The largest numbers of commensal bacteria reside within our intestinal tract, with
an increasing density from mouth to anus. Recently, a new estimate for the total number
of bacteria (3.8×1013) in the 70 kg ‘reference man’ was reported.1 For human cells,
the same authors revised past estimates to 3.0×1013 cells, out of which approximately
90% belong to the haematopoietic lineage. Hence, the widely cited 10:1 ratio of bacteria
versus human cells received an update, showing that the number of bacteria in the
body is actually of the same order as the number of human cells, and that the cumulative
bacterial mass is about 200 g. Still, this large number of bacteria highlights their
importance in maintaining health and metabolism. Different parts of the intestinal
tract have different functions, tissue structure varies accordingly and gradients
exist for several physicochemical parameters such as nutrients, pH or oxygen levels.2
Consequently, microbiota composition varies along the gut, but also between luminal
and mucosa-attached communities of the same intestinal segment, and even along the
crypt-villus axis in the epithelium. Thus, host–microbiota interactions are likely
regionally specific and the local crosstalk determines intestinal function and physiology.
Probably each human individual carries its own ‘microbial fingerprint’ (especially
when considering genomic variation within the bacterial species’ populations), which
is why forensic scientists started to exploit the use of this non-human organ.3
Recent large-scale analyses of population-based cohorts with >1000 samples validated
that body mass index, age at sampling and gender are important covariates that need
to be included in microbiome association analyses.4 In sum, Falony et al identified
18 covariates, including stool consistency, dietary factors and blood traits, cumulatively
explaining 7.7% of the total variation in the gut microbiota, leaving 92.3% of the
interindividual microbial variation unexplained. In a recent host-microbiome genome-wide
association study, 42 genetic loci were identified that explain another 10% of microbiome
variability.5 Although these percentages need to be confirmed in other cohorts and
for other ethnicities, both studies show that (A) a large portion of microbiome variability
remains to be explained and (B) any microbiome study needs to account for covariates
to prevent false-positive and false-negative results. This means for the field that
the meta-data for a particular sample is at least as important for the data analysis
as the actual microbiome data itself.
The main subtypes of IBD are Crohn’s disease (CD) and UC, affecting together more
than 2.5 million European individuals. More than 200 genetic susceptibility loci have
been identified for IBD, a contribution that significantly improved our disease understanding.
In contrast to the static human genome, microbiome composition is more dynamic and
can thus be more easily modified by, for example, probiotics, prebiotics, antibiotics
or faecal microbiota transfer (FMT) from healthy donors to patients by enema or via
the nasoduodenal route. The latter was demonstrated to be perhaps the best method
to treat life-threatening recurrent Clostridium difficile infections,6–8 but is a
controversial method for IBD and is therefore still regarded as an experimental therapy.
Nevertheless, a recent multicentre placebo-controlled trial in Australia (the Faecal
Microbiota Transplantation in Ulcerative Colitis (FOCUS) study) demonstrated efficacy
of FMT for treating UC (p=0.021; enemas given at 5 days a week for overall 8 weeks).9
Based on this finding, it was suggested that future work should focus on defining
the optimum treatment intensity and the role of donor–recipient matching based on
microbial profiles. The growing body of studies using FMT and gnotobiotics started
to move the field of microbiota research away from correlation to causality by proving
a functional involvement of the microbiota for health and disease.10–12
Microbiome studies in IBD have been consistent in finding reduced diversity in patients
compared with healthy controls (HC).13 14 A lack of microbial diversity seems to be
a general theme in several diseases (obesity, diabetes, asthma, atopic dermatitis,
and so on), suggesting that a species-rich ecosystem is more robust against environmental
influences, as functionally related microbes exist in an intact ecosystem that can
compensate for the function of another species that has disappeared. Consequently,
diversity seems to be a good indicator of a ‘healthy gut.’15 A large study that analysed
the microbiota in 447 treatment-naïve patients with new-onset CD (treatment effects
on the microbiota can likely be excluded in this unique cohort) also identified different
bacteria, which were more abundant in patients compared with controls and vice versa.16
Gevers et al also developed a microbial dysbiosis index that was correlated with the
paediatric CD activity index. The dysbiotic signal in their study was stronger in
the mucosa-attached microbial communities compared with faecal bacteria, suggesting
that disease-relevant and more adherent bacteria are probably ‘diluted’ in the stool.
