bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: March 5, 2024
Abstract
All
organisms
are
subjected
to
multiple
stresses
usually
occurring
at
the
same
time,
requiring
activation
of
appropriate
signalling
pathways
respond
all
or
by
prioritizing
response
one
stress
factor.
Plants,
as
sessile
organisms,
particularly
impacted
constantly
changing
environment
that
is
often
unfavourable
even
hostile.
Because
experimental
complexity
studying
organism
stressors
simultaneously,
experiments
conducted
considering
individual
factor
time.
An
alternative
consists
in
performing
silico
integration
those
data
on
single
response.
Currently
used
methods
integrate
unpaired
consist
meta-analysis
finding
differentially
expressed
genes
for
each
condition
separately
and
then
selecting
commonly
regulated
ones.
Although
these
approaches
allowed
find
valuable
results,
they
mainly
identify
specific
signatures
very
few
signature
responding
lack
modulated
differently
condition.
For
this
purpose,
we
developed
HIVE
(Horizontal
Integration
analysis
using
Variational
AutoEncoders)
single-stress
transcriptomics
datasets
composed
experiments.
Briefly,
coupled
a
variational
autoencoder,
alleviates
batch
effects,
with
random
forest
regression
SHAP
explainer
select
relevant
specifically
stresses.
We
illustrate
functionality
study
transcriptional
changes
several
different
plants
namely
Arabidopsis
thaliana
,
rice,
maize,
wheat,
grapevine
peanut
collecting
publicly
available
stress,
either
biotic
and/or
abiotic,
jointly
analyse
them.
performed
better
than
differential
expression
analysis,
state-of-the-art
tool
horizontal
allowing
novel
promising
candidates
responsible
triggering
effective
defence
responses
npj Systems Biology and Applications,
Journal Year:
2024,
Volume and Issue:
10(1)
Published: Aug. 23, 2024
Abstract
Unraveling
how
cellular
signaling
is
remodeled
upon
perturbation
crucial
for
understanding
disease
mechanisms
and
identifying
potential
drug
targets.
In
this
pursuit,
computational
tools
generating
mechanistic
hypotheses
from
multi-omics
data
have
invaluable
potential.
Here,
we
present
a
newly
implemented
version
(2.0)
of
SignalingProfiler
,
multi-step
pipeline
to
draw
on
the
events
impacting
phenotypes.
2.0
derives
context-specific
networks
by
integrating
proteogenomic
with
prior
knowledge-causal
network.
This
freely
accessible
flexible
tool
that
incorporates
statistical,
footprint-based,
graph
algorithms
accelerate
integration
interpretation
data.
Through
benchmarking
process
three
proof-of-concept
studies,
demonstrate
tool’s
ability
generate
hierarchical
recapitulating
novel
known
perturbed
phenotypic
outcomes,
in
both
human
mice
contexts.
summary,
S
ignalingProfiler
addresses
emergent
need
derive
biologically
relevant
information
complex
extracting
interpretable
networks.
International Journal of Molecular Sciences,
Journal Year:
2024,
Volume and Issue:
25(22), P. 12375 - 12375
Published: Nov. 18, 2024
Blood
is
an
important
component
for
maintaining
animal
lives
and
synthesizing
sugars,
lipids,
proteins
in
organs.
Revealing
the
relationship
between
genes
metabolite
expression
milk
somatic
cell
count
(SCC),
fat
percentage,
protein
lactose
percentage
blood
helpful
understanding
molecular
regulation
mechanism
of
formation.
Therefore,
we
separated
buffy
coat
plasma
from
Xinjiang
Brown
cattle
(XJBC)
Chinese
Simmental
(CSC),
which
exhibit
high
low
SCC/milk
percentage/milk
percentage/lactose
percentages,
respectively.
The
metabolites
was
detected
via
RNA-Seq
LC-MS/MS,
Based
on
weighted
gene
coexpression
network
analysis
(WGCNA)
functional
enrichment
differentially
expressed
(DEGs),
further
found
that
mainly
affected
SCC
percentage.
Immune
or
inflammatory-response-related
pathways
were
involved
SCC,
joint
metabolome
transcriptome
indicated
that,
blood,
metabolism
purine,
glutathione,
glycerophospholipid,
glycine,
arginine,
proline
are
also
associated
with
while
lipid
amino-acid-related
Finally,
related
DEGs
DEMs
identified
blood.
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: March 5, 2024
Abstract
All
organisms
are
subjected
to
multiple
stresses
usually
occurring
at
the
same
time,
requiring
activation
of
appropriate
signalling
pathways
respond
all
or
by
prioritizing
response
one
stress
factor.
Plants,
as
sessile
organisms,
particularly
impacted
constantly
changing
environment
that
is
often
unfavourable
even
hostile.
Because
experimental
complexity
studying
organism
stressors
simultaneously,
experiments
conducted
considering
individual
factor
time.
An
alternative
consists
in
performing
silico
integration
those
data
on
single
response.
Currently
used
methods
integrate
unpaired
consist
meta-analysis
finding
differentially
expressed
genes
for
each
condition
separately
and
then
selecting
commonly
regulated
ones.
Although
these
approaches
allowed
find
valuable
results,
they
mainly
identify
specific
signatures
very
few
signature
responding
lack
modulated
differently
condition.
For
this
purpose,
we
developed
HIVE
(Horizontal
Integration
analysis
using
Variational
AutoEncoders)
single-stress
transcriptomics
datasets
composed
experiments.
Briefly,
coupled
a
variational
autoencoder,
alleviates
batch
effects,
with
random
forest
regression
SHAP
explainer
select
relevant
specifically
stresses.
We
illustrate
functionality
study
transcriptional
changes
several
different
plants
namely
Arabidopsis
thaliana
,
rice,
maize,
wheat,
grapevine
peanut
collecting
publicly
available
stress,
either
biotic
and/or
abiotic,
jointly
analyse
them.
performed
better
than
differential
expression
analysis,
state-of-the-art
tool
horizontal
allowing
novel
promising
candidates
responsible
triggering
effective
defence
responses
