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5
Influence of Diet and Dietary
Components on the Microbiome
A
s the workshop progressed, speakers explored in greater depth the
impact of diet on the microbiome, how dietary influences on the
microbiome contribute to human health and disease, and ways to
modulate the microbiome to build and maintain health through the use of
prebiotics and probiotics in food products. This chapter summarizes that
discussion.
HUMAN BREAST MILK1
Through evolutionary experimentation, mammals have spent the last
120 million years successfully developing “the most efficient, effective
and adaptable means of postnatal nutrient provision that has ever arisen
among vertebrates: lactation.” —Blackburn (1993)
That a majority of people suffer from diet-dependent diseases raises
the question, Is it possible to prevent disease through diet? In his explora-
tion of the preventive potential of the human diet, Bruce German focuses
his research on the one food that evolved to be preventive: human breast
milk. The cost–benefit trade-off associated with human milk is key to
under tanding milk’s preventive potential, German explained. Everything in
s
human milk costs the mother. “The mother is literally dissolving her tissues
to make milk,” he said. Yet the third most abundant component in milk,
the oligosaccharides, are undigestible by the infant. How can this be? How
1 This section summarizes the presentation of Bruce German.
81
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82 THE HUMAN MICROBIOME, DIET, AND HEALTH
can such an abundant material of such a costly phenotype be undigestible
by the individual for whom it is intended, that is, the infant?
What Are Human Milk Oligosaccharides (HMOs), and
What Health Benefit Do They Provide the Infant?
Glycobiology2 is “disastrously, catastrophically complex,” German
said. According to German, the number of possible glycans, or oligosac-
charides, in a biological system is in the billions, based on the number of
ways that sugars (the basic structural units of glycans) and linkages (the
bonds between sugars) can be combined. This makes sense given that oligo-
saccharides on cell surfaces are the basis of a recognition system across all
life forms. Carlito Lebrilla developed a methodology for analyzing glycan
complexity in human milk, based on innovative separation technologies
and very high-efficiency, high-accuracy mass spectrometry (Ninonuevo et
al., 2006). His research team has constructed an annotated database of the
nearly 200 highly variable structural compositions of HMO (Wu et al.,
2010, 2011).
David Mills was among the first to address the question, What do
HMOs do? He hypothesized that HMOs serve as a food source for the
infant microbiome. However when he and his colleagues tested bacterial
growth on HMO as a sole food source, none but Bifidobacterium infantis
grew (Ward et al., 2007). “Perhaps we shouldn’t be surprised,” German
said, given that B. infantis is a dominant member of the breast-fed-infant
microbiome. Moreover, Mills and his group have discovered that only very
specific strains of B. infantis grow on HMO medium. Even bacteria that
grow very well on a variety of other sugar media are unable to grow on
HMO medium (Marcobal et al., 2010; Ward et al., 2006). (The only other
genus that appears to be able to grow on HMO medium is Bacteroides.
However, as both German and, later during the question-and-answer pe-
riod, Mills explained, B. infantis readily outcompetes Bacteroides when the
two are grown together.) “What we are discovering about this remarkable
interaction between milk oligosaccharides and this particular bacterium is
remarkable,” German said. “Mothers are literally recruiting another life
form to babysit their babies and using the oligosaccharides to direct the
microbiome.”
How does the system work? For example, if oligosaccharides are serv-
ing as a food source for B. infantis, which oligosaccharides are being
consumed? Mills and his group have discovered that unlike other bifido
bacteria, B. infantis selectively cleaves and eliminates sialic acid– ontaining
c
2 Glycobiology is the study of the structure, biosynthesis, and biology of glycans, also called
oligosaccharides (i.e., sugar chains).
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INFLUENCE OF DIET AND DIETARY COMPONENTS 83
oligosaccharides (LoCascio et al., 2007; Ward et al., 2007). Only an es-
timated 4 to 38 percent of HMOs are sialyated; a higher proportion are
fucosylated (40 to 70 percent) (Ninonuevo et al., 2006). Moreover, Mills’s
team has also identified which B. infantis genes cleave what HMO linkages
(Sela et al., 2011). Interestingly, in German’s opinion, the expression of
the bacterial enzyme that actually cleaves the sialyated oligosaccharides is
regulated by the abundance of HMOs in the growth medium. There is other
evidence indicating that human milk sugars interact with the microbiome in
ways that increase the value of the microbiome to the infant. For example,
German mentioned the research of Helen Raybould’s group on B. infantis
and its role in endocrine signaling in the infant intestine (Chichlowski et
al., 2012).
