Biology subjects

Willems, R. J.

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

3 recordsLinked to original sources

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↗