insulin/IGF-1 receptor is a major known determinant of dauer
formation, stress resistance, longevity, and metabolism in Caenorhabditis elegans. In the past, whole-genome
transcript profiling was used extensively to study differential gene
expression in response to reduced insulin/IGF-1 signaling, including
the expression levels of metabolism-associated genes. Taking advantage
of the recent developments in quantitative liquid chromatography mass
spectrometry (LC–MS)-based proteomics, we profiled the proteomic
changes that occur in response to activation of the DAF-16 transcription
factor in the germline-less glp-4(bn2);daf-2(e1370) receptor mutant. Strikingly, the daf-2 profile
suggests extensive reorganization of intermediary metabolism, characterized
by the upregulation of many core intermediary metabolic pathways.
These include glycolysis/gluconeogenesis, glycogenesis, pentose phosphate
cycle, citric acid cycle, glyoxylate shunt, fatty acid β-oxidation,
one-carbon metabolism, propionate and tyrosine catabolism, and complexes
I, II, III, and V of the electron transport chain. Interestingly,
we found simultaneous activation of reciprocally regulated metabolic
pathways, which is indicative of spatiotemporal coordination of energy
metabolism and/or extensive post-translational regulation of these
enzymes. This restructuring of daf-2 metabolism is
reminiscent to that of hypometabolic dauers, allowing the efficient
and economical utilization of internal nutrient reserves and possibly
also shunting metabolites through alternative energy-generating pathways
to sustain longevity.
Caenorhabditis elegans; gene expression; mass spectrometry; metabolism; physiology; aging
The integrity of quantitative proteomic
experiments depends on
the reliability and the robustness of the protein extraction, solubilization,
and digestion methods utilized. Combinations of detergents, chaotropes,
and mechanical disruption can yield successful protein preparations;
however, the methods subsequently required to eliminate these added
contaminants, in addition to the salts, nucleic acids, and lipids
already in the sample, can result in significant sample losses and
incomplete contaminant removal. A recently introduced method for proteomic
sample preparation, filter-aided sample preparation (FASP), cleverly
circumvents many of the challenges associated with traditional protein
purification methods but is associated with significant sample loss.
Presented here is an enhanced FASP (eFASP) approach that incorporates
alternative reagents to those of traditional FASP, improving sensitivity,
recovery, and proteomic coverage for processed samples. The substitution
of 0.2% deoxycholic acid for urea during eFASP digestion increases
tryptic digestion efficiency for both cytosolic and membrane proteins
yet obviates needed cleanup steps associated with use of the deoxycholate
sodium salt. For classic FASP, prepassivating Microcon filter surfaces
with 5% TWEEN-20 reduces peptide loss by 300%. An express eFASP method
uses tris(2-carboxyethyl)phosphine and 4-vinylpyridine to alkylate
proteins prior to deposition on the Microcon filter, increasing alkylation
specificity and speeding processing.
filter-aided sample preparation; quantitative
proteomics; detergent; ammonium deoxycholate; MSE
most common markers for monitoring patients with diabetes are
glucose and HbA1c, but additional markers such as glycated human serum
albumin (HSA) have been identified that could address the glycation
gap and bridge the time scales of glycemia between transient and 2–3
months. However, there is currently no technical platform that could
measure these markers concurrently in a cost-effective manner. We
have developed a new assay that is able to measure glucose, HbA1c,
glycated HSA, and glycated apolipoprotein A-I (apoA-I) for monitoring
of individual blood glycemia, as well as cysteinylated HSA, S-nitrosylated
HbA, and methionine-oxidized apoA-I for gauging oxidative stress and
cardiovascular risks, all in 5 μL of blood. The assay utilizes
our proprietary multinozzle emitter array chip technology to enable
the analysis of small volumes of blood, without complex sample preparation
prior to the online and on-chip liquid chromatography–nanoelectrospray
ionization mass spectrometry. Importantly, the assay employs top-down
proteomics for more accurate quantitation of protein levels and for
identification of post-translational modifications. Further, the assay
provides multimarker, multitime-scale, and multicompartment monitoring
of blood glycemia. Our assay readily segregates healthy controls from
Type 2 diabetes patients and may have the potential to enable better
long-term monitoring and disease management of diabetes.
