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Immune checkpoint inhibitors (ICIs) have improved the prognosis in multiple cancer types. However, ICIs can induce immune-related adverse events such as immune-mediated enterocolitis (IMC). The gut microbiota may be implicated in IMC development. Therefore, we investigated fecal microbiota transplantation (FMT) as a treatment option for 2 patients with metastatic cancer suffering from refractory IMC. The patients were treated with, respectively, 1 and 3 FMTs after vancomycin pre-treatment. We monitored defecation frequency, fecal calprotectin, and microbiota composition. After FMT, both patients improved in defecation frequency, were discharged from the hospital, and received lower dosage of immunosuppressive therapy. Patient 1 developed an invasive pulmonary aspergillosis deemed to be related to prolonged steroid exposure. Patient 2 suffered from a Campylobacter jejuni infection after the first FMT and was treated with meropenem, resulting in a low-diversity microbiota profile and increased calprotectin levels and defecation frequency. After a second and third FMT, bacterial diversity increased and defecation frequency and calprotectin levels decreased. Pre-FMT, both patients showed low bacterial richness, but varying bacterial diversity. After FMT, diversity and richness were similar to healthy donor levels. In conclusion, FMT resulted in improvement of IMC symptoms and corresponding microbial changes in 2 cancer patients with refractory IMC. While more research is warranted, microbiome-modulation could be a promising new therapeutic option for IMC.

Background: Gut colonisation by extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli is a risk factor for developing overt infection. The gut microbiome can provide colonisation resistance against enteropathogens, but it remains unclear whether it confers resistance against ESBL-producing E coli. We aimed to identify a potential role of the microbiome in controlling colonisation by this antibiotic-resistant bacterium.Methods: For this matched case-control study, we used faeces from 2751 individuals in a Dutch cross-sectional population study (PIENTER-3) to culture ESBL-producing bacteria. Of these, we selected 49 samples that were positive for an ESBL-producing E coli (ESBL-positive) and negative for several variables known to affect microbiome composition. These samples were matched 1:1 to ESBL-negative samples on the basis of individuals' age, sex, having been abroad or not in the past 6 months, and ethnicity. Shotgun metagenomic sequencing was done and taxonomic species composition and functional annotations (ie, microbial metabolism and carbohydrate-active enzymes) were determined. Targeted quantitative metabolic profiling (proton nuclear magnetic resonance spectroscopy) was done to investigate metabolomic profiles and combinations of univariate (t test and Wilcoxon test), multivariate (principal coordinates analysis, permutational multivariate analysis of variance, and partial least-squares discriminant analysis) and machine-learning approaches (least absolute shrinkage and selection operator and random forests) were used to analyse all the molecular data.Findings: No differences in diversity parameters or in relative abundance were observed between ESBL-positive and ESBL-negative groups based on bacterial species-level composition. Machine-learning approaches using microbiota composition did not accurately predict ESBL status (area under the receiver operating characteristic curve [AUROC]=0.41) when using either microbiota composition or any of the functional profiles. The metabolome also did not differ between ESBL groups, as assessed by various methods including random forest (AUROC=0- 61).Interpretation: By combining multiomics 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 finding might suggest that microbiome-mediated colonisation resistance against ESBL-producing E, coli is not as relevant as it is against other enteropathogens and antibiotic-resistant bacteria. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.

