Analysis of induced sputum supematant is a minimally invasive approach to study the epithelial lining fluid and, thereby, provide insight into normal lung biology and the pathobiology of lung diseases. We present here a novel proteomics approach to sputum analysis developed within the U-BIOPRED (unbiased biomarkers predictive of respiratory disease outcomes) international project. We present practical and analytical techniques to optimize the detection of robust biomarkers in proteomic studies. The normal sputum proteome was derived using data-independent HDMSE applied to 40 healthy nonsmoking participants, which provides an essential baseline from which to compare modulation of protein expression in respiratory diseases. The "core" sputum proteome (proteins detected in >= 40% of participants) was composed of 284 proteins, and the extended proteome (proteins detected in >= 3 participants) contained 1666 proteins. Quality control procedures were developed to optimize the accuracy and consistency of measurement of sputum proteins and analyze the distribution of sputum proteins in the healthy population. The analysis showed that quantitation of proteins by HDMSE is influenced by several factors, with some proteins being measured in all participants' samples and with low measurement variance between samples from the same patient. The measurement of some proteins is highly variable between repeat analyses, susceptible to sample processing effects, or difficult to accurately quantify by mass spectrometry. Other proteins show high interindividual variance. We also highlight that the sputum proteome of healthy individuals is related to sputum neutrophil levels, but not gender or allergic sensitization. We illustrate the importance of design and interpretation of disease biomarker studies considering such protein population and technical measurement variance.
Tree biotechnology will soon reach a mature state where it will influence the overall supply of fiber, energy and wood products. We are now ready to make the transition from identifying candidate genes, controlling important biological processes, to discovering the detailed molecular function of these genes on a broader, more holistic, systems biology level. In this paper, a strategy is outlined for informative data generation and integrated modeling of systematic changes in transcript, protein and metabolite profiles measured from hybrid aspen samples. The aim is to study characteristics of common changes in relation to genotype-specific perturbations affecting the lignin biosynthesis and growth. We show that a considerable part of the systematic effects in the system can be tracked across all platforms and that the approach has a high potential value in functional characterization of candidate genes.
We have investigated whether postexercise ingestion of carbohydrates in combination with proteins generates a different systemic metabolic response, as compared to the sole ingestion of carbohydrate or water, in the early recovery phase following exercise. In addition, metabolic patterns related to fitness level were studied together with individual responses to nutritional modulation. Twenty-four male subjects were exposed to 90 min of ergometer-cycling. Each participant was subject to four identical test-sessions, including ingestion of one of four beverages (water, low-carbohydrate beverage, high-carbohydrate beverage, and low-carbohydrate−protein beverage (LCHO-P)) immediately after cycling. Blood was collected at six time points, one pre- and five postexercise. Extracted blood serum was subject to metabolomic characterization by gas chromatography/time-of-flight mass spectrometry (GC−TOF MS). Data was processed using hierarchical multivariate curve resolution (HMCR), and multivariate statistical analysis was carried out using orthogonal partial least-squares (OPLS). Predictive metabolomics, including predictive HMCR and OPLS classification, was applied to ensure efficient sample processing and validation of detected metabolic patterns. Separation of subjects in relation to ingested beverage was detected and interpreted. Pseudouridine was suggested as a novel marker for pro-anabolic effect following LCHO-P ingestion, which was supported by the detected decrease of the catabolic marker 3-methylhistidine. Separation of subjects according to fitness level was achieved, and nutritional modulation by LCHO-P was shown to improve the metabolic status of less fit subjects in the recovery phase. In addition, the potential of the methodology for detection of early signs of insulin resistance was also demonstrated.
Although dendritic cells (DCs) control the priming of the adaptive immunity response, a comprehensive description of their behavior at the protein level is missing. The introduction of the into the field of DC research would therefore be highly beneficial. quantitative proteomic technique of metabolic labeling (SILAC) To achieve this, we applied SILAC labeling to primary bone marow-derived DCs (BMDCs). These cells combine both biological relevance and experimental feasibility, as their in vitro generation permits the use of C-13/N-15-labeled amino acids.. Interestingly, BMDCs appear to exhibit a very active arginine metabolism. Using standard cultivation conditions, similar to 20% of all protein-incorporated proline was a byproduct of heavy arginine degradation. In addition, the dissipation of N-15 from labeled arginine to the whole proteome was observed. The latter decreased the mass accuracy in MS and affected the natural isotopic distribution of peptides. SILAC-connected metabolic issues were shown to be enhanced by GM-CSF, which is used for the differentiation of DC progenitors. Modifications of the cultivation procedure suppressed the arginine-related effects, yielding cells with a proteome labeling efficiency of >= 90%. Importantly, BMDCs generated according to the new cultivation protocol preserved their resemblance to inflammatory DCs in vivo, as evidenced by their response to LPS treatment.
