Future prospects for dissecting
inter-individual variability in the absorption, distribution and elimination of plant
bioactives of relevance for cardiometabolic endpoints
verfasst von:
Rikard Landberg, Claudine Manach, Frederiek-Maarten Kerckhof, Anne-Marie Minihane, Rasha Noureldin M. Saleh, Baukje De Roos, Francisco Tomas-Barberan, Christine Morand, Tom Van de Wiele
The health-promoting potential of food-derived plant bioactive
compounds is evident but not always consistent across studies. Large
inter-individual variability may originate from differences in digestion,
absorption, distribution, metabolism and excretion (ADME). ADME can be modulated
by age, sex, dietary habits, microbiome composition, genetic variation, drug
exposure and many other factors. Within the recent COST Action POSITIVe,
large-scale literature surveys were undertaken to identify the reasons and
extent of inter-individual variability in ADME of selected plant bioactive
compounds of importance to cardiometabolic health. The aim of the present review
is to summarize the findings and suggest a framework for future studies designed
to investigate the etiology of inter-individual variability in plant bioactive
ADME and bioefficacy.
Results
Few studies have reported individual data on the ADME of bioactive
compounds and on determinants such as age, diet, lifestyle, health status and
medication, thereby limiting a mechanistic understanding of the main drivers of
variation in ADME processes observed across individuals. Metabolomics represent
crucial techniques to decipher inter-individual variability and to stratify
individuals according to metabotypes reflecting the intrinsic capacity to absorb
and metabolize bioactive compounds.
Conclusion
A methodological framework was developed to decipher how the
contribution from genetic variants or microbiome variants to ADME of bioactive
compounds can be predicted. Future study design should include (1) a larger
number of study participants, (2) individual and full profiling of all possible
determinants of internal exposure, (3) the presentation of individual ADME data
and (4) incorporation of omics platforms, such as genomics, microbiomics and
metabolomics in ADME and efficacy studies.
Hinweise
This article is based upon work from COST Action FA1403 POSITIVe
(Interindividual variation in response to consumption of plant food bioactives and
determinants involved) supported by COST (European Cooperation in Science and
Technology; www.cost.eu).
Abkürzungen
ADME
Absorption, distribution, metabolism and excretion
ABCA1
ATP-binding cassette transporter 1
APOB
Apolipoprotein B
BC01/2
Beta-carotene oxygenase ½
CD36
Cluster of differentiation 36
COMT
Catechol-O-methyltransferase
CRP
C-reactive protein
IMG
Integrated microbial genome
GWAS
Genome-wide association studies
HMP
Human microbiome project
JGI
Joint genome institute
LPL
Lipoprotein lipase
MAF
Minor allele frequency
MTTP
Microsomal triglyceride transfer protein
PNLIP
Pancreatic lipase
WG1
Working group 1
SDG
Secoisolariciresinol diglucoside
SNP
Singe-nucleotide polymorphism
SR-B1
Scavenger receptor class B type 1
UGT
Urine-5’-diphosphate glucuronosyltransferases
TFBS
Transcription factor binding site
TSS
Transcription start sites
TES
Transcription end site
VEP
Variant effect predictor
SIFT
Sorting intolerant from tolerant
Introduction
Cardiometabolic disease, including cardiovascular diseases, type 2
diabetes, obesity, and their risk factors is the leading cause of morbidity and
mortality worldwide [1]. It is estimated
that patient care and indirect costs represent more than 192 billion euros a year
for the EU economy. Population studies have shown that up to 80% of cardiometabolic
disease could be prevented through lifestyle changes [2] and that dietary behavior may be most important [3], which offers tremendous public health
potential. A large number of observational and interventional studies have provided
evidence for the beneficial effects of a diet rich in plant-based foods on
cardiometabolic health [4‐6]. Although the
mechanisms are far from fully understood, bioactive compounds such as polyphenols,
carotenoids and phytosterols are being investigated for their health-promoting
effects in the context of cardiometabolic diseases. Establishing optimal dose–effect
relationships is hampered by the fact that most bioactive compounds have poor to
modest bioavailability and there is typically a large inter-individual variation in
absorption, distribution, metabolism and excretion (ADME) of such compounds.
Moreover, there is also a large inter-individual variability in observed health
effects due to inter-individual variation pharmacodynamics parameters, independent
from differences in ADME [7]. This
clouds the associated health effects from consumption of plant foods and hampers the
identification of the effects of particular bioactive compounds in specific
subpopulations. Several determinants such as genetic variability, gut microbiota
composition, age or sex may explain this inter-individual variability but few
studies have collected such information in a way that facilitates a systematic study
and conclusions to be drawn. To exemplify, the bioavailability of lutein in healthy
human subjects relies on variants in genes encoding proteins involved in carotenoid
absorption and metabolism [8]. Another
well-known example of a marked inter-individual variation in plant bioactive
metabolism is the gut microbial conversion of soy isoflavones into equol and its
impact on host physiology [9]. Only 30%
of the western population has a microbiota capable of producing equol and these
producers are known to gain more health benefits (improved vasomotricity, lower
blood LPL and CRP levels) from soy consumption than non-producers [10].
