How metabolomics can help in understanding the host-gut microbe interactions: Jingyuan Fu

In conversation with the Principal Investigator of the LifeLines-DEEP cohort, Jingyuan Fu, we talk to her about the recent findings and her thoughts on the role of metabolomics in gut microbiome research.

Microbiome, or the study of the combined genetic material of micro-organisms in a particular environment, has recently been receiving a lot of attention in the academic circles due to its versatile and exciting impact on the host’s health. Nightingale also got the opportunity to analyse one of the biggest microbiome sample sets from the LifeLines-DEEP cohort to support a systems medicine study. In conversation with the Principal Investigator of the cohort, Jingyuan Fu, we talk to her about the recent findings and her thoughts on the role of metabolomics in gut microbiome research. Excerpts:


1.     Could you tell us more about your body of work and what are you currently working on?

My research is on systems medicine with a focus on using an integrative omics approach. Our research in LifeLines and the LifeLines-DEEP cohort, a subset of Lifelines participants with more data layers, aims to understand host-microbe interactions in complex diseases.

We are studying a large, prospective, population-based human cohort to understand how the interaction between three variables — diet, host and microbiome — influence metabolic fitness and immune senescence (the ageing of the immune system) in relation to the development of complex diseases, including cardiovascular illnesses, fatty liver diseases and type 2 diabetes.

I’m excited about the way our research is making use of the deep omics and phenotypic data collected in Lifelines. This cohort is exceptionally exciting because in this longitudinal study participants have been followed for the past 10 years and will be continued to be followed for the coming 20 years. Having data from the same people over such a long period of their lives enables us to use a systems biology approach to unravel the molecular processes that lead from genotype to phenotype. This will allow us to develop disease risk models that combine genetics, omics and microbiome data to predict an individual’s disease development, the knowledge that is important for the development of personalised medicine.

2.     Why did you choose metabolomics for this study?

Metabolomics is an important readout of human physiological status and represents the downstream products of multiple interactions between genes, proteins, diet and gut microbiome. Moreover, the impact of the gut microbiome on human health and disease is largely transmitted via their metabolites. Understanding host-microbe metabolic interactions can thus highlight metabolic entities that might be manipulated via the gut microbiome for disease prevention and treatment.

3.     In LifeLines-DEEP, you are combining different types of omics. What are these and what are the benefits of this combined approach?

Integration of multiple omics dataset yields more insights into the down-steam effects of genetic variants on molecular traits. It also shows how these traits can converge into molecular pathways that interact with the gut microbiome and environmental factors, thereby contributing to the susceptibility risk for complex traits and diseases. Integration of omics datasets provides a more holistic molecular perspective on the human body compared to traditional approaches.

This has been an on-going work in our group for some time. We have, for instance, performed genetic association studies to gene expression (Zhernakova et al. Nat Gen 2017), DNA methylation (Bonder et al. Nat Gen 2017), proteomics (Zhernakova et al. Nat Gen 2018) and the gut microbiome (Bonder et al. Nat Gen 2016). We have also assessed the inter-individual variation of the gut microbiome and its associations to environmental factors and host physiological status (Zhernakova et al. Science 2016), as well as investigated its contribution to blood lipids (Fu et al. Cir Res 2015) and blood proteomics (Zhernakova et al. Nat Gen 2018). Recently, we also applied a Mendelian Randomization approach to assess the causal directionalities between different omics levels (Sanna et al. Nat Gen 2019).

4.     Your recent results show how gut microbiome is linked to cardiovascular disease risk. Can you tell us more about the study and your findings?

The study was aimed to identify gut microbial factors that can influence plasma metabolites and the metabolic risk of cardiovascular disease. It was supported by the Dutch Heart Foundation and is part of a collaboration between the LifeLines-DEEP cohort, headed by Prof. C. Wijmenga, Prof. A. Zhernakova and Prof. J. Fu at the University Medical Center Groningen and an obese-cohort, headed by Prof. M. Netea at Radboud University Medical Center.

We had two complementary advantages here. First, the gut microbiome and plasma metabolism data were obtained using the same techniques, namely metagenomics sequencing and NMR-based Nightingale metabolomics profiling. Second, each cohort had specific clinical aspects and that allowed us to assess the microbial effects in healthy individuals versus obese ones and in the metabolic risk of CVD versus the onset of the disease.

We were able to identify many associations between the gut microbiome and human plasma metabolites related to the metabolic pathways of the gut microbiota. Moreover, 48 of these bacterial pathways were associated with a metabolic risk score for cardiovascular diseases, and some were also associated with the presence of carotid plaques in these obese individuals. We then conducted a deeper analysis of the interaction between microbe, diet, metabolism and inflammation and assessed the impact of several key bacterial metabolites in cardiovascular disease, including short-chain fatty acids (SCFAs) and trimethylamine N-oxide (TMAO). The results suggest some novel functional links between the gut microbiome and cardiovascular disease risk.

5.     In your paper, you have stated that plasma can be more beneficial to study certain aspects of microbial activities than faeces. In which cases do you think it’s worth considering using plasma samples?

Short-chain fatty acids (SCFAs) are products of microbial fermentation of dietary fibres, serving as important molecules of host-microbe crosstalk. We show that plasma levels of SCFAs, rather their faecal abundances, were more relevant to CVD risk. This is because 95% of the SCFAs produced in the gut are rapidly absorbed by colonocytes and used as an energy source. Thus, plasma SCFAs are more important to access the risk, while the combination of the two data sources can help map the distribution, utilisation and excretion of microbial metabolic products.

6.     What’s next? What’s in store for this research project in the future?

The next steps would be to further enrich our omics and functional genomics data in LifeLines-DEEP, including metabolic and immune profiling; take advantage of the longitudinal data to construct omics-based risk models for phenotypic prediction, and move from association to causality through state-of-the-art bacterial culturomics and organ-on-a-chip technologies.

7.     Recently, gut-related research has received a lot of attention and one might get the impression that gut impacts almost all aspects of human health. In your opinion, what are the most exciting topics currently in gut-related research and why?

The gut microbiome is emerging as an important player in human health and disease, with numerous associations between microbiome and health being established in the past few years. Now, we need to understand the underlying causality and mechanisms for this correlation as this will pave the way towards clinical translation.

Here, metabolomics will play an important part because the impact of the gut microbiome on host metabolism and immunity occurs largely via its metabolism. There is a great need to understand bacterial metabolism, for example, by linking its biosynthesis gene clusters to its metabolic products.

Further reading:

If you are interested how blood and urine metabolomics can be used in gut-microbiome research, here’s more.




1.     Zhernakova et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nature Genetics 2017 Jan;49(1):139-145.

2.     Bonder et al. Disease variants alter transcription factor levels and methylation of their binding sites. Nature Genetics, 2017 Jan;49(1):131-138.

3.     Zhernakova et al. Individual variations in cardiovascular-disease-related protein levels are driven by genetics and gut microbiome. Nature Genetics 2018 Nov;50(11):1524-1532.

4.     Bonder et al. The effect of host genetics on the gut microbiome. Nature Genetics 2016 Nov;48(11):1407-1412.

5.     Zhernakova et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 2016 Apr 29;352(6285):565-9.

6.     Fu et al. The Gut Microbiome Contributes to a Substantial Proportion of the Variation in Blood Lipids. Circulation Research. 2015 Oct 9;117(9):817-24.

7.     Sanna et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nature Genetics 2019 Apr;51(4):600-605.

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