MEA; microfluidic chip; top-down
proteomics; LC−MS; blood; diabetes; multimarker
Researchers are increasingly turning
to label-free MS1 intensity-based
quantification strategies within HPLC–ESI–MS/MS workflows
to reveal biological variation at the molecule level. Unfortunately,
HPLC–ESI–MS/MS workflows using these strategies produce
results with poor repeatability and reproducibility, primarily due
to systematic bias and complex variability. While current global normalization
strategies can mitigate systematic bias, they fail when faced with
complex variability stemming from transient stochastic events during
HPLC–ESI–MS/MS analysis. To address these problems,
we developed a novel local normalization method, proximity-based intensity
normalization (PIN), based on the analysis of compositional data.
We evaluated PIN against common normalization strategies. PIN outperforms
them in dramatically reducing variance and in identifying 20% more
proteins with statistically significant abundance differences that
other strategies missed. Our results show the PIN enables the discovery
of statistically significant biological variation that otherwise is
falsely reported or missed.
normalization; label-free quantification; proteomics; peptidomics; bioinformatics
mass spectrometry relies crucially on algorithms that match
peptides to spectra. We describe a method to evaluate the accuracy
of these algorithms based on the masses of parent proteins before
trypsin endoprotease digestion. Measurement of conformance to parent
proteins provides a score for comparison of the performances of different
algorithms as well as alternative parameter settings for a given algorithm.
Tracking of conformance scores for spectrum matches to proteins with
progressively lower expression levels revealed that conformance scores
are not uniform within data sets but are significantly lower for less
abundant proteins. Similarly peptides with lower algorithm peptide-spectrum
match scores have lower conformance. Although peptide mass spectrometry
data is typically filtered through decoy analysis to ensure a low
false discovery rate, this analysis confirms that the filtered data
should not be considered as having a uniform confidence. The analysis
suggests that use of different algorithms and multiple standardized
parameter settings of these algorithms can increase significantly
the numbers of peptides identified. This data set can be used as a
resource for future algorithm assessment.
peptide mass spectrometry; trypsin; OMSSA; SEQUEST; Mascot; algorithm parameter sets; parent-protein conformance; decoy analysis
(Cd2+) is a toxic heavy metal and a well-known
human carcinogen. The toxic effects of Cd2+ on biological
systems are diverse and thought to be exerted through a complex array
of mechanisms. Despite the large number of studies aimed to elucidate
the toxic mechanisms of action of Cd2+, few have been targeted
toward investigating the ability of Cd2+ to disrupt multiple
cellular pathways simultaneously and the overall cellular responses
toward Cd2+ exposure. In this study, we employed a quantitative
proteomic method, relying on stable isotope labeling by amino acids
in cell culture (SILAC) and LC–MS/MS, to assess the Cd2+-induced simultaneous alterations of multiple cellular pathways
in cultured human skin fibroblast cells. By using this approach, we
were able to quantify 2931 proteins, and 400 of them displayed significantly
changed expression following Cd2+ exposure. Our results
unveiled that Cd2+ treatment led to the marked upregulation
of several antioxidant enzymes (e.g., metallothionein-1G, superoxide
dismutase, pyridoxal kinase, etc.), enzymes associated with glutathione
biosynthesis and homeostasis (e.g., glutathione S-transferases, glutathione
synthetase, glutathione peroxidase, etc.), and proteins involved in
cellular energy metabolism (e.g., glycolysis, pentose phosphate pathway,
and the citric acid cycle). Additionally, we found that Cd2+ treatment resulted in the elevated expression of two isoforms of
dimethylarginine dimethylaminohydrolase (DDAH I and II), enzymes known
to play a key role in regulating nitric oxide biosynthesis. Consistent
with these findings, we observed elevated formation of nitric oxide
in human skin (GM00637) and lung (IMR-90) fibroblast cells following
Cd2+ exposure. The upregulation of DDAH I and II suggests
a role of nitric oxide synthesis in Cd2+-induced toxicity
in human cells.
