scholarly journals Deciphering microbial interactions in synthetic human gut microbiome communities

2017 ◽  
Author(s):  
Ophelia S. Venturelli ◽  
Alex C. Carr ◽  
Garth Fisher ◽  
Ryan H. Hsu ◽  
Rebecca Lau ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Community Assembly ◽  
Gut Microbiome ◽  
Community Dynamics ◽  
Microbial Interactions ◽  
Model Parameters ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Pairwise Interactions ◽  
Key Species

ABSTRACTThe human gut microbiota comprises a dynamic ecological system that contributes significantly to human health and disease. The ecological forces that govern community assembly and stability in the gut microbiota remain unresolved. We developed a generalizable model-guided framework to predict higher-order consortia from time-resolved measurements of lower-order assemblages. This method was employed to decipher microbial interactions in a diverse 12-member human gut microbiome synthetic community. We show that microbial growth parameters and pairwise interactions are the major drivers of multi-species community dynamics, as opposed to context-dependent (conditional) interactions. The inferred microbial interaction network as well as a top-down approach to community assembly pinpointed both ecological driver and responsive species that were significantly modulated by microbial inter-relationships. Our model demonstrated that negative pairwise interactions could generate history-dependent responses of initial species proportions on physiological timescales that frequently does not originate from bistability. The model elucidated a topology for robust coexistence in pairwise assemblages consisting of a negative feedback loop that balances disparities in monospecies fitness levels. Bayesian statistical methods were used to evaluate the constraint of model parameters by the experimental data. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis for competitive and cooperative interactions in the community. However, these data failed to predict influential organisms shaping community assembly. In sum, these methods defined the ecological roles of key species shaping community assembly and illuminated network design principles of microbial communities.

scholarly journals Alterations in human gut microbiome composition and metabolism after exposure to glyphosate and Roundup and/or a spore-based formulation using the SHIME® technology

2021 ◽  
Author(s):  
Robin Mesnage ◽  
Marta Calatayud ◽  
Cindy Duysburgh ◽  
Massimo Marzorati ◽  
Michael Antoniou
Keyword(s):  
Gut Microbiota ◽  
Gut Microbiome ◽  
Large Scale ◽  
Epidemiological Studies ◽  
Human Gut ◽  
Microbiome Composition ◽  
Human Gut Microbiome ◽  
Ammonium Production ◽  
Profiling Techniques ◽  
Microbial Environment

Despite extensive research into the toxicology of the herbicide glyphosate, there are still major unknowns regarding its effects on the human gut microbiome. As a step in addressing this knowledge gap, we describe for the first time the effects of glyphosate and a Roundup glyphosate-based herbicide on infant gut microbiota using SHIME technology, which mimics the entire gastrointestinal tract. SHIME microbiota culture was undertaken in the presence of a concentration of 100 mg/L (corresponding to a dose of 1.6 mg/kg/day) glyphosate and the same glyphosate equivalent concentration of Roundup, which is in the range of the US chronic reference dose, and subjected to molecular profiling techniques to assess outcomes. Roundup and to a lesser extent glyphosate caused an increase in fermentation activity, resulting in acidification of the microbial environment. This was also reflected by an increase in lactate and acetate production concomitant to a decrease in the levels of propionate, valerate, caproate and butyrate. Ammonium production reflecting proteolytic activities was increased by Roundup exposure. Global metabolomics revealed large scale disturbances in metabolite profiles, including an increased abundance of long chain polyunsaturated fatty acids (n3 and n6). Although changes in bacterial composition measured by qPCR and 16S rRNA sequencing were less clear, our results suggested that lactobacilli had their growth stimulated as a result of microenvironment acidification. Co-treatment with the spore-based probiotic formulation MegaSporeBiotic reverted some of the changes in short-chain fatty acid levels. Altogether, our results suggest that glyphosate can exert effects on human gut microbiota at permitted regulatory levels of exposure, highlighting the need for epidemiological studies aimed at evaluating the effects of glyphosate herbicides on human gut microbiome function.

scholarly journals Natural and after colon washing fecal samples: the two sides of the coin for investigating the human gut microbiome

2021 ◽  
Author(s):  
Elisabetta Piancone ◽  
Bruno Fosso ◽  
Mariangela De Robertis ◽  
Elisabetta Notario ◽  
Annarita Oranger ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Gut Microbiome ◽  
Digital Pcr ◽  
Shannon Index ◽  
Individual Variability ◽  
Rrna Gene ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Taxonomic Analysis ◽  
Paired Samples

