Fate of agrochemicals in wood chip denitrifying reactors and their impacts on wood chip microbial ecology

Subsurface tile drainage systems have contributed towards increasing agricultural production, but have also contributed towards water pollution by rapidly transporting excessive nutrient and agrochemicals to surface water and ground water. One of the pollution control strategies is to treat the tile drainage water or the contaminated subsurface water with denitrifying bioreactors. Wood chips have been used in denitrifying bioreactors, providing organic carbon and attachment surface area for denitrifiers. The focus of this research is to investigate fate of agrochemicals in wood chips from the in situ reactors and their potential effects on denitrification and the denitrifiers. The selected agrochemicals for study are atrazine, enrofloxacin, monensin and sulfamethazine. Partition coefficients of atrazine, enrofloxacin, monensin and sulfamethazine were determined by single–point sorption experiments by using wood chips from an in situ reactor. Of the four chemicals tested, enrofloxacin had the highest partition coefficient (Kow) while sulfamethazine had the lowest. Atrazine and monensin had moderate sorption coefficients. In addition, partition coefficients for the four chemicals for wood chips were larger than the partition coefficients for soils obtained close to the in situ reactor. Freundlich distribution coefficients (Kf) for isotherm studies for the four chemicals were in the order of (highest to lowest): enrofloxacin \u3e monensin \u3e atrazine \u3e sulfamethazine. Desorption hysteresis were found for enrofloxacin, atrazine and sulfamethazine when the wood chips were desorbed by water. For monensin, the desorption aqueous phase concentrations were larger than the adsorption aqueous phase content. A possible reason for the larger desorption concentration was that the monensin adsorbed onto wood chips were on the eternal surface of the wood chips due to its larger molecular structure which allowed monensin to be easily desorbed. Only 5% of enrofloxacin, 14% of monensin, 23% of sulfamethazine and 25% of atrazine were recovered from the wood chips after two desorption and an acetonitrile–water extraction indicating the strong binding of the chemicals onto wood chips. Degradation studies with atrazine, enrofloxacin, and sulfamethazine onto wood chips indicate that a large majority of the chemical mass was removed from the aqueous phase within the first 48 hours followed by a slow removal over time. Dissipation rates were estimated using the availability-adjusted first-order degradation model. Disappearance of sulfamethazine was slower than disappearance of enrofloxacin and atrazine. No impact on denitrifiers as measured by the denitrification potential assays, most-probable-number (MPN) and nosZ1 copy number was found for atrazine at an initial concentration of 5 mg L-1. The MPN was reduced under enrofloxacin treatment after 2 days of the incubation; however, at the end of the experiment the denitrifier MPN was similar to control treatment MPN. Sulfamethazine was found to initially impact the denitrification (both MPN, nosZ1 copy number and denitrification potential) but after 5 days the denitrification potential assays, most-probable–number (MPN) and nosZ1 copy number were found to be similar to that of the control

Deciphering the complex interplay between microbiota, HPV, inflammation and cancer through cervicovaginal metabolic profiling