Background It is important to understand the consequences of pre-emptive antibiotic treatment in neonates, as disturbances in microbiota development during this key developmental time window might affect early and later life health outcomes. Despite increasing knowledge regarding the detrimental effect of antibiotics on the gut microbiota, limited research focussed on antibiotic treatment duration. We determined the effect of short and long amoxicillin/ceftazidime administration on gut microbiota development during the immediate postnatal life of preterm and term infants. Methods Faeces was collected from 63 (pre) term infants at postnatal weeks one, two, three, four and six. Infants received either no (control), short-term (ST) or long-term (LT) postpartum amoxicillin/ceftazidime treatment. Results Compared to control infants, ST and LT infants’ microbiota contained significantly higher abundance of Enterococcus during the first two postnatal weeks at the expense of Bifidobacterium and Streptococcus. Short and long antibiotic treatment both allowed for microbiota restoration within the first six postnatal weeks. However, Enterococcus and Bifidobacterium abundances were affected in fewer ST than LT infants. Conclusions Intravenous amoxicillin/ceftazidime administration affects intestinal microbiota composition by decreasing the relative abundance of Escherichia-Shigella and Streptococcus, while increasing the relative abundance of Enterococcus and Lactobacillus species during the first two postnatal weeks. Thriving of enterococci at the expense of bifidobacteria and streptococci should be considered as aspect of the cost-benefit determination for antibiotic prescription.

Intestinal helminths are highly prevalent in low-SES children and could contribute to poor health outcomes either directly or via alteration of the gut microbiome and gut barrier function. We analysed parasitic infections and gut microbiota composition in 325 children attending high- and low-SES schools in Makassar, Indonesia before and after albendazole treatment. Lactulose/Mannitol Ratio (LMR, a marker of gut permeability); I-FABP (a surrogate marker of intestinal damage) as well as inflammatory markers (LBP) were measured. Helminth infections were highly prevalent (65.6%) in low-SES children. LMR and I-FABP levels were higher in low-SES children (geomean (95%CI): 4.03 (3.67–4.42) vs. 3.22 (2.91–3.57); p. adj

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.

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.

Background Nursing home residents have increased rates of intestinal colonisation with multidrug-resistant organisms (MDROs). We assessed the colonisation and spread of MDROs among this population, determined clinical risk factors for MDRO colonisation and investigated the role of the gut microbiota in providing colonisation resistance against MDROs. Methods We conducted a prospective cohort study in a Dutch nursing home. Demographical, epidemiological and clinical data were collected at four time points with 2-month intervals (October 2016–April 2017). To obtain longitudinal data, faecal samples from residents were collected for at least two time points. Ultimately, twenty-seven residents were included in the study and 93 faecal samples were analysed, of which 27 (29.0%) were MDRO-positive. Twelve residents (44.4%) were colonised with an MDRO at at least one time point throughout the 6-month study. Results Univariable generalised estimating equation logistic regression indicated that antibiotic use in the previous 2 months and hospital admittance in the previous year were associated with MDRO colonisation. Characterisation of MDRO isolates through whole-genome sequencing revealed Escherichia coli sequence type (ST)131 to be the most prevalent MDRO and ward-specific clusters of E. coli ST131 were identified. Microbiota analysis by 16S rRNA gene amplicon sequencing revealed no differences in alpha or beta diversity between MDRO-positive and negative samples, nor between residents who were ever or never colonised. Three bacterial taxa (Dorea, Atopobiaceae and Lachnospiraceae ND3007 group) were more abundant in residents never colonised with an MDRO throughout the 6-month study. An unexpectedly high abundance of Bifidobacterium was observed in several residents. Further investigation of a subset of samples with metagenomics showed that various Bifidobacterium species were highly abundant, of which B. longum strains remained identical within residents over time, but were different between residents. Conclusions Our study provides new evidence for the role of the gut microbiota in colonisation resistance against MDROs in the elderly living in a nursing home setting. Dorea, Atopobiaceae and Lachnospiraceae ND3007 group may be associated with protection against MDRO colonisation. Furthermore, we report a uniquely high abundance of several Bifidobacterium species in multiple residents and excluded the possibility that this was due to probiotic supplementation.

