Biology subjects

Zwittink, R. D.

Publications and source records attributed to Zwittink, R. D..

6 recordsLinked to original sources

bioRxiv2023

Fungal-bacterial gut microbiota interactions in patients with Clostridioides difficile colonisation and infection

ObjectivesThe bacterial microbiota is well-recognised for its role in Clostridioides difficile colonisation and infection, while fungi and yeasts remain understudied. The aim of this study was to analyse the mycobiota and its interactions with the bacterial microbiota in light of C. difficile colonisation and infection. MethodsThe mycobiota was profiled by ITS2 sequencing of faecal DNA from infected patients (CDI; n = 29), asymptomatically colonised patients (CDC; n = 38) and hospitalised controls with C. difficile negative stool culture (Controls; n = 38). Previously published 16S rRNA gene sequencing data of the same cohort were used additionally for machine learning and fungal-bacterial network analysis. ResultsCDI patients were characterised by a significantly higher abundance of Candida spp. (MD 0.270 {+/-} 0.089, P = 0.002) and Candida albicans (MD 0.165 {+/-} 0.082, P = 0.023) compared to Controls. Additionally, they were deprived of Aspergillus spp. (MD -0.067 {+/-} 0.026, P = 0.000) and Penicillium spp. (MD -0.118 {+/-} 0.043, P = 0.000) compared to CDC patients. Network analysis revealed a positive association between several fungi and bacteria in CDI and CDC, although the analysis did not reveal a direct association between Clostridioides spp. and fungi. Furthermore, the microbiota machine learning model outperformed the models based on the mycobiota and the joint microbiota-mycobiota model. The microbiota classifier successfully distinguished CDI from CDC (AUROC = 0.884) and CDI from Controls (AUROC = 0.905). Blautia and Bifidobacterium were marker genera associated with CDC patients and Controls. ConclusionsThe gut mycobiota differs between CDI, CDC, and Controls, and may affect Clostridioides spp. through indirect interactions. The identification of bacterial marker genera associated with CDC and Controls warrants further investigation. Although the mycobiotas predictive value of C. difficile status was low, fungal-bacterial interactions might be considered when diagnosing and treating C. difficile infection.

microbiology↗

bioRxiv2021

Impacts of dietary exposure to pesticides on faecal microbiome metabolism in adult twins

Concerns have been raised as to whether the consumption of foodstuffs contaminated with pesticides can contribute to the development of chronic human diseases by affecting microbial community function in the gut. We provide the first associations between urinary pesticide excretion and the composition and function of the faecal microbiome in 65 twin pairs in the UK. Biomonitoring of exposure to 186 common insecticide, herbicide, or fungicide residues showed the presence of pyrethroid and/or organophosphorus insecticide residues in all urine samples, while the herbicide glyphosate was found in 45% of individuals. Other pesticides such as DEET, imidacloprid or dithiocarbamate fungicides were less frequently detected. While the geographic location or the rural/urban environment had no influence on pesticide urinary excretion, food frequency questionnaires showed that DMTP levels, a metabolite of organophosphates, was higher with increased consumption of fruit and vegetables. Multivariable association between urinary pesticide excretion and faecal microbial composition and function were determined with shotgun metagenomics and metabolomics. A total of 34 associations between pesticide residues concentrations and faecal metabolite concentrations were detected. Glyphosate excretion was positively associated to an increased bacterial species richness, as well as to fatty acid metabolites and phosphate levels. The insecticide metabolite Br2CA, reflecting deltamethrin exposure, was positively associated with the mammalian phytoestrogens enterodiol and enterolactone, and negatively associated with some N-methyl amino acids. Urine metabolomics performed on a subset of samples did not reveal associations with the excretion of pesticide residues. Our results highlight the need for future interventional studies to understand effects of pesticide exposure on the gut microbiome and possible health consequences.

pharmacology and toxicology↗

bioRxiv2021

Asymptomatic gut colonization by extended-spectrum beta-lactamase-producing Escherichia coli is not associated with an altered gut microbiome or metabolome in Dutch adults