As long as information about determinants of inter-individual
variability is not collected and strategies to take them into account are lacking,
it is likely that the number of studies with conflicting results will continue to
grow. This will prevent policy makers to establish evidence-based dietary
guidelines, consumers will not be convinced to adopt the recommended dietary habits
and the food and nutraceutical industry will be hesitant to invest in product
innovation that gets the highest health benefit from food products and/or targets
specific consumer subpopulations.
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The main goal of the COST POSITIVe action was, therefore, to build an
open European scientific network to tackle the question of inter-individual
variation in response to consumption of plant bioactive foods and work with industry
and regulatory bodies to translate findings to product innovation and refined
dietary recommendations. Within COST POSITIVe, working group 1 (WG1) aimed to
improve the understanding of factors affecting inter-individual variation in
bioavailability of bioactive compounds from plant-based foods. WG1 brought together
scientists from various disciplines: nutritionists expert in the bioavailability of
different classes of bioactive compounds, microbiologists, geneticists,
epidemiologists, food technologists, and experts in metabolomics [11].
Processes including absorption, distribution, metabolism and excretion
(ADME) of selected bioactive compounds were included when mapping determinants of
inter-individual variability. The WG had three aims: (1) to identify the main
factors that modulate the ADME of plant food bioactive compounds and to improve
methods and tools to assess individual exposure. Bioactive compound classes were
selected based on their presence in plant-based foods and to what extent they have
been studied and/or have established effects on cardiometabolic risk factors in
humans. To address the aim, WG1 established a joint data input structure that was
followed for all compound classes and started with a detailed literature data
analysis to collect reported determinants of inter-individual variability in ADME
after oral intake of selected plant bioactive compounds (Fig. 1). Eight working subgroups were formed to deal with
the selected compound classes who processed the literature data according to
predefined guidelines and generated one review or opinion paper per compound class.
In parallel to the working groups focusing on dissecting factors important for
interpersonal variation of ADME of specific compounds, separate working groups were
formed to address aim (2) the compilation of existing knowledge to identify key
genes and enzymes related to human and gut microbial biotransformation with major
importance for interpersonal variability in the ADME of selected bioactive
compounds. Finally, aim (3) was to develop a framework for how metabolomics can be
applied to analyze and assess true internal exposure to bioactive metabolites at the
individual level and interpersonal variability in ADME. These activities were
covered by the creation of the metabolomics subgroup.
Fig. 1
Structure of the COST Positive working group 1 focusing on
the inter-individual variability in absorption, distribution,
metabolism and excretion of selected plant food bioactive
compounds
×
Determinants of interpersonal variability
The first step of WG1 was to select compound classes of interest and to
set up the criteria for a literature review to collect the correct information to
allow for the dissection of the determinants of inter-individual variability in ADME
of selected bioactive compounds. Following a questionnaire in which research
interests and expertise were probed, participants organized themselves into
different compound subgroups focusing on anthocyanins, carotenoids, lignans and
phenolic acids, ellagitannins, flavanones, flavonols, catechins and phytosterols
(Fig. 2). A data entry template was
established to collate literature information and indicate what articles cover
determinants such as age, sex, genetics and microbiome (Fig. 2a). Over 3000 papers were reviewed, from which 511
were eventually selected as the basis to write several reviews. So far, this has
resulted in six publications that have been published, accepted or submitted
[12‐16].
Fig. 2
a Scheme of possible
determinants covered within the literature survey. b Molecular structures of the compounds
tackled within the literature survey
×
Lignans
As an example, the literature surveys from lignans resulted in 443
articles fulfilling the search criteria including 96 human studies [15]. The main findings from this extensive
literature survey were that variability in plasma levels of enterolactone, one
of the main end-products formed by gut microbiota from plant lignans, was large
and that gut microbiota and antibiotics were found to be the most important
determinants for internal exposure. In addition, age and sex also appeared to be
important determinants of plasma enterolactone concentrations and variability.
Higher plasma concentrations of enterolactone were found for older individuals
and female individuals. This could be primarily explained by the slower gut
transit that is typically observed in these aged individuals and the higher
lignan intake that was noted for female individuals. Ethnicity also was a
determinant of plasma enterolactone and primarily related to altered dietary
habits. Finally, a clear link between enterolactone plasma concentrations and
health status and lifestyle was observed, with individuals with a high BMI and
smoking showing lower circulating plasma levels of enterolactone. The mechanisms
behind the effects of specific determinants have not been elucidated in all
cases. An important finding from the literature review of lignans was the
involvement of gut microbiota in the biotransformation and bioavailability of
lignans and its role as a main determinant for plasma enterolactone
concentrations. For example, the metabolism of secoisolariciresinol diglucoside
(SDG, a major plant lignan) entails subsequent steps of O-deglycosylation, demethylation, dehydroxylation and
dehydrogenation. Interestingly, not a single microbe is able to completely
metabolize SDG into enterodiol and enterolactone: SDG metabolism is always
relying on a joint action from different microorganisms [17]. This results in observations that
microbiome diversity and composition are clearly correlating with the
enterolactone status of human individuals. As the microbiome is largely affected
by dietary behavior, it is not surprising that dietary fiber intake associates
with a higher microbial diversity and in turn results in higher plasma levels of
enterolactone. Conversely, intake of energy and lipids was negatively correlated
with plasma enterolactone concentrations.