The real question, in German’s opinion, is whether the association
between HMOs and B. infantis persists as a phenotype in “real life.” That
is, “does is really influence the bacteria in living babies?” Data on micro-
biome development through the first 12 weeks of an infant’s life show that
initially Bifidobacterium is not present in the microbiome (manuscript in
preparation), but by week 12 it emerges as a dominant member of the
microbiome. Evidence from fecal sampling indicates that HMOs are not
being digested during the first weeks of life, presumably because there are
no bifidobacteria to digest them, but they begin to disappear from the infant
feces at the same time Bifidobacterium begins to dominate the microbiome
(manuscript in preparation). Thus, the association between HMOs and
B. infantis is a “true symbiotic relationship,” German said. “It’s as impor-
tant to feed the bacteria in the baby as the baby.”
German suggested that knowledge of human milk–microbiome sym-
biosis could be translated into practice in several ways. For example, he
mentioned Mark Underwood’s research on the effects of administering a
combination of B. infantis and HMOs to premature infants (manuscript in
preparation).
HOST-MICROBE INTERACTIONS IN THE PERINATAL PERIOD3
There are very few data on the development of the gut microbiota in
healthy infants, let alone how diet impacts that microbiota. Yet, there is a
plethora of clinical and epidemiological data suggesting that breast-feeding
promotes mucosal immune development and protects against many diseases.
These data, combined with the fact that human milk contains a variety of
bioactive proteins, carbohydrates, and lipids not present in infant formula,
raise questions about whether and how the infant gut microbiota differs
between breast-fed and formula-fed infants. Sharon Donovan’s long-term
3 This section summarizes the presentation of Sharon Donovan.
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84 THE HUMAN MICROBIOME, DIET, AND HEALTH
research goal is to use noninvasive approaches to define how early nutri-
tion shapes host-microbe interactions and influences intestinal development
in breast-fed versus formula-fed infants. She hopes that the knowledge
gained can be used to identify selective additives, such as bioactive proteins,
p
rebiotics (including HMO), and probiotics that can be added to infant
formula to provide some of the health benefits afforded by breast-feeding.
Differential Expression of Microbial Genes in
Breast-Fed Versus Formula-Fed Infants
In what Donovan described as a “proof-of-concept” study, she and
colleagues used a method developed by Robert Chapkin (Davidson et al.,
1995) for isolating exfoliated epithelial cells from stool to identify genes
differentially expressed in breast-fed versus formula-fed infants (Chapkin
et al., 2010). Specifically, they analyzed stool samples collected at 3 months
of age from vaginally delivered term infants who were medically certified
as healthy and who were either exclusively breast-fed (N = 12) or formula-
fed (N = 10) (Chapkin et al., 2010). The researchers gained institutional
review board (IRB) approval to train the mothers themselves to collect the
samples at home. The initial messenger RNA (mRNA) expression microar-
ray analysis yielded 4,250 genes that were expressed in all infants. Of those,
about 1,200 were significantly differentially expressed between breast-fed
and formula-fed infants. Due to the small sample size and thus greater
potential for false discovery, the scientists compared these 1,200 genes to a
list of 546 that they had predicted could be differentially expressed based
on their known roles in intestinal biology. This yielded 146 differentially
expressed genes, to which researchers applied a linear discriminant analysis
and coefficient of determination analyses developed by Edward Dougherty
and colleagues (Dougherty et al., 2009; Kim et al., 2000) to identify the
genes that best classified breast-fed versus formula-fed infants and those
that were master regulators, respectively.
The strongest classifier was EPAS1, which encodes a protein involved
in cellular response to hypoxia. Given that necrotizing enterocolitis (NEC)
is associated with tissue hypoxia and that human milk has been shown to
protect preterm infants from NEC, Donovan speculated that upregulation
of EPAS1 in breast-fed infants might be helping those babies’ guts to toler-
ate hypoxic episodes.
Other genes that qualified as good classifiers are summarized in
Table 5-1. The linear discriminant analysis methodology used allowed in-
vestigators to identify not just single genes that could be considered good
classifiers of breast-fed versus formula-fed infants, but also two- and three-
gene combinations (Chapkin et al., 2010).