Cd2+; mass spectrometry; protein
quantitation; stable isotope labeling by amino acids in cell
culture; reactive oxygen species; nitric oxide synthesis
Arsenic is a widely-distributed environmental component that is associated with a variety of cancer and non-cancer adverse health effects. Additional lifestyle factors, such as diet, contribute to the manifestation of disease. Recently, arsenic was found to increase inflammation and liver injury in a dietary model of fatty liver disease. The purpose of the present study was to investigate potential mechanisms of this diet-environment interaction via a high throughput metabolomics approach. GC×GC-TOF MS was used to identify metabolites that were significantly increased or decreased in the livers of mice fed a Western diet (a diet high in fat and cholesterol) and co-exposed to arsenic-contaminated drinking water. The results showed that there are distinct hepatic metabolomic profiles associated with eating a high fat diet, drinking arsenic-contaminated water, and the combination of the two. Among the metabolites that were decreased when arsenic exposure was combined with a high fat diet were short-chain and medium-chain fatty acid metabolites and the anti-inflammatory amino acid, glycine. These results are consistent with the observed increase in inflammation and cell death in the livers of these mice, and they point to potentially novel mechanisms by which these metabolic pathways could be altered by arsenic in the context of diet-induced fatty liver disease.
GC×GC-TOF MS; metabolomics; liver; sodium arsenite; high fat diet
Label-free quantitation of proteins analyzed by tandem mass spectrometry uses either integrated peak intensity from the parent-ion mass analysis (MS1) or features from fragment-ion analysis (MS2), such as spectral counts or summed fragment-ion intensity. We directly compared MS1 and MS2 quantitation by analyzing human protein standards diluted into Escherichia coli extracts on an Orbitrap mass spectrometer. We found that summed MS2 intensities were nearly as accurate as integrated MS1 intensities, and both outperformed MS2 spectral counting in accuracy and linearity. We compared these results to those obtained from two low-resolution ion-trap mass spectrometers; summed MS2 intensities from LTQ and LTQ Velos instruments were similar in accuracy to those from the Orbitrap. Data from all three instruments are available via ProteomeXchange with identifier PXD000602. Abundance measurements using MS1 or MS2 intensities had limitations, however. While measured protein concentration was on average well correlated with the known concentration, there was considerable protein-to-protein variation. Moreover, not all human proteins diluted to a mole fraction of 10−3 or lower were detected, with a strong fall-off below 10−4 mole fraction. These results show that MS1 and MS2 intensities are simple measures of protein abundance that are on average accurate, but should be limited to quantitation of proteins of intermediate to higher fractional abundance.
Intensity; spectral counts; Orbitrap; ion trap
Myeloid-derived suppressor cells (MDSC) are present in most cancer patients where they inhibit natural anti-tumor immunity and are an obstacle to anti-cancer immunotherapies. They mediate immune suppression through their production of proteins and soluble mediators that prevent the activation of tumor-reactive T lymphyocytes, polarize macrophages towards a tumor-promoting phenotype, and facilitate angiogenesis. The accumulation and suppressive potency of MDSC is regulated by inflammation within the tumor microenvironment. Recently exosomes have been proposed to act as intercellular communicators, carrying active proteins and other molecules between sender cells and receiver cells. In this report we describe the proteome of exosomes shed by MDSC induced in BALB/c mice by the 4T1 mammary carcinoma. Using bottom-up proteomics, we have identified 412 proteins. Spectral counting identified 63 proteins whose abundance was altered > 2-fold in the inflammatory environment. The pro-inflammatory proteins S100A8 and S100A9, previously shown to be secreted by MDSC and to be chemotactic for MDSC, are abundant in MDSC-derived exosomes. Bioassays reveal that MDSC-derived exosomes polarize macrophages towards a tumor-promoting type 2 phenotype, in addition to possessing S100A8/A9 chemotactic activity. These results suggest that some of the tumor-promoting functions of MDSC are implemented by MDSC-shed exosomes.