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Carotenoids
The focus was on lycopene, β-carotene and lutein, which are the
three main carotenoid compounds found in blood and tissue. From the factors that
are most likely to impact carotenoid uptake, distribution, metabolism and
excretion, genetic variations in proteins involved in carotenoid metabolism were
the most important [12,
14]. These involve digestion
enzymes (PNLIP), metabolizing enzymes (BCO1/2), carrier proteins (SR-B1, CD36,
ABCA1 and more) as well as proteins governing secretion into chylomicrons (APOB
and MTTP), biotransformation enzymes in the blood and liver and proteins
involved in the distribution to target tissues. Interestingly, although gut
microbiota are likely not involved in the direct metabolism of carotenoids, they
may have an indirect impact on carotenoid ADME through their modulation of bile
salt profiles and excretion [12].
As for many other bioactive compounds, determinants such as disease status, body
weight and BMI, sex and lifestyle habits (smoking, alcohol consumption) turned
out to additionally contribute to inter-individual variability.
Flavonols
Despite the large impact of inter-individual variability in ADME of
flavonols, only 10 out of 55 articles reported the participants’ individual data
and none of the studies were actually designed to decipher the underlying
determinants of inter-individual variability [13]. Variability was smaller for metabolites resulting from
small intestinal digestion and absorption than for metabolites that were derived
from colon microbiota. While dietary habits, genetic polymorphisms and
microbiota composition are reported to impact variability in flavonol ADME, it
is difficult to accurately assess their individual contribution, given the
information presented in the publications. As for many bioactive compounds,
there is a need for more detailed intervention studies of larger size,
collection of more information about the characteristics of the enrolled
individuals, such as age, diet, sex, diet, lifestyle and health status, and
attention for obtaining individual pharmacokinetic data of quercetin and all of
its metabolites.
Phenolic acids
Phenolic acids are widely present in plant-based foods in free or
conjugated forms. Their bioavailability depends on the free/conjugated form and
is affected by food processing. Phenolic acids are metabolized both by the host
and gut microbiota, resulting in conjugations and structural modifications of
the compounds. Only a few studies have investigated inter-individual variability
in ADME and health responses to phenolic acid-rich foods and these studies were
reviewed and data summarized [18].
As for many other bioactive compounds, phenolic acid metabolite profiles and
health responses of phenolic acids show large inter-individual variability,
which seems to be related to the metabolic status, sex, dietary habits, and
genetic polymorphisms, but the co-ingestion of other dietary bioactive compounds
may also contribute. For phenolic acids, it may be of particular interest to
design future studies to allow potential confounding effects of other food
phytochemicals and their metabolites present in concomitance with phenolic acids
to be revealed. Moreover, since phenolic acid metabolites are highly dependent
on gut microbiota, a better understanding of the gut-host interplay and
microbiome biochemistry is becoming highly relevant in understanding the impact
of phenolic acids and their response/non-response in health outcomes.
Ellagitannins and ellagic acid
These (poly)phenols belong to the family of hydrolysable tannins.
They are poorly absorbed in the stomach, small intestine and colon. However,
they are extensively metabolized by the gut microbiota to urolithins, which are
readily absorbed. Urolithin Phase II metabolites reach plasma concentrations as
high as 10 µM [19]. Urolithins show
several biological effects on cardiometabolic risk biomarkers [20‐22], anti-inflammatory effects at different
organs (gut, vascular tissues and neuronal tissues) [23], and some interesting effects recently
observed on muscular performance and exercise recovery [20]. Inter-individual variability in the
ADME of urolithins has been reported, and three different metabotypes A, B and
0, have been described [19]. These
metabotypes associate with the occurrence of specific bacterial strains in the
gut and show that gut microbiota composition can be potentially a relevant
determinant in the ADME of ellagitannins and ellagic acid, and, therefore, an
important determinant of their cardiometabolic health effects. In fact, it has
been shown that individuals of metabotype B respond better to the administration
of pomegranate ellagitannins leading to significant decreases in cardiometabolic
risk biomarkers, while metabotype A does not show the same effects [20]. Specific bacterial strains have been
associated with the production metabotype A and B urolithins [24]. However, no matching of metabotypes A,
B and 0 with the gut microbiome enterotypes has been observed [25].
Flavanones
A large inter-individual variability in the ADME of citrus
flavanones has been reported and high, medium, and low flavanone metabolites
excreters have been identified in different studies after the intake of
flavanone rutinosides, citrus flavanone extracts and citrus juices [26‐28]. One key factor in this variability is
the necessity of gut microbiota to convert the un-absorbable flavanone
rutinosides (rhamnosyl(1-6)glucosides, hesperidin, narirutin, naringin,
neohesperidin and eriocitrin) to the readily absorbable aglycones (hesperetin,
naringenin and eriodictyol). Human intestinal cells do not have the rhamnosidase
activity needed, while many gut bacterial species do have rhamnosidase activity,
including bifidobacteria, lactobacilli and Bacteroides spp. Despite the demonstrated high inter-individual
variability in flavanone ADME, no correlation with gut microbiota composition
has been studied so far, and no correlation of flavanone absorption with the
effects on cardiometabolic health biomarkers has been demonstrated.
