Cereal products derived from wheat, sorghum, rice and oats alter the infant gut microbiota in vitro

Durand

Bloom

et al. 2018

Preprint

We analyzed the artificial gut dataset described below using a MALLARD model that is generative and assumes there exists an unobserved microbial composition ( " ; the state) that evolves through time ( Fig. 1A) due to stochastic biological variations ( " ). 115We regard the state sequence ( $ , … , " , … , ' ) as the true microbial dynamics in a timeseries. Random technical variations ( " ) are then added to the true system state ( " ) resulting in the composition " (Fig. 1B). We observe " through a multinomial counting process. This formulation is similar to the constant level model commonly used in Bayesian time-series analysis (34). By separately modeling the process generating " 120 and " with distinct covariance matrices ( and , respectively), we can decouple biological and technical variations in artificial gut datasets (Fig. 1C). Visually, we found this model provided a good fit to artificial gut data (Fig. S1). Daily and hourly gut microbiota time-series in an artificial human gut 125We applied our model to an artificial gut that was constructed using continuousflow anaerobic bioreactor systems that have been validated as models of human gut microbiota (1,6,15, 35,36). The same starting human fecal inoculum was seeded into replicate ex vivo vessels (n=4) and cultured for 1 month (Fig. 2 and S2). Throughout the experiment pH, temperature, media input rates, and oxygen concentration were all 130 fixed (Methods). To introduce microbial dynamics into our systems, a single bolus of Bacteroides ovatus isolated from the stool donor was administered to the system on Day 23 (Methods). The B. ovatus bolus did not have discernable effects on microbial dynamics, but the media it was suspended in appeared to induce minor shifts in the relative abundances of select bacterial taxa that were visible with hourly sampling (Fig. 135

Section: Introductionmentioning

confidence: 99%

Dynamic linear models guide design and analysis of microbiota studies within artificial human guts

Durand

Bloom

et al. 2018

Preprint

“…As our fermentation experiments used previously frozen fecal samples, we verified that total SCFA production was strongly correlated between fresh samples and twice freeze-thawed samples (p < 0.0001, ρ = 0.75, Spearman correlation; Figure S4A). Since we elected to not provide our fermentation reactions with nutrients in excess of what was contained in the fecal slurries, we verified that there existed strong correlation in total SCFA production between PBS-grown and colonic medium-grown cultures (46), both when supplied with dextrin and inulin (Dextrin: p = 0.001, ρ = 0.68; inulin: p = 0.02, ρ = 0.51; Spearman correlations; Figure S5). We found that total SCFA production over control was positively correlated with the pH of starting fecal slurries (p = 0.003, ρ = 0.46; Spearman correlation; Figure S6A).…”

Section: Resultsmentioning

confidence: 93%

“…Once thawed, stool was weighed and placed into a polyethylene filter bag with 0.33mm pore size (Whirl-Pak B01385) and 10mL of anaerobic 1X PBS was added for each gram of stool, resulting in a 10% w/v fecal slurry, similar to previous studies (41, 42, 70, 71). During our validation experiments, a medium designed to simulate colonic contents was used in place of 1X PBS to create stool slurries (46). The filter bag was then closed and placed into a stomacher (Seward Stomacher 80) where the contents were homogenized on the medium speed setting for 60 seconds.…”

Section: Methodsmentioning

confidence: 99%

“…To validate our methods, namely our use of a 5% fecal slurry in PBS, without supplementation of other nutrient components, we compared SCFA production with our methods to SCFA production when stool was instead resuspended in a medium designed to simulate the large intestine. We used a slightly modified medium derived from Gamage et al 2017 (46). The media contained, per liter: peptone 0.5g, yeast extract 0.5g, NaHCO3 6g, hemin solution (0.5% (w/v) hemin and 0.2% (w/v) NaOH) 100uL, L-cysteine HCl monohydrate 0.53g, bile salts 0.5g, Vitamin Supplement (ATCC MD-VS) 1mL, K2HPO4 0.228g, KH2PO4 0.228g, (NH4)2SO4 0.228g, NaCl 0.456g, MgSO4 0.0456g, CaCl2 0.0460g, Trace Mineral Supplement (ATCC MD-TMS) 1mL, and 287uL glacial acetic acid.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Short-chain fatty acid production by gut microbiota from children with obesity is linked to bacterial community composition and prebiotic choice