Donovan speculated that these gene expression differences might ex-
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INFLUENCE OF DIET AND DIETARY COMPONENTS 85
TABLE 5-1 Exfoliated Epithelial Cell Genes Identified as Good
Classifiers of Breast-Fed (BF) Versus Formula-Fed (FF) Infants, Based on
a Linear Discriminant Analysis of Genetic Material Collected from Stool
Samples
Fold change
Gene Name Function (BF/FF)
EPAS1 Transcription factor (TF); cellular response to hypoxia 3.3
NR5A2 TF, encodes liver receptor homologue-1 (LRH-1); 2.8
development
NR3C1 Encodes glucocorticoid receptor 5.5
PCDH7 Encodes protocadherin-7; membrane protein 3.9
ITGB2 Encodes integrin beta-2 (CD18); ICAM-1 receptor 2.5
FGF5 Encodes fibroblast growth factor 5; mitogenesis and cell 2.0
survival
TJP1 Encodes ZO-1; intercellular tight junctions 2.2
MYB TF, transcriptional transactivation; proto-oncogene 2.8
EPIM Syntaxin 2/epimorphin; epithelial cell morphogenesis 2.5
BAD BCL2-associated agonist of apoptosis 4.0
SOURCE: Chapkin et al., 2010.
plain some of the clinical and epidemiological evidence that has accumu-
lated over the years showing that breast-fed babies’ guts develop differently
and are less leaky than those of formula-fed babies. For example, the ex-
pression of TJPI, which encodes ZO-1, an intercellular protein that plays
an important role in regulating tight junctions, was also upregulated in the
cells from breast-fed babies. Additionally, expression of NR3C1, which
encodes a glucocorticoid receptor that plays a role in gut differentiation,
was fivefold higher in breast-fed than in formula-fed infants.
Using MetaCore, a bioinformatics tool that provides information about
function, researchers found that some of the strongest signals were with
combinations of genes that encode signaling pathways involved in funda-
mental pathways of intestinal stem cell proliferation and differentiation,
such as WNT and NOTCH.
Donovan suggested that these various gene and gene network classifiers
could serve as potential biomarkers for differentiating between breast-fed
and formula-fed infants. Also, it would be interesting to see if addition
of any of these to infant formula, in the form of a prebiotic or probiotic,
would shift the gut microbiota toward the direction of breast-fed infants.
Next, Donovan and her team tested the hypothesis that the integra-
tion of infant (host) epithelial cell transcriptome and functionally profiled
microbiome can be used to suggest important regulatory pathways of the
microbiome affecting intestinal development in the first few months of life
(Schwartz et al., 2012). Community-wide microbial gene expression in stool
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86 THE HUMAN MICROBIOME, DIET, AND HEALTH
from the same breast-fed (N = 6) and formula-fed (N = 6) infants from their
earlier work (Chapkin et al., 2010) using established protocols (Poroyko et
al., 2010) was evaluated using 454 pyrosequencing of DNA libraries cre-
ated from stool samples. Taxonomic composition of the metagenome was
analyzed with the metagenomics analysis server MG-RAST using similarity
to a large nonredundant protein database. Using the same database, the
sequence alignments for known microbial metabolic functions were tested
against the SEED subsystems.4 At the phyla level, all formula-fed infants
shared the same distinct signature, whereas breast-fed infants were more
variable. Three of the breast-fed infants had similar profiles, but the other
three “were sort of going to the beat of their own drummer,” said onovan.
D
She noted that all of the formula-fed infants were receiving the same for-
mula but that breast milk composition can be highly variable.
Using established protocols for evaluating community-wide microbial
gene expression in stool samples, the team observed a greater total percent-
age (i.e., percentage of total 16S ribosomal RNA [rRNA]) of Actinobacte-
ria, Proteobacteria, and Bacteroidetes in breast-fed piglets as a group and
a greater total percentage of Firmicutes and no Bacteroidetes in formula-
fed infants as a group (see Figure 5-1) (Donovan et al., 2012). Donovan
remarked that these findings warrant follow-up, given Peter Turnbaugh
and colleagues’ observation that obesity in mice is associated with a higher
Firmicutes:Bacteroidetes ratio. In addition, epidemiological studies have
demonstrated that breast-feeding protects against the development of child-
hood obesity.
Which Human Genes Respond to Bacterial Signals?
An overarching theme of the workshop discussion was the importance
and growing interest in understanding not just what bacteria are present in
the microbiome, but how those bacteria are signaling in a way that impacts
the human host biology. For example, Donovan expressed curiosity about
whether differences in microbial gene expression between breast-fed and
formula-fed infants impact host gene expression. A comparison of the func-
tional SEED categories of the stool metagenome of breast-fed and formula-
fed infants demonstrated a significantly higher proportion of virulence genes
in the breast-fed infants. Next, the scientists applied a systematic and statisti-
cally rigorous analytic framework for the simultaneous examination of both
host and microbial responses to dietary or environmental components in
the early neonatal period. Specifically, using canonical correlation analysis,
4 SEED is an open-source software platform that seeks to curate microbial genomic data
into subsystems-based functional annotation (e.g., amino acid metabolism). More information
is available online: www.theseed.org (accessed August 28, 2012).