OMICs-strategies for assessment of interpersonal variability
Metabotypes in the field of plant food bioactives
Individuals that share similar metabolic phenotype, i.e., that may
have similar pharmacokinetics and/or bioavailability of plant bioactive
compounds and/or similar response patterns of such, may be grouped intometabotypes. The general concept of
metabotypes was already introduced 20 years ago by Gavaghan et al. [29] proposing a metabonomic approach to
relate metabolic balance and metabolite excretion data to host phenotype and
genotype. In 2008, the metabotype concept was expanded with a transgenomic
approach to link up microbiome profiles with metabolic phenotypes in the host
[30] followed by the proposal
of Bolca et al. [31] to actively
consider gut microbial metabotyping when elucidating health effects from plant
bioactives. Yet, the general metabotype concept suffers from its very broad and
often subjective definition, given the multitude and variety of study types
(epidemiological vs. intervention), metabolic pathways (host vs. microbiome),
study objectives and endpoints of interest where it has been considered useful
[32]. For plant food
bioactives, while most evidence for the existence of separate human metabotypes
stems from differences in microbial metabolic potency, human genetic
polymorphisms may also significantly contribute to different (metabolic)
phenotypes. To exemplify, polymorphisms in the CYP1A2 encoding genes,
responsible for caffeine biotransformation, were previously found to lie at the
basis for a modulated risk of hypertension and myocardial infarction for those
individuals that display the low caffeine-metabolizing phenotype [33, 33, 34]. Riedl
et al. [32] made a plea for a
stricter metabotype definition, not in the broad sense where it is unrealistic
to make a fit-for-all definition, but rather use sub-definitions that are
fit-for-purpose. In the context of plant bioactives, we propose to define
metabotypes as the manifestation of human population subgroups that have
different metabolic phenotypes for phytochemicals, which could be captured by
differences in the metabolome profiles in bodily fluids (plasma, urine, feces,
tissues, etc.) after the intake of specific plant food or bioactive compounds.
Thus, a metabotype can be defined on the basis of a small number of specific
metabolites (e.g., equol or urolithin metabotypes) that reflect a particular
metabolic capacity and are sufficient to distinguish meaningful subgroups.
Besides, metabotyping can be based on wider metabolic profiles, e.g., using
untargeted metabolomics, showing the potential to classify subjects in subgroups
with distinct internal exposures to bioactive metabolites in fasting state or as
a response to a nutritional challenge. Identifying consumers with different
metabotypes can be of major importance to identify those that will have
particular health benefit from plant food bioactive compounds as well as risk
groups of consumption. This could be the starting point for personalized
nutrition concepts, and identification of biomarkers of specific metabotypes
differentially associated with response/non-response of the intake of bioactive
compounds is highly warranted [35].
In addition, basic understanding about the required optimal dose of plant food
bioactive compounds that will ensure the best health benefit is currently
lacking for most compounds. Phytosterols are an exception as it has been shown
that a daily intake of 2 g is needed to lower total- and LDL cholesterol
[36]. However, the optimal dose
that brings out the best health benefit most likely differs across individuals
and metabotypes.
To accelerate the development of personalized nutrition strategies
based on metabotyping, simple and high-throughput methods for the analysis of
bioactive compounds and their metabolites at large scale are needed, with robust
detection, identification and quantification of plant bioactive parent compounds
and metabolites. Methods for analytical coverage of representative plant
bioactive compounds and metabolites in human biological samples were worked out
and tested in different labs involved in the POSITIVe network [37]. Exploration of interpersonal variation
according to variations in microbiome composition or gene polymorphisms is two
additional fundaments for stratification of individuals. This requires a
profound understanding of the biochemical pathways that include
biotransformation enzymes and transport proteins, both from the gut microbiome
perspective as well as from the human body perspective.
MS-based metabolomics as a methodology of choice to address plant
bioactives
Assessing exposure to plant food bioactive compounds
The internal exposure to polyphenols, carotenoids and other
plant food bioactive compounds is largely, but not only, dependent on the
level of intake of their food sources. As already mentioned above, the
internal exposure is also modulated by food matrix effects as well as by
intrinsic and environmental factors affecting the ADME capacity of
individuals for xenobiotics [38]. For example, the gut microbiome may confer or not to the
host the capacity to produce metabolites such as equol or urolithins.
Assessing the exposure to plant food bioactive compounds by the measurement
of food intakes using dietary questionnaires followed by conversion into
intakes of bioactive compounds using food composition tables is thus not
reliable enough and may mask potentially existing relationships with health
and diseases. Pioneering studies with isoflavones and ellagitannins have
shown that in some cases, health benefits associated with plant food
consumption may be restricted to population subgroups with particular
metabotypes [20, 39]. Profiling of metabolites present in
biofluids, rather than estimating the intake through dietary assessment and
food composition tables appears as the best approach to reflect the true
exposure to bioactive compounds and their metabolites, and is more closely
related with pharmacodynamics at any time point. Targeted metabolomics has
been used for years to study the bioavailability of bioactive compounds from
plants and more recently to assess kinetics concordance with the improvement
of health outcomes [40].