extracellular vesicles; exosomes; myeloid-derived suppressor cells; chemotaxis; macrophages; proteomics; spectral counting; tumors; protein S100A8; immune suppression
In clinical settings, biopsies are routinely used to determine cancer type and grade based on tumor cell morphology, as determined via histochemical or immunohistochemical staining. Unfortunately, in a significant number of cases, traditional biopsy results are either inconclusive or do not provide full subtype differentiation, possibly leading to inefficient or ineffective treatment. Glycomic profiling of the cell membrane offers an alternate route towards cancer diagnosis. In this study, isomer-sensitive nano-LC/MS was used to directly obtain detailed profiles of the different N-glycan structures present on cancer cell membranes. Membrane N-glycans were extracted from cells representing various subtypes of breast, lung, cervical, ovarian, and lymphatic cancer. Chip-based porous graphitized carbon nano-LC/MS was used to separate, identify, and quantify the native N-glycans. Structure-sensitive N-glycan profiling identified hundreds of glycan peaks per cell line, including multiple isomers for most compositions. Hierarchical clusterings based on Pearson correlation coefficients were used to quickly compare and separate each cell line according to originating organ and disease subtype. Based simply on the relative abundances of broad glycan classes (e.g. high mannose, complex/hybrid fucosylated, complex/hybrid sialylated, etc.) most cell lines were readily differentiated. More closely-related cell lines were differentiated based on several-fold differences in the abundances of individual glycans. Based on characteristic N-glycan profiles, primary cancer origins and molecular subtypes could be distinguished. These results demonstrate that stark differences in cancer cell membrane glycosylation can be exploited to create an MS-based biopsy, with potential applications towards cancer diagnosis and direction of treatment.
mass spectrometry; LC/MS; N-glycans; cell membrane; cancer; molecular subtype
Sensitive and specific biomarkers for pancreatic cancer are currently unavailable. The high mortality associated with adenocarcinoma of the pancreatic epithelium justifies the broadest possible search for new biomarkers that can facilitate early detection or monitor treatment efficacy. Protein glycosylation is altered in many cancers, leading many to propose that glycoproteomic changes may provide suitable biomarkers. In order to assess this possibility for pancreatic cancer, we have performed an in-depth LC-MS/MS analysis of the proteome and MSn-based characterization of the N-linked glycome of a small set of pancreatic ductal fluid obtained from normal, pancreatitis, intraductal papillary mucinous neoplasm (IPMN), and pancreatic adenocarcinoma patients. Our results identify a set of seven proteins that were consistently increased in cancer ductal fluid compared to normal (AMYP, PRSS1, GP2-1, CCDC132, REG1A, REG1B, and REG3A) and one protein that was consistently decreased (LIPR2). These proteins are all directly or indirectly associated with the secretory pathway in normal pancreatic cells. Validation of these changes in abundance by Western blotting revealed increased REG protein glycoform diversity in cancer. Characterization of the total N-linked glycome of normal, IPMN, and adenocarcinoma ductal fluid clustered samples into three discrete groups based on the prevalence of 6 dominant glycans. Within each group, the profiles of less prevalent glycans were able to distinguish normal from cancer on this small set of samples. Our results emphasize that individual variation in protein glycosylation must be considered when assessing the value of a glycoproteomic marker, but also indicate that glycosylation diversity across human subjects can be reduced to simpler clusters of individuals whose N-linked glycans share structural features.
Pancreatic cancer; Proteomics; Biomarker; N-linked glycan; Glycomics
This paper describes an algorithm to assist in relative quantitation of peptide post-translational modifications using Stable Isotope Labeling by Amino acids in Cell culture (SILAC). The described algorithm first determines the normalization factor and then calculates SILAC ratios for a list of target peptide masses using precursor ion abundances. Four yeast histone mutants were used to demonstrate the effectiveness of this approach for quantitation of peptide post-translational modifications changes. The details of the algorithm’s approach for normalization and peptide ratio calculation are described. The examples demonstrate the robustness of the approach as well as its utility to rapidly determine changes in peptide posttranslational modifications within a protein.