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INFLUENCE OF DIET AND DIETARY COMPONENTS 87
Breast-fed (BF) Formula-fed (FF)
Verrucomicrobia Un_sub_classified
Un_sub_classified Firmicutes 3.2% 3.3%
7.0% 9.8%
Proteo-
bacteria
11.1% Firmicutes
Bacteroidetes
35.1%
19.6% Actinobacteria
Proteobacteria 37.1% Actinobacteria
26.4%
46.4%
• BF had more total Proteobacteria and Bacteriodetes
• FF had more Firmicutes and no Bacteriodetes
FIGURE 5-1 Results from a study on breast-fed versus formula-fed human infants
showing variation in gut microbial composition.
SOURCE: Donovan et al., 2012.
Figure 5-1
Donovan and her team identified associations between the microbiome viru-
lence genes and 11 host immunity defense genes (TACR1, VAV2, ALOX5,
NDST, REL, BPILI, AOC3, KLRF1, DUOX2, IL1A, and SP2) (Schwartz
et al., 2012). Donovan speculated on the potential biomarker usefulness of
microbial sequencing, in this case as a way to predict host defense mecha-
nisms. These findings suggest that simultaneously examining the multivariate
structure underlying the microbiome and gut transcriptome leverages richer
and fuller information content compared to analyses focusing on single data
sets (e.g., only host transcriptome data or only microbiome data) and only
single variables (e.g., gene-by-gene differential expression testing). The use
of canonical correlation analysis can support the formulation of hypothesis-
based studies by accurately identifying those genes active in commensal
microbiome and host activities (Schwartz et al., 2012).
What Components in the Infant Diet Affect the Intestinal Microbiota?
Nutrients and bioactive components in human milk directly influence
the development of the infant’s immune system, actively protect the infant
from pathogenic infection, and facilitate establishment of the microbiota,
the last of which is required to activate the mucosal immune system. Recent
data suggest that HMOs contribute to many of these activities (Donovan
et al., 2012). Oligosaccharides are the third most predominant component
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88 THE HUMAN MICROBIOME, DIET, AND HEALTH
of milk, after lactose and fat, and up to 200 different structural forms
have been identified in human milk (Wu et al., 2010, 2011). Since HMOs
are resistant to digestion by the infant and pass into the colon, Donovan
described them as the fiber of human milk, a fact that she said hasn’t really
been appreciated until recently. In the colon, they potentially function in a
variety of ways, including as substrates for fermentation and the production
of short-chain fatty acids. They can also serve as prebiotics for beneficial
bacteria. Donovan referred to David Mills’s very elegant data showing that
Bifidobacterium infantis metabolizes specific HMOs (see the previous sec-
tion for a more detailed description of work conducted in the Mills labora-
tory and by Bruce German).
HMO composition is influenced partly by secretor status of the mother
and whether she has the 2-fucosyltransferase gene; non-secretor mothers do
not produce 2'-fucosyllactose (2'-FL), which is one of the primary HMOs in
the milk of secretor mothers. Therefore, Donovan and others are exploring
potential predictive associations between HMO composition and infant gut
microbiota. Systematic evaluation of the impact of HMO on infant devel-
opment, however, has been limited by the lack of sufficient quantities of
pure HMOs to conduct animal or human feeding studies. However, in the
near future, this limitation will be overcome through improved synthetic
approaches, opening avenues of investigation into the biology of HMOs.
Additionally, the availability of noninvasive methods of assessing outcomes
in human infants (Chapkin et al., 2010; Schwartz et al., 2012) and high-
throughput methods for measuring HMOs (Wu et al., 2010, 2011) and the
infant microbiome (Schwartz et al., 2012) will facilitate our understanding
of the role of HMOs in host-microbe interactions in the developing infant
(Donovan et al., 2012).
THE RESISTOME AS A DRIVER OF THE MICROBIOME5
Food is not the only major driver of the microbiome. So too is the way
we raise food, Ellen Silbergeld stated. Most food animals are grown very
intensively, including through the use of animal feeds that contain antibiot-
ics. Food and Drug Administration (FDA) data indicate that 80 percent of
total antimicrobial production in the United States in 2009 was for use in
animal feed.6 Silbergeld stressed that the use of antibiotics in animal feed
is not for veterinary medical purposes; rather, antibiotics are added to feed
5 Thissection summarizes the presentation of Ellen Silbergeld.
6 These values were calculated by the Center for a Livable Future based on data pro-
vided by the Food and Drug Administration. For more information, read the posting on its
website: http://www.livablefutureblog.com/2010/12/new-fda-numbers-reveal-food-animals-
consume-lion%E2%80%99s-share-of-antibiotics (accessed September 19, 2012).