Background: Dysbiotic vaginal microbiota have been implicated as contributors to persistent HPV-mediated cervical carcinogenesis and genital inflammation with mechanisms unknown. Given that cancer is a metabolic disease, metabolic profiling of the cervicovaginal microenvironment has the potential to reveal the functional interplay between the host and microbes in HPV persistence and progression to cancer. Methods: Our study design included HPV-negative/positive controls, women with low-grade and high-grade cervical dysplasia, or cervical cancer (n = 78). Metabolic fingerprints were profiled using liquid chromatography-mass spectrometry. Vaginal microbiota and genital inflammation were analysed using 16S rRNA gene sequencing and immunoassays, respectively. We used an integrative bioinformatic pipeline to reveal host and microbe contributions to the metabolome and to comprehensively assess the link between HPV, microbiota, inflammation and cervical disease. Findings: Metabolic analysis yielded 475 metabolites with known identities. Unique metabolic fingerprints discriminated patient groups from healthy controls. Three-hydroxybutyrate, eicosenoate, and oleate/vaccenate discriminated (with excellent capacity) between cancer patients versus the healthy participants. Sphingolipids, plasmalogens, and linoleate positively correlated with genital inflammation. Non-Lactobacillus dominant communities, particularly in high-grade dysplasia, perturbed amino acid and nucleotide metabolisms. Adenosine and cytosine correlated positively with Lactobacillus abundance and negatively with genital inflammation. Glycochenodeoxycholate and carnitine metabolisms connected non-Lactobacillus dominance to genital inflammation. Interpretation: Cervicovaginal metabolic profiles were driven by cancer followed by genital inflammation, HPV infection, and vaginal microbiota. This study provides evidence for metabolite-driven complex host-microbe interactions as hallmarks of cervical cancer with future translational potential. (C) 2019 The Authors. Published by Elsevier B.V.Flinn Foundation [1974]; Banner Foundation in Obstetrics and Gynecology Research; National Institutes of Health NCI [P30 CA023074]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Microbiome après une chirurgie bariatrique et connaissances microbiennes sur la perte de poids chirurgicale

Obesity is a worldwide epidemic accompanied by multiple comorbidities. Bariatric surgery is currently the most efficient treatment for morbid obesity and its comorbidities. The etiology of obesity is unknown, although genetic, environmental, and most recently, microbiome elements have been recognized as contributors to this rising epidemic. The role of the gut microbiome in weight-loss or weight-gain warrantsinvestigation, and bariatric surgery provides a good model to study influences of the microbiome on host metabolism. The underlying goals of my research were to analyze (i) the factors that change the microbiome after bariatric surgery, (ii) the effects of different types of bariatric surgeries on the gut microbiome and metabolism, (iii) the role of the microbiome on the success of bariatric surgery, and (iv) temporal and spatial changes of the microbiome after bariatric surgery. Roux-en-Y gastric bypass (RYGB) rearranges the gastrointestinal tract and reduces gastric acid secretions. Therefore, pH could be one of the factors that change microbiome after RYGB. Using mixed-cultures and co-cultures of species enriched after RYGB, I showed that as small as 0.5 units higher gut pH can aid in the survival of acid sensitive microorganisms after RYGB and alter gut microbiome function towards the production of weight loss associated metabolites. By comparing microbiome after two different bariatric surgeries, RYGB and laparoscopic adjustable gastric banding (LAGB), I revealed that gut microbiome structure and metabolism after RYGB are remarkably different than LAGB, and LAGB change microbiome minimally. Given the distinct RYGB alterations to the microbiome, I examined the contribution of the microbiome to weight loss. Analyses revealed that Fusobacterium might lessen the success of RYGB by producing putrescine, which may enhance weight-gain and could serve as biomarker for unsuccessful RYGB. Finally, I showed that RYGB alters the luminal and the mucosal microbiome. Changes in gut microbial metabolic products occur in the short-term and persist over the long-term. Overall, the work in this dissertation provides insight into how the gut microbiome structure and function is altered after bariatric surgery, and how these changes potentially affect the host metabolism. These findings will be helpful in subsequent development of microbiome-based therapeutics to treat obesity.L'obésité est une épidémie mondiale accompagnée de multiples comorbidités. La chirurgie bariatrique est actuellement le traitement le plus efficace de l'obésité morbide et de ses comorbidités. L’étiologie de l’obésité est inconnue, bien que des éléments génétiques, environnementaux et, plus récemment, du microbiome aient été reconnus comme contribuant à cette épidémie croissante. Le rôle du microbiome intestinal dans la perte ou la prise de poids est justifiéL’investigation et la chirurgie bariatrique constituent un bon modèle pour étudier les influences du microbiome sur le métabolisme de l’hôte. Les objectifs sous-jacents de mes recherches étaient d'analyser (i) les facteurs qui modifient le microbiome après une chirurgie bariatrique, (ii) les effets de différents types de chirurgies bariatriques sur le microbiome intestinal et le métabolisme, (iii) le rôle du microbiome sur le succès de la chirurgie bariatrique, et (iv) changements temporels et spatiaux du microbiome après une chirurgie bariatrique. Le pontage gastrique Roux-en-Y (RYGB) réorganise le tractus gastro-intestinal et réduit les sécrétions d'acide gastrique. Par conséquent, le pH pourrait être l’un des facteurs qui modifient le microbiome après le RYGB. En utilisant des cultures mixtes et des co-cultures d'espèces enrichies après le RYGB, j'ai montré qu'un pH intestinal aussi petit que 0,5 unité plus élevé peut contribuer à la survie des micro-organismes sensibles aux acides après le RYGB et modifier la fonction du microbiome intestinal vers la production de métabolites associés à la perte de poids. En comparant le microbiome après deux chirurgies bariatriques différentes, le RYGB et l'anneau gastrique réglable laparoscopique (LAGB), j'ai révélé que la structure et le métabolisme du microbiome intestinal après le RYGB sont remarquablement différents de ceux du LAGB, et que le LAGB modifie le microbiome de manière minime. Compte tenu des altérations distinctes du RYGB dans le microbiome, j’ai examiné la contribution du microbiome à la perte de poids. Les analyses ont révélé que Fusobacterium pourrait diminuer le succès du RYGB en produisant de la putrescine, ce qui pourrait améliorer la prise de poids et pourrait servir de biomarqueur en cas d'échec du RYGB. Enfin, j’ai montré que le RYGB altère le microbiome luminal et muqueux. Les changements dans les produits métaboliques microbiens intestinaux se produisent à court terme et persistent à long terme. Dans l’ensemble, les travaux de cette thèse donnent un aperçu de la manière dont la structure et la fonction du microbiome intestinal sont modifiées après une chirurgie bariatrique, et de la manière dont ces changements affectent potentiellement le métabolisme de l’hôte. Ces résultats seront utiles au développement ultérieur de thérapies basées sur le microbiome pour traiter l’obésité

