Biology subjects

van Schaik, W.

Publications and source records attributed to van Schaik, W..

5 recordsLinked to original sources

bioRxiv2017

Prediction of the intestinal resistome by a novel 3D-based method

The intestinal microbiota is considered to be a major reservoir of antibiotic resistance determinants (ARDs) that could potentially be transferred to bacterial pathogens. Yet, this question remains hypothetical because of the difficulty to identify ARDs from intestinal bacteria. Here, we developed and validated a new annotation method (called pairwise comparative modelling, PCM) based on homology modelling in order to characterize the Human resistome. We were able to predict 6,095 ARDs in a 3.9 million protein catalogue from the Human intestinal microbiota. We found that predicted ARDs (pdARDs) were distantly related to known ARDs (mean amino-acid identity 29.8%). Among 3,651 pdARDs that were identified in metagenomic species, 3,489 (95.6%) were assumed to be located on the bacterial chromosome. Furthermore, genes associated with mobility were found in the neighbourhood of only 7.9% (482/6,095) of pdARDs. According to the composition of their resistome, we were able to cluster subjects from the MetaHIT cohort (n=663) into 6 \"resistotypes\". Eventually, we found that the relative abundance of pdARDs was positively associated with gene richness, but not when subjects were exposed to antibiotics. Altogether, our results support that most ARDs in the intestinal microbiota should be considered as intrinsic genes of commensal microbiota with a low risk of transfer to bacterial 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↗

bioRxiv2017

Gut microbiota and resistome dynamics in intensive care patients receiving selective digestive tract decontamination

BackgroundCritically ill patients hospitalized in an Intensive Care Unit (ICU) are at increased risk of acquiring potentially life-threatening infections with opportunistic pathogens. The gut microbiota of ICU patients forms an important reservoir for these infectious agents. To suppress gut colonization with opportunistic pathogens, a prophylactic antibiotic regimen, termed Selective decontamination of the digestive tract (SDD), may be used. SDD has previously been shown to improve clinical outcome in ICU patients, but the impact of ICU hospitalization and SDD on the gut microbiota remains largely unknown. Here, we characterize the composition of the gut microbiota and its antimicrobial resistance genes ( the resistome) of ICU patients during SDD.\n\nResultsDuring ICU-stay, 30 fecal samples of ten patients were collected. Additionally, feces were collected from five of these patients after transfer to a medium-care ward and cessation of SDD. As a control group, feces from ten healthy subjects were collected twice, with a one-year interval. Gut microbiota and resistome composition were determined using 16S rRNA phylogenetic profiling and nanolitre-scale quantitative PCRs.\n\nThe microbiota of the ICU patients differed from the microbiota of healthy subjects and was characterized by low microbial diversity, decreased levels of E. coli and of anaerobic Gram-positive, butyrate-producing bacteria of the Clostridium clusters IV and XIVa, and an increased abundance of Bacteroidetes and enterococci. Four resistance genes (aac(6')-Ii, ermC, qacA, tetQ), providing resistance to aminoglycosides, macrolides, disinfectants and tetracyclines respectively, were significantly more abundant among ICU patients than in healthy subjects, while a chloramphenicol resistance gene (catA) and a tetracycline resistance gene (tetW) were more abundant in healthy subjects.\n\nConclusionsThe microbiota and resistome of ICU patients and healthy subjects were noticeably different, but importantly, levels of E. coli remained low during ICU hospitalization, presumably due to SDD therapy. Selection for four antibiotic resistance genes was observed, but none of these are of particular concern as they do not contribute to clinically relevant resistance. Our data support the ecological safety of SDD, at least in settings with low levels of circulating antibiotic resistance.

microbiology↗

bioRxiv2017

Fitness determinants of vancomycin-resistant Enterococcus faecium during growth in human serum

Enterococcus faecium is a commensal of the human gastrointestinal tract and a frequent cause of bloodstream infections in hospitalized patients. Here, we identify genes that contribute to growth of E. faecium in human serum. We first sequenced the genome of E. faecium E745, a vancomycin-resistant clinical isolate, to completion and then compared its transcriptome during exponential growth in rich medium and in human serum by RNA-seq. This analysis revealed that 27.8% of genes on the E. faecium E745 genome were differentially expressed in these two conditions. A gene cluster with a role in purine biosynthesis was among the most upregulated genes in E. faecium E745 upon growth in serum. A high-throughput transposon sequencing (Tn-seq) approach was used to identify conditionally essential genes in E. faecium E745 during growth in serum. Genes involved in de novo nucleotide biosysnthesis (including pyrK_2, pyrF, purD, purH) and a gene encoding a phosphotransferase system subunit (manY_2) were thus identified to be contributing to E. faecium growth in human serum. Transposon mutants in pyrK_2, pyrF, purD, purH and manY_2 were isolated from the library and their impaired growth in human serum was confirmed. In addition, the pyrK_2 and manY_2 mutants also exhibited significantly attenuated virulence in an intravenous zebrafish infection model. We conclude that genes involved in carbohydrate and nucleotide metabolism of E. faecium are essential for growth in human serum and contribute to the pathogenesis of this organism.

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↗