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For any enquiries related to the Microbiome and Diet research area, please contact

Fiona Pereira
CSM Research Manager
csm@imperial.ac.uk

+44 (0)20 7594 3197

Probing the host-microbe-diet interface

The gut microbiota refers to the micro-organisms (bacteria, archaea, fungi, protozoa, helminths) and viruses found in the human gastrointestinal tract, while the gut microbiome refers specifically to the DNA and genes associated with the prokaryotes, eukaryotes and viruses that contribute to the gut microbiota. There are as many microbial cells present in the gut as there are human cells in the body, all perform an array of metabolic biotransformations (some of which are not performed by their human hosts), exert an influence on digestive function, and influence the immune system.

Despite some overlap in terms of the bacteria, archaea and viruses present, the exact composition of the gut microbiota varies from individual to individual and in the different regions of the gastrointestinal tract, with the density and complexity of microbial populations increasing from stomach to large intestine. Numerous intrinsic (pH, oxygen availability, bacterial co-operation and antagonism, antimicrobial peptides, transit time, peristalsis, immunoglobulin-containing mucin secretions) and extrinsic (diet, medications) factors affect the composition and functions of the gut microbiota. Age and clinical conditions such as obesity, inflammatory bowel disease, neurodegenerative diseases, liver disease, chronic fatigue syndrome, and type II diabetes also alter the gut microbiota and interactions with its human host.

Diet is clearly relevant in relation to human health in its own right, but it is the interaction between human dietary inputs and the gut microbiome that is currently providing some of the most fascinating insight. Understanding how different diets affect - and are affected by - the human microbiome has the potential to unlock a variety of clinically relevant opportunities, including precise dietary regimens for improved clinical outcomes, and new drug targets.

By integrating clinical, dietary, microbiota/microbiome, gene expression and metabolite data, we identify mechanisms through which all components of the microbiota and their functions influence human health and identify targeted means of manipulating the microbiota to improve human health.


Find out more about our key focus areas:

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Metagenomics

Metagenomic studies that examine the microbiome in great detail have revealed reductions in microbial gene richness and changes in functional capabilities of the faecal microbiome are signatures of obesity, liver disease and type II diabetes, and can be modified by dietary intervention. They have also demonstrated the gut microbiome harbours 150 times more genes than the human genome (3.3 M vs 22,000), significantly increasing the repertoire of functional genes available to humans, and contributing to energy harvesting from food and production of microbiota-associated metabolites that can be detected in human blood, urine, saliva and cerebrospinal fluid. Microbiota-associated metabolites and products such as lipopolysaccharide influence human health at the gastrointestinal, organ and systemic levels, and microbiota-associated metabolites mediate epigenetic programming in multiple host tissues.

Diet and Health

Diet plays a fundamental role in human health and disease. What we eat, how much, and when, all influence central biochemical processes that underpin our health status. Expertise in metabolic phenotyping and compositional analysis by NMR spectroscopy and MS techniques provides CSM researchers with the opportunity to understand the metabolic consequences of different diets, explore rich clinical and population biosample archives to understand dietary behaviors, and augment epidemiological analyses by providing objective measures of dietary exposure, which are notoriously difficult to capture accurately by traditional means. By identifying the components of complex dietary patterns that are linked to health status, researchers in CSM are actively involved in gathering together the scientific evidence that may help inform health policy relevant to clinical decision making and public health. 

Infection and Immunity

Mammalian responses to microbial / parasitic infection occur at many levels and include both the immune and metabolic pathways. Understanding those responses to infection in a mammalian host that are common, and those that are specific to a given infectious agent. Responses to inflammation are not restricted to infection, and the immune response profile through the course of disease processes provides a valuable source of complementary information in the context of systems biology. The linkage of the immune and metabolic responses provides an opportunity to gain a more comprehensive view of the biochemical processes that underlie response to infection and the influence of disease processes on mammalian health. Researchers in CSM have recently embarked on a programme of study focused on the interface of the immune system and metabolism using NMR and MS-based metabonomics in parallel with multiplexed cytokine profiling.

Infection and Immunity

Key members within Microbiome and Diet

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  • Book chapter
    Li JV, Saric J, Sabrina D, 2018,

    1H NMR-based Metabolic Profiling in Infectious Disease Research

    , NMR-based Metabolomics, Editors: Keun, Publisher: Royal Society of Chemistry, ISBN: 9781849736435

    This chapter highlights the application of 1H NMR spectroscopy-based metabolic profiling in infection research, specifically on HIV/AIDS, tuberculosis, malaria and the neglected tropical diseases, such as Schistosomiasis. We describe the use of this approach to investigate the metabolic responses of the host to infectious agents in both in vivo and in vitro models, as well in natural human infections. These metabolic signatures hold significant promise in leading to early and robust diagnosis of a range infectious diseases, including parasitic infections, where often a unique set of metabolites has been found to be associated with infection. In addition, metabolic profiling, together with measures of immune responses and gut microbial composition, provides mechanistic insight into the pathogen–host interactions through the immune–gut microbiota–metabolic axis.