Quantitative proteomics; global normalization factor; ratio/charge filter; peptide clustering; sliding time window
Despite recent developments in treatment strategies, castrate resistant prostate cancer (CRPC) is still the second leading cause of cancer associated mortality among American men, the biological underpinnings of which are not well understood. To this end, we measured levels of 150 metabolites and examined the rate of utilization of 184 metabolites in metastatic androgen dependent prostate cancer (AD) and CRPC cell lines using a combination of targeted mass spectrometry and metabolic phenotyping. Metabolic data were used to derive biochemical pathways that were enriched in CRPC, using Oncomine Concept Maps (OCM). The enriched pathways were then examined in-silico for their association with treatment failure (i.e., prostate specific antigen (PSA) recurrence or biochemical recurrence) using published clinically annotated gene expression data sets. Our results indicate that a total of 19 metabolites were altered in CRPC compared to AD cell lines. These altered metabolites mapped to a highly interconnected network of biochemical pathways that describe UDP glucuronosyltransferase (UGT) activity. We observed an association with time to treatment failure in an analysis employing genes restricted to this pathway in three independent gene expression data sets. In summary, our studies highlight the value of employing metabolomic strategies in cell lines to derive potentially clinically useful predictive tools.
Prostate Cancer; Castrate Resistant Prostate Cancer; Metabolomics; Metabolic Phenotyping; Liquid-chromatography Mass Spectrometry; Oncomine Concept Map and Biochemical Recurrence
Chemical crosslinking coupled with mass spectrometry provides structural information that is useful for probing protein conformations and providing experimental support for molecular models. “Zero-length” crosslinks have greater value for these applications than longer crosslinks because they provide more stringent distance constraints. However, this method is less commonly utilized because it cannot take advantage of isotopic labels, MS-labile bonds, or enrichment tags to facilitate identification. In this study, we combined label-free precursor ion quantitation and targeted tandem mass spectrometry with a new software tool, Zero-length Crosslink Miner (ZXMiner), to form a multi-tiered analysis strategy. A major, critical objective was to simultaneously achieve very high accuracy with essentially no false positive crosslink identifications, while maintaining a good depth of analysis. Our strategy was optimized on several proteins with known crystal structures. Comparison of ZXMiner to several existing crosslink analysis software showed that other algorithms detected less true positive crosslinks and were far less accurate. Although prior use of zero-length crosslinking was typically restricted to small proteins, ZXMiner and the associated strategy enables facile analysis of very large protein complexes. This was demonstrated by identification of zero-length crosslinks using purified 526 kDa spectrin heterodimers and intact red cell membranes and membrane skeletons.
chemical crosslinking; mass spectrometry; software
D-cycloserine is an effective second line antibiotic used as a last resort to treat multi (MDR)- and extensively (XDR)- drug resistant strains of Mycobacterium tuberculosis. D-cycloserine interferes with the formation of peptidoglycan biosynthesis by competitive inhibition of Alanine racemase (Alr) and D-Alanine-D-alanine ligase (Ddl). Although, the two enzymes are known to be inhibited, the in vivo lethal target is still unknown. Our NMR metabolomics work has revealed that Ddl is the primary target of DCS, as cell growth is inhibited when the production of D-alanyl-D-alanine is halted. It is shown that inhibition of Alr may contribute indirectly by lowering the levels of D-alanine thus allowing DCS to outcompete D-alanine for Ddl binding. The NMR data also supports the possibility of a transamination reaction to produce D-alanine from pyruvate and glutamate, thereby bypassing Alr inhibition. Furthermore, the inhibition of peptidoglycan synthesis results in a cascading effect on cellular metabolism as there is a shift toward the catabolic routes to compensate for accumulation of peptidoglycan precursors.
Tuberculosis; NMR metabolomics; mycobacteria; D-cycloserine
One can interpret fragmentation spectra stemming from peptides in mass spectrometry-based proteomics experiments using so called database search engines. Frequently, one also runs post-processors such as Percolator to assess the confidence, infer unique peptides and increase the number of identifications. A recent search engine, MS-GF+, has shown promising results, due to a new and efficient scoring algorithm. However, MS-GF+ provides few statistical estimates about the peptide-spectrum matches, hence limiting the biological interpretation. Here, we enabled Percolator-processing for MS-GF+ output, and observed an increased number of identified peptides for a wide variety of datasets. In addition, Percolator directly reports p values and false discovery rate estimates, such as q values and posterior error probabilities, for peptide-spectrum matches, peptides and proteins, functions that are useful for the whole proteomics community.