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INFLUENCE OF DIET AND DIETARY COMPONENTS 89
as growth promotants. This is a new phenomenon in the history of antimi-
crobials and one with significant implications for what Silbergeld referred
to as the “resistome.”
The Resistome
The term “resistome” was introduced by Wright (2007), who defined
it as the collection of all the antibiotic resistance genes and their precursors
in the entire microbial community of both pathogenic and nonpathogenic
bacteria. Genes within the resistome encode molecular changes that confer
phenotypic resistance to both general and specific antibiotic molecules.
They are often clustered in cassettes and are transferable by plasmids, creat-
ing a pleiotropic efficacy. Multigene cassettes can encode other phenotypes,
not just resistance.
An important feature of the resistome is that resistance genes are read-
ily transferred from one bacterial cell to another via horizontal, or lateral,
gene transfer (usually via plasmid-mediated transfers but also by conjuga-
tion). The classic model of antibiotic resistance describes a population of
diverse organisms encountering antibiotic pressure, with some organisms
being susceptible and some resistant and with the susceptible organisms
dying and the resistant organisms persisting (Sommer and Dantas, 2011).
However, that model does not account for the dynamic nature of horizontal
gene transfer and the fact that a population of initially susceptible bacteria
can accumulate expressible resistance genes over time. Even after a stressor
is withdrawn, this system can be permanently altered by the experience. In
a study of humans exposed to ciprofloxacin, Dethlefsen and Relman (2011)
showed that resistant phenotypes persisted even after exposure ended.
Increasingly, horizontal gene transfer involves not just the sharing of
single genes, but also the sharing of cassettes of multiple genes. Silbergeld
explained how the extensive use of antimicrobials exerts multiple and
repeated pressures on bacterial populations, resulting in sequential acquisi-
tion of resistance genes and buildup of multigene cassettes (Canton and
Ruiz-Garbajosa, 2011). As empirical evidence of the buildup of transferable
multigene cassettes, Silbergeld mentioned U.S. Department of Agriculture
(USDA) data showing growth over time of extended multidrug resistance
phenotypes of Escherichia coli in domestic animals (i.e., chicken, swine, cat,
dog, dairy cattle) (Lindsey et al., 2011).
Not only is the resistome accruing more resistance genes, either singly
or bundled in multigene cassettes, it also appears to be accumulating net-
works of preferential horizontal gene transfers, or “cliques” (Skippington
and Ragan, 2011). Again, extensive antimicrobial use is exerting selective
pressure, in this case for more active networks of horizontal gene transfer.
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90 THE HUMAN MICROBIOME, DIET, AND HEALTH
The bacteria involved in any given network, or clique, are not necessarily
in close proximity and may not even share the same ecology.
Silbergeld described the resistome as being analogous to cloud com-
puting, because it is a resource that can be externalized and accessed by
various groups of bacteria and because the transfer of resistance genes
via horizontal gene transfer is like transferring downloaded bytes of data.
She stressed the importance of recognizing that the resistome encompasses
both pathogenic and nonpathogenic bacteria and may include most of the
bacteria in a specific microbiome.
From the Modern Livestock Farm to Humans:
Implications for the Resistome
Selective pressures exerted by extensive antibiotic use abound in the
modern livestock farm, which Silbergeld described as an “impressive labo-
ratory for driving microbial evolution.” Danzeisen et al. (2011) sampled
the microbiomes of chicken ceca after feeding chickens either control feed,
feed with monensin (an antibiotic), or feed with virginiamycin (another
antibiotic) and detected significant differences in microbiome content (e.g.,
percentage of total microbes represented by Lachnospiraceae versus Ru-
minococaceae). However, it is not just the flora that is changing in the
face of increased antimicrobial pressure. Researchers are also reporting an
increased prevalence of antibiotic-resistant phenotypes in food animals fed
antibiotic-containing diets (Looft et al., 2012) (see Figure 5-2).
Importantly, the increased prevalence of resistant phenotypes being ob-
served in the guts of farm animals persists not just in their microbiome but
in the resistome at large, mainly because of the practice of land disposal of
animal wastes without required pretreatment. For example, Nandi et al.
(2004) traced the movement of resistance genes from poultry litter into the
soil environment where poultry waste was deposited. Eventually, humans can
potentially become exposed to those same resistance genes via several routes.
One way to represent the resistome and the way it transcends, or ex-
tends across, all of these different microbiomes, from farm animals to soil
bacteria to the human gastrointestinal (GI) tract, is as a nested system of
increasing complexity, where components occupy different spaces in the
ecosystem and events that occur can eventually impact the human micro-
biome (Davis et al., 2011). Silbergeld’s research group is conducting an
ongoing study of the historical ecology of the Chesapeake Bay to see if the
appearance of resistance genes in sediment correlates with the introduction
of intensive poultry production and the use of antibiotics in poultry feed.