To date there are several studies focusing on the importance of gut microbiome for human health, however the selection of a universal sampling matrix representative of the microbial biodiversity associated to the gastrointestinal (GI) tract, still represents a challenge. Here we present a study in which, through a deep metabarcoding analysis of the 16S rRNA gene, we compared two sampling matrices, feces (F) and colonic lavage liquid (LL), in order to evaluate their accuracy to represent the complexity of the human gut microbiome. A training set of 37 volunteers was attained and paired F and LL samples were collected from each subject. A preliminary absolute quantification of total 16S rDNA, performed by droplet digital PCR (ddPCR), confirmed that sequencing and taxonomic analysis were performed on same total bacterial abundance obtained from the two sampling methods. The taxonomic analysis of paired samples revealed that, although specific taxa were predominantly or exclusively observed in LL samples, as well as other taxa were detectable only or were predominant in stool, the microbiomes of the paired samples F and LL in the same subject hold overlapping taxonomic composition. Moreover, LL samples revealed a higher biodiversity than stool at all taxonomic ranks, as demonstrated by the Shannon Index and the Inverse Simpson's Index. We also found greater inter-individual variability than intra-individual variability in both sample matrices. Finally, functional differences were unveiled in the gut microbiome detected in the F and LL samples. A significant overrepresentation of 22 and 13 metabolic pathways, mainly occurring in Firmicutes and Proteobacteria, was observed in gut microbiota detected in feces and LL samples, respectively. This suggests that LL samples may allow for the detection of microbes adhering to the intestinal mucosal surface as members of the resident flora that are not easily detectable in stool, most likely representative of a diet-influenced transient microbiota. This first comparative study on feces and LL samples for the study of the human gut microbiome demonstrates that the use of both types of sample matrices may represent a possible choice to obtain a more complete view of the human gut microbiota in response to different biological and clinical questions.

scholarly journals Impact of Different Exercise Modalities on the Human Gut Microbiome

Sports ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 14
Author(s):  
Dierdra Bycura ◽  
Anthony C. Santos ◽  
Arron Shiffer ◽  
Shari Kyman ◽  
Kyle Winfree ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Gut Microbiome ◽  
Sequence Data ◽  
Exercise Program ◽  
Rrna Gene ◽  
Human Gut ◽  
Post Intervention ◽  
Cross Sectional ◽  
Intervention Phase ◽  
Human Gut Microbiome

In this study we examined changes to the human gut microbiome resulting from an eight-week intervention of either cardiorespiratory exercise (CRE) or resistance training exercise (RTE). Twenty-eight subjects (21 F; aged 18–26) were recruited for our CRE study and 28 subjects (17 F; aged 18–33) were recruited for our RTE study. Fecal samples for gut microbiome profiling were collected twice weekly during the pre-intervention phase (three weeks), intervention phase (eight weeks), and post-intervention phase (three weeks). Pre/post VO2max, three repetition maximum (3RM), and body composition measurements were conducted. Heart rate ranges for CRE were determined by subjects’ initial VO2max test. RTE weight ranges were established by subjects’ initial 3RM testing for squat, bench press, and bent-over row. Gut microbiota were profiled using 16S rRNA gene sequencing. Microbiome sequence data were analyzed with QIIME 2. CRE resulted in initial changes to the gut microbiome which were not sustained through or after the intervention period, while RTE resulted in no detectable changes to the gut microbiota. For both CRE and RTE, we observe some evidence that the baseline microbiome composition may be predictive of exercise gains. This work suggests that the human gut microbiome can change in response to a new exercise program, but the type of exercise likely impacts whether a change occurs. The changes observed in our CRE intervention resemble a disturbance to the microbiome, where an initial shift is observed followed by a return to the baseline state. More work is needed to understand how sustained changes to the microbiome occur, resulting in differences that have been reported in cross sectional studies of athletes and non-athletes.

scholarly journals Deciphering microbial interactions in synthetic human gut microbiome communities

2018 ◽  
Vol 14 (6) ◽  
Author(s):  
Ophelia S Venturelli ◽  
Alex V Carr ◽  
Garth Fisher ◽  
Ryan H Hsu ◽  
Rebecca Lau ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Microbial Interactions ◽  
Human Gut ◽  
Human Gut Microbiome

scholarly journals Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference

2019 ◽  
Vol 244 (6) ◽  
pp. 445-458 ◽  
Author(s):  
Anders B Dohlman ◽  
Xiling Shen
Keyword(s):  
Human Health ◽  
Gut Microbiome ◽  
Network Inference ◽  
Therapeutic Strategies ◽  
Microbial Interactions ◽  
Interaction Networks ◽  
Human Gut ◽  
Statistical Tools ◽  
Human Gut Microbiome ◽  
Network Analyses