  • Journal article
    Posma JM, Garcia Perez I, Heaton JC, Burdisso P, Mathers JC, Draper J, Lewis M, Lindon JC, Frost G, Holmes E, Nicholson JKet al., 2017,

    An integrated analytical and statistical two-dimensional spectroscopy strategy for metabolite identification: application to dietary biomarkers

    , Analytical Chemistry, Vol: 89, Pages: 3300-3309, ISSN: 1086-4377

    A major purpose of exploratory metabolic profiling is for the identification of molecular species that are statistically associated with specific biological or medical outcomes; unfortunately the structure elucidation process of unknowns is often a major bottleneck in this process. We present here new holistic strategies that combine different statistical spectroscopic and analytical techniques to improve and simplify the process of metabolite identification. We exemplify these strategies using study data collected as part of a dietary intervention to improve health and which elicits a relatively subtle suite of changes from complex molecular profiles. We identify three new dietary biomarkers related to the consumption of peas (N-methyl nicotinic acid), apples (rhamnitol) and onions (N-acetyl-S-(1Z)-propenyl-cysteine-sulfoxide) that can be used to enhance dietary assessment and assess adherence to diet. As part of the strategy, we introduce a new probabilistic statistical spectroscopy tool, RED-STORM (Resolution EnhanceD SubseT Optimization by Reference Matching), that uses 2D J-resolved ¹H-NMR spectra for enhanced information recovery using the Bayesian paradigm to extract a subset of spectra with similar spectral signatures to a reference. RED-STORM provided new information for subsequent experiments (e.g. 2D-NMR spectroscopy, Solid-Phase Extraction, Liquid Chromatography prefaced Mass Spectrometry) used to ultimately identify an unknown compound. In summary, we illustrate the benefit of acquiring J-resolved experiments alongside conventional 1D ¹H-NMR as part of routine metabolic profiling in large datasets and show that application of complementary statistical and analytical techniques for the identification of unknown metabolites can be used to save valuable time and resource.

  • Journal article
    Garcia Perez I, Posma JM, Gibson R, Chambers ES, Hansen TH, Vestergaard H, Hansen T, Beckmann M, Pedersen O, Elliott P, Stamler J, Nicholson JK, Draper J, Mathers JC, Holmes E, Frost Get al., 2017,

    Objective assessment of dietary patterns using metabolic phenotyping: a randomized, controlled, crossover trial

    , The Lancet Diabetes & Endocrinology, Vol: 5, Pages: 184-195, ISSN: 2213-8587

    Background: The burden of non-communicable diseases, such as obesity, diabetes, coronary heart disease and cancer, can be reduced by the consumption of healthy diets. Accurate monitoring of changes in dietary patterns in response to food policy implementation is challenging. Metabolic profiling allows simultaneous measurement of hundreds of metabolites in urine, many of them influenced by food intake. We aim to classify people according to dietary behaviour and enhance dietary reporting using metabolic profiling of urine.Methods: To develop metabolite models from 19 healthy volunteers who attended a clinical research unit for four day periods on four occasions. We used the World Health Organisation’s healthy eating guidelines (increase fruits, vegetables, wholegrains, dietary fibre and decrease fats, sugars, and salt) to develop four dietary interventions lasting for four days each that ranged from a diet associated with a low to high risk of developing non-communicable disease. Urine samples were measured by 1H-NMR spectroscopy. This study is registered as an International Standard Randomized Controlled Trial, number ISRCTN 43087333. INTERMAP U.K. (n=225) and a healthy-eating Danish cohort (n=66) were used as free-living validation datasets.Findings: There was clear separation between the urinary metabolite profiles of the four diets. We also demonstrated significant stepwise differences in metabolite levels between the lowest and highest metabolic risk diets and developed metabolite models for each diet. Application of the derived metabolite models to independent cohorts confirmed the association between urinary metabolic and dietary profiles in INTERMAP (P<0•001) and the Danish cohort (P<0•001).Interpretation: Urinary metabolite models, developed in a highly controlled environment, can classify groups of free-living people into consumers of dietary profiles associated with lower or higher non-communicable disease risk based on multivariate m

  • Journal article
    Chilloux J, Dumas ME, 2017,

    Are gut microbes responsible for post-dieting weight rebound?

    , Cell Metabolism, Vol: 25, Pages: 6-7, ISSN: 1932-7420

    One of the dieting conundrums in the age of the obesity epidemic is the cycle of weight loss and regain known as the "yo-yo effect." Thaiss et al. (2016) demonstrate that the microbiome plays a key role in this phenomenon and that simple dietary supplementations can reset the weight-rebound clock.

  • Journal article
    Li JV, Swann J, Marchesi JR, 2017,

    Biology of the Microbiome 2: Metabolic Role

    , Gastroenterology Clinics of North America, Vol: 46, Pages: 37-47, ISSN: 0889-8553

    The human microbiome is a new frontier in biology and one that is helping to define what it is to be human. Recently, we have begun to understand that the “communication” between the host and its microbiome is via a metabolic superhighway. By interrogating and understanding the molecules involved we may start to know who the main players are, and how we can modulate them and the mechanisms of health and disease.

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

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