L'exposition longitudinale aux médicaments antiépileptiques façonne la composition du microbiome intestinal, du résistome et du métabolome

International audienc

Effects of Multiple Electron Acceptors on Microbial Interactions in a Hydrogen-Based Biofilm

To investigate interactions among multiple electron acceptors in a H₂-fed biofilm, we operated a membrane biofilm reactor with H₂-delivery capacity sufficient to reduce all acceptors. ClO₄^– and O₂ were input electron acceptors in all stages at surface loadings of 0.08 ± 0.006 g/m²-d (1.0 ± 0.7 e^– meq/m²-d) for ClO₄^– and 0.51 g/m²-d (76 e^– meq/m²-d) for O₂. SO₄^2– was added in Stage 2 at 3.77 ± 0.39 g/m²-d (331 ± 34 e^– meq/m²-d), and NO₃^– was further added in Stage 3 at 0.72 ± 0.03 g N/m²-d (312 ± 13 e^– meq/m²-d). At steady state for each stage, ClO₄^–, O₂, and NO₃^– (when present in the influent) were completely reduced; measured SO₄^2– reduction decreased from 78 ± 4% in Stage 2 to 59 ± 4% in Stage 3, when NO₃^– was present. While perchlorate-reducing bacteria (PRB), assayed by qPCR targeting the <i>pcrA</i> gene, remained stable throughout, sulfate-reducing bacteria (SRB), assayed by the <i>dsrA</i> gene, increased almost 3 orders of magnitude when significant SO₄^2– reduction occurred in stage 2. The abundance of denitrifying bacteria (DB), assayed by the <i>nirK</i> and <i>nirS</i> genes, increased in Stage 3, while SRB remained at high numbers, but did not increase. Based on pyrosequencing analyses, <i>β-Proteobacteria</i> dominated in Stage 1, but <i>ε-Proteobacteria</i> became more important in Stages 2 and 3, when the input of multiple electron acceptors favored genera with broader electron-accepting capabilities. <i>Sulfuricurvum</i> (a sulfur oxidizer and NO₃^– reducer) and <i>Desulfovibrio</i> (a SO₄^2– reducer) become dominant in Stage 3, suggesting redox cycling of sulfur in the biofilm