A new and general methodology is described for the targeted enrichment and subsequent direct mass spectrometric characterization of sample subsets bearing various chemical functionalities from highly complex mixtures of biological origin. Specifically, sample components containing a chemical moiety of interest are first selectively labeled with perfluoroalkyl groups, and the entire sample is then applied to a perfluoroalkyl-silylated porous silicon (pSi) surface. Due to the unique hydrophobic and lipophobic nature of the perfluorinated tags, unlabeled sample components are readily removed using simple surface washes, and the enriched sample fraction can then directly be analyzed by desorption/ionization on silicon mass spectrometry (DIOS-MS). Importantly, this fluorous-based enrichment methodology provides a single platform that is equally applicable to both peptide as well as small molecule focused applications. The utility of this technique is demonstrated by the enrichment and mass spectrometric analysis of both various peptide subsets from protein digests as well as amino acids from serum.
Mass spectrometry analysis was used to target three different aspects of the viral infection process: the expression kinetics of viral proteins, changes in the expression levels of cellular proteins, and the changes in cellular metabolites in response to viral infection. The combination of these methods represents a new, more comprehensive approach to the study of viral infection revealing the complexity of these events within the infected cell. The proteins associated with measles virus (MV) infection of human HeLa cells were measured using a label-free approach. On the other hand, the regulation of cellular and Flock House Virus (FHV) proteins in response to FHV infection of Drosophila cells was monitored using stable isotope labeling. Three complementary techniques were used to monitor changes in viral protein expression in the cell and host protein expression. A total of 1500 host proteins was identified and quantified, of which over 200 proteins were either up- or down-regulated in response to viral infection, such as the up-regulation of the Drosophila apoptotic croquemort protein, and the down-regulation of proteins that inhibited cell death. These analyses also demonstrated the up-regulation of viral proteins functioning in replication, inhibition of RNA interference, viral assembly, and RNA encapsidation. Over 1000 unique metabolites were also observed with significant changes in over 30, such as the down-regulated cellular phospholipids possibly reflecting the initial events in cell death and viral release. Overall, the cellular transformation that occurs upon viral infection is a process involving hundreds of proteins and metabolites, many of which are structurally and functionally uncharacterized.
Francisella tularensis is a highly infectious intracellular pathogen that has evolved an efficient strategy to subvert host defense response to survive inside the host. The molecular mechanisms regulating these host-pathogen interactions and especially those that are initiated at the time of the bacterial entry via its attachment to the host plasma membrane likely predetermine the intracellular fate of pathogen. Here, we provide the evidence that infection of macrophages with F. tularensis leads to changes in protein composition of macrophage-derived lipid rafts, isolated as detergent-resistant membranes (DRMs). Using SILAC-based quantitative proteomic approach, we observed the accumulation of autophagic adaptor protein p62 at the early, stages of microbe-host cell interaction. We confirmed the colocalization of the p62 with ubiquitinated and LC3-decorated intracellular F. tularensis microbes with its maximum at 1 h postinfection. Furthermore, the infection of p62-knockdown host cells led to the transient increase in the intracellular number of microbes up to 4 h after in vitro infection. Together, these data suggest that the activation of the autophagy pathway in F. tularensis infected macrophages, which impacts the early phase of microbial proliferation, is subsequently circumvented by ongoing infection.
During the courser of infection, the common human pathogen Streptococcus pyogenes encounters plasma. We show that plasma causes S. pyogenes to rapidly remodel its cellular metabolism and virulence pathways. We also identified a variant of the major virulence factor, M1 protein, lacking 13 amino acids at the NH2-terminus in bacteria grown with plasma. The pronounced effect of plasma on protein expression, suggests this is an important adaptive mechanism with implications for S. pyogenes pathogenicity.
A method for predictive metabolite profiling based on resolution of GC-MS data followed by multivariate data analysis is presented and applied to three different biofluid data sets (rat urine, aspen leaf extracts, and human blood plasma). Hierarchical multivariate curve resolution (H-MCR) was used to simultaneously resolve the GC-MS data into pure profiles, describing the relative metabolite concentrations between samples, for multivariate analysis. Here, we present an extension of the H-MCR method allowing treatment of independent samples according to processing parameters estimated from a set of training samples. Predictions or inclusion of the new samples, based on their metabolite profiles, into an existing model could then be carried out, which is a requirement for a working application within, e.g., clinical diagnosis. Apart from allowing treatment and prediction of independent samples the proposed method also reduces the time for the curve resolution process since only a subset of representative samples have to be processed while the remaining samples can be treated according to the obtained processing parameters. The time required for resolving the 30 training samples in the rat urine example was approximately 13 h, while the treatment of the 30 test samples according to the training parameters required only approximately 30 s per sample (approximately 15 min in total). In addition, the presented results show that the suggested approach works for describing metabolic changes in different biofluids, indicating that this is a general approach for high-throughput predictive metabolite profiling, which could have important applications in areas such as plant functional genomics, drug toxicity, treatment efficacy and early disease diagnosis.