Some experts consider antibiotic-resistant genes to be environ ental
m
pollutants that bioaccumulate over time (Martinez, 2009). Although
a
ntibiotics are generally not very persistent in the environment, if humans
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INFLUENCE OF DIET AND DIETARY COMPONENTS 91
10000
Normalized number of 16S
8000
sequences
6000
Desulfovibrio
Campylobacter
4000
Escherichia/Shigella
Succinivibrio
2000
0
Nonmed Med Nonmed Med
day 0 day 0 day 14 day 14
150
145
Escherichia
Number of reads
20 Shigella
Oxalobacter
15 Desulfovibrio
Prevotella
10 Parabacteroides
Chitinophaga
5
Bacteroides
Other
0
Nonmed Med Nonmed Med
day 0 day 0 day 14 day 14
NonMed Day 0 Med Day 0
10-3 NonMed Days 3-21 Med Days 3-21
ab a
(ARG / 16S rRNA) copy numbers
a
ab a a
a
ab a ab
10-4 ab
ab ab
b
b
10-5 ab
b b b
b b
b
10-6 b b
10-7
Class A Beta Tetracycline Sulfonamide Aminoglycoside Resistance- Major Facilitator
Lactamase Efflux Pump Resistance O-phospho- Superfamily
transferase Division Transporter
Transporter
FIGURE 5-2 Results from a study showing a shift in gut microbiota (top figures)
and an increased prevalence of antibiotic-resistant phenotypes (bottom figure) in
food animals fed antibiotic-containing diets (“GPA feeds”).
NOTES: ARG = antibiotic-resistant gene; med = antibiotic-containing diet; nonmed
= control. Columns with the same letter are not statistically significant (p > 0.05)
within each resistance type.
SOURCE: Looft et al., 2012.
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110 THE HUMAN MICROBIOME, DIET, AND HEALTH
leverage this science on the microbiome to develop products that maintain
or improve health? Establishing causal relationship allows for microbes to
be targeted, and the next step is to identify specific active ingredients and
components that target these microbes and their impact on host health. Or,
as van Hylckama Vlieg expressed, “Science provides increased rationale for
functional food concepts using pre- and probiotics that bring a clear health
benefit to consumers.”
Leveraging the Microbiome for Health
In fact, humans have been leveraging the microbiome for a long time,
initially through animal husbandry and consumption of fermented foods
and moving toward science-based evidence for dietary intake. Fermented
foods are important constituents of the human diet worldwide, and use of
these foods dates back approximately 10,000 years (Evershed et al., 2008).
These fermentations are often carried out with lactic acid bacteria. As van
Hylckama Vlieg explained, lactic acid bacteria are also natural inhabitants
of the human GI tract, so foods fermented by lactic acid bacteria are effec-
tively supplementing the indigenous microbiota. In fact, lactic acid bacteria
could be considered “domesticated” microbes. By substituting “microbe”
for “animal” or “plant,” they fit the Webster’s dictionary definition of
domesticated: “to adapt (an animal or plant) to life in intimate association
with and to the advantage of humans.”16
As an example of how Danone has leveraged the microbiome for
health, van Hylckama Vlieg highlighted work on a fermented food product
containing Bifidobacterium animalis subsp. lactis strain CNCM I-2494. The
impact of FMP on the gut microbiome and host health was explored using
a specific mouse model (T-bet-/- and Rag2-/- knockout mice, also known as
TRUC mice) that spontaneously develops gut inflammation resembling hu-
man ulcerative colitis (Garrett et al., 2007). Garrett et al. (2007) reported
that antibiotics reverse inflammation in TRUC mice, indicating a microbial
etiology. Plus, when they co-housed TRUC and wild-type mice, they ob-
served colitis in the wild-type mice as well, indicating the communicability
of whatever gut microbes were associated with inflammation in the TRUC
mice.
While a population of individuals with ulcerative colitis is far removed
from Danone’s target population, using that sort of extreme model can pro-
vide cleaner data and reveal mechanisms more readily than would be possi-
ble using other methods. Using the TRUC mouse model, Veiga et al. (2010)
analyzed the TRUC gut microbiota by 16S rDNA sequencing and observed
low levels of Bifidobacterium in the colon compared to non-inflamed (wild-
16 See http://www.merriam-webster.com/dictionary/domesticate.
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INFLUENCE OF DIET AND DIETARY COMPONENTS 111
type) mice. They observed that feeding the TRUC mice 100 milligrams
of FMP per day decreased intestinal inflammation (<0.0001 compared to
TRUC mice fed the same amount of nonfermented food product) and in-
creased levels of short-chain fatty acids (acetic acid, propionic acid, butyric
acid). They also observed increased levels of lactate-consuming bacteria in
the FMP-fed TRUC mice. The researchers showed “very elegantly,” accord-
ing to van Hylckama Vlieg, that the newly altered intestinal environment
in FMP-fed mice inhibits growth of colitogenic bacteria. Research on the
impact of FMPs on TRUC mouse microbiome and host health is ongoing.
