TO
STUDY THE  FUNCTION, STRUCTURE OF AN
HEALTHY HUMAN MICROBIAME

 

INTRODUCTION:-

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The diversity of
microbes within a given body habitat can be defined as the number and abundance
distribution of distinct types of organisms, which has been linked to several
human diseases: low diversity in the gut inflammatory bowel diseases, for
example, and high diversity in the vagina to bacterial vaginosis . For this
large study involving microbiome samples collected from healthy volunteers at
two distinct geographic locations in the United States, we have defined the
microbial communities at each body habitat, encountering 81–99% of predicted
genera and saturating the range of overall community configurations . Oral and
stool communities were especially diverse in terms of community membership,
expanding prior observations , and vaginal sites harboured particularly simple
communities . This study established that these patterns of alpha diversity differed
markedly from comparisons between samples from the same habitat among subjects
. For example, the saliva had among the highest median alpha diversities of
operational taxonomic units (OTUs, roughly species level classification, but
one of the lowest beta diversities—so although each individual’s saliva was
ecologically rich, members of the population shared similar organisms.
Conversely, the antecubital fossae (skin) were intermediate in alpha diversity.
The vagina had the lowest alpha diversity, with quite low beta diversity at the
genus level but very high among OTUs due to the presence of distinct
Lactobacillus spp. The primary patterns of variation in community structure
followed the major body habitat groups (oral, skin, gut and vaginal), defining
as a result the complete range of population-wide between-subject variation in
human microbiome habitats . Within-subject variation over time was consistently
lower than between-subject variation, both in organismal composition and in
metabolic function . The uniqueness of each individual’s microbial community
thus seems to be stable over time (relative to the population as a whole),
which may be another feature of the human microbiome specifically associated
with health.

 

CARRIAGE
OF SPECIFIC MICROBES

 Inter-individual variation in the microbiome
proved to be specific, functionally relevant and personalized. of the oral
cavity. The genus dominates the oropharynx16, with different species abundant
within each sampled body habitat even at the species level, marked differences
in carriage within each habitat among individuals . As the ratio of pan- to
core-genomes is high in many human-associated microbes17, this variation in
abundance could be due to selective pressures acting on pathways differentially
present among Streptococcus species or strains. Indeed, we observed extensive
strain-level genomic variation within microbial species in this population,
enriched for host-specific structural variants around genomic islands. Even
with respect to the single, gene losses associated with these events were
common, for example differentially eliminating S. mitis carriage of the V-type
ATPase or choline binding proteins cbp6 and cbp12 among subsets of the host
population. These losses were easily observable by comparison to reference
isolate genomes, and these initial findings indicate that microbial strain- and
host-specific gene gains and polymorphisms may be similarly ubiquitous

The
Human Gut Microbiome:  Toolkit behind the
Science

The widespread
application of 16S rRNA gene sequencing for detection of bacterial pathogens
and microbial ecology has provided a robust technical platform for the
evaluation of the bacterial composition of the human microbiome. Sequencing of
2 primary targets within bacterial 16S rRNA genes yielded valuable
compositional data pertaining to the human fecal microbiome of 242 healthy
adults. In the Human Microbiome Project, 18 different body sites were sampled
and sequenced. Stool specimens were the single specimen type used to study the
intestinal microbiome. Previously published studies demonstrated the variation
in composition of the gut microbiome among locations within the
gastrointestinal tract in different mammalian species. For example, 16S rRNA
gene sequencing has been deployed to study the maturation of murine cecal
microbiota, and these studies demonstrated the existence of a large number of
yet-unidentified bacteria that inhabit the mammalian intestine.. Thus, it is
essential to develop robust experimental models of the human microbiome to
delineate important mechanistic processes in the development of human disease
states.

 Gut Microbiome: Composition to Function and
Metabolism

The gut microbial
community includes approximately bacteria that normally reside in the
gastrointestinal tract, reaching a microbial cell number that greatly exceeds
the number of human cells of the body. The collective genome of these
microorganisms (the microbiome) contains millions of genes (a rapidly expanding
number) compared to roughly 20 000–25 000 genes in the human genome. This
microbial “factory” contributes to a broad range of biochemical and metabolic
functions that the human body could not otherwise perform. Although
diet-induced changes in gut microbiota occur within a short time frame (1–3–4
days after a diet switch), the changes are readily reversible. In animal
models, the ratio of the most prominent intestinal bacterial phyla, the
Bacteroidetes and Firmicutes, is altered in response to dietary changes.
Disruption of the energy equilibrium leads to weight gain. Mouse model studies
have demonstrated the relationship between energy equilibrium, diet, and the
composition of the gut microbiome. Transplantation of the gut microbiota from
obese donors resulted in increased adiposity in recipients compared to a
similar transfer from lean donors.