Assessment of disease activity in patients with rheumatoid arthritis (RA) is of importance in the evaluation of treatment. The most important measure of disease activity is the Disease Activity Score counted in 28 joints (DAS28). In this study, we evaluated whether metabolic profiling could complement current measures of disease activity. Fifty-six patients, in two separate studies, were followed for two years after commencing anti-TNF therapy. DAS28 was assessed, and metabolic profiles were recorded at defined time points. Correlations between metabolic profile and DAS28 scores were analyzed using multivariate statistics. The metabolic responses to lowering DAS28 scores varied in different patients but could predict DAS28 scores at the individual and subgroup level models. The erythrocyte sedimentation rate (ESR) component in DAS28 was most correlated to the metabolite data, pointing to inflammation as the primary effect driving metabolic profile changes. Patients with RA had differing metabolic response to changes in DAS28 following anti-TNF therapy. This suggests that discovery of new metabolic biomarkers for disease activity will derive from studies at the individual and subgroup level. Increased inflammation, measured as ESR, was the main common effect seen in metabolic profiles from periods associated with high DAS28.
Pancreatic cancer is the seventh leading cause of cancer-related death worldwide, with a 5 year survival rate as low as 9%. One factor complicating the management of pancreatic cancer is the lack of reliable tools for early diagnosis. While up to 50% of the adult population has been shown to develop precancerous pancreatic cysts, limited and insufficient approaches are currently available to determine whether a cyst is going to progress into pancreatic cancer. Recently, we used metabolomics approaches to identify candidate markers of disease progression in patients diagnosed with intraductal papillary mucinous neoplasms (IPMNs) undergoing pancreatic resection. Here, we enrolled an independent cohort to verify the candidate markers from our previous study with orthogonal quantitative methods in plasma and cyst fluid from serous cystic neoplasm and IPMN (either low- or high-grade dysplasia or pancreatic ductal adenocarcinoma). We thus validated these markers with absolute quantitative methods through the auxilium of stable isotope-labeled internal standards in a new independent cohort. Finally, we identified novel markers of IPMN status and disease progression—including amino acids, carboxylic acids, conjugated bile acids, free and carnitine-conjugated fatty acids, purine oxidation products, and trimethylamine-oxide. We show that the levels of these metabolites of potential bacterial origin correlated with the degree of bacterial enrichment in the cyst, as determined by 16S RNA. Overall, our findings are interesting per se, owing to the validation of previous markers and identification of novel small molecule signatures of IPMN and disease progression. In addition, our findings further fuel the provoking debate as to whether bacterial infections may represent an etiological contributor to the development and severity of the disease in pancreatic cancer, in like fashion to other cancers (e.g., Helicobacter pylori and gastric cancer).
The aim of this study was to evaluate three principally different top-down protein prefractionation methods for plasma: high-abundance protein depletion, size fractionation and peptide ligand affinity beads, focusing in particular on compatibility with downstream analysis, reproducibility and analytical depth. Our data clearly demonstrates the benefit of high-abundance protein depletion. However, MS/MS analysis of the proteins eluted from the high-abundance protein depletion column show that more proteins than aimed for are removed and, in addition, that the depletion efficacy varies between the different high-abundance proteins. Although a smaller number of proteins were identified per fraction using the peptide ligand affinity beads, this technique showed to be both robust and versatile. Size fractionation, as performed in this study, focusing on the low molecular weight proteome using a combination of gel filtration chromatography and molecular weight cutoff filters, showed limitations in the molecular weight cutoff precision leading detection of high molecular weight proteins and, in the case of the cutoff filters, high variability. GeLC-MS/MS analysis of the fractionation methods in combination with pathway analysis demonstrates that increased fractionation primarily leads to high proteome coverage of pathways related to biological functions of plasma, such as acute phase reaction, complement cascade and coagulation. Further, the prefractionation methods in this study induces limited effect on the proportion of tissue proteins detected, thereby highlighting the importance of extensive or targeted downstream fractionation.