Reduced Incidence of <i>Prevotella</i> and Other Fermenters in Intestinal Microflora of Autistic Children

<div><p>High proportions of autistic children suffer from gastrointestinal (GI) disorders, implying a link between autism and abnormalities in gut microbial functions. Increasing evidence from recent high-throughput sequencing analyses indicates that disturbances in composition and diversity of gut microbiome are associated with various disease conditions. However, microbiome-level studies on autism are limited and mostly focused on pathogenic bacteria. Therefore, here we aimed to define systemic changes in gut microbiome associated with autism and autism-related GI problems. We recruited 20 neurotypical and 20 autistic children accompanied by a survey of both autistic severity and GI symptoms. By pyrosequencing the V2/V3 regions in bacterial 16S rDNA from fecal DNA samples, we compared gut microbiomes of GI symptom-free neurotypical children with those of autistic children mostly presenting GI symptoms. Unexpectedly, the presence of autistic symptoms, rather than the severity of GI symptoms, was associated with less diverse gut microbiomes. Further, rigorous statistical tests with multiple testing corrections showed significantly lower abundances of the genera <i>Prevotella</i>, <i>Coprococcus</i>, and unclassified <i>Veillonellaceae</i> in autistic samples. These are intriguingly versatile carbohydrate-degrading and/or fermenting bacteria, suggesting a potential influence of unusual diet patterns observed in autistic children. However, multivariate analyses showed that autism-related changes in both overall diversity and individual genus abundances were correlated with the presence of autistic symptoms but not with their diet patterns. Taken together, autism and accompanying GI symptoms were characterized by distinct and less diverse gut microbial compositions with lower levels of <i>Prevotella</i>, <i>Coprococcus</i>, and unclassified <i>Veillonellaceae</i>.</p></div

Top 10 genera generating the highest area under curves in a receiver operating characteristics curve.

<p>A highest area under curves (AUC) value of 0.5 indicates no predictive, while an AUC of 1 indicates perfect ability to predict.</p

Comparison of gut microbiota within the genus <i>Prevotella</i> between neurotypical and autistic children.

<p>(A) Heat map profile and dendrogram (A01-A19: autistic children, N01–N20: neurotypical children). A red, orange, and blue scale bar represents scores of autistic symptoms, GI problems, and a log scale of the percentile abundance from a total bacteria, respectively. (B) Phylogenetic tree within the genus <i>Prevotella</i>. (C) The weighted UniFrac analysis with <i>Prevotella</i> copri-like 16 OTUs. Jackknife counts over 50 out of 100 are shown.</p

Comparison on bacterial richness and diversity between neurotypical and autistic children.

<p>(A) Rarefaction curves showing unique OTUs at the 95% threshold (a box graph at the rarefied sequence number), comparison of (B) Chao1 estimators and (C) phylogenetic diversity (PD) index between neurotypical (blue-colored box) and autistic (red-colored box) groups at different similarity thresholds (*: P<0.05, **: P<0.01 by one-tailed Mann-Whitney test).</p

Genus level comparison of gut microbiota between neurotypical and autistic children.

<p>(A) Heat map profile and dendrogram of all identified genera (A01-A19: autistic children, N01–N20: neurotypical children). A red, orange, and blue scale bar represents scores of autistic symptoms, GI problems, and a log scale of the percentile abundance from a total bacteria, respectively. (B) Principal Component Analysis at the genus level from the autistic and neurotypical children. Blue- and red-, and black-colored dots represent neurotypical, autistic samples, and 16 selected genera, respectively. Three genera representing enterotypes (23) were identified in bold. (C) The gradient of <i>Prevotella</i> and <i>Bacteroides</i> through neurotypical and autistic children (*: P<0.05 by Mann-Whitney test).</p