This extensive sampling
of the human microbiome across many subjects and body habitats provides an
initial characterization of the normal microbiota of healthy adults in a
Western population. The large sample size and consistent sampling of many sites
from the same individuals allows for the first time an understanding of the
relationships among microbes, and between the microbiome and clinical
parameters, that underpin the basis for individual variation—variation that may
ultimately be critical for understanding microbiome-based disorders. Clinical
studies of the microbiome will be able to leverage the resulting extensive
catalogues of taxa, pathways and genes1 , although they must also still include
carefully matched internal controls. The uniqueness of each individual’s
microbiome even in this reference population argues for future studies to
consider prospective within-subjects designs where possible. The HMP’s unique
combination of organismal and functional data across body habitats,
encompassing both 16S and metagenomic profiling, together with detailed
characterization of each subject, has allowed us and subsequent studies to move
beyond the observation of variability in the human microbiome to ask how and
why these microbial communities vary so extensively. Many details remain for
further work to fill in, building on this reference study. How do early
colonization and lifelong change vary among body habitats? Do epidemiological
patterns of transmission of beneficial or harmless microbes mirror patterns of
transmission of pathogens? Which co-occurrences among microbes reflect shared
response to the environment, as opposed to competitive or mutualistic
interactions? How large a role does host immunity or genetics play in shaping
patterns of diversity, and how do the patterns observed in this North American
population compare to those around the world? Future studies building on the
gene and organism catalogues established by the Human Microbiome Project, including
increasingly detailed investigations of metatranscriptomes and metaproteomes,
will help to unravel these open questions and allow us to more fully understand
the links between the human microbiome, health and disease.

 

The Gut Microbiome and Body Metabolism: Obesity and Inflammation

The incidence of overweight and obesity has reached epidemic
proportions. Data reported by the CDC and the National Health and Nutrition
Examination Survey indicated that, in 2008, an estimated 1.5 billion adults
were overweight, and more than 200 million men and almost 300 million women
were obese by these criteria. Worldwide obesity has more than doubled in the
last 2 decades. Obesity is associated with a cluster of metabolic and systemic
disorders such as insulin resistance, type 2 diabetes, fatty liver disease,
atherosclerosis, and hypertension. The major cause of obesity is a positive
energetic balance resulting from an increased energy intake from the diet and a
decreased energy output associated with low physical activity. In addition to
alterations in diet and physical activity resulting in obesity, genetic
differences contribute to obesity and cause differences in energy storage and
expenditure. Furthermore, growing evidence suggests that the gut microbiota
represents an important factor contributing to the host response to nutrients.
A landmark study by Turnbaugh et al.was one of the first studies to show how
the gene content in the gut microbiota contributes to obesity. The microbiomes
obtained from the distal gut of genetically obese leptin-deficient mice (ob/ob)
and their lean littermates (ob/+ and +/+) were compared. In this study,
investigators reported that the microbiota in the ob/ob mice contained genes
encoding enzymes that hydrolyze indigestible dietary polysaccharides. Increased
amounts of fermentation end products (such as acetate and butyrate) and
decreased calories were found in the feces of obese mice. These data suggest
that the gut microbiota in this mouse model promoted the extraction of
additional calories from the diet.