BackgroundGut colonization by antibiotic resistant E. coli strains, including extended-spectrum beta-lactamase (ESBL)-producing E. coli is a risk factor for developing overt infection. The gut microbiome can provide colonization resistance against enteropathogens, but it remains unclear whether it confers resistance against potentially pathogenic ESBL-producing E. coli. MaterialsFrom a Dutch cross-sectional population study (PIENTER-3), feces from 2751 individuals were used to culture ESBL-producing bacteria. Of these, we selected 49 samples which were positive for an ESBL-producing Escherichia coli (ESBL+), and negative for a variety of variables known to affect microbiome composition. These were matched in a 1:1 ratio to ESBL- samples based on age, sex, having been abroad in the past six months and ethnicity. Shotgun metagenomic sequencing was performed and taxonomic species composition and functional annotations (microbial metabolism and carbohydrate-active enzymes) were determined. Targeted quantitative metabolic profiling (1H NMR-spectroscopy) was performed to investigate metabolomic profiles. ResultsNo differences in alpha or beta diversity were observed, nor in relative abundance, between ESBL+ and ESBL- individuals based on bacterial species level composition. Machine learning approaches based on microbiota composition did not accurately predict ESBL status (area under the receiver operating characteristic curve (AUROC)=0.53), neither when based on functional profiles. The metabolome did also not convincingly differ between ESBL groups as assessed by a variety of approaches, including machine learning through random forest (AUROC=0.61). ConclusionUsing a combination of multi-omics and machine learning approaches, we conclude that asymptomatic gut carriage of ESBL-producing E. coli is not associated with an altered microbiome composition or function. This may suggest that microbiome-mediated colonization resistance against ESBL-producing E. coli is not as relevant as it is against other enteropathogens.

microbiology↗

bioRxiv2020

Multi-omics phenotyping of the gut-liver axis allows health risk predictability from in vivo subchronic toxicity tests of a low-dose pesticide mixture

Human health effects from chronic exposure to mixtures of pesticide residues are little investigated. We compared standard histopathology and serum biochemistry measures and multi-omics analyses in an in vivo subchronic toxicity test of a mixture of six pesticide active ingredients frequently detected in foodstuffs (azoxystrobin, boscalid, chlorpyrifos, glyphosate, imidacloprid and thiabendazole). Sprague-Dawley rats were administered with the pesticide mixture with each ingredient at its regulatory permitted acceptable daily intake. Analysis of water and feed consumption, body weight, histopathology and serum biochemistry showed little or no physiological effects from exposure to the pesticide mixture. In marked contrast, analysis of the host-gut microbiome axis using serum and caecum metabolomics revealed that nicotinamide and tryptophan metabolism were affected, which suggested the initiation of a cell danger response, including adaptation to oxidative stress. Only limited effects were detected on the caecum microbiota by shotgun metagenomics. Further analyses of in vitro bacterial cultures showed that growth of Lactobacillus rhamnosus and Escherichia coli strains was negatively impacted by the pesticide mixture at concentrations that were not inhibitory when exposure was to a single agent. Transcriptomics of the liver showed that 257 genes had their expression changed. Gene functions affected included those involved in the regulation of response to hormones and correlated with previously reported transcriptome changes following administration of nicotinamide. Genome-wide DNA methylation analysis of the same liver samples showed that 4255 CpG sites were differentially methylated (> 10% difference). Overall, we demonstrated that unlike standard blood biochemical and organ histological analysis, in-depth molecular profiling using a combination of high-throughput -omics methods in laboratory animals exposed to low concentrations of pesticides reveals metabolic effects on the gut-liver axis, which can potentially be used as biomarkers for the prediction of future negative health outcomes. Our data suggest that adoption of multi-omics as part of regulatory risk assessment procedures will result in more accurate outcome measures, with positive public health implications.

pharmacology and toxicology↗

bioRxiv2019

Shotgun metagenomics and metabolomics reveal glyphosate alters the gut microbiome of Sprague-Dawley rats by inhibiting the shikimate pathway