Holmes

Dressman

et al. 2020

Preprint

23Pediatric obesity remains a public health burden and continues to increase in 24 prevalence. The gut microbiota plays a causal role in obesity and is a promising 25 therapeutic target. Specifically, the microbial production of short-chain fatty acids 26 (SCFA) from the fermentation of otherwise indigestible dietary carbohydrates may 27 protect against pediatric obesity and metabolic syndrome. Still, it has not been 28 demonstrated that therapies involving microbiota-targeting carbohydrates, known as 29 prebiotics, will enhance gut bacterial SCFA production in children and adolescents with 30 obesity (age 10-18). Here, we used an in vitro system to examine the SCFA production 31 by fecal microbiota from 17 children with obesity when exposed to five different 32 commercially available over-the-counter (OTC) prebiotic supplements. We found 33 microbiota from all 17 patients actively metabolized most prebiotics. Still, supplements 34 varied in their acidogenic potential. Significant inter-donor variation also existed in SCFA 35 production, which 16S rRNA sequencing supported as being associated with differences 36 in the host microbiota composition. Last, we found that neither fecal SCFA 37 concentration, microbiota SCFA production capacity, nor markers of obesity positively 38 correlated with one another. Together, these in vitro findings suggest the hypothesis that 39 OTC prebiotic supplements may be unequal in their ability to stimulate SCFA production 40 in children and adolescents with obesity, and that the most acidogenic prebiotic may 41 differ across individuals. 42 43 44 IMPORTANCE Pediatric obesity remains a major public health problem in the US, where 45 17% of children and adolescents are obese, and rates of pediatric 'severe obesity' are 46 increasing. Children and adolescents with obesity face higher health risks, and non-47 invasive therapies for pediatric obesity often have limited success. The human gut 48 microbiome has been implicated in adult obesity, and microbiota-directed therapies can 49 aid weight loss in adults with obesity. However, less is known about the microbiome in 50 pediatric obesity, and microbiota-directed therapies are understudied in children and 51 adolescents. Our research has two important findings: 1) dietary prebiotics (fiber) cause 52 the microbiota from adolescents with obesity to produce more SCFA, and 2) the 53 effectiveness of each prebiotic is donor-dependent. Together, these findings suggest 54 that prebiotic supplements could help children and adolescents with obesity, but that 55 these therapies may not be one-size-fits-all. 56 developed long-term complications, we pursued three specific lines of inquiry: 1) whether 132 different types of prebiotics lead to differences in SCFA production by gut microbiota from 133 adolescents with obesity; 2) whether the effects of prebiotics are shaped by inter-134 individual differences in gut microbiota structure; and, 3) whether fecal SCFA production 135 is associated with protection from obesity. 136

“…In a second set of demonstration experiments, we used the MicDrop platform to explore the dynamics of human gut microbiota in artificial gut systems. Such systems have been used when in vivo microbiota research is challenging, including measuring the effects of nutrition on the infant gut, systematic antibiotic testing, and investigating chemotherapy-induced dysbioses 47,48,49 . Still, a recurring challenge of artificial gut models has been their inability to completely reconstruct in vivo microbial communities.…”

Section: Resultsmentioning

confidence: 99%

High-throughput isolation and culture of human gut bacteria with droplet microfluidics

et al. 2019

Preprint

30Isolation and culture of gut bacteria enable testing for microbial roles in disease and 31 may also lead to novel therapeutics. However, the diversity of human gut microbial 32 communities (microbiota) impedes comprehensive experimental studies of individual 33 bacterial taxa. Here, we combine advances in droplet microfluidics and high-throughput 34 DNA sequencing to develop a platform for isolating and assaying microbiota members 35 in picoliter droplets (MicDrop). MicDrop can be used to create millions of distinct 36 bacterial colonies in a single experiment while using off-the-shelf parts compact enough 37 to fit in an anaerobic chamber. In proof-of-concept experiments, we used the platform to 38 characterize inter-individual metabolic variation among hundreds of polysaccharide-39 degrading gut bacteria from nine stool donors. We also used MicDrop to test the 40 hypothesis that growth kinetics of individual gut bacterial taxa are associated with long-41 term community dynamics in an artificial gut. These demonstrations suggest the 42 MicDrop platform could support future diagnostic efforts to personalize microbiota-43 directed therapies, as well as to provide comprehensive new insights into the ecology of 44 human gut microbiota. 45