Cigarette smoke (CS)-mediated oxidative stress induces several signaling cascades, including kinases, which results in chromatin modifications (histone acetylation/deacetylation and histone methylation/demethylation). We have previously reported that CS induces chromatin remodeling in pro-inflammatory gene promoters; however, the underlying site-specific histone marks formed in histones H3 and H4 during CS exposure in lungs in vivo and in lung cells in vitro, which can either drive gene expression or repression are not known. We hypothesize that CS exposure in mouse and human bronchial epithelial cells (H292) can cause site-specific posttranslational histone modifications (PTMs) that may play an important role in the pathogenesis of CS-induced chronic lung diseases. We used a bottom-up mass spectrometry approach to identify some potentially novel histone marks, including acetylation, mono-methylation and di-methylation in specific lysine and arginine residues of histones H3 and H4 in mouse lungs and H292 cells. We found that CS-induced distinct posttranslational histone modification patterns in histone H3 and histone H4 in lung cells, which may be considered as usable biomarkers for CS-induced chronic lung diseases. These identified histone marks (histone H3 and histone H4) may play an important role in epigenetic state during the pathogenesis of smoking-induced chronic lung diseases, such as chronic obstructive pulmonary disease and lung cancer.
Oxidants; chromatin; lung; acetylation; methylation; chronic obstructive pulmonary disease; mass spectrometry; lung cancer
Mammalian IQGAP proteins all feature multiple ~50 amino acid sequence repeats near their N-termini and little is known about the function of these “Repeats”. We have expressed and purified the Repeats from human IQGAP1 in order to identify binding partners. We used mass spectrometry to identify 42 mouse kidney proteins that associate with the IQGAP1 Repeats including the ERM proteins ezrin, radixin and moesin. ERM proteins have an N-terminal FERM domain (four point one, ezrin, radixin, moesin) through which they bind to protein targets and phosphatidylinositol 4,5-bisphosphate (PIP2), and a C-terminal actin-binding domain, and function to link the actin cytoskeleton to distinct locations on the cell cortex. Isothermal titration calorimetry (ITC) reveals that the IQGAP1 Repeats directly bind to the ezrin FERM domain while no binding is seen for full-length “autoinhibited” ezrin or a version of full-length ezrin intended to mimic the activated protein. ITC also indicates that the ezrin FERM domain binds to the Repeats from IQGAP2 but not the Repeats from IQGAP3. We conclude that IQGAP1 and IQGAP2 are positioned at the cell cortex by ERM proteins. We propose that the IQGAP3 Repeats may likewise bind to FERM domains signaling purposes.
IQGAP1; Repeats; ezrin; FERM domain
Anterior gradient 2 (AGR2) is a secreted, cancer-associated protein in many types of epithelial cancer cells. We developed a highly sensitive targeted mass spectrometric assay for quantification of AGR2 in urine and serum. Digested peptides from clinical samples were processed by PRISM (high pressure and high resolution separations coupled with intelligent selection and multiplexing), which incorporates high pH reversed-phase LC separations to fractionate and select target fractions for follow-on LC-SRM analyses. The PRISM-SRM assay for AGR2 showed a reproducibility of <10% CV and LOQ values of ~130 pg/mL in serum and ~10 pg per 100 μg total protein mass in urine, respectively. A good correlation (R2 = 0.91) was observed for the measurable AGR2 concentrations in urine between SRM and ELISA. Based on an initial cohort of 37 subjects, urinary AGR2/PSA concentration ratios showed a significant difference (P = 0.026) between non-cancer and cancer. Large clinical cohort studies are needed for the validation of AGR2 as a useful diagnostic biomarker for prostate cancer. Our work validated the approach of identifying candidate secreted protein biomarkers through genomics and measurement by targeted proteomics, especially for proteins where no immunoassays are available.