Health effects of pesticides are not always accurately detected using the current battery of regulatory toxicity tests. We compared standard histopathology and serum biochemistry measures and multi-omics analyses in a subchronic toxicity test of a mixture of six pesticides frequently detected in foodstuffs (azoxystrobin, boscalid, chlorpyrifos, glyphosate, imidacloprid and thiabendazole) in Sprague-Dawley rats. Analysis of water and feed consumption, body weight, histopathology and serum biochemistry showed little effect. Contrastingly, serum and caecum metabolomics revealed that nicotinamide and tryptophan metabolism were affected, which suggested activation of an oxidative stress response. This was not reflected by gut microbial community composition changes evaluated by shotgun metagenomics. Transcriptomics of the liver showed that 257 genes had their expression changed. Gene functions affected included the regulation of response to steroid hormones and the activation of stress response pathways. Genome-wide DNA methylation analysis of the same liver samples showed that 4,255 CpG sites were differentially methylated. Overall, we demonstrated that in-depth molecular profiling in laboratory animals exposed to low concentrations of pesticides allows the detection of metabolic perturbations that would remain undetected by standard regulatory biochemical measures and which could thus improve the predictability of health risks from exposure to chemical pollutants., Using a multi-omics platform, Mesnage et al present an extensive dataset that reports the effects of low-dose pesticides frequently detected in food on Sprague-Dawley rats. This study suggests potential metabolic biomarkers that may predict health risks from exposure to chemical pollutants.

BACKGROUND: There is intense debate on whether glyphosate can inhibit the shikimate pathway of gastrointestinal microorganisms, with potential health implications.OBJECTIVES: We tested whether glyphosate or its representative EU herbicide formulation Roundup MON 52276 affects the rat gut microbiome.METHODS: We combined cecal microbiome shotgun metagenomics with serum and cecum metabolomics to assess the effects of glyphosate [0.5, 50, 175 mg=kg body weight oBW thorn per day] or MON 52276 at the same glyphosate-equivalent doses, in a 90-d toxicity test in rats.RESULTS: Glyphosate and MON 52276 treatment resulted in ceca accumulation of shikimic acid and 3-dehydroshikimic acid, suggesting inhibition of 5-enolpyruvylshikimate-3-phosphate synthase of the shikimate pathway in the gut microbiome. Cysteinylglycine, c-glutamylglutamine, and valylglycine levels were elevated in the cecal microbiome following glyphosate and MON 52276 treatments. Altered cecum metabolites were not differentially expressed in serum, suggesting that the glyphosate and MON 52276 impact on gut microbial metabolism had limited consequences on physiological biochemistry. Serum metabolites differentially expressed with glyphosate treatment were associated with nicotinamide, branched-chain amino acid, methionine, cysteine, and taurine metabolism, indicative of a response to oxidative stress. MON 52276 had similar, but more pronounced, effects than glyphosate on the serum metabolome. Shotgun metagenomics of the cecum showed that treatment with glyphosate and MON 52276 resulted in higher levels of Eggerthella spp., Shinella zoogleoides, Acinetobacter johnsonii, and Akkermansia muciniphila. Shinella zoogleoides was higher only with MON 52276 exposure. In vitro culture assays with Lacticaseibacillus rhamnosus strains showed that Roundup GT plus inhibited growth at concentrations at which MON 52276 and glyphosate had no effect.DISCUSSION: Our study highlights the power of multi-omics approaches to investigate the toxic effects of pesticides. Multi-omics revealed that glyphosate and MON 52276 inhibited the shikimate pathway in the rat gut microbiome. Our findings could be used to develop biomarkers for epidemiologi cal studies aimed at evaluating the effects of glyphosate herbicides on humans. https://doi.org/10.1289/EHP6990

