Accumulating
evidence
shows
that
the
gastric
bacterial
community
may
contribute
to
development
of
cancer
(GC).
However,
reported
alterations
microbiota
were
not
always
consistent
among
literature.
To
assess
reproducible
signals
in
during
progression
GC
across
studies,
we
performed
a
meta-analysis
nine
publicly
available
16S
datasets
with
standard
tools
state-of-the-art.
Despite
study-specific
batch
effect,
significant
changes
composition
microbiome
found
carcinogenesis,
especially
when
Helicobacter
pylori
(HP)
reads
removed
from
analyses
mitigate
its
compositional
effect
as
they
accounted
for
extremely
large
proportions
sequencing
depths
many
samples.
Differential
microbes,
including
Fusobacterium,
Leptotrichia,
and
several
lactic
acid
bacteria
such
Bifidobacterium,
Lactobacillus,
Streptococcus
anginosus,
which
frequently
significantly
enriched
patients
compared
gastritis
had
good
discriminatory
capacity
distinguish
samples
gastritis.
Oral
microbes
precancerous
stages.
Intriguingly,
observed
mutual
exclusivity
different
HP
species
studies.
In
addition,
comparison
between
fluid
mucosal
suggested
their
convergent
dysbiosis
disease
progression.
Taken
together,
our
systematic
analysis
identified
novel
microbial
patterns
carcinogenesis.
Nucleic Acids Research,
Год журнала:
2023,
Номер
51(W1), С. W310 - W318
Опубликована: Май 11, 2023
Abstract
Microbiome
studies
have
become
routine
in
biomedical,
agricultural
and
environmental
sciences
with
diverse
aims,
including
diversity
profiling,
functional
characterization,
translational
applications.
The
resulting
complex,
often
multi-omics
datasets
demand
powerful,
yet
user-friendly
bioinformatics
tools
to
reveal
key
patterns,
important
biomarkers,
potential
activities.
Here
we
introduce
MicrobiomeAnalyst
2.0
support
comprehensive
statistics,
visualization,
interpretation,
integrative
analysis
of
data
outputs
commonly
generated
from
microbiome
studies.
Compared
the
previous
version,
features
three
new
modules:
(i)
a
Raw
Data
Processing
module
for
amplicon
processing
taxonomy
annotation
that
connects
directly
Marker
Profiling
downstream
statistical
analysis;
(ii)
Metabolomics
help
dissect
associations
between
community
compositions
metabolic
activities
through
joint
paired
metabolomics
datasets;
(iii)
Statistical
Meta-Analysis
identify
consistent
signatures
by
integrating
across
multiple
Other
improvements
include
added
multi-factor
differential
interactive
visualizations
popular
graphical
outputs,
updated
methods
prediction
correlation
analysis,
expanded
taxon
set
libraries
based
on
latest
literature.
These
are
demonstrated
using
dataset
recent
type
1
diabetes
study.
is
freely
available
at
microbiomeanalyst.ca.
Journal of Clinical Investigation,
Год журнала:
2022,
Номер
132(7)
Опубликована: Март 31, 2022
Next-generation
sequencing
(NGS)
technology
has
advanced
our
understanding
of
the
human
microbiome
by
allowing
for
discovery
and
characterization
unculturable
microbes
with
prediction
their
function.
Key
NGS
methods
include
16S
rRNA
gene
sequencing,
shotgun
metagenomic
RNA
sequencing.
The
choice
which
methodology
to
pursue
a
given
purpose
is
often
unclear
clinicians
researchers.
In
this
Review,
we
describe
fundamentals
NGS,
focus
on
We
also
discuss
pros
cons
each
as
well
important
concepts
in
data
variability,
study
design,
clinical
metadata
collection.
further
present
examples
how
studies
have
disease
pathophysiology
across
diverse
contexts,
including
development
diagnostics
therapeutics.
Finally,
share
insights
might
be
integrated
into
advance
research
care
coming
years.
Nature Communications,
Год журнала:
2022,
Номер
13(1)
Опубликована: Ноя. 15, 2022
Abstract
Parkinson’s
disease
(PD)
may
start
in
the
gut
and
spread
to
brain.
To
investigate
role
of
microbiome,
we
conducted
a
large-scale
study,
at
high
taxonomic
resolution,
using
uniform
standardized
methods
from
end.
We
enrolled
490
PD
234
control
individuals,
deep
shotgun
sequencing
fecal
DNA,
followed
by
metagenome-wide
association
studies
requiring
significance
two
(ANCOM-BC
MaAsLin2)
declare
association,
network
analysis
identify
polymicrobial
clusters,
functional
profiling.
Here
show
that
over
30%
species,
genes
pathways
tested
have
altered
abundances
PD,
depicting
widespread
dysbiosis.