Advances in high-throughput sequencing have ushered in a new era of research into the gut microbiome and its role in human health and disease. However, due to the unique characteristics of microbiome survey data, their use for the detection of ecological interaction networks remains a considerable challenge, and a field of active methodological development. In this review, we discuss the landscape of existing statistical and experimental methods for detecting and characterizing microbial interactions, as well as the role that host and environmental metabolic signals play in mediating the behavior of these networks. Numerous statistical tools for microbiome network inference have been developed. Yet due to tool-specific biases, the networks identified by these methods are often discordant, motivating a need for the development of more general tools, the use of ensemble approaches, and the incorporation of prior knowledge into prediction. By elucidating the complex dynamics of the microbial interactome, we will enhance our understanding of the microbiome’s role in disease, more precisely predict the microbiome’s response to perturbation, and inform the development of future therapeutic strategies for microbiome-related disease. Impact statement This review provides a comprehensive description of experimental and statistical tools used for network analyses of the human gut microbiome. Understanding the system dynamics of microbial interactions may lead to the improvement of therapeutic approaches for managing microbiome-associated diseases. Microbiome network inference tools have been developed and applied to both cross-sectional and longitudinal experimental designs, as well as to multi-omic datasets, with the goal of untangling the complex web of microbe-host, microbe-environmental, and metabolism-mediated microbial interactions. The characterization of these interaction networks may lead to a better understanding of the systems dynamics of the human gut microbiome, augmenting our knowledge of the microbiome’s role in human health, and guiding the optimization of effective, precise, and rational therapeutic strategies for managing microbiome-associated disease.

scholarly journals Metabolic Modeling of CommonEscherichia coliStrains in Human Gut Microbiome

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yue-Dong Gao ◽  
Yuqi Zhao ◽  
Jingfei Huang
Keyword(s):  
Gut Microbiome ◽  
Metabolic Networks ◽  
High Throughput Sequencing ◽  
Carbon Sources ◽  
Microbial Interactions ◽  
Dynamic Responses ◽  
Sequencing Data ◽  
Human Gut ◽  
E Coli ◽  
Human Gut Microbiome

The recent high-throughput sequencing has enabled the composition ofEscherichia colistrains in the human microbial community to be profiled en masse. However, there are two challenges to address: (1) exploring the genetic differences betweenE. colistrains in human gut and (2) dynamic responses ofE. colito diverse stress conditions. As a result, we investigated theE. colistrains in human gut microbiome using deep sequencing data and reconstructed genome-wide metabolic networks for the three most commonE. colistrains, includingE. coliHS, UTI89, and CFT073. The metabolic models show obvious strain-specific characteristics, both in network contents and in behaviors. We predicted optimal biomass production for three models on four different carbon sources (acetate, ethanol, glucose, and succinate) and found that these stress-associated genes were involved in host-microbial interactions and increased in human obesity. Besides, it shows that the growth rates are similar among the models, but the flux distributions are different, even inE. colicore reactions. The correlations between human diabetes-associated metabolic reactions in theE. colimodels were also predicted. The study provides a systems perspective onE. colistrains in human gut microbiome and will be helpful in integrating diverse data sources in the following study.

scholarly journals Microbe-seq: high-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome

2020 ◽  
Author(s):  
Wenshan Zheng ◽  
Shijie Zhao ◽  
Yehang Yin ◽  
Huidan Zhang ◽  
David M. Needham ◽  
...  
Keyword(s):  
Microbial Communities ◽  
High Throughput ◽  
Gut Microbiome ◽  
Bacterial Species ◽  
Microbial Interactions ◽  
Single Individual ◽  
Human Gut ◽  
Single Strain ◽  
Human Gut Microbiome

AbstractWe present Microbe-seq, a high-throughput single-microbe method that yields strain-resolved genomes from complex microbial communities. We encapsulate individual microbes into droplets with microfluidics and liberate their DNA, which we amplify, tag with droplet-specific barcodes, and sequence. We use Microbe-seq to explore the human gut microbiome; we collect stool samples from a single individual, sequence over 20,000 microbes, and reconstruct nearly-complete genomes of almost 100 bacterial species, including several with multiple subspecies strains. We use these genomes to probe genomic signatures of microbial interactions: we reconstruct the horizontal gene transfer (HGT) network within the individual and observe far greater exchange within the same bacterial phylum than between different phyla. We probe bacteria-virus interactions; unexpectedly, we identify a significant in vivo association between crAssphage, an abundant bacteriophage, and a single strain of Bacteroides vulgatus. Microbe-seq contributes high-throughput culture-free capabilities to investigate genomic blueprints of complex microbial communities with single-microbe resolution.

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