Hookworms are soil-transmitted helminths that use immune-evasive strategies to persist in the human duodenum where they are responsible for anemia and protein loss. Given their location and immune regulatory effects, hookworms likely impact the bacterial microbiota. However, microbiota studies struggle to deconvolute the effect of hookworms from confounders such as coinfections and malnutrition. We thus used an experimental human hookworm infection model to explore temporal changes in the gut microbiota before and during hookworm infection. Volunteers were dermally exposed to cumulative dosages of 50, 100 or 150 L3 Necator americanus larvae. Fecal samples were collected for microbiota profiling through 16S rRNA gene amplicon sequencing at weeks zero, four, eight, fourteen and twenty. During the acute infection phase (trial week zero to eight) no changes in bacterial diversity were detected. During the established infection phase (trial week eight to twenty), bacterial richness (Chao1, p = .0174) increased significantly over all volunteers. No relation was found between larval dosage and diversity, stability or relative abundance of individual bacterial taxa. GI symptoms were associated with an unstable microbiota during the first eight weeks and rapid recovery at week twenty. Barnesiella, amongst other taxa, was more abundant in volunteers with more GI symptoms throughout the study. In conclusion, this study showed that clinical GI symptoms following N. americanus infection are associated with temporary microbiota instability and relative abundance of specific bacterial taxa. These results suggest a possible role of hookworm-induced enteritis on microbiota stability.

Bacterial vaginosis (BV) is perceived as a condition of disrupted vaginal microbiota, but remains of unknown aetiology. In this study, vaginal microbiota composition was determined in twenty-one women with BV, before and after treatment with metronidazole or clindamycin. Microbiota composition varied greatly between women and defining a (un)healthy vaginal microbiota state remains elusive, challenging BV diagnosis and treatment. While relative abundance of Lactobacillus increased after antibiotic treatment in two-third of women, its abundance was not associated with treatment outcome. Instead, remaining complaints of abnormal vaginal discharge were more common after metronidazole treatment and associated with increased relative abundance of Ureaplasma.

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.

Diet and body mass index (BMI) have been shown to affect the gut microbiota of children, but studies are largely performed in developed countries. Here, we conducted a cross-sectional investigation on the differences in the bacterial gut microbiota between normal-weight and overweight urban Filipino children, and determined the relationship between their energy, macronutrient and dietary fiber intakes, and their gut microbiota composition and diversity. Forty-three children (normal-weight, n = 32, overweight, n = 11) participated in the study. Energy and fiber intakes were collected using a semi-quantitative Food Frequency Questionnaire (FFQ). The gut microbiota was profiled using 16S rRNA gene amplicon sequencing of the V3&ndash, V4 region. The diet of the children was a mixture of traditional and Western patterns. There were no significant differences in energy, macronutrients and energy-adjusted fiber intakes between the normal-weight and overweight groups, but there were significantly more children meeting the recommended fiber intake in the overweight group. Alpha and beta bacterial diversities did not significantly differ between weight groups. Relative abundance of Bifidobacterium, Turicibacter and Clostridiaceae 1 were higher in the normal-weight than overweight children, and Lachnospira was higher in overweight children.

To understand the relationship between the gut microbiota and the health profile of Indonesians, it is important to elucidate the characteristics of the bacterial communities that prevail in this population. To this end, we profiled the faecal bacterial community of 140 Indonesian schoolchildren in urban Makassar. The core microbiota of Indonesian schoolchildren consisted of Bifidobacterium, Collinsella, and multiple members of the Lachnospiraceae and Ruminicoccaceae families, but the relative abundance of these taxa varied greatly among children. Socioeconomic status (SES) was the main driver for differences in microbiota composition. Multiple bacterial genera were differentially abundant between high and low SES children, including Bifidobacterium, Lactobacillus, Prevotella, and Escherichia-Shigella. In addition, the microbiota of high SES children was less diverse and strongly associated with body mass index (BMI). In low SES children, helminth infection was prevalent and positively associated with Olsenella, Enterohabdus, Lactobacillus, and Mogibacterium abundance, while negatively associated with relative abundance of Prevotella. Protozoa infection was also prevalent, and positively associated with Rikenellaceae, while it was negatively associated with the relative abundance of Romboutsia and Prevotella. In conclusion, Indonesian schoolchildren living in urban Makassar share a core microbiota, but their microbiota varies in diversity and relative abundance of specific bacterial taxa depending on socioeconomic status, nutritional status, and intestinal parasites infection.