AGR2; PSA; prostate cancer; PRISM-SRM; human urine; human serum
Sepsis is commonly caused by community-acquired pneumonia (CAP) and may develop into severe sepsis, characterized by multiple organ failure. The risk of severe sepsis among CAP patients and subsequent mortality increases sharply after the age of 65. The molecular mechanisms associated with this age-related risk are not fully understood. To better understand factors involved with increased incidence and mortality of severe sepsis in the elderly, we used a nested case-control study of patients enrolled in a multicenter observational cohort of 2,320 participants with CAP. We identified a total of 39 CAP patients 50-65 and 70-85 years old who did or did not develop severe sepsis. Plasma samples were obtained on presentation to the emergency department and prior to therapeutic interventions. A semi-quantitative plasma proteomics workflow was applied which incorporated tandem immunoaffinity depletion, iTRAQ labeling, strong cation exchange fractionation, and nanoflow-liquid chromatography coupled to high resolution mass spectrometry. In total, 772 proteins were identified, of which, 58 proteins exhibit statistically significant differences in expression levels amongst patients with severe sepsis as a function of age. Differentially-expressed proteins are involved in pathways such as acute phase response, coagulation signaling, atherosclerosis signaling, lipid metabolism, and production of nitric oxide and reactive oxygen species. This study provides insight into factors that may explain age-related differences in incidence of severe sepsis in the elderly.
sepsis; severe sepsis; proteomics; CAP; plasma; aging; immunosenescence; pneumonia
Deamidation of asparagine and glutamine residues is a common post-translational modification. Researchers often rely on mass spectrometric based proteomic techniques for the identification of these post-translational sites. Mass spectral analysis of deamidated peptides is complicated and often misassigned due to overlapping 13C peak of the amidated form with the deamidated monoisotopic peak; these two peaks are only separated by 19.34 mDa. For proper assignment it is inherently important to use a mass spectrometer with high mass measurement accuracy and high resolving power. Herein, mouse brain tissue lysate was prepared using filter-aided sample preparation (FASP) method and Stage Tip fractionation followed by analysis on a nanoLC coupled to a quadrupole orbitrap (Q-Exactive) mass spectrometer to accurately identify more than 5400 proteins. Mass spectral data was processed using MASCOT and ProteoIQ for accurate identification of peptides and proteins. MASCOT search values for precursor and MS/MS mass tolerances were investigated, and it was determined data searched with greater than 5 ppm precursor mass tolerance resulted in the misassignment of deamidated peptides. Peptides that were identified with a mass measurement accuracy of ± 5 ppm were correctly assigned.
Biological assays formatted as microarrays have become a critical tool for the generation of the comprehensive datasets required for systems-level understanding of biological processes. Manual annotation of data extracted from images of microarrays, however, remains a significant bottleneck, particularly for protein microarrays due to the sensitivity of this technology to weak artifact signal. In order to automate the extraction and curation of data from protein microarrays, we describe an algorithm called Crossword that logically combines information from multiple approaches for segmenting microarrays and a novel method for removing artifacts. Artifact removal is accomplished by segregating structured pixels from the background noise using iterative clustering and pixel connectivity. Correlation of the location of structured pixels across image channels is used to identify and remove artifact pixels from the image prior to data extraction. This component improves the accuracy of datasets while reducing the requirement for time-consuming visual inspection of the data. Crossword enables a fully automated protocol that is robust to significant spatial and intensity aberrations. Overall, the average amount of user intervention is reduced by an order of magnitude and the data quality is increased through artifact removal and reduced user variability. The increase in throughput should aid the further implementation of microarray technologies in clinical studies.
