The composition and metabolic output of the human gut microbiome is shaped by several environmental factors, an important one being diet. To better understand the relationship between diet and microbiome function researchers must develop methods to integrate dietary and microbiome data. This is challenging due to several factors. Including the sheer complexity and volume of data to be captured and analysed; and the lack of standardised methods for collecting, analyzing, and interpreting data.
At the Microbiome Data Congress 2023, Boston, 13th-14th November, leading microbiome researchers from industry and academia will come together to discuss pressing microbiome data challenges such as “Integrating dietary and microbiome data”.
We spoke with Microbiome Data Congress 2023’s esteemed speakers to get their perspective on the subject:
What are the biggest challenges in integrating diet and microbiome data, and how do you address them?
Abigail Johnson, Assistant Professor, University of Minnesota
“Diet is hard to capture, which has implications when understanding how to connect diet data to the microbiome. When we record diet, we are working with people who may misremember what they eat or forget that they ate at all. People also tend to underestimate the amount they eat and often — intentionally or unintentionally — underreport what they eat. People also eat episodically, so we have to estimate their usual intake from a very small snapshot that might not represent their overall diet. During diet data entry, we need to connect the foods people report eating to dietary databases. Sometimes, this means selecting a similar — but not identical — food. Once the data is connected to a database, we rely on that database for information about nutrients and food components. The values in these databases are not measured from the food the person ate; they are estimates from reference databases. These reference databases are also built to measure essential nutrients for human health, so many potentially fermentable or bioactive food components are not included in the databases at all. For example, fibre is often a single variable that doesn’t reflect the chemical complexity of the different components that make up fibre in different fruits and vegetables. Taken together, these challenges result in a large amount of measurement error in dietary data.
Once data is collected, we must decide the best way to compare diet with the microbiome. In some cases, it makes sense to look at specific dietary components, such as fibre, in relation to the microbiome. In other cases, we want to look at the overall dietary pattern with a data-driven approach or by comparing diet to a reference index such as the healthy eating index. One approach I use to integrate diet data with the microbiome is to use food-level data. This avoids some of the limitations associated with nutrient estimation. I’ve developed methods that borrow tree-based techniques from microbiome analysis and applied them to dietary data. This allows us to keep highly detailed food-level analysis while statistically accounting for similarities and differences between food groups when exploring dietary patterns.”
What are some of the key findings and insights that have emerged from research on the relationship between diet and the microbiome?
Rajita Menon, Associate Director, Modelling & Statistics at Vedanta Biosciences
“Diet has a profound effect on the formation, succession, and modulation of the gut microbiome – nutrient production and exchange guide complex microbial ecology. We have learned that differences in diet are an important driver of the heterogeneity in the human microbiome: regional heterogeneity can be driven by a combination of diet and lifestyle, and temporal heterogeneity by diet-linked plasticity over timescales of years, weeks, or days within the same person. In fact, microbiota changes over a single day illustrate dependencies between circadian fluctuations in the gut microbiome and dietary patterns. I think an important lesson here is not to ignore diet as a critical variable when designing studies and/or interpreting microbiome data. Recent work has shown diet is a major confounding factor that undermines the inference of causal relationships between microbes and disease in translational or clinical studies. This underscores the practical need to capture usable diet-related metadata in such studies, ideally selecting matched controls upstream of analysis. If we can generalize findings from individual studies and get specific about the ways that dietary elements interact with microbes, this helps the development and improvement of microbiome-directed drugs.”
What methodologies and technologies do you use to analyze and integrate large sets of data on diet and the microbiome?
Manuel Kleiner, Associate Professor of Microbiomes and Complex Microbial Communities, North Carolina State University
“We integrate genome-centric metagenomics with metaproteomics to study diet-microbiota interactions in the gut. In our case we use the metagenomic data mostly as a tool to obtain protein sequences from species in the microbiota. These protein-sequences then serve as the basis for metaproteomics. Metaproteomics is an approach to identify and quantify thousands of proteins from complex microbiome samples using high resolution mass spectrometry data. The proteins are identified by matching the mass spectrometry data to the protein sequences from the microbiota metagenomes. The presence and abundances of proteins can tell us a lot about the “phenotype” of microbes and a microbiome on the molecular level providing insights into interactions, metabolism and physiology. One big advantage of metaproteomics for the study of diet-microbiota-host interactions, as compared to DNA and RNA sequencing based approaches, is that with metaproteomics we quantify proteins not only from species in the microbiota, but also from the diet and the host giving us insights into all three players in this tripartite interaction. So metaproteomics by its very nature integrates the diet and microbiome aspect in a single dataset”
What are some of the most promising areas of research for understanding the role of the microbiome in human health, and how can integrating diet and microbiome data contribute to these areas of study?
Eric Patridge, Principal Scientist – Computational Systems Biology, Viome Life Sciences
“Efforts to identify connections between human health and the microbiome generally place far more importance on the presence of specific microbes rather than what those microbes are doing. In thinking about some of these connections, there are a few names which stand out: Oxalobacter formigenes, Akkermansia muciniphila, Gordonibacter urolithinfaciens, Lactobacillus rhamnosus, and Faecalibacterium prausnitzii. These microbes are well known for their reported benefits, but the abundance of these microbes is not intrinsically beneficial (and too much of any microbe could be detrimental). Really, we consider these microbes as beneficial because of their role in metabolic transformations, which is dependent on both the diet and other players in the gut. For example, Oxalobacter formigenes is often thought to be particularly beneficial for those with kidney stones, but this would really only be true if the microbe is actively expressing certain genes and oxalates are present. Therefore, it is important for research to detail what microbes are actively doing rather than just their names or whether they are present.
General dietary guidelines are made available by countries and organizations from around the globe, but these are predominantly focused on macronutrient valuations in an effort to address world hunger and primary malnourishment. Importantly, these dietary guidelines are not intended to prevent chronic diseases, even though many nutritional staples and products have great potential to serve this goal. Aside from common staples and products, there are also many potential adjunct nutritional therapies which are not widely known or they are generally inaccessible. In order to leverage any potential adjunct nutritional therapies, it is vital to first identify associations between therapies and symptoms/conditions. Then, it follows, if these staples or products are dependent on the microbiome, detailing those requirements are equally important. Public health would certainly benefit from greater knowledge about which nutritional therapies can effectively maintain health and which are specifically effective because of the microbiome.”
Other sessions at MDC 2023 include:
- Deciphering Microbiome Biogeography: Longitudinal and Spatial Data
- Analyzing and interpreting Non-Bacterial Microbiome data
- Methodologies for Modelling Host-Microbiome Interactions
- Distinguishing Strains: Methodologies for Strain-level Analyses of Microbiomes
- Comparing and Validating Analytical Tools and Statistical Methods