81 research outputs found
Quantitative Proteomic Profiling Reveals Differentially Regulated Proteins in Cystic Fibrosis Cells
The
most prevalent cause of cystic fibrosis (CF) is the deletion
of a phenylalanine residue at position 508 in CFTR (ÎF508-CFTR)
protein. The mutated protein fails to fold properly, is retained in
the endoplasmic reticulum via the action of molecular chaperones,
and is tagged for degradation. In this study, the differences in protein
expression levels in CF cell models were assessed using a systems
biology approach aided by the sensitivity of MudPIT proteomics. Analysis
of the differential proteome modulation without a priori hypotheses
has the potential to identify markers that have not yet been documented.
These may also serve as the basis for developing new diagnostic and
treatment modalities for CF. Several novel differentially expressed
proteins observed in our study are likely to play important roles
in the pathogenesis of CF and may serve as a useful resource for the
CF scientific community
Behavioral and Proteomic Analysis of Stress Response in Zebrafish (<i>Danio rerio</i>)
The
purpose of this study is to determine the behavioral and proteomic
consequences of shock-induced stress in zebrafish (<i>Danio rerio</i>) as a vertebrate model. Here we describe the behavioral effects
of exposure to predictable and unpredictable electric shock, together
with quantitative tandem mass tag isobaric labeling workflow to detect
altered protein candidates in response to shock exposure. Behavioral
results demonstrate a hyperactivity response to electric shock and
a suppression of activity to a stimulus predicting shock. On the basis
of the quantitative changes in protein abundance following shock exposure,
eight proteins were significantly up-regulated (HADHB, hspa8, hspa5,
actb1, mych4, atp2a1, zgc:86709, and zgc:86725). These proteins contribute
crucially in catalytic activities, stress response, cation transport,
and motor activities. This behavioral proteomic driven study clearly
showed that besides the rapid induction of heat shock proteins, other
catalytic enzymes and cation transporters were rapidly elevated as
a mechanism to counteract oxidative stress conditions resulting from
elevated fear/anxiety levels
Behavioral and Proteomic Analysis of Stress Response in Zebrafish (<i>Danio rerio</i>)
The
purpose of this study is to determine the behavioral and proteomic
consequences of shock-induced stress in zebrafish (<i>Danio rerio</i>) as a vertebrate model. Here we describe the behavioral effects
of exposure to predictable and unpredictable electric shock, together
with quantitative tandem mass tag isobaric labeling workflow to detect
altered protein candidates in response to shock exposure. Behavioral
results demonstrate a hyperactivity response to electric shock and
a suppression of activity to a stimulus predicting shock. On the basis
of the quantitative changes in protein abundance following shock exposure,
eight proteins were significantly up-regulated (HADHB, hspa8, hspa5,
actb1, mych4, atp2a1, zgc:86709, and zgc:86725). These proteins contribute
crucially in catalytic activities, stress response, cation transport,
and motor activities. This behavioral proteomic driven study clearly
showed that besides the rapid induction of heat shock proteins, other
catalytic enzymes and cation transporters were rapidly elevated as
a mechanism to counteract oxidative stress conditions resulting from
elevated fear/anxiety levels
Comparison of Protein Expression Ratios Observed by Sixplex and Duplex TMT Labeling Method
Stable isotope labeling via isobaric derivatization of
peptides
is a universally applicable approach that enables concurrent identification
and quantification of proteins in different samples using tandem mass
spectrometry. In this study, we evaluated the performance of amine-reactive
isobaric tandem mass tag (TMT), available as duplex and sixplex sets,
with regard to their ability to elucidate protein expression changes.
Using rat brain tissue from two different developmental time points,
postnatal day 1 (p1) and 45 (p45), as a model system, we compared
the protein expression ratios (p45/p1) observed using duplex TMT tags
in triplicate measurements versus sixplex tag in a single LCâMS/MS
analysis. A correlation of 0.79 in relative protein abundance was
observed in the proteins quantified by these two sets of reagents.
However, more proteins passed the criteria for significant fold change
(â1.0 †log<sub>2</sub> ratio (p45/p1) â„ +1.0
and <i>p</i> < 0.05) in the sixplex analysis. Nevertheless,
in both methods most proteins showing significant fold change were
identified by multiple spectra, increasing their quantification precision.
