. Data summaryRaw core-genome alignment (1.1 MB, Harvest suite v1.1.2), including the 1,644 Clade A isolates and the complete E. faecium AUS0004 (accession number CP003351) as a reference is available under the following gitlab repository https://gitlab.com/sirarredondo/efm_gwes.

genomics↗

bioRxiv2019

gplas: a comprehensive tool for plasmid analysis using short-read graphs

SummaryPlasmids can horizontally transmit genetic traits, enabling rapid bacterial adaptation to new environments and hosts. Short-read whole-genome sequencing data is often applied to large-scale bacterial comparative genomics projects but the reconstruction of plasmids from these data is facing severe limitations, such as the inability to distinguish plasmids from each other in a bacterial genome. We developed gplas, a new approach to reliably separate plasmid contigs into discrete components using sequence composition, coverage, assembly graph information and clustering based on a pruned network of plasmid unitigs. Gplas facilitates the analysis of large numbers of bacterial isolates and allows a detailed analysis of plasmid epidemiology based solely on short read sequence data.\n\nAvailability and implementationGplas is written in R, Bash and uses a Snakemake pipeline as a workflow management system. Gplas is available under the GNU General Public License v3.0 at https://gitlab.com/sirarredondo/gplas.git\n\nContacta.c.schurch@umcutrecht.nl

bioinformatics↗

bioRxiv2019

CRISPR-Cas9 mediated genome editing in vancomycin resistant Enterococcus faecium

The Gram-positive bacterium Enterococcus faecium is becoming increasingly prevalent as a cause of hospital-acquired, antibiotic-resistant infections. There is thus an urgent need to mechanistically characterize the traits that contribute to the emergence of E. faecium as a multidrug resistant opportunistic pathogen. A fundamental part of research into E. faecium biology relies on the ability to generate targeted mutants, but this process is currently labour-intensive and time-consuming, taking 4 to 5 weeks per mutant. In this report we describe a method relying on the high recombination rates of E. faecium and the application of the Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-Cas9 genome editing tool to more efficiently generate targeted mutants in the E. faecium chromosome. Using this tool and the multi-drug resistant clinical E. faecium strain E745, we generated a deletion mutant in the lacL gene, which encodes the large subunit of the E. faecium {beta}-galactosidase. Blue/white screening using 5-bromo-4-chloro-3-indolyl-{beta}-D-galactopyranoside (X-gal) could be used to distinguish between the wild-type and lacL deletion mutant. We also inserted two copies of gfp into the intrinsic E. faecium macrolide resistance gene msrC to generate stable green fluorescent cells. We conclude that CRISPR-Cas9 can be used to generate targeted genome modifications in E. faecium in 3 weeks, with limited hands-on time. This method can potentially be implemented in other Gram-positive bacteria with high intrinsic recombination rates.

microbiology↗

bioRxiv2019

Genomes of a major nosocomial pathogen Enterococcus faecium are shaped by adaptive evolution of the chromosome and plasmidome

Enterococcus faecium is a gut commensal of many mammals but is also recognized as a major nosocomial human pathogen, as it is listed on the WHO global priority list of multi-drug resistant organisms. Previous research has suggested that nosocomial strains have multiple zoonotic origins and are only distantly related to those involved in human commensal colonization. Here we present the first comprehensive population-wide joint genomic analysis of hospital, commensal and animal isolates using both short- and long-read sequencing techniques. This enabled us to investigate the population plasmidome, core genome variation and genome architecture in detail, using a combination of machine learning, population genomics and genome-wide co-evolution analysis. We observed a high level of genome plasticity with large-scale inversions and heterogeneous chromosome sizes, collectively painting a high-resolution picture of the adaptive landscape of E. faecium, and identified plasmids as the main indicator for host-specificity. Given the increasing availability of long-read sequencing technologies, our approach could be widely applied to other human and animal pathogen populations to unravel fine-scale mechanisms of their evolution.

genomics↗

bioRxiv2018

Group IIA secreted phospholipase A2 in human serum kills commensal but not clinical Enterococcus faecium isolates

Human innate immunity employs cellular and humoral mechanisms to facilitate rapid killing of invading bacteria. The direct killing of bacteria by human serum is mainly attributed to the activity of the complement system that forms pores in Gram-negative bacteria. Although Gram-positive bacteria are considered resistant to serum killing, we here uncover that normal human serum effectively kills Enterococcus faecium. Comparison of a well-characterized collection of commensal and clinical E. faecium isolates revealed that human serum specifically kills commensal E. faecium strains isolated from normal gut microbiota, but not clinical isolates. Inhibitor studies show that the human group IIA secreted phospholipase A2 (hGIIA), but not complement, is responsible for killing of commensal E. faecium strains in human normal serum. This is remarkable since hGIIA concentrations in non-inflamed serum were considered too low to be bactericidal against Gram-positive bacteria. Mechanistic studies showed that serum hGIIA specifically causes permeabilization of commensal E. faecium membranes. Altogether, we find that a normal serum concentration of hGIIA effectively kills commensal E. faecium and that hGIIA resistance of clinical E. faecium could have contributed to the ability of these strains to become opportunistic pathogens in hospitalized patients.\n\nImportanceHuman normal serum contains antimicrobial components that effective kill invading Gram-negative bacteria. Although Gram-positive bacteria are generally considered resistant to serum killing, here we show that normal human effectively kills the Gram-positive Enterococcus faecium strains that live as commensals in the gut of humans. In contrast, clinical E. faecium strains that are responsible for opportunistic infections in debilitated patients are resistant against human serum. The key factor in serum responsible for killing is group IIA secreted phospholipase A2 (hGIIA) that effectively destabilizes commensal E. faecium membranes. We believe that hGIIA resistance by clinical E. faecium could have contributed to the ability of these strains to cause opportunistic infections in hospitalized patients. Altogether, understanding mechanisms of immune defense and bacterial resistance could aid in further development of novel anti-infective strategies against medically important multidrug resistant Gram-positive pathogens.

