One
way
to
improve
both
the
ecological
performance
and
functionality
of
probiotic
bacteria
is
by
combining
them
with
a
prebiotic
in
form
synbiotic.
However,
degree
which
such
synbiotic
formulations
strain
humans
has
not
been
tested
systematically.
Our
goal
was
use
randomized,
double-blind,
placebo-controlled,
parallel-arm
clinical
trial
obese
compare
physiological
impact
galactooligosaccharides
(GOS)
strains
Bifidobacterium
adolescentis
IVS-1
(autochthonous
selected
via
vivo
selection)
lactis
BB-12
(commercial
allochthonous
human
gut)
when
used
on
their
own
or
as
combinations.
After
3
weeks
consumption,
strain-specific
quantitative
real-time
PCR
16S
rRNA
gene
sequencing
were
performed
fecal
samples
assess
changes
microbiota.
Intestinal
permeability
determined
measuring
sugar
recovery
urine
GC
after
consumption
mixture.
Serum-based
endotoxin
exposure
also
assessed.
reached
significantly
higher
cell
numbers
than
(P
<
0.01)
and,
remarkably,
its
administration
induced
an
increase
total
bifidobacteria
that
comparable
GOS.
Although
GOS
showed
clear
bifidogenic
effect
resident
gut
microbiota,
only
non-significant
trend
administered
Post-aspirin
sucralose:lactulose
ratios
reduced
groups
=
0.050),
+
0.022),
0.010),
while
sucralose
excretion
0.002)
0.020),
indicating
improvements
colonic
but
no
synergistic
effects.
No
markers
endotoxemia
observed.
This
study
demonstrated
"autochthony"
larger
provision
substrate,
likely
due
competitive
interactions
members
combinations
this
did
demonstrate
functional
synergism,
our
findings
clearly
pro-
components
themselves
improved
permeability,
providing
rational
for
pathologies
underlying
leakiness
gut.
Frontiers in Microbiology,
Год журнала:
2017,
Номер
8
Опубликована: Ноя. 15, 2017
Datasets
collected
by
high-throughput
sequencing
(HTS)
of
16S
rRNA
gene
amplimers,
metagenomes
or
metatranscriptomes
are
commonplace
and
being
used
to
study
human
disease
states,
ecological
differences
between
sites,
the
built
environment.
There
is
increasing
awareness
that
microbiome
datasets
generated
HTS
compositional
because
they
have
an
arbitrary
total
imposed
instrument.
However,
many
investigators
either
unaware
this
assume
specific
properties
data.
The
purpose
review
alert
dangers
inherent
in
ignoring
nature
data,
point
out
derived
from
studies
can
should
be
treated
as
compositions
at
all
stages
analysis.
We
briefly
introduce
illustrate
pathologies
occur
when
data
analyzed
inappropriately,
finally
give
guidance
resources
examples
for
analysis
using
Nucleic Acids Research,
Год журнала:
2017,
Номер
45(W1), С. W180 - W188
Опубликована: Апрель 12, 2017
The
widespread
application
of
next-generation
sequencing
technologies
has
revolutionized
microbiome
research
by
enabling
high-throughput
profiling
the
genetic
contents
microbial
communities.
How
to
analyze
resulting
large
complex
datasets
remains
a
key
challenge
in
current
studies.
Over
past
decade,
powerful
computational
pipelines
and
robust
protocols
have
been
established
enable
efficient
raw
data
processing
annotation.
focus
shifted
toward
downstream
statistical
analysis
functional
interpretation.
Here,
we
introduce
MicrobiomeAnalyst,
user-friendly
tool
that
integrates
recent
progress
statistics
visualization
techniques,
coupled
with
novel
knowledge
bases,
comprehensive
common
outputs
produced
from
MicrobiomeAnalyst
contains
four
modules
-
Marker
Data
Profiling
module
offers
various
options
for
community
profiling,
comparative
prediction
based
on
16S
rRNA
marker
gene
data;
Shotgun
supports
exploratory
analysis,
metabolic
network
shotgun
metagenomics
or
metatranscriptomics
Taxon
Set
Enrichment
Analysis
helps
interpret
taxonomic
signatures
via
enrichment
against
>300
taxon
sets
manually
curated
literature
public
databases;
finally,
Projection
Public
allows
users
visually
explore
their
reference
pattern
discovery
biological
insights.
is
freely
available
at
http://www.microbiomeanalyst.ca.