The recently published longitudinal IBD microbiome study of Halfvarson and colleagues
revealed that the microbiome of patients with IBD varies more over time compared with
controls, corroborating that patients with IBD harbour a disturbed microbial ecosystem.17
The highest volatility of the microbiota, that is, dynamic deviation from a ‘healthy
plane,’ was observed for patients with CD with ileal inflammation (lower volatility
in colonic CD and UC). In an elegant visualisation study, Swidsinski et al demonstrated
that the number of mucosa-associated bacteria increases with disease severity18 and
together with reduced diversity, an increase in bacterial load seems to be a key feature
of several diseases.19 Notably, diet can rapidly alter the gut microbiome with effects
on disease susceptibility such as pathogenic infection or chronic inflammation.20–22
For example, removal of carbohydrates from the diet promotes the growth of mucus-utilising
bacteria and alters microbial biogeography by attracting luminal bacteria to the mucus
layer, which increases competition for beneficial mucus-resident bacteria such as
Akkermansia muciniphila or Faecalibacterium prausnitzii, which in turn shifts epithelial
responses, in particular goblet cell function.23 24 Oral but even intravenous iron
replacement therapy distinctly alters the gut microbiota and metabolome in patients
with IBD.25 Recent studies even suggest that changes in nutrition during the course
of human history may have led to decreased microbiota diversity and the increased
incidence of chronic inflammatory diseases and obesity.24 26 27 Another recent study
has highlighted the potential effect of a disturbed host–microbiota crosstalk in IBD.
This is evidenced by a striking uncoupling of host transcriptional patterns and microbiota
signatures in IBD, with the affected pathways indicating a loss of cometabolic functions.28
It therefore seems imperative to correct for dietary variance in microbiota-related
data and to explore dietary modulation of the metabolic properties of the gut ecosystem
(eg, enrichment of dietary fibre) as a possible therapeutic approach for IBD. Figure
1 provides a schematic overview on known gut microbiome changes in IBD.
Figure 1
Microbial signatures of a healthy gut and IBD. Under healthy homeostasis the microbiota
is diverse. Goblet cells produce a thick colonic mucus layer, which creates a physical
barrier against the microbiota, but also harbours a specific mucus-resident microbiota
enriched in, for example, short-chain fatty acid producing bacteria Roseburia and
Faecalibacterium prausnitzii. Immune sensory cells such as DC and MΦ sample microbial
patterns and induce a T cell profile dominated by IL-10 producing Treg lymphocytes
leading to homeostasis. However, the composition of the microbiota is less diverse
in patients with IBD with fewer Bacteroidetes mainly attributed to loss of Prevotella
species and expansion of Actinobacteria, Proteobacteria such as adherent invasive
Escherichia coli and Fusobacteria. Mucosal function is also altered, for example,
lipid metabolism, illustrating a cometabolism of the metaorganism. A reduction in
Paneth and goblet cell number along with their impaired functions—secretion of antimicrobial
substances and mucus—leads to decreased mucus thickness, reduced mucosal integrity
and finally to an impaired barrier function. This increases bacterial translocation
and stimulates activation of DCs and MΦs, which then induce an altered T cell profile
with increased IFNγ/TNFα-producing Th1, IL-6/TNFα-producing Th2 and IL-17-producing
Th17 lymphocytes resulting in a proinflammatory response and tissue damage, which
in turn stabilises the dysbiotic microbiota and a chronic inflammatory tone. DC, dendritic
cells; IFNγ, interferon gamma; IL, interleukin; MΦ, macrophages; TNFα, tumour necrosis
factor alpha.