Additionally, the fold change in p45 rats against p1, observed in
TMT experiments, was corroborated by a metabolic labeling strategy
where relative quantification of differentially expressed proteins
was obtained using <sup>15</sup>N-labeled p45 rats as an internal
standard
Modified MuDPIT Separation Identified 4488 Proteins in a System-wide Analysis of Quiescence in Yeast
A modified multidimensional protein
identification technology (MudPIT)
separation was coupled to an LTQ Orbitrap Velos mass spectrometer
and used to rapidly identify the near-complete yeast proteome from
a whole cell tryptic digest. This modified online two-dimensional
liquid chromatography separation consists of 39 strong cation exchange
steps followed by a short 18.5 min reversed-phase (RP) gradient. A
total of 4269 protein identifications were made from 4189 distinguishable
protein families from yeast during log phase growth. The âMicroâ
MudPIT separation performed as well as a standard MudPIT separation
in 40% less gradient time. The majority of the yeast proteome can
now be routinely covered in less than a daysâ time with high
reproducibility and sensitivity. The newly devised separation method
was used to detect changes in protein expression during cellular quiescence
in yeast. An enrichment in the GO annotations âoxidation reductionâ,
âcatabolic processingâ and âcellular response
to oxidative stressâ was seen in the quiescent cellular fraction,
consistent with their long-lived stress resistant phenotypes. Heterogeneity
was observed in the stationary phase fraction with a less dense cell
population showing reductions in KEGG pathway categories of âRibosomeâ
and âProteasomeâ, further defining the complex nature
of yeast populations present during stationary phase growth. In total,
4488 distinguishable protein families were identified in all cellular
conditions tested
Modified MuDPIT Separation Identified 4488 Proteins in a System-wide Analysis of Quiescence in Yeast
A modified multidimensional protein
identification technology (MudPIT)
separation was coupled to an LTQ Orbitrap Velos mass spectrometer
and used to rapidly identify the near-complete yeast proteome from
a whole cell tryptic digest. This modified online two-dimensional
liquid chromatography separation consists of 39 strong cation exchange
steps followed by a short 18.5 min reversed-phase (RP) gradient. A
total of 4269 protein identifications were made from 4189 distinguishable
protein families from yeast during log phase growth. The âMicroâ
MudPIT separation performed as well as a standard MudPIT separation
in 40% less gradient time. The majority of the yeast proteome can
now be routinely covered in less than a daysâ time with high
reproducibility and sensitivity. The newly devised separation method
was used to detect changes in protein expression during cellular quiescence
in yeast. An enrichment in the GO annotations âoxidation reductionâ,
âcatabolic processingâ and âcellular response
to oxidative stressâ was seen in the quiescent cellular fraction,
consistent with their long-lived stress resistant phenotypes. Heterogeneity
was observed in the stationary phase fraction with a less dense cell
population showing reductions in KEGG pathway categories of âRibosomeâ
and âProteasomeâ, further defining the complex nature
of yeast populations present during stationary phase growth. In total,
4488 distinguishable protein families were identified in all cellular
conditions tested
Quantitative Proteomic Profiling Reveals Differentially Regulated Proteins in Cystic Fibrosis Cells
The
most prevalent cause of cystic fibrosis (CF) is the deletion
of a phenylalanine residue at position 508 in CFTR (ÎF508-CFTR)
protein. The mutated protein fails to fold properly, is retained in
the endoplasmic reticulum via the action of molecular chaperones,
and is tagged for degradation. In this study, the differences in protein
expression levels in CF cell models were assessed using a systems
biology approach aided by the sensitivity of MudPIT proteomics. Analysis
of the differential proteome modulation without a priori hypotheses
has the potential to identify markers that have not yet been documented.