image segmentation; artifact removal; image quality control; automated image processing; pixel clustering; pixel connectivity
Many of the functional proteins and lipids in HDL particles are potentially glycosylated yet very little is known about the glycoconjugates of HDL. In this study, HDL was isolated from plasma by sequential micro-ultracentrifugation, followed by glycoprotein and glycolipid analysis. N-glycans, glycopeptides, and gangliosides were extracted and purified followed by analysis with nano-HPLC-Chip Q-TOF MS and MS/MS. HDL particles were found to be highly sialylated. Most of the N-glycans (~90%) from HDL glycoproteins were sialylated with one or two neuraminic acids (Neu5Ac). The most abundant N-glycan was a biantennary complex type glycan with two sialic acids (Hexose5HexNAc4Neu5Ac2), and was found in multiple glycoproteins using site-specific glycosylation analysis. The observed O-glycans were all sialylated and most contained a core 1 structure with two Neu5Acs, including those that were associated with apolipoprotein CIII (ApoC-III) and fetuin A. GM3 (monosialoganglioside, NeuAc2-3Gal1-4Glc-Cer) and GD3 (disialoganglioside, NeuAc2-8NeuAc2-3Gal1-4Glc-Cer) were the major gangliosides in HDL. A 60% GM3 and 40% GD3 distribution was observed. Both GM3 and GD3 were composed of heterogeneous ceramide lipid tails, including d18:1/16:0 and d18:1/23:0. This report describes for the first time a glycomic approach for analyzing HDL, highlighting that HDL are highly sialylated particles.
HDL; glycomics; glycoproteomics; gangliosides; sialylation; mass spectrometry
Proteomic analysis of bronchoalveolarBlavageBfluid (BALF) in chronic obstructive pulmonary disease (COPD) patients may provide new biomarkers and deeper understanding of the disease mechanisms but remains challenging. Here we describe an ionBcurrentBbased strategy for comparative analysis of BALF proteomes from patients with moderate and stable COPD vs. healthy controls. The strategy includes an efficient preparation procedure providing quantitative recovery and a nanoBLC/MS analysis with a long, heated column. Under optimized conditions, high efficiency and reproducibility were achieved for each step, enabling a “20Bplex” comparison of clinical subjects (n=10/group). Without depletion/fractionation, a total of 423 unique protein groups were quantified under stringent criteria with at least two quantifiable peptides. SeventyBsix proteins were determined as significantlyBaltered in COPD, which represent a diversity of biological processes such as alcohol metabolic process, gluconeogenesis/glycolysis, inflammatory response, proteolysis, and oxidation reduction. Interestingly, altered alcohol metabolism responding to oxidant stress is a novel observation in COPD. The prominently elevated key enzymes involved in alcohol metabolism (e.g. ADH1B, ALDH2&ALDH3A1) may provide a reasonable explanation for a bewildering observation in COPD patients known for decades: the underestimation of the blood alcohol concentrations through breath tests. These discoveries could provide new insights for identifying novel biomarkers and pathological mediators in clinical studies.
Biomarker Discovery; Bronchoalveolar Lavage Fluid; Chronic Obstructive Pulmonary Disease; Peptide Extracted Ion Current
Acute respiratory distress syndrome (ARDS) remains a significant hazard to human health and is clinically challenging because there are no prognostic biomarkers and no effective pharmacotherapy. The lung compartment metabolome may detail the status of the local environment that could be useful in ARDS biomarker discovery and the identification of drug target opportunities. However, neither the utility of bronchoalveolar lavage fluid (BALF) as a biofluid for metabolomics nor the optimal analytical platform for metabolite identification are established. To address this, we undertook a study to compare metabolites in BALF samples from patients with ARDS and healthy controls using a newly developed liquid chromatography (LC)-mass spectroscopy (MS) platform for untargeted metabolomics. Following initial testing of three different high performance liquid chromatography (HPLC) columns, we determined that reversed phase (RP)-LC and hydrophilic interaction chromatography (HILIC), were the most informative chromatographic methods because they yielded the most and highest quality data. Following confirmation of metabolite identification, statistical analysis resulted in 37 differentiating metabolites in the BALF of ARDS compared with health across both analytical platforms. Pathway analysis revealed networks associated with amino acid metabolism, glycolysis and gluconeogenesis, fatty acid biosynthesis, phospholipids and purine metabolism in the ARDS BALF. The complementary analytical platforms of RPLC and HILIC-LC generated informative, insightful metabolomics data of the ARDS lung environment.
biomarkers; critical illness; metabolomics; lung injury; bioinformatics; phospholipids; lactate; xanthine oxidase; hippurate; pharmacotherapy