In the highlighted study, Pascal and colleagues29 report on an analysis of the faecal
microbiome for a large IBD panel from four different countries (40 HC (unrelated HCs)/71 HR
(healthy relatives from patients)/34 CD/74 UC from Spain, 54 CD from Belgium, 977
healthy twins from the UK and 158 patients with anorexia from Germany). In sum, they
included about 1246 non-IBD and 162 IBD samples. The hypervariable region V4 of the
16S rRNA gene was targeted for microbiome profiling. Pascal et al replicate the findings
of Gevers et al
16 that F. prausnitzii is reduced/absent in patients with CD and that patients have
a higher abundance of Escherichia and Fusobacterium.
The key finding of their study is a ‘microbial signature’ that may be used to discriminate
between CD and non-CD by eight prokaryotic groups: Faecalibacterium (genus from family
Clostridiaceae), Peptostreptococcaceae (family from order Clostridiales), Anaerostipes
(genus from family of Lachnospiraceae), Methanobrevibacter (genus from family Methanobacteriaceae),
Christensenellaceae (family from order Clostridiales) and Collinsella (genus from
family Coriobacteriaceae) being decreased in patients with CD; Fusobacterium (genus
from family Fusobacteriaceae) and Escherichia (genus from family Enterobacteriaceae)
having a higher relative abundance in patients with CD. The overall sensitivity of
this diagnostic signature was 80%, with specificities of 89%–94% depending on the
comparison. In comparison, assessing faecal calprotectin (a heterodimer of S100A8
(calgranulin A) and S100A9 (calgranulin B)), an established, non-invasive test that
measures intestinal inflammation, has a sensitivity of 83% and specificity of 84%
for distinguishing organic from functional intestinal diseases in symptomatic patients.30
However, diagnosis of IBD, and especially distinguishing between the different subtypes
of IBD, still relies on a combination of diagnostic tests, including endoscopic and
histological analyses, which showed high accuracy in a 12-month long follow-up, with
only 1%–2% of patients with CD and UC being identified as false positives, but 9%–12%
of patients with CD and UC being reclassified as another subform of IBD.31
While the study of Pascal et al employed a large sample panel in order to identify
potential microbiomarkers of interest, a few words of caution are necessary. First
of all, the findings still need to be validated by other centres using well-powered
sample collections that include HC, CD, UC and IBD-related diseases, that is, differential
diagnoses such as IBS. In a first attempt, Pascal et al applied their test to another
cohort sequenced in another centre in France with another method (V3-V5 instead of
V4). This led to a significantly lower sensitivity and specificity for the prediction
of CD versus UC (60% and 68% respectively) and a lower sensitivity (60%) for the prediction
of CD versus HC. While these results still suggest a non-random signal in the data,
they also imply that larger and multicentre trials are clearly needed to produce a
more robust and clinically useful diagnostic signature. It is noteworthy that Pascal
et al included a very large UK control panel, making up almost half of their sample
size, while they did not include a matched patient panel from the same geographical
region. Anthropometric features as well as nutritional patterns can have a large impact
on the gut’s microbial community and are a valuable resource in the investigation
of connections between disease states and the microbiota. While these data will certainly
lead to more complex models for the determination and distinguishing of dysbiotic
states, they will most certainly lead to higher sensitivity and specificity of microbiome-based
classifiers and should be considered for future approaches.
The regional specificity of the intestinal microbiota to different anatomical sites
might partially explain why the authors failed for CD but were able to describe UC-specific
microbiomarkers.2 16 Faeces may be a suitable surrogate for the colonic microbiota
but not for that of the small intestine. Thus, the diagnosis of CD using microbiomarkers
seems to require sampling of the local microbiota using biopsies.
To date, endoscopy is the gold standard to diagnose IBD and assess mucosal inflammation.