PD-associated
species
form
clusters
grow
or
shrink
together,
some
compete.
microbiome
is
permissive,
evidenced
overabundance
pathogens
immunogenic
components,
dysregulated
neuroactive
signaling,
preponderance
molecules
induce
alpha-synuclein
pathology,
over-production
toxicants;
with
reduction
anti-inflammatory
neuroprotective
factors
limiting
capacity
recover.
validate,
human
findings
were
observed
experimental
models;
reconcile
resolve
literature;
provide
broad
foundation
wealth
concrete
testable
hypotheses
discern
PD.
ISME Communications,
Год журнала:
2022,
Номер
2(1)
Опубликована: Окт. 6, 2022
Abstract
The
many
microbial
communities
around
us
form
interactive
and
dynamic
ecosystems
called
microbiomes.
Though
concealed
from
the
naked
eye,
microbiomes
govern
influence
macroscopic
systems
including
human
health,
plant
resilience,
biogeochemical
cycling.
Such
feats
have
attracted
interest
scientific
community,
which
has
recently
turned
to
machine
learning
deep
methods
interrogate
microbiome
elucidate
relationships
between
its
composition
function.
Here,
we
provide
an
overview
of
how
latest
studies
harness
inductive
prowess
artificial
intelligence
methods.
We
start
by
highlighting
that
data
–
being
compositional,
sparse,
high-dimensional
necessitates
special
treatment.
then
introduce
traditional
novel
discuss
their
strengths
applications.
Finally,
outlook
pipelines,
focusing
on
bottlenecks
considerations
address
them.
Nature Communications,
Год журнала:
2022,
Номер
13(1)
Опубликована: Июль 8, 2022
Hepatocellular
carcinoma
(HCC)
is
a
leading
cause
of
cancer-related
deaths
worldwide,
and
therapeutic
options
for
advanced
HCC
are
limited.
Here,
we
observe
that
intestinal
dysbiosis
affects
antitumor
immune
surveillance
drives
liver
disease
progression
towards
cancer.
Dysbiotic
microbiota,
as
seen
in
Nlrp6
Microbiome
research
is
now
moving
beyond
the
compositional
analysis
of
microbial
taxa
in
a
sample.
Increasing
evidence
from
large
human
microbiome
studies
suggests
that
functional
consequences
changes
intestinal
may
provide
more
power
for
studying
their
impact
on
inflammation
and
immune
responses.
Although
16S
rRNA
one
most
popular
cost-effective
method
to
profile
compositions,
marker-gene
sequencing
cannot
direct
information
about
genes
are
present
genomes
community
members.
Bioinformatic
tools
have
been
developed
predict
function
with
gene
data.
Among
them,
PICRUSt2
has
become
prediction
tools,
which
generates
community-wide
pathway
abundances.
However,
no
state-of-art
inference
available
test
differences
abundances
between
comparison
groups.
We
ggpicrust2,
an
R
package,
do
extensive
differential
abundance(DA)
analyses
publishable
visualization
highlight
signals.
Nature Communications,
Год журнала:
2023,
Номер
14(1)
Опубликована: Май 31, 2023
The
gut
microbiome
is
emerging
as
a
key
modulator
of
human
energy
balance.
Prior
studies
in
humans
lacked
the
environmental
and
dietary
controls
precision
required
to
quantitatively
evaluate
contributions
microbiome.
Using
Microbiome
Enhancer
Diet
(MBD)
designed
deliver
more
substrates
colon
therefore
modulate
microbiome,
we
quantified
microbial
host
balance
controlled
feeding
study
with
randomized
crossover
design
young,
healthy,
weight
stable
males
females
(NCT02939703).
In
metabolic
ward
where
environment
was
strictly
controlled,
measured
intake,
expenditure,
output
(fecal
urinary).
primary
endpoint
within-participant
difference
metabolizable
between
experimental
conditions
[Control,
Western
(WD)
vs.
MBD].
secondary
endpoints
were
enteroendocrine
hormones,
hunger/satiety,
food
intake.
Here
show
that,
compared
WD,
MBD
leads
an
additional
116
±
56
kcals
(P
<
0.0001)
lost
feces
daily
thus,
lower
for
(89.5
0.73%;
range
84.2-96.1%
on
95.4
0.21%;
94.1-97.0%
WD;
P
without
changes
hunger/satiety
or
intake
>
0.05).
Microbial
16S
rRNA
gene
copy
number
(a
surrogate
biomass)
increases
0.0001),
beta-diversity
(whole
genome
shotgun
sequencing;
=
0.02),
fermentation
products
increase
0.01)
WD
along
significant
system
0.0001).
substantial
interindividual
variability
explained
part
by
fecal
SCFAs
biomass.
Our
results
reveal
complex
host-diet-microbiome
interplay
that
modulates
