Response to Comment on Sofi et al. pH of Drinking Water Influences the Composition of Gut Microbiome and Type 1 Diabetes Incidence. Diabetes 2014;63:632–644

Sofi, M. Hanief; Johnson, Benjamin M.; Gudi, Radhika; Wolf, Kyle J.; Lorenz, Robin G.; Vasu, Chenthamarakshan

doi:10.2337/db15-0554

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Previous studies have reported divergent effects of water acidification on the NOD mouse model [ 16 , 17 , 46 ], suggesting that water treatment method can have a significant influence on model phenotypes. Since these studies were performed in different institutions [ 47 ], these divergent effects of water decontamination in the NOD model may be due to an interaction with some other unrecognized variable (diet, bedding, caging, facility, etc.). Since several of these husbandry related variables are known to alter the GM [ 14 ], understanding how these factors interact to alter the GM will aid in addressing these experimental discrepancies.…”

Section: Discussionmentioning

confidence: 99%

Effects of water decontamination methods and bedding material on the gut microbiota

2018

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Rodent models are invaluable to understanding health and disease in many areas of biomedical research. Unfortunately, many models suffer from lack of phenotype reproducibility. Our laboratory has shown that differences in gut microbiota (GM) can modulate phenotypes of models of colon cancer and inflammatory bowel disease. We and others have also shown that a number of factors associated with rodent research, including vendor, cage system, and bedding can alter GM. The objective of this study was to expand these studies to examine the effect of additional bedding materials and methods of water decontamination on GM diversity and composition. To this end, Crl:CD1 (ICR) mice were housed on corn cob or compressed paper chip bedding and provided water that was decontaminated by four different methods: autoclaving with reverse osmosis, autoclaving with hydrochloric acid, autoclaving with sulfuric acid, and autoclaving alone. Feces was collected at day 0, and at day 28 (endpoint), fecal and cecal samples were collected. DNA was extracted from samples, amplified by PCR using conserved bacterial primer sets and subjected to next generation sequencing. Sequence data were analyzed using Qiime and groups were compared using principal coordinate analysis (PCoA) and permutational multivariate analysis of variance (PERMANOVA). Two factor PERMANOVA of cecal GM data revealed significant changes when comparing bedding and water decontamination methods, while no significant effects were noted in the fecal GM data. Subsequent PERMANOVA and PCoA of cecal data revealed that several combinations of bedding and water decontamination methods resulted in differing GM, highlighting the complexity by which environmental factors interact to modulate GM.

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Section: Discussionmentioning

confidence: 99%

Effects of water decontamination methods and bedding material on the gut microbiota

2018

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“…Despite this complexity, global sequencing surveys of natural communities have revealed ubiquitous patterns relating the abundance of the genes that make up these pathways to local environmental variables. For example, bacterial metabolic capabilities vary with nutrient levels in marine systems (1), and gene content changes with soil pH (2)(3)(4) and host diet (5)(6)(7).…”

Section: Introductionmentioning

confidence: 99%

Global patterns in gene content of soil microbiomes emerge from microbial interactions

Crocker,

Lee,

Chakraverti-Wuerthwein

et al. 2023

Preprint

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Microbial metabolism sustains life on Earth. Global sequencing surveys reveal ubiquitous patterns linking the taxa and genes responsible for metabolism with environmental conditions. One explanation for these patterns is environmental filtering: local conditions select for strains with particular traits. However, filtering assumes ecological interactions do not influence patterns. Here, we demonstrate a clear example of interactions driving global patterns in topsoil microbiomes. We find a global trade-off between two nitrate reduction genes and pH:nargene abundances increase whilenapdecreases with declining pH. Experiments contradict filtering by revealing strains possessing Nar thrive at low pH due to an interaction with Nap strains that mitigates nitrite toxicity. Thus, associations between environmental variables and gene abundances arise from environmentally modulated interactions driven by conserved metabolic traits.

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“…Contrastingly, a study by Sofi and colleagues found that changing the drinking water from acidic (pH ≈ 3) to neutral (pH ≈ 7) decreased diabetes incidence and rate of disease progression, accompanied by a reduction in the abundance of the genus Bacteroides and some species of Prevotella ( P. oris , P. melaninogenica , P. loescheii , and P. copri ), whereas other species of Prevotella ( P. multiformis and P. shahii ) were increased along with species of Parabacteroides ( P. goldsteinii and P. distasonis ) 15 . Differences in baseline gut microbiota, along with difference in other environmental circumstances, have been proposed as an explanation for the conflicting results regarding diabetes incidence and rate of progression 16 , 17 . Importantly, both studies showed substantial differences in the gut microbiota of mice drinking acidic and neutral water, demonstrating that drinking water pH can have a profound impact on the gut microbial community.…”

Section: Introductionmentioning

confidence: 99%

The effect of drinking water pH on the human gut microbiota and glucose regulation: results of a randomized controlled cross-over intervention

Hansen

Thomassen

Madsen

et al. 2018

Sci Rep

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Studies in rodent models have shown that alterations in drinking water pH affect both the composition of the gut microbiota and host glucose regulation. To explore a potential impact of electrochemically reduced alkaline (pH ≈ 9) versus neutral (pH ≈ 7) drinking water (2 L/day) on human intestinal microbiota and host glucose metabolism we conducted a randomized, non-blinded, cross-over study (two 2-week intervention periods, separated by a 3-week wash-out) in 29 healthy, non-smoking Danish men, aged 18 to 35 years, with a body mass index between 20.0 to 27.0 kg m-2. Volunteers were ineligible if they had previously had abdominal surgery, had not been weight stabile for at least two months, had received antibiotic treatment within 2 months, or had a habitual consumption of caloric or artificially sweetened beverages in excess of 1 L/week or an average intake of alcohol in excess of 7 units/week. Microbial DNA was extracted from faecal samples collected at four time points, before and after each intervention, and subjected to 16S rRNA gene amplicon sequencing (Illumina MiSeq, V4 region). Glycaemic regulation was evaluated by means of an oral glucose tolerance test.No differential effect of alkaline versus neutral drinking water was observed for the primary outcome, overall gut microbiota diversity as represented by Shannon’s index. Similarly, neither a differential effect on microbiota richness or community structure was observed. Nor did we observe a differential effect on the abundance of individual operational taxonomic units (OTUs) or genera. However, analyses of within period effects revealed a significant (false discovery rate ≤5%) increase in the relative abundance of 9 OTUs assigned to order Clostridiales, family Ruminococcaceae, genus Bacteroides, and species Prevotella copri, indicating a potential effect of quantitative or qualitative changes in habitual drinking habits. An increase in the concentration of plasma glucose at 30 minutes and the incremental area under the curve of plasma glucose from 0 30 and 0 120 minutes, respectively, was observed when comparing the alkaline to the neutral intervention. However, results did not withstand correction for multiplicity. In contrast to what has been reported in rodents, a change in drinking water pH had no impact on the composition of the gut microbiota or glucose regulation in young male adults. The study is registered at www.clinicaltrials.gov (NCT02917616).

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Response to Comment on Sofi et al. pH of Drinking Water Influences the Composition of Gut Microbiome and Type 1 Diabetes Incidence. Diabetes 2014;63:632–644

Cited by 3 publications

References 3 publications

Effects of water decontamination methods and bedding material on the gut microbiota

Effects of water decontamination methods and bedding material on the gut microbiota

Global patterns in gene content of soil microbiomes emerge from microbial interactions

The effect of drinking water pH on the human gut microbiota and glucose regulation: results of a randomized controlled cross-over intervention

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