Metabolite data are a good basis for stratification of subjects according to
their molecular phenotype reflecting the capacity to produce particular
metabolites, and, therefore, one defined aspect of the metabotype.
Untargeted metabolomics
Untargeted metabolomics offers an even greater potential for a
comprehensive phenotyping of internal exposure embracing the diversity of
plant food compounds from various families. Furthermore, owing to its
exploratory nature, it is a useful approach to bring new knowledge on still
unfamiliar metabolites. Although the number of studies using untargeted
metabolomics has exploded the last years, it is still a young discipline,
which requires improvements and harmonization of its methods and tools.
Metabolomics applied in the field of plant food bioactive compounds is
facing the same impediments as in other application domains. Analytical
options for untargeted metabolic profiling are multiple and not harmonized,
and a large proportion of the signals detected cannot be identified. As
these are key limitations, the WG1 of this COST Action evaluated and
improved the analytical coverage of untargeted methods for plant food
bioactives and contributed to the development of databases and tools to
facilitate the identification of plant food metabolites in metabolomics
profiles.
Plant food bioactives and their derived human metabolites
represent a broad variety of chemical structures, with masses from 100 to
> 1300 Da and a large spectrum of polarity, from very polar small
microbial metabolites to hydrophobic compounds such as carotenoids or
phytosterols. Analyzing simultaneously such an array of chemicals, even with
an untargeted approach, represents more than a challenge and analytical
choices have to be made. Up to now, metabolomics platforms developed their
own profiling methods independently, and no standardization effort has been
undertaken as yet. Platforms often do not have a precise knowledge of their
own coverage until they have analyzed thousands of standards. POSITIVe
organized the first international initiative in the field of plant food
bioactives, in the form of a multiplatform test comparing the analytical
coverage of 11 LC–MS and 2 GC–MS methods currently used by the partners
[41]. Results provided
insights for optimization and harmonization of methods. A quality control
mixture of 12 inexpensive plant food metabolites was proposed to assess the
analytical coverage and resolution of any untargeted LC–MS method. Methods
of preparation for plasma, urine and other sample types are also key as
ranges of metabolites can unintentionally be discarded. Optimization of
preparation methods for plant food bioactive compounds is ongoing.
Inter-laboratory validated reference methods will be essential to facilitate
the comparison of findings across studies.
Use of online databases in the identification of plant-based compounds
and their metabolites
Untargeted MS profiling methods are widely used but typically,
only 5–20% of the detected signals are identified, leaving a vast pool of
potential information unresolved. Databases are essential tools in the
identification process. They are queried with experimental spectral data and
return the chemical structures matching these data, as hypotheses of
identification to be further confirmed. Many compound-centered or
spectra-centered databases with various contents are available online and
must be used in a complementary manner for a wider chemical coverage. Among
the most commonly used are HMDB (www.hmdb.ca), Metlin (https://metlin.scripps.edu/), PubChem (https://pubchem.ncbi.nlm.nih.gov/), ChemSpider (www.chemspider.com/), MassBank (http://massbank.eu/), MoNA (http://mona.fiehnlab.ucdavis.edu/), NIST, Golm Metabolome database, LipidMaps (https://www.lipidmaps.org/), mzCloud (https://www.mzcloud.org/), GNPS (https://gnps.ucsd.edu/), ReSpect (http://spectra.psc.riken.jp/) and FooDB (http://foodb.ca/). The detected signals are not easily identified when the
corresponding compounds are not yet present in one of these databases, which
is the case for many metabolites of less studied food phytochemicals.
PhytoHub (http://phytohub.eu/) is an online database conceived to facilitate the
identification of food phytochemicals and their metabolites in metabolomics
profiles [42]. It contains
> 1700 compounds and is continuously updated by invited experts. The
literature survey conducted by WG1 of the COST Action POSITIVe led to a
major upgrade of PhytoHub. About 200 metabolites of polyphenols not yet
recorded in any database were added, associated with the original
literature. PhytoHub can now be searched to get the list of metabolites
observed or expected in biofluids after consumption of a given food. For
example, when searched for apple, a list of 195 metabolites is obtained,
along with the information necessary to identify them in urine or plasma
metabolomic profiles. In addition, for compounds whose metabolism has not
yet been studied in humans, the most likely host and microbial metabolites
can be predicted by Biotransformer (http://biotransformer.ca/), a new open-access tool that applies prediction rules
elaborated from machine-learning algorithms and expert knowledge including
PhytoHub data [43]. Integration
of more chemical, biological and spectral data for phytochemical metabolites
in metabolomic databases will be key for a better understanding of food
phytochemical ADME and associated inter-individual variation.