There is intense debate as to whether glyphosate can interfere with aromatic amino acid biosynthesis in microorganisms inhabiting the gastrointestinal tract, which could potentially lead to negative health outcomes. We have addressed this major gap in glyphosate toxicology by using a multi-omics strategy combining shotgun metagenomics and metabolomics. We tested whether glyphosate (0.5, 50, 175 mg/kg bw/day), or its representative EU commercial herbicide formulation MON 52276 at the same glyphosate equivalent doses, has an effect on the rat gut microbiome in a 90-day subchronic toxicity test. Clinical biochemistry measurements in blood and histopathological evaluations showed that MON 52276 but not glyphosate was associated with statistically significant increase in hepatic steatosis and necrosis. Similar lesions were also present in the liver of glyphosate-treated groups but not in the control group. Caecum metabolomics revealed that glyphosate inhibits the enzyme 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase in the shikimate pathway as evidenced by an accumulation of shikimic acid and 3-dehydroshikimic acid. Levels of caecal microbiome dipeptides involved in the regulation of redox balance ({gamma}-glutamylglutamine, cysteinylglycine, valylglycine) had their levels significantly increased. Shotgun metagenomics showed that glyphosate affected caecum microbial community structure and increased levels of Eggerthella spp. and Homeothermacea spp.. MON 52276, but not glyphosate, increased the relative abundance of Shinella zoogleoides. Since Shinella spp. are known to degrade alkaloids, its increased abundance may explain the decrease in solanidine levels measured with MON 52776 but not glyphosate. Other glyphosate formulations may have different effects since Roundup(R) GT Plus inhibited bacterial growth in vitro at concentrations at which MON 52276 did not present any visible effect. Our study highlights the power of a multiomics approach to investigate effects of pesticides on the gut microbiome. This revealed the first biomarker of glyphosate effects on rat gut microbiome. Although more studies will be needed to ascertain if there are health implications arising from glyphosate inhibition of the shikimate pathway in the gut microbiome, our findings can be used in environmental epidemiological studies to understand if glyphosate can have biological effects in human populations. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/870105v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@66578corg.highwire.dtl.DTLVardef@fddcd9org.highwire.dtl.DTLVardef@ae4ffforg.highwire.dtl.DTLVardef@5f7ed_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2019

Toward standards in clinical microbiome studies: comparison of three DNA extraction methods and two bioinformatic pipelines

When studying the microbiome using next generation sequencing, DNA extraction method, sequencing procedures and bioinformatic processing are crucial to obtain reliable data. Method choice has been demonstrated to strongly affect the final biological interpretation. We assessed the performance of three DNA extraction methods and two bioinformatic pipelines for bacterial microbiota profiling through 16S rRNA gene amplicon sequencing, using positive and negative controls for DNA extraction and sequencing, and eight different types of high- or low-biomass samples. Performance was evaluated based on quality control passing, DNA yield, richness, diversity and compositional profiles. All DNA extraction methods retrieved the theoretical relative bacterial abundance with maximum three-fold change, although differences were seen between methods, and library preparation and sequencing induced little variation. Bioinformatic pipelines showed different results for estimating richness, but diversity and compositional profiles were comparable. DNA extraction methods were successful for feces and oral swabs and variation induced by DNA extraction methods was lower than inter-subject (biological) variation. For low-biomass samples, a mixture of genera present in negative controls and sample-specific genera, possibly representing biological signal, were observed. We conclude that the tested bioinformatic pipelines perform equally with pipeline-specific advantages and disadvantages. Two out of three extraction methods performed equally well, while one method was less accurate regarding retrieval of compositional profiles. Lastly, we demonstrate the importance of including negative controls when analyzing low bacterial biomass samples.\n\nIMPORTANCEMethod choice throughout the workflow of a microbiome study, from sample collection to DNA extraction and sequencing procedures, can greatly affect results. This study evaluated three different DNA extraction methods and two bioinformatic pipelines by including positive and negative controls, and various biological specimens. By identifying an optimal combination of DNA extraction method and bioinformatic pipeline use, we hope to contribute to increased methodological consistency in microbiome studies. Our methods were not only applied to commonly studied samples for microbiota analysis, e.g. feces, but also for more rarely studied, low-biomass samples. Microbiota composition profiles of low-biomass samples (e.g. urine and tumor biopsies) were not always distinguishable from negative controls, or showed partial overlap, confirming the importance of including negative controls in microbiome studies, especially when low bacterial biomass is expected.

microbiology↗