We read with interest the article by Schierwagen et al ,1 who reported on their findings regarding the blood microbiome. Since our research group is working in a hospital setting, recently, we have also considered investigating the blood microbiome using collected blood samples that are potentially available. However, based on recent literature,2 3 we considered the presence of a blood microbiome very unlikely and did not further venture in this direction. We were therefore extremely excited to read this work, but the results quickly raised concerns in our research group. In another area of microbiota research, we recently …

Method 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 microbiota studies. Our methods were applied not only to commonly studied samples for microbiota analysis, e.g., feces, but also to more rarely studied, low-biomass samples. Microbiota composition profiles of low-biomass samples (e.g., urine and tumor biopsy specimens) were not always distinguishable from negative controls, or showed partial overlap, confirming the importance of including negative controls in microbiota studies, especially when low bacterial biomass is expected., When studying the microbiome using next-generation sequencing, the 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 a maximum 3-fold change, although differences were seen between methods, and library preparation and sequencing induced little variation. Bioinformatic pipelines showed different results for observed 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 intersubject (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 again demonstrate the importance of including negative controls when analyzing low-bacterial-biomass samples. IMPORTANCE Method 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 microbiota studies. Our methods were applied not only to commonly studied samples for microbiota analysis, e.g., feces, but also to more rarely studied, low-biomass samples. Microbiota composition profiles of low-biomass samples (e.g., urine and tumor biopsy specimens) were not always distinguishable from negative controls, or showed partial overlap, confirming the importance of including negative controls in microbiota studies, especially when low bacterial biomass is expected.

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 (γ-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® 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

Antibiotic treatment is common practice in the neonatal ward for the prevention and treatment of sepsis, which is one of the leading causes of mortality and morbidity in preterm infants. Although the effect of antibiotic treatment on microbiota development is well recognised, little attention has been paid to treatment duration. We studied the effect of short and long intravenous antibiotic administration on intestinal microbiota development in preterm infants. Faecal samples from 15 preterm infants (35 ± 1 weeks gestation and 2871 ± 260 g birth weight) exposed to no, short (≤ 3 days) or long (≥ 5 days) treatment with amoxicillin/ceftazidime were collected during the first six postnatal weeks. Microbiota composition was determined through 16S rRNA gene sequencing and by quantitative polymerase chain reaction (qPCR). Short and long antibiotic treat ment significantly lowered the abundance of Bifidobacterium right after treatment (p = 0.027) till postnatal week three (p = 0.028). Long treatment caused Bifidobacterium abundance to remain decreased till postnatal week six (p = 0.009). Antibiotic treatment was effective against members of the Enterobacteriaceae family, but allowed Enterococcus to thrive and remain dominant for up to two weeks after antibiotic treatment discontinuation. Community richness and diversity were not affected by antibiotic treatment, but were positively associated with postnatal age (p

Objective: Development of the gastrointestinal tract and immune system can be modulated by the gut microbiota. Establishment of the intestinal microbiota, in its turn, is affected by host and environmental factors. As such, development of the gut microbiota is greatly impacted in preterm infants, who have an immature gut and are exposed to factors like hospitalization, caesarean section, antibiotics, and respiratory support. Design: We analyzed fecal microbiota composition and activity of ten preterm infants (gestational age 25–30 weeks; birthweight 630–1750 g) during the first six postnatal weeks through metaproteomics (LC-MS/MS) and 16S-rRNA gene sequencing. Results: A gestational-age-dependent microbial signature is observed, enabling microbiota-based differentiation between extremely preterm (25–27 weeks gestation) and very preterm (30 weeks gestation) infants. In very preterm infants, the intestinal microbiota developed toward a Bifidobacterium-dominated community and was associated with high abundance of proteins involved in carbohydrate and energy metabolism. Extremely preterm infants remained predominantly colonized by facultative anaerobes and were associated with proteins involved in membrane transport and translation. Delayed colonization by obligate anaerobes could be associated with antibiotic treatment and respiratory support. Conclusion: We speculate that gestational age and its associated intensity of care (e.g. antibiotics and respiratory support) affects intestinal microbiota composition and activity in preterm infants. As the gut microbiota plays a major role in development of the neonate, gestational age and its associated factors could set the stage for early and later life health complications via interference with microbiota development.