Obtaining hypotheses of identification for unknowns using
databases is one step in the process. Hypotheses must be validated by
comparison of experimental MS/MS spectra or retention time with those of
authentic standards. Unfortunately, many standards are expensive or not
commercially available. A collaborative platform called FoodComEx (http://foodcomex.org/) is facilitating the sharing of standards, and several
chemists from POSITIVe already proposed some precious synthesized
metabolites of polyphenols there [44]. MS/MS fragmentation is widely used to support or
discard hypotheses of identification, while information associated to the
retention time has not been considered enough. We evaluated the potential of
retention time prediction by PredRet [45]. This online tool can predict the retention time of
compounds in a chromatographic system as soon as they have been
experimentally determined in other registered chromatographic systems. We
compiled retention time data for 471 plant food metabolites in 24
chromatographic systems used in 19 platforms across Europe. Predictions were
very accurate, with a median prediction error of 0.03–0.76 min depending on
the systems. Such level of precision represents a great help to accelerate
the identification process, with the possibility to discard hypotheses
outside the predicted retention time window. PredRet is free to use for new
contributors, and the number and the accuracy of predictions will increase
with the number of shared data.
Coordination of efforts and large collaborative initiatives are
essential to improve and harmonize the assessment of individual exposures to
plant food bioactive compounds using untargeted metabolomics. The COST
Action POSITIVe has been instrumental in building a community with shared
interests, which will hopefully continue to interact and expand efforts to
collectively address the most important remaining challenges.
Genetics
As for other phenotypes [46], it is likely that up to 50% of the cardiometabolic
response to intake of plant bioactives is attributable to genetic variation.
Variation in genes that modulate plant bioactive ADME and bioavailability as
well as the pharmacodynamics of the bioactive compounds, are likely to be
important. In the general population, about 99.4% of DNA is common between
individuals with the remaining 0.6% defining phenotype and response to the
environment including dietary exposure. The 2015 output from the 1000 Genome Consortium, indicated that there are
typically 88 million variants in a human genome [47], including gross structural alterations affecting
> 1000 bases/nucleotides in the DNA sequence, such as copy number variation,
deletions, insertion and inversion, as well as single-nucleotide polymorphisms
(SNPs), where a single base in a nucleotide is changed. The identification of
which variants are important in modulating human health represents an enormous
challenge. As SNPs constitute > 90% of all genetic variability, and are of
high relevance for public health, targeted genotyping, where the focus is SNPs
in key genes involved in the metabolic pathway of interest, is a widely used
approach. In addition, genome-wide association studies (GWAS) are been conducted (https://www.genome.gov/20019523/, http://www.ebi.ac.uk/gwas/), in which genetic information across the genome can be related
to a particular trait, e.g., plant bioactive concentrations.
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To date the limited investigations for plant food bioactives have
taken a candidate gene approach, focusing on one or a small number of variants
in a gene encoding for plant bioactive phase I or II metabolism proteins, with
the justification for the selected gene variants rarely provided, and the
functional consequences of genotype often unknown. Perhaps the most extensively
studied genotype relevant to flavonoid ADME, is a Catechol-O-methyltransferase (COMT) missense mutation
(rs4680), with a G to A base change resulting in a valine to methionine amino
acid substitution at position 158 of the protein. This polymorphism is thought
to produce a less-stable protein, with a reduced enzyme activity [48]. In human trials, this genotype has been
associated with flavan-3-ol/metabolite plasma and urinary concentrations, breast
cancer risk and vascular responses following green tea (source of dietary
flavan-3-ol) consumption [49‐51], although in the Minnesota Green
Tea Trial, no overall impact of 12-month intervention or no
genotype*treatment interactions on adiposity or cardiometabolic health, was
observed [52].
Twenty-two isoforms of the Urine-5’-diphosphate glucuronosyltransferases (UGTs) gene
superfamily [53] catalyze the
transfer of a glucuronic acid from UDP glucuronic acid to a host of endogenous
and exogenous compounds including prescribed medications and plant bioactive
compounds, affecting their metabolism. The impact of UGT genotypes on the
endogenous concentrations of these compounds, the incidence of associated
cancers and response to select drugs has been reported (for review see
[39]). Although the
bi-directional relationship between UGT and plant bioactive has been reported
[54, 55], the impact of UGT genotype on plant
bioactive concentrations and efficacy remains to be established.
The ongoing COB trial (NCT01922869) is taking a more comprehensive
approach and is sequencing 112 genes, involved in all stages of plant bioactive
metabolism from digestion to elimination, to establish their impact on flavonoid
ADME over 48 h. No human GWAS investigation of plant bioactive metabolism is
available.
Methodological approaches for the identification of gene variants of
interest with respect to plant food bioactives
Once genes of interest are identified, large publicly available
databases including HapMap and more recently
the 1000 Genome Project (where the genomes of a large number of people sequenced
and their genotypic data stored) are used to select potentially important
variants. Their selection should be on the basis of their known or predicted
impact on protein concentration, structure or function, or pathogenicity, or
variants which tag and act as genetic biomarkers for a particular gene
region.