microbiology↗

bioRxiv2017

In-Depth Resistome Analysis by Targeted Metagenomics

We developed ResCap, a targeted sequence capture platform based on SeqCapEZ technology, to analyse resistomes and other genes related to antimicrobial resistance (heavy metals, biocides and plasmids). ResCap includes probes for 8,667 canonical resistance genes (7,963 antibiotic resistance genes and 704 genes conferring resistance to metals or biocides), plus 2,517 relaxase genes (plasmid markers). Besides, it includes 78.600 genes homologous to the previous ones (47,806 for antibiotics and 30,794 for biocide or metals). ResCap enriched 279-fold the targeted sequences detected by metagenomic shotgun sequencing and improves their identification. Novel bioinformatic approaches allow quantifying \"gene abundance\" and \"gene diversity\". ResCap, the first targeted sequence capture specifically developed to analyse resistomes, enhances the sensitivity and specificity of available metagenomic methods to analyse antibiotic resistance in complex populations, enables the analysis of other genes related to antimicrobial resistance and opens the possibility to accurately study other complex microbial systems.

microbiology↗

bioRxiv2016

On the (im)possibility to reconstruct plasmids from whole genome short-read sequencing data

Plasmids are autonomous extra-chromosomal elements in bacterial cells that can carry genes that are important for bacterial survival. To benchmark algorithms for automated plasmid sequence reconstruction from short read sequencing data, we selected 42 publicly available complete bacterial genome sequences which were assembled by a combination of long- and short-read data. The selected bacterial genome sequence projects span 12 genera, containing 148 plasmids. We predicted plasmids from short-read data with four different programs (PlasmidSPAdes, Recycler, cBar and PlasmidFinder) and compared the outcome to the reference sequences.\n\nPlasmidSPAdes reconstructs plasmids based on coverage differences in the assembly graph. It reconstructed most of the reference plasmids (recall = 0.82) but approximately a quarter of the predicted plasmid contigs were false positives (precision = 0.76). PlasmidSPAdes merged 83 % of the predictions from genomes with multiple plasmids in a single bin. Recycler searches the assembly graph for sub-graphs corresponding to circular sequences and correctly predicted small plasmids but failed with long plasmids (recall = 0.12, precision = 0.30). cBar, which applies pentamer frequency composition analysis to detect plasmid-derived contigs, showed an overall recall and precision of 0.78 and 0.64. However, cBar only categorizes contigs as plasmid-derived and does not bin the different plasmids correctly within a bacterial isolate. PlasmidFinder, which searches for matches in a replicon database, had the highest precision (1.0) but was restricted by the contents of its database and the contig length obtained from de novo assembly (recall = 0.36).\n\nSurprisingly, PlasmidSPAdes and Recycler detected single isolated components corresponding to putative novel small plasmids (<10 kbp) which were also predicted as plasmids by cBar.\n\nThis study shows that it is possible to automatically predict plasmid sequences, but only for small plasmids. The reconstruction of large plasmids (>50 kbp) containing repeated sequences remains challenging and limits the high-throughput analysis of WGS data.\n\nAuthor SummaryShort read sequencing of the DNA of bacteria is often used to understand characteristics such as antibiotic resistance. However the assembly of short read sequencing data with the goal of reconstructing a complete genome is often fragmented and leaves gaps. Therefore independently replicating DNA fragments called plasmids cannot easily be identified from an assembly. Lately a number of programs have been developed to enable the automated prediction of the sequences of plasmids. Here we tested these programs by comparing their outcomes with complete genome sequences. None of the tested programs were able to fully and unambiguously predict distinct plasmid sequences. All programs performed best with the prediction of plasmids smaller than 50 kbp. Larger plasmids were only correctly predicted if they were present as a single contig in the assembly. While predictions by PlasmidSPAdes and cBar contained most of the plasmids, they were merged with or indistinguishable from other plasmids and sometimes chromosome sequences. PlasmidFinder missed most plasmids but all its predictions were correct. Without manual steps or long-read sequencing information, plasmid reconstruction from short read sequencing data remains challenging.

microbiology↗