The gut microbiome is critical in providing resistance against colonization by exogenous microorganisms. The mechanisms via which the gut microbiota provide colonization resistance (CR) have not been fully elucidated, but they include secretion of antimicrobial products, nutrient competition, support of gut barrier integrity, and bacteriophage deployment. However, bacterial enteric infections are an important cause of disease globally, indicating that microbiota-mediated CR can be disturbed and become ineffective. Changes in microbiota composition, and potential subsequent disruption of CR, can be caused by various drugs, such as antibiotics, proton pump inhibitors, antidiabetics, and antipsychotics, thereby providing opportunities for exogenous pathogens to colonize the gut and ultimately cause infection. In addition, the most prevalent bacterial enteropathogens, including Clostridioides diflicile, Salmonella enterica serovar Typhimurium, enterohemorrhagic Escherichia coli, Shigella flexneri, Campylobacter jejuni, Vibrio cholerae, Yersinia enterocolitica, and Listeria monocytogenes, can employ a wide array of mechanisms to overcome colonization resistance. This review aims to summarize current knowledge on how the gut microbiota can mediate colonization resistance against bacterial enteric infection and on how bacterial enteropathogens can overcome this resistance.

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.IMPORTANCEMethod 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.

Good scientific practice is important in all areas of science. In recent years this has gained more and more attention, especially considering the ‘scientific reproducibility crisis’. While most researchers are aware of the issues with good scientific practice, not all of these issues are necessarily clear, and the details can be very complicated. For many years it has been accepted to perform and publish sequencing based microbiome studies without including proper controls. Although in recent years more scientists realize the necessity of implementing controls, this poses a problem due to the complexity of the field. Another concern is the inability to properly interpret the information gained from controls in microbiome studies. Here, we will discuss these issues and provide a comprehensive overview of problematic points regarding controls in microbiome research, and of the current standards in this area., Current issues and standards with positive and negative controls in microbiome research are discussed.

Bloodstream infections and graft-versus-host disease are common complications after hematopoietic stem cell transplantation (HSCT) procedures, associated with the gut microbiota that acts as a reservoir for opportunistic pathogens. Selective gut decontamination (SGD) and total gut decontamination (TGD) during HSCT have been associated with a decreased risk of developing these complications after transplantation. However, because studies have shown conflicting results, the use of these treatments remains subject of debate. In addition, their impact on the gut microbiota is not well studied. The aim of this study was to elucidate the dynamics of the microbiota during and after TGD and to compare these with the dynamics of SGD. In this prospective, observational, single center study fecal samples were longitudinally collected from 19 children eligible for allogenic HSCT (TGD, n=12; SGD, n=7), weekly during hospital admission and monthly after discharge. In addition, fecal samples were collected from 3 family stem cell donors. Fecal microbiota structure of patients and donors was determined by 16S rRNA gene amplicon sequencing. Microbiota richness and diversity markedly decreased during SGD and TGD and gradually increased after cessation of decontamination treatment. During SGD, gut microbiota composition was relatively stable and dominated by Bacteroides, whereas it showed high inter- and intraindividual variation and low Bacteroides abundance during TGD. In some children TGD allowed the genera Enterococcus and Streptococcus to thrive during treatment. A gut microbiota dominated by Bacteroides was associated with increased predicted activity of several metabolic processes. Comparing the microbiota of recipients and their donors indicated that receiving an SCT did not alter the patient's microbiota to become more similar to that of its donor. Overall, our findings indicate that SGD and TGD affect gut microbiota structure in a treatment-specific manner. Whether these treatments affect clinical outcomes via interference with the gut microbiota needs to be further elucidated. (C) 2019 American Society for Blood and Marrow Transplantation.