PLoS Computational Biology,
Год журнала:
2021,
Номер
17(11), С. e1009442 - e1009442
Опубликована: Ноя. 16, 2021
It
is
challenging
to
associate
features
such
as
human
health
outcomes,
diet,
environmental
conditions,
or
other
metadata
microbial
community
measurements,
due
in
part
their
quantitative
properties.
Microbiome
multi-omics
are
typically
noisy,
sparse
(zero-inflated),
high-dimensional,
extremely
non-normal,
and
often
the
form
of
count
compositional
measurements.
Here
we
introduce
an
optimized
combination
novel
established
methodology
assess
multivariable
association
with
complex
population-scale
observational
studies.
Our
approach,
MaAsLin
2
(Microbiome
Multivariable
Associations
Linear
Models),
uses
generalized
linear
mixed
models
accommodate
a
wide
variety
modern
epidemiological
studies,
including
cross-sectional
longitudinal
designs,
well
data
types
(e.g.,
counts
relative
abundances)
without
covariates
repeated
To
construct
this
method,
conducted
large-scale
evaluation
broad
range
scenarios
under
which
straightforward
identification
meta-omics
associations
can
be
challenging.
These
simulation
studies
reveal
that
2’s
model
preserves
statistical
power
presence
measures
multiple
covariates,
while
accounting
for
nuances
controlling
false
discovery.
We
also
applied
dataset
from
Integrative
Human
(HMP2)
project
which,
addition
reproducing
results,
revealed
unique,
integrated
landscape
inflammatory
bowel
diseases
(IBD)
across
time
points
omics
profiles.
Nature Communications,
Год журнала:
2020,
Номер
11(1)
Опубликована: Июль 14, 2020
Abstract
Differential
abundance
(DA)
analysis
of
microbiome
data
continues
to
be
a
challenging
problem
due
the
complexity
data.
In
this
article
we
define
notion
“sampling
fraction”
and
demonstrate
major
hurdle
in
performing
DA
is
bias
introduced
by
differences
sampling
fractions
across
samples.
We
introduce
methodology
called
Analysis
Compositions
Microbiomes
with
Bias
Correction
(
ANCOM-BC
),
which
estimates
unknown
corrects
induced
their
among
The
absolute
are
modeled
using
linear
regression
framework.
This
formulation
makes
fundamental
advancement
field
because,
unlike
existing
methods,
it
(a)
provides
statistically
valid
test
appropriate
p-values,
(b)
confidence
intervals
for
differential
each
taxon,
(c)
controls
False
Discovery
Rate
(FDR),
(d)
maintains
adequate
power,
(e)
computationally
simple
implement.
Although
much
work
has
linked
the
human
microbiome
to
specific
phenotypes
and
lifestyle
variables,
data
from
different
projects
have
been
challenging
integrate
extent
of
microbial
molecular
diversity
in
stool
remains
unknown.
Using
standardized
protocols
Earth
Microbiome
Project
sample
contributions
over
10,000
citizen-scientists,
together
with
an
open
research
network,
we
compare
specimens
primarily
United
States,
Kingdom,
Australia
one
another
environmental
samples.
Our
results
show
unexpected
range
beta-diversity
microbiomes
compared
samples;
demonstrate
utility
procedures
for
removing
effects
overgrowth
during
room-temperature
shipping
revealing
phenotype
correlations;
uncover
new
molecules
kinds
communities
metabolome;
examine
emergent
associations
among
microbiome,
metabolome,
plants
that
are
consumed
(rather
than
relying
on
reductive
categorical
variables
such
as
veganism,
which
little
or
no
explanatory
power).
We
also
living
resource
cross-cohort
comparison
confirm
existing
between
psychiatric
illness
reveal
change
within
individual
surgery,
providing
a
paradigm
education.
IMPORTANCE
citizen
science,
self-selected
cohort
samples
through
mail
at
room
temperature
recaptures
many
known
clinically
collected
cohorts
reveals
ones.
Of
particular
interest
is
integrating
n
=
1
study
population
data,
showing
after
events
surgery
can
exceed
differences
distinct
biomes,
effect
diverse
diet,
untargeted
metabolomics
hundreds