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In addition to the gene itself, variants in adjacent regulatory
regions, for example transcription factor binding site (TFBS) and enhancers,
which can influence gene expression, are also likely to be important. Therefore,
variants in genes regions within 10 Kb from the transcription start sites (TSS)
and transcription end site (TES) for each gene should be included. The second
step is to determine the effect of these variants. The Variant Effect Predictor (VEP) [56] tool is used to provide information regarding variants’
locations, consequences (e.g. ,missense or frameshift), minor allele frequencies
(MAFs) and pathogenicity prediction established using Sorting Intolerant from Tolerant (SIFT), Polyphen, and others. The next step is to search publicly
available datasets that have records of studies showing associations of the
selected variants with disease incidence or biomarkers. Some of the commonly
used datasets include GWAS, dbSNP, Humsavar and pharmacogenetic
databases.
Using all this gathered information, the final step, is to select a
tagging SNP for each haplotype block (250 kb max) together with SNPs in the
inter-block regions, to provide a comprehensive list of variants covering the
whole gene region. Priorities for SNPs’ selection include:
1.
SNPs previously published in online
databases,
2.
SNPs with predicted pathogenicity established using
SIFT and Polyphen,
3.
SNPs in promotor regions, exons, 3’UTR and
regulatory regions, and
4.
SNPs with a MAF ≥ 0.5
Such an approach will result in a list of SNPs which can be used to
probe human clinical trial and cohort data, for genotype*ADME*phenotype
associations.
At present, research is at the early stages of identifying the main
genetic determinants of plant bioactive metabolism and efficacy. It is hoped
that specific genotypes will emerge which could identify subgroups who are
likely to most benefit from increased intake of selected plant bioactive
compounds. When evidence is sufficient, such information may be used in routine
health examinations and possibly be used for individual dietary
recommendations.
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The role of gut microbiota in inter-individual variability of plant
bioactives
A thorough understanding of the apparent inter-individual
variability in ADME of plant food bioactive compounds is occluded by the lack of
insight in the role of gut microbiota in these processes. Gut microbiota can be
involved in the breakdown of the food matrix and thus contribute to the bio
accessibility of a bioactive which then becomes available for intestinal
absorption. In addition, gut microbiota can directly or indirectly (through
cleavage of phase-II metabolites) metabolize parent compounds into metabolites
with modified bioactivity and susceptibility of intestinal uptake. Finally,
alterations in gut microbiome composition and functionality can modulate gut
epithelial barrier function resulting in a higher or lower uptake of bioactive
molecules of interest. WG1 of the COST Action POSITIVe chose to focus on the
microbiome’s metabolic potency and deemed it essential to fully exploit
currently available “omics” platforms to study microbial markers of internal
exposure and microbial markers of effect. This will enable stratification of
human individuals into metabotypes and/or responders based on their microbial
metagenome profile (Fig. 3a).
Fig. 3
a Schematic overview of
the workflow to identify genes encoding microbial enzymes
responsible for specific metabolic steps of bioactives.b Krona™ plot of microbial
genomes yielding genes that encode the methyltransferase I
component of the O-demethylase enzyme system
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Knowledge of involvement of gut microbiota in the metabolism of
plant bioactive compounds requires analytical coverage of possible metabolites
and knowledge of metabolic pathways. The groups establishing the reviews of
specific bioactive compounds (Fig. 1)
were asked to—if at all possible—pinpoint the most crucial step in the (human or
microbial) metabolic pathway for the plant bioactive of interest. Identification
of microbial genes/gene cassettes of interest that are encoding specialized and
unique metabolizing enzymes or transport proteins that may differ amongst
individuals depending on their gut microbiome composition was attempted. The
search strategy employed was so-called “gene-centric microbial genome and
metagenome mining”.
The method consists of (1) screening literature for the crucial
microbial metabolic steps that were identified by experts working on the
specific compound classes, (2) if absent, screen for microbial enzymes and their
encoding genes that are known to perform a comparable metabolic step, (3) use
JGI’s (Joint Genome Institute) integrated microbial genome and microbiome
(IMG/ER) to filter known HMP (Human Microbiome Project) genomes for these genes (https://img.jgi.doe.gov/imgm_hmp) and export those and (4) use the exported genes to query HMP
metagenomes, the 10 million gene catalogue from the former MetaHIT FP7 project
or other metagenome databases and subsequently obtain a stratification of human
individuals based on their microbiome metagenomic’s information. This approach
of stratifying individuals according to their personal microbial (meta)genomic
profiles has rarely been attempted before. Several knowledge gaps were
identified primarily relate to a lack of accurate gene annotation and database
implementation.
Since not all plant bioactive compounds could be covered, a set of
model compounds (isoxanthohumol, lignans, flavan-3-ols, ellagitannins,
isoflavones, rutin, chlorogenic acid and anthocyanin) was chosen to evaluate
employing the gene-centric metagenomic screening strategy. We illustrate our
approach with the microbial metabolism of lignans and isoxanthohumol. As
mentioned in the section above, enterolactone has been extensively investigated
with regard to health effects mainly related to hormonal-dependent cancers.