Meanwhile, another study with the same FMP product on the gut mi-
crobiomes of healthy twin pairs and gnotobiotic mice demonstrated that
FMPs do not cause any major perturbation of the dominant microbiota
of healthy human subjects. However, this product does trigger distinct re-
sponses in the activity of the microbiome detected through transcriptomics
(McNulty et al., 2011). Also, in both the mouse model and human samples,
the metabolic activity of gut Bifidobacterium animalis subsp. lactis is dra-
matically altered. B. animalis subsp. lactis harvests and grows on the xylo-
oligosaccharides derived from dietary components, which demonstrates
that strain is becoming an active member in the gut microbial community.
Microbiome Biomarkers: Implications for Personalized Nutrition
In addition to research on mouse models and human samples, Danone
is a partner of the MetaHIT Consortium. Acknowledging controversy
about the biological relevance as expressed during the meeting regarding
the existence of enterotypes, van Hylckama Vlieg remarked that regardless,
“the observation is there” (Arumugam et al., 2011). The question is, What
are the implications for the food industry? Do observations of enterotypes
or any other clusters of markers in the microbiome indicate that people
have specific nutritional needs, perhaps specific probiotic needs, depending
on their microbiota compositions? It may be possible that these issues will
lead to the emergence of personalized, or categorized, nutrition in coming
years.
In preparation for these coming years, Danone is building and main-
taining a culture collection of more than 3,000 microbes, mainly lactic acid
bacteria. The collection includes strains isolated from multiple geographic
areas (i.e., from various countries), ecoystems (i.e., from dairy products,
cereals, plants, and stools), and time periods (i.e., from the 1960s through
the present) and contains more than 80 species. They have sequenced
nearly 100 genomes in the collection. “It is a very powerful platform for
discovery,” van Hylckama Vlieg said. Much of its power stems from its
focus on strain diversity. In a comparative genome hybridization study of
42 strains of Lactobacillus plantarum, Siezen and colleagues (2010) found
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112 THE HUMAN MICROBIOME, DIET, AND HEALTH
that while about 70 percent of the genome of L. plantarum is shared among
all strains, there are many genes not shared by all strains and many strain-
specific genes.
Strain individuality raises the key question: What is the correlation with
functional diversity? Currently, Danone investigators have been exploring
the “pangenome” of multiple strains of two species, L. rhamnosus and
L. paracasei, for gene-function correlations. Researchers are building a ge-
nome diversity database and coupling that with an extensive phenotyping
program. Such studies will help to identify both common activities shared
by many strains and specific features that can only be delivered by a few
strains.
According to van Hylckama Vlieg, Danone scientists are looking at
phenotypes related to carbohydrate utilization and short-chain fatty acid
production, antimicrobial activity, and immune modulation. As an example
of the type of results they are collecting, a sequencing analysis of 12 strains
of L. rhamnosus and 30 strains of L. paracasei indicated that about 30
to 40 percent of the genome is “core,” that is, shared among all strains.
However, up to 25 percent of genes are strain-specific, with different strains
having different immune function effects (i.e., based on an NF-kappa B-type
screen on HT29 cells).
DISCUSSION
Most of the discussion during the question-and-answer period at the
end of this session revolved around three major issues: (1) functional con-
sequences of modulating the microbiome through food, (2) whether there
are any known adverse effects of prebiotic and probiotic interventions, and
(3) experimental design and study size.
Focus on Function
A recurring theme of the workshop was the importance of functional,
not just compositional, changes to the microbiome as a result of dietary
(or antibiotic) intervention. For example, as summarized in this chapter,
Johan van Hylckama Vlieg mentioned research results demonstrating that
Activia does not cause any major perturbation of the microbiota but does
trigger distinct transcriptomic responses related to the metabolic activity of
Bifidobacterium animalis. Also, George Fahey commented on the results of
a study showing several positive metabolic outcomes associated with oligo-
fructose consumption in mice. Finally, James Versalovic elaborated on the
many ways that probiotics can impact host immunity. An audience member
asked whether, given that food products appear to impact the microbiome
not by recolonizing (so not by changing the taxonomic makeup of the mi-
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INFLUENCE OF DIET AND DIETARY COMPONENTS 113
crobiome), by rather through signaling (changing the way the microbiome
is functioning), does it matter whether the food product in question is a
live organism (i.e., a probiotic) or an inert substance (i.e., a prebiotic)? In
other words, is the differentiation between probiotic and prebiotic a false
dichotomy? Are the conceptual barriers between probiotic and prebiotic
artificial?
Versalovic agreed that as the field moves forward, hopefully the dia-
logue will move beyond definitions and focus more on how food is impact-
ing health via its effect on the microbiome. The value of the discovery of
histamine as a microbially produced molecule that impacts host immunity
isn’t the histamine itself. Rather, its value is that “it has pointed us in a
whole new direction—to look at amino acid conversions.” Are other amino
acids being converted by microbes? What other enzymatic machinery is the
microbiome providing for the biochemical conversion of dietary informa-
tion into biological signals? Versalovic said, “We’re on entirely new trails
in the wilderness.” His research group is also studying another microbially
catalyzed bioconversion of a host dietary substance into a biological signal,
that is, glutamate into gamma-aminobutyric acid (GABA). He speculated
on the potential regulatory role of the gut microbiome with respect to how
much dietary information is actually being converted in vivo. “What’s re-
ally intriguing here,” he said, “is that that may be how the microbiome is
really contributing to health and physiology … providing the enzymatic
machinery, the metabolic pathways, at a particular location. It could be the
GI tract. It could be the airways. It could be the oral cavity … providing
that machinery that then allows [for the conversion of] the dietary content
into [a] signal for the body.”
Adverse Effects?
An audience member asked whether there were any known adverse
e
ffects of prebiotic or probiotic interventions. Mary Ellen Sanders replied
that the National Institutes of Health (NIH) recently commissioned a review
on probiotic safety that covered hundreds of prevention and treatment-of-
disease studies of a variety of microbes (Hempel et al., 2011). The review
concluded that most studies have not been adequately designed to address
safety. Sanders speculated that the lack of safety data is likely due to the
fact that probiotics have always been viewed as a food. They have not been
viewed as drugs or as something that potentially could cause problems,
at least not when administered to healthy people. So in the past, most re-
searchers jumped right into efficacy studies, bypassing what would be the
equivalent of a phase I drug study. Today, more researchers are designing
their studies with safety factors in mind. Meanwhile, to Sanders’s knowl-
edge, at least for the most commonly researched microbes there is no or
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114 THE HUMAN MICROBIOME, DIET, AND HEALTH
very little association of use in a normal population with any adverse effects
of real concern. There are some notable exceptions, she said. For example,
Besselink et al. (2008) reported increased mortality after administration of
probiotics to patients with severe pancreatitis. Sanders cautioned that not
only do different microorganisms have different safety patterns, but that
the health status of the host also influences safety.
Dan Levy emphasized the need to examine strain-specific safety effects.
He said, “The tendency has been to say, well, we’re exposed to all of these
lactic acid bacteria with no adverse effects.” But not all lactic acid bacte-
ria are the same. While some strains may cause no adverse effects, that is
not necessarily the case for all strains. This is especially worrisome when
a strain is chosen for a unique property, that very uniqueness suggesting
that the strain does not behave like others, particularly when selecting for
strains intended to have specific and perhaps novel physiological effects on
the consumer. “You have to stop lumping all lactic acid bacteria together,”
Levy said. “We’re looking at specific physiological mechanisms associated
with specific strains, and we have to study the good, the bad, and the ugly.”
Experimental Design and Study Size
The discussion of microbiome markers of health and disease prompted
a couple of comments on experimental design. Ellen Silbergeld expressed
concern that too many studies on the microbiome are “almost entirely
underpowered,” raising serious questions about the value of the informa-
tion being provided by those studies. If a study is too underpowered to
provide any evidence of an effect, then what is the value of that study? “If
you really want to move this field forward,” she said, “you really have to
start considering your study design.” In response, Johan van Hylckama
Vlieg remarked that many small studies, such as the twin study that he
mentioned, are intended to be exploratory and hypothesis-generating. They
are not intended to yield the same type of results that large-scale clinical
trials provide. Silbergeld then wondered whether any of the larger stud-
ies actually meet the criteria of a clinical trial. It is not clear that any do.
Versalovic questioned whether large-scale clinical trials are even appropri-
ate for microbiome studies. Given such extreme individual-level variation in
microbiome composition and function, he said, “I don’t know that we can
do trials the same way anymore,” indicating the need for further discussion
on issues of study design.
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INFLUENCE OF DIET AND DIETARY COMPONENTS 115
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