Proteomic analysis revealed that the oomyceticide phosphite exhibits multi-modal action in an oomycete pathosystem.

Phytopathogenic oomycetes constitute some of the most devastating plant pathogens and cause significant crop and horticultural yield and economic losses. The phytopathogen Phytophthora cinnamomi causes dieback disease in native vegetation and several crops. The most commonly used chemical to control P. cinnamomi is the oomyceticide phosphite. Despite its widespread use, the mode of action of phosphite is not well understood and it is unclear whether it targets the pathogen, the host, or both. Resistance to phosphite is emerging in P. cinnamomi isolates and other oomycete phytopathogens. The mode of action of phosphite on phosphite-sensitive and resistant isolates of the pathogen and through a model host was investigated using label-free quantitative proteomics. In vitro treatment of sensitive P. cinnamomi isolates with phosphite hinders growth by interfering with metabolism, signalling and gene expression; traits that are not observed in the resistant isolate. When the model host Lupinus angustifolius was treated with phosphite, proteins associated with photosynthesis, carbon fixation and lipid metabolism in the host were enriched. Increased production of defence-related proteins was also observed in the plant. We hypothesise the multi-modal action of phosphite and present two models constructed using comparative proteomics that demonstrate mechanisms of pathogen and host responses to phosphite. SIGNIFICANCE: Phytophthora cinnamomi is a significant phytopathogenic oomycete that causes root rot (dieback) in a number of horticultural crops and a vast range of native vegetation. Historically, areas infected with phosphite have been treated with the oomyceticide phosphite despite its unknown mode of action. Additionally, overuse of phosphite has driven the emergence of phosphite-resistant isolates of the pathogen. We conducted a comparative proteomic study of a sensitive and resistant isolate of P. cinnamomi in response to treatment with phosphite, and the response of a model host, Lupinus angustifolius, to phosphite and its implications on infection. The present study has allowed for a deeper understanding of the bimodal action of phosphite, suggested potential biochemical factors contributing to chemical resistance in P. cinnamomi, and unveiled possible drivers of phosphite-induced host plant immunity to the pathogen.

Canagliflozin Attenuates PromarkerD Diabetic Kidney Disease Risk Prediction Scores.

PromarkerD is a biomarker-based blood test that predicts kidney function decline in people with type 2 diabetes (T2D) who may otherwise be missed by current standard of care tests. This study examined the association between canagliflozin and change in PromarkerD score (Δ score) over a three-year period in T2D participants in the CANagliflozin cardioVascular Assessment Study (CANVAS). PromarkerD scores were measured at baseline and Year 3 in 2008 participants with preserved kidney function (baseline eGFR ≥60 mL/min/1.73 m2). Generalized estimating equations were used to assess the effect of canagliflozin versus placebo on PromarkerD scores. At baseline, the participants (mean age 62 years, 32% females) had a median PromarkerD score of 3.9%, with 67% of participants categorized as low risk, 14% as moderate risk, and 19% as high risk for kidney function decline. After accounting for the known acute drop in eGFR following canagliflozin initiation, there was a significant treatment-by-time interaction (p < 0.001), whereby participants on canagliflozin had decreased mean PromarkerD scores from baseline to Year 3 (Δ score: -1.0% [95% CI: -1.9%, -0.1%]; p = 0.039), while the scores of those on placebo increased over the three-year period (Δ score: 6.4% [4.9%, 7.8%]; p < 0.001). When stratified into PromarkerD risk categories, participants with high risk scores at baseline who were randomized to canagliflozin had significantly lower scores at Year 3 (Δ score: -5.6% [-8.6%, -2.5%]; p < 0.001), while those on placebo retained high scores (Δ score: 4.5% [0.3%, 8.8%]; p = 0.035). This post hoc analysis of data from CANVAS showed that canagliflozin significantly lowered PromarkerD risk scores, with the effect greatest in those T2D participants who were classified at study entry as at high risk of a subsequent decline in kidney function.

Immunoaffinity Mass Spectrometry Diagnostic Tests for Multi-Biomarker Assays.

Immunoaffinity mass spectrometry as an approach for diagnostic biomarker assays combines the advantages of antibody selectivity with the multiplexing and analytical performance of mass spectrometry. A method has been developed to detect and quantify three protein biomarkers for a diabetic kidney disease prognostic assay, PromarkerD. The methodology reflects an immunoaffinity approach compatible with higher throughput and robust clinical application. After preparation and purification of antibody-bead conjugates for the three target proteins, an immunoaffinity capture step provides a solution for reduction, alkylation, and digestion on-bead. Targeted mass spectrometry provides a quantitative measure of each biomarker in a rapid 8 min run using a microflow LCMS workflow.

Semi-Automated Lectin Magnetic