Enterolactone is formed by microbiota-dependent metabolism of plant lignans, in
several steps as illustrated by secoisolariciresinol (SECO) diglucoside
converted to enterodiol and to enterolactone in the final step. The O-demethylation of SECO is a crucial metabolic step
in the bioactivation process. O-demethylation
is not only crucial for lignan activation but also for the conversion of
hop-derived isoxanthohumol into its bioactive metabolite 8-prenylnaringenin
[57]. Interestingly, O-demethylase is a four-component enzyme system
that has been well described in several microorganisms [58, 59]. It consists of methyltransferase I, corrinoid protein,
methyltransferase II and an activating enzyme. Methyltransferase I typically
transfers a methyl group from a substrate (SECO) to the cobalamin (vitamin
B12)-corrinoid protein while methyltransferase II
subsequently transfers the methyl group to tetrahydrofolate, yielding
methyl-tetrahydrofolate. The activating enzyme is required to render the
corrinoid protein into its appropriate redox status making it in turn receptive
again for methylation. While Eubacterium
limosum ZL-II is known to convert SECO into enterodiol, its
genome is unfortunately not completely available. Chen et al. [60], therefore, used the genome of E. limosum KIST612 as a reference to further
characterize the four-component O-demethylase. We used the same reference organism’s proteome and
genome to screen the IMG database for available putative O-demethylase encoding genes. Interestingly, IMG database
exploration for functional protein-encoding genes upstream a metabolic pathway
yielded a lower number of positive bacterial genomes. To exemplify, screening
for the more general methyltransferase II resulted in positive hits for 2010
bacterial genomes, the corrinoid protein gave 272 bacterial genomes, while
methyltransferase I—which probably has higher substrate specificity—only yielded
96 bacterial genomes (Fig. 3b).
This reduction in number of bacterial genomes increases the
feasibility of stratifying individuals based on their phylogenetic microbiome
composition and making predictions on their probability of harboring a
SECO-converting microbiome. A similar gene-centric mining of metagenomic
databases can serve as a basis for stratifying individuals based on the presence
of functional genes and explore whether specific determinants (ethnicity, diet,
health status, sex, age, etc.) form a confounding factor for this
stratification. However, this proposal to use gene-centric metagenomic mining
does not suffice to identify metabotypes that group individuals based on their
ability to generate a high plant-based bioactive concentration in vivo. In-depth
and independent validation with separate intervention studies, including the
incorporation of all metadata from study participants, need to further prove the
usefulness of this approach and indicate to what extent the microbiome is
actually involved in a bioactive’s ADME and predict which individuals would
experience the largest health effects based on their microbial
metagenome.
The identification of putatively involved microbes is still
complicated due to some specific issues. The gene-centric metagenomic screening
relies on available information of metabolic pathways, which are for many
compounds still uncharacterized. Moreover, if functional genes are less well
conserved across microbial genomes, gene-centric metagenome mining could yield
false negatives depending on the cutoffs that are used. Third, knowledge of gene
sequence information does not immediately lead to gene annotation. In such case,
KEGG pathways cannot be explored and database screening can only be conducted
based on the gene identifier (Gene ID). In addition to the fact that it is
difficult to connect an individual’s metagenome with its metadata, it is clear
that future research progress in this field will require constantly updated
databases and proper gene annotations.
Conclusions
Available evidence suggests that ADME and bioefficacy of plant food
bioactive compounds varies several fold between individuals. Although age, sex, BMI,
genotype, the gut microbiome and background diet have been shown to be important for
interpersonal variability in selected bioactive compounds, a comprehensive
understanding of the individual and interactive contribution of these variables to
plant bioactive ADME, and ultimately their impact on tissue function and overall
‘health’, is in its relative infancy. Advancement in current knowledge will require
ADME studies, with well-characterized plant food bioactive sources and doses,
sensitive metabolomic analysis of tissues and biofluids, along with detailed
phenotyping of trial participants for all potentially modulating variables listed
above. Such trials should have sufficient sample sizes to draw conclusions regarding
variable*ADME associations and derived publications should make data from each trial
participant publically available. This would allow integration of data from
individual investigations, thereby increasing sample size, and with appropriate
statistical approaches facilitating the interactive impact of variables on bioactive
ADME to be assessed and modeled. Integrated information may be used to identify
metabotypes not only reflecting formation/non-formation of specific metabolites from
plant-based bioactive compounds but also of general metabolic phenotype associated
with response/non-response to specific bioactive compounds. This could contribute to
the development of meaningful dietary recommendations and food product innovation
aimed to increase plant food bioactive status and efficacy in all individuals,
taking into account the different metabotypes.
Acknowledgements
This article is based upon work from COST Action FA1403 POSITIVe
(Interindividual variation in response to consumption of plant food bioactives and
determinants involved) supported by COST (European Cooperation in Science and
Technology; www.cost.eu). The authors also acknowledge all the partners involved in working
group 1 of the COST Action POSITIVe.
Compliance with ethical standards
Conflict of interest
On behalf of all the authors, the corresponding author states that
there is no conflict of interest.
Open AccessThis article is distributed
under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
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Future prospects for dissecting inter-individual variability in the absorption, distribution and elimination of plant bioactives of relevance for cardiometabolic endpoints
verfasst von
Rikard Landberg Claudine Manach Frederiek-Maarten Kerckhof Anne-Marie Minihane Rasha Noureldin M. Saleh Baukje De Roos Francisco Tomas-Barberan Christine Morand Tom Van de Wiele