These may also serve as the basis for developing new diagnostic and
treatment modalities for CF. Several novel differentially expressed
proteins observed in our study are likely to play important roles
in the pathogenesis of CF and may serve as a useful resource for the
CF scientific community
Off-Line Multidimensional Liquid Chromatography and Auto Sampling Result in Sample Loss in LC/LCâMS/MS
Large-scale
proteomics often employs two orthogonal separation
methods to fractionate complex peptide mixtures. Fractionation can
involve ion exchange separation coupled to reversed-phase separation
or, more recently, two reversed-phase separations performed at different
pH values. When multidimensional separations are combined with tandem
mass spectrometry for protein identification, the strategy is often
referred to as multidimensional protein identification technology
(MudPIT). MudPIT has been used in either an automated (online) or
manual (offline) format. In this study, we evaluated the performance
of different MudPIT strategies by both label-free and tandem mass
tag (TMT) isobaric tagging. Our findings revealed that online MudPIT
provided more peptide/protein identifications and higher sequence
coverage than offline platforms. When employing an off-line fractionation
method with direct loading of samples onto the column from an eppendorf
tube via a high-pressure device, a 5.3% loss in protein identifications
is observed. When off-line fractionated samples are loaded via an
autosampler, a 44.5% loss in protein identifications is observed compared
with direct loading of samples onto a triphasic capillary column.
Moreover, peptide recovery was significantly lower after offline fractionation
than in online fractionation. Signal-to-noise (S/N) ratio, however,
was not significantly altered between experimental groups. It is likely
that offline sample collection results in stochastic peptide loss
due to noncovalent adsorption to solid surfaces. Therefore, the use
of the offline approaches should be considered carefully when processing
minute quantities of valuable samples
PSEA-Quant: A Protein Set Enrichment Analysis on Label-Free and Label-Based Protein Quantification Data
The majority of large-scale
proteomics quantification methods yield
long lists of quantified proteins that are often difficult to interpret
and poorly reproduced. Computational approaches are required to analyze
such intricate quantitative proteomics data sets. We propose a statistical
approach to computationally identify protein sets (e.g., Gene Ontology
(GO) terms) that are significantly enriched with abundant proteins
with reproducible quantification measurements across a set of replicates.
To this end, we developed PSEA-Quant, a protein set enrichment analysis
algorithm for label-free and label-based protein quantification data
sets. It offers an alternative approach to classic GO analyses, models
protein annotation biases, and allows the analysis of samples originating
from a single condition, unlike analogous approaches such as GSEA
and PSEA. We demonstrate that PSEA-Quant produces results complementary
to GO analyses. We also show that PSEA-Quant provides valuable information
about the biological processes involved in cystic fibrosis using label-free
protein quantification of a cell line expressing a CFTR mutant. Finally,
PSEA-Quant highlights the differences in the mechanisms taking place
in the human, rat, and mouse brain frontal cortices based on tandem
mass tag quantification. Our approach, which is available online,
will thus improve the analysis of proteomics quantification data sets
by providing meaningful biological insights
Modified MuDPIT Separation Identified 4488 Proteins in a System-wide Analysis of Quiescence in Yeast
A modified multidimensional protein
identification technology (MudPIT)
separation was coupled to an LTQ Orbitrap Velos mass spectrometer
and used to rapidly identify the near-complete yeast proteome from
a whole cell tryptic digest. This modified online two-dimensional
liquid chromatography separation consists of 39 strong cation exchange
steps followed by a short 18.5 min reversed-phase (RP) gradient. A
total of 4269 protein identifications were made from 4189 distinguishable
protein families from yeast during log phase growth. The âMicroâ
MudPIT separation performed as well as a standard MudPIT separation
in 40% less gradient time. The majority of the yeast proteome can
now be routinely covered in less than a daysâ time with high
reproducibility and sensitivity. The newly devised separation method
was used to detect changes in protein expression during cellular quiescence
in yeast. An enrichment in the GO annotations âoxidation reductionâ,
âcatabolic processingâ and âcellular response
to oxidative stressâ was seen in the quiescent cellular fraction,
consistent with their long-lived stress resistant phenotypes. Heterogeneity
was observed in the stationary phase fraction with a less dense cell
population showing reductions in KEGG pathway categories of âRibosomeâ
and âProteasomeâ, further defining the complex nature
of yeast populations present during stationary phase growth. In total,
4488 distinguishable protein families were identified in all cellular
conditions tested
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