Using DNA Metabarcoding To Evaluate the Plant Component of Human Diets: a Proof of Concept

Reese, Aspen T.; Kartzinel, Tyler R.; Petrone, Brianna L.; Pringle, Robert M.; David, Lawrence A.

doi:10.1128/msystems.00458-19

Cited by 24 publications

(29 citation statements)

References 40 publications

(66 reference statements)

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“…Caveats including the observer effect exist in professional-assisted reporting as well, thus novel objective measurement techniques (e.g. DNA metabarcoding 25 ) or combinations of approaches may better estimate real effects. Regardless, well-reported associations between disease states and host variables were recapitulated in our dataset (e.g.…”

Section: Caveats To Data Interpretationmentioning

confidence: 99%

Host variables confound gut microbiota studies of human disease

Vujkovic-Cvijin

Sklar

Jiang

et al. 2020

Nature

381

319

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Low concordance between studies that examine the microbiota in human diseases is a pervasive challenge that limits capacity to identify causal relationships between host-associated microbes and pathology. Risks of obtaining false positives in human microbiota research are exacerbated by wide inter-individual heterogeneity in microbiota composition 1 likely due to population-wide differences in human lifestyle and physiological variables 2 that exert differential impacts on the microbiota. Herein, we infer the greatest, generalized sources of heterogeneity in human gut microbiota profiles and, further, identify human lifestyle and physiological characteristics that, if not evenly matched between cases and controls, confound microbiota analyses to produce spurious microbial associations with human diseases. Surprisingly, we identify alcohol consumption frequency and bowel movement quality as unexpectedly strong sources of gut microbiota variance that differ in distribution between healthy and diseased subjects and can confound study designs. We demonstrate that for numerous prevalent, high-burden human diseases, matching cases and controls for confounding variables reduces observed microbiota differences and incidence of spurious associations. Thus, we present a list of recommended host variables to capture in human microbiota studies for the purpose of matching comparison groups, which we anticipate will increase robustness and reproducibility in resolving true disease-associated gut microbiota members in human disease.

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Section: Caveats To Data Interpretationmentioning

confidence: 99%

Host variables confound gut microbiota studies of human disease

Vujkovic-Cvijin

Sklar

Jiang

et al. 2020

Nature

381

319

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“…Food sources and overall dietary composition should ultimately be included in analysis of dietary intervention studies that aim to connect dietary changes to microbial compositional shifts. Incorporation of nutrient origin analysis using new multivariate methods as employed by Johnson et al in their 2019 observational longitudinal study, or DNA metabarcoding techniques such as those employed by Reese et al, may provide further insight into the effect of food sources on the diet-microbiota relationship [35,116].…”

Section: Nutrient Centred Designs Vs Whole Food Approaches To Dietarmentioning

confidence: 99%

Effect of Diet on the Gut Microbiota: Rethinking Intervention Duration

et al. 2019

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The human gut is inhabited by trillions of microorganisms composing a dynamic ecosystem implicated in health and disease. The composition of the gut microbiota is unique to each individual and tends to remain relatively stable throughout life, yet daily transient fluctuations are observed. Diet is a key modifiable factor influencing the composition of the gut microbiota, indicating the potential for therapeutic dietary strategies to manipulate microbial diversity, composition, and stability. While diet can induce a shift in the gut microbiota, these changes appear to be temporary. Whether prolonged dietary changes can induce permanent alterations in the gut microbiota is unknown, mainly due to a lack of long-term human dietary interventions, or long-term follow-ups of short-term dietary interventions. It is possible that habitual diets have a greater influence on the gut microbiota than acute dietary strategies. This review presents the current knowledge around the response of the gut microbiota to short-term and long-term dietary interventions and identifies major factors that contribute to microbiota response to diet. Overall, further research on long-term diets that include health and microbiome measures is required before clinical recommendations can be made for dietary modulation of the gut microbiota for health.

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“…Improvement in dietary assessment by shortening the time of data collection and reducing participant workload would improve the overall quality of dietary intervention trials. Recent work has applied the technique of metabarcoding to evaluate the plant component of diet from stool samples using the trnL-P6 marker gene (114). Similar techniques have previously been used to assess diet in non-human primate populations (115,116); demonstrating that DNA from plant species persists through digestion and can be identified in stool.…”

Section: Improved Dietary Assessment Methodologies Could Improve Dietmentioning

confidence: 99%

A Guide to Diet-Microbiome Study Design

et al. 2020

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Intense recent interest in understanding how the human gut microbiome influences health has kindled a concomitant interest in linking dietary choices to microbiome variation. Diet is known to be a driver of microbiome variation, and yet the precise mechanisms by which certain dietary components modulate the microbiome, and by which the microbiome produces byproducts and secondary metabolites from dietary components, are not well-understood. Interestingly, despite the influence of diet on the gut microbiome, the majority of microbiome studies published to date contain little or no analysis of dietary intake. Although an increasing number of microbiome studies are now collecting some form of dietary data or even performing diet interventions, there are no clear standards in the microbiome field for how to collect diet data or how to design a diet-microbiome study. In this article, we review the current practices in diet-microbiome analysis and study design and make several recommendations for best practices to provoke broader discussion in the field. We recommend that microbiome studies include multiple consecutive microbiome samples per study timepoint or phase and multiple days of dietary history prior to each microbiome sample whenever feasible. We find evidence that direct effects of diet on the microbiome are likely to be observable within days, while the length of an intervention required for observing microbiome-mediated effects on the host phenotype or host biomarkers, depending on the outcome, may be much longer, on the order of weeks or months. Finally, recent studies demonstrating that dietmicrobiome interactions are personalized suggest that diet-microbiome studies should either include longitudinal sampling within individuals to identify personalized responses, or should include an adequate number of participants spanning a range of microbiome types to identify generalized responses.

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Using DNA Metabarcoding To Evaluate the Plant Component of Human Diets: a Proof of Concept

Cited by 24 publications

References 40 publications

Host variables confound gut microbiota studies of human disease

Host variables confound gut microbiota studies of human disease

Effect of Diet on the Gut Microbiota: Rethinking Intervention Duration

A Guide to Diet-Microbiome Study Design

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