Fig: human microbian and its importance

A Comprehensive Human-Associated Microbial Census

Sequencing of
the 16S rRNA gene is an effective method for interrogating the taxonomic
composition of microbial communities. This gene is ubiquitous within the
prokaryotic domain and can be effectively PCR-amplified from even previously unknown
organisms. The analysis of microbial communities through the sequencing of 16S
rRNA gene was common long before the influx of high throughput sequencing (HTS)
data , making this gene one of the most highly represented within GenBank. HTS approaches
to 16S rRNA sequence analysis typically include targeted Illumina or 454 reads
of up to a few hundred nucleotides, each targeting uniquely identifiable variable
regions of the gene that can be used as unique microbial identifiers .The HMP
planned to comprehensively characterize the taxonomic composition of the
microbiome by averaging 5,000 454 FLX 16S rRNA gene sequences from all 300
subjects, 18 body sites, and multiple time points. This design, combined with
more than a 1,000-fold increase in sequencing throughput over the course of the
HMP, forced the consortium to develop novel tools for processing large 16S rRNA
gene datasets, tackling issues specific to 454 sequence data quality, and addressing
novel biological questions that were previously inaccessible due to limited sample
sizes. An interesting question addressed by these
data is the presence or absence of stable community configurations in different
human body sites, such as enterotypes in the gut. Identifying groups of highly
similar microbial communities among many samples is a difficult unsupervised machine
learning problem, akin to that of clustering or discovering molecular subtypes
in cancer gene expression data. Work to better understand the topic is ongoing,
and the HMP’s survey of many body sites offered the chance to contrast
community organization within distinct ecologies. The vaginal microbiome, for
example, has been observed to occupy one of five main states characterized by
differing Lactobacillus spp. abundances. This proved to be the case in the HMP
as well, in contrast to a more complex continuum of community configurations
occupied by the gut microbiota, particularly when meta-analyzed with the MetaHIT
cohort. As the presence of community types in distinct ecosystems may be
influenced by environmental factors that can themselves vary continuously, such
as diet, care must be taken in future computational efforts to reproducibly
identify microbial community types within habitats where they do occur.

 

 

Putting the Pieces Together:Metagenomic SequenceAssembly

 

The taxonomic
composition of the human microbiome is thus one step in understanding the role
microbes play in our health, and it is well complemented by sequencing of
microbial communities’ entire genomic contents to catalog their biological
functions. Thus, the HMP carried out extensive deep sequencing on a subset of
its subjects and body sites using the Illumina platform. While portions of the HMP’s
16S rRNA gene analysis were based on extensions of established experimental and
computational approaches, this approach to whole-metagenome sequencing was a
foray into new territory.

The sequencing
technology itself was (and still is) rapidly evolving, and metagenomic datasets
of comparable size, read length, and ecological diversity did not previously exist.
In the relatively short period between an initial pilot which were already
difficult to interpret in microbial communities containing hundreds or
thousands of taxa. It thus necessitated development of a scalable end-to-end
shotgun pre-processing and quality control pipeline, including duplicate read
removal, quality and length trimming, host sequence removal, and whole-sample
quality control. In the end, the HMP generated over 8 Tbp of raw sequence data,
representing two lanes of paired-end Illumina sequencing for each of over 700
samples (targeting 10 Gbp/ sample) as well as a small collection of samples,
which were also sequenced with the Roche/454 instrument to investigate the
impact of longer reads on metagenome assembly. The design of this
whole-metagenome sequencing experiment warrants a brief discussion. As the HMP
was started, little information was available about the genomic diversity of
the communities being assayed. The use of Illumina sequencing in metagenomics
projects was still being debated, the main argument against this technology
being the very short length of the reads being generated (just 100 bp compared
to close to 400 bp achievable by Roche/454 and over 1,000 bp routinely achieved
through Sanger sequencing). As detailed below, the feasibility of assembling the
resulting data into large enough chunks to enable meaningful analyses was by no
means obvious. At the same time, analyzing the reads themselves, rather than
assembled contigs, was considered insufficiently accurate, although both assembly
and read-based analyses ultimately proved successful. The choice of depth of
sequencing, ”just” two lanes of the instrument, was chosen to be sufficient to
generate roughly 1-fold coverage of the Escherichia coli genome within gut
microbiome samples estimated to occur in most individuals at 0.1%–5% relative
abundance. The human distal gut was the body site for which the most prior knowledge
was available due to extensive studies of the fecal microbiome, particularly due
to insights from the MetaHIT project—a European-led study aimed at characterizing
the human gut microbiome in health and disease.

  

References

1.      Huber M, Knottnerus JA, Green,
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we define health?” (PDF). BMJ. 343:
d4163. doi:10.1136/bmj.d4163. PMID 21791490.

2.      World Health
Organization. WHO definition
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Records of the World Health Organization, no. 2, p. 100) and entered into force
on 7 April 1948. In Grad,
Frank P. (2002). “The Preamble of the Constitution of the World Health
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982.

3.      World Health
Organization. (2006). Constitution of
the World Health Organization – Basic Documents,
Forty-fifth edition, Supplement, October 2006.

4.     Jadad
AR, O’Grady L: How should health be defined? (2008). “How should health be
defined?”. BMJ (Clinical research ed.). 337: a2900. doi:10.1136/bmj.a2900. PMID 19073663.

5.     Callahan
D. (1973). “The WHO definition of ‘health'”. The Hastings Center
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6.      Taylor
S, Marandi A (2008). “How should health be defined?”. BMJ. 337:
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