Francisella tularensis (F. tularensis) is highly infectious for humans via aerosol route and untreated infections with the highly virulent subsp. tularensis can be fatal. Our knowledge regarding key virulence determinants has increased recently but is still somewhat limited. Surface proteins are potential virulence factors and therapeutic targets, and in this study, we decided to target three genes encoding putative membrane lipoproteins in F. tularensis LVS. One of the genes encoded a protein with high homology to the protein family of disulfide oxidoreductases DsbA. The two other genes encoded proteins with homology to the VacJ, a virulence determinant of Shigella flexneri. The gene encoding the DsbA homologue was verified to be required for survival and replication in macrophages and importantly also for in vivo virulence in the mouse infection model for tularemia. Using a combination of classical and shotgun proteome analyses, we were able to identify several proteins that accumulated in fractions enriched for membrane-associated proteins in the dsbA mutant. These proteins are substrate candidates for the DsbA disulfide oxidoreductase as well as being responsible for the virulence attenuation of the dsbA mutant.
In the present study, we performed a metabolomics analysis to evaluate a MODY5/RCAD mouse mutant line as a potential model for HNF1B-associated diseases. Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) of gut, kidney, liver, muscle, pancreas, and plasma samples uncovered the tissue specific metabolite distribution. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) was used to identify the differences between MODY5/RCAD and wild-type mice in each of the tissues. The differences included, for example, increased levels of amino acids in the kidneys and reduced levels of fatty acids in the muscles of the MODY5/RCAD mice. Interestingly, campesterol was found in higher concentrations in the MODY5/RCAD mice, with a four-fold and three-fold increase in kidneys and pancreas, respectively. As expected, the MODY5/RCAD mice displayed signs of impaired renal function in addition to disturbed liver lipid metabolism, with increased lipid and fatty acid accumulation in the liver. From a metabolomics perspective, the MODY5/RCAD model was proven to display a metabolic pattern similar to what would be suspected in HNF1B-associated diseases. These findings were in line with the presumed outcome of the mutation based on the different anatomy and function of the tissues as well as the effect of the mutation on development.
The aim of this study was to evaluate how the cytokine profiles differed between autoantibody based subgroups of systemic lupus erythematosus (SLE). SLE is a systemic autoimmune disease, characterized by periods of flares (active disease) and remission (inactive disease). The disease can affect many organ systems, e.g., skin, joints, kidneys, heart, and the central nervous system (CNS). SLE patients often have an overproduction of cytokines, e.g., interferons, chemokines, and interleukins. The high cytokine levels are part of the systemic inflammation, which can lead to tissue injury. In the present study, SLE patients were divided into five groups based on their autoantibody profiles. We thus defined these five groups: ANA negative, antiphospholipid (aPL) positive, anti-Sm/anti-RNP positive, Sjögren’s syndrome (SS) antigen A and B positive, and patients positive for more than one type of autoantibodies (other SLE). Cytokines were measured using Mesoscale Discovery (MSD) multiplex analysis. On the basis of the cytokine data, ANA negative patients were the most deviating subgroup, with lower levels of interferon (IFN)-γ, tumor necrosis factor (TNF)-α, interleukin (IL)-12/IL-23p40, and interferon gamma-induced protein (IP)-10. Despite low cytokine levels in the ANA negative group, autoantibody profiles did not discriminate between different cytokine patterns.
Mass spectrometry (MS) is an established technology in drug metabolite analysis and is now expanding into endogenous metabolite research. Its utility derives from its wide dynamic range, reproducible quantitative analysis, and the ability to analyze biofluids with extreme molecular complexity. The aims of developing mass spectrometry for metabolomics range from understanding basic biochemistry to biomarker discovery and the structural characterization of physiologically important metabolites. In this review, we will discuss the techniques involved in this exciting area and the current and future applications of this field.
We employed stereotactic microdialysis to sample extracellular fluid intracranially from glioblastoma patients, before and during the first five days of conventional radiotherapy treatment. Microdialysis catheters were implanted in the contrast enhancing tumor as well as in the brain adjacent to tumor (BAT). Reference samples were collected subcutaneously from the patients' abdomen. The samples were analyzed by gas chromatography-time-of-flight mass spectrometry (GC-TOF MS), and the acquired data was processed by hierarchical multivariate curve resolution (H-MCR) and analyzed with orthogonal partial least-squares (OPLS). To enable detection of treatment-induced alterations, the data was processed by individual treatment over time (ITOT) normalization. One-hundred fifty-one metabolites were reliably detected, of which 67 were identified. We found distinct metabolic differences between the intracranially collected samples from tumor and the BAT region. There was also a marked difference between the intracranially and the subcutaneously collected samples. Furthermore, we observed systematic metabolic changes induced by radiotherapy treatment among both tumor and BAT samples. The metabolite patterns affected by treatment were different between tumor and BAT, both containing highly discriminating information, ROC values of 0.896 and 0.821, respectively. Our findings contribute to increased molecular knowledge of basic glioblastoma pathophysiology and point to the possibility of detecting metabolic marker patterns associated to early treatment response.