Since endoscopy is an expensive and invasive procedure with the risk for complications,
alternative scores, for example, the Harvey-Bradshaw index for CD32 or the simple
clinical colitis activity index for UC,33 which include non-invasive measures such
as general state of health, faecal properties and frequency or abdominal pain have
been developed. These scores, however, do not correlate well with mucosal inflammation34
and therefore inflammatory markers such as C-reactive protein or faecal calprotectin
are additionally used to evaluate disease activity. Still, levels of C-reactive protein
are not specific for the intestine, but rather a general marker of systemic inflammation
and faecal calprotectin correlates with UC but not CD and thus seems to be a marker
of inflammation only for the colon.35 36 Thus, other non-invasive biomarkers are needed
as surrogates for a healthy or inflamed gut ecosystem. Explorative metabolomics of
faecal water showed already good differentiation of colonic versus ileal CD with a
clear association of metabolites to the core metabolome or to specific microbiota,37
motivating thus also the use of high-resolution analytical technologies for the description
of novel molecule biomarkers. The concept of determining non-invasive microbiomarkers
in stool, which can aid gastroenterologists in the diagnosis of IBD and in distinguishing
between different intestinal diseases, is a very attractive one and warrants future
investigations.29 38 However, specific bacterial species that are clearly associated
with disease activity are not yet identified. With IBD on the rise worldwide and a
strong geographical influence on intestinal microbiota signatures,39 analysis of a
core taxa signature (combination of bacterial taxa) in large transethnic cohorts or
determining functional deficits of the microbial community seems to be required.16
29 40 More sophisticated sequencing-based approaches (see table 1) such as metagenomic/metatranscriptomic
shotgun sequencing or metabolic modelling,41 which capture the functional capacity
of the microbiota, overcome many limitations of 16S sequencing, yet should be complemented
with phenotypic characterisation of individual bacteria using culture-based techniques,
proteomics and metabolomics. With more complete microbiome reference data sets becoming
available, and with larger international sample panels being collated—similar to the
genetics community where several 10 000 DNAs and genome-wide genetic data sets were
analysed in a joint effort (see www.ibdgenetics.org)—we are certain that microbial
signatures of ‘caviar value’ may be derived in the future. Until then, we alert the
scientists to the ‘caveats’ that come along with the field of biomarker research and
call for more large-scale efforts by the community.
Table 1
Overview of sequencing-based methods to characterise microbiota composition and function
Full-length 16S rRNA gene sequencing
Targeted 16S rRNA gene amplicon sequencing
Metagenome sequencing
Metatranscriptomics
Single-cell analysis
Technique
Clade-specific amplification of 16S rRNA gene, vector-based cloning and sequencing
Long-read-based amplification and sequencing of large 16S rRNA gene fragments
PCR-based amplification of target followed by sequencing
Sequencing of entire DNA extracted from samples
Sequencing of entire RNA extracted from samples
Cultivation-based isolation of single bacterial clones
Emulsion or droplet-based single-cell amplification
Target
Entire 16S rRNA gene
Entire 16S rRNA gene
Variable regions, eg, V1-V2; V3-V4; V4; V6
All DNA molecules
All transcribed RNA molecules
Multiple complete genomes
Single bacterial cells
Potential bias
Gene copy-number bias
Amplification bias
Primer/region-specific amplification bias; gene copy-number bias
Transcriptionally more active bacteria are over-represented
Anaerobic and hard-to-culture bacteria are under-represented
More abundant taxa over-represented
Sequencing technology
Sanger
PacBio or Oxford Nanopore
MiSeq
HiSeq/NextSeq
HiSeq/NextSeq
Any NGS technology
HiSeq/NextSeq
Advantages
Species-level resolution
High throughput; species-level resolution
Low cost; high throughput
Information on encoded functional repertoire; high-resolution taxonomic assignment;
captures also viruses, fungi and archaea
Information on actively expressed functional content
Reconstruction of multiple complete genomes from complex communities
Reconstruction of multiple complete genomes from complex communities
Disadvantages
No information on functional repertoire; low throughput; much hands-on time
No information on functional repertoire; high error rates; higher cost per base than
MiSeq
No information on functional repertoire; low resolution on species level
High cost; high computational burden; large amounts of unannotated data
High cost; high computational burden; large amounts of unannotated data Removal of
16S rRNA required
Tedious culturing and selection approaches (eg, media, oxygen); high cost
High cost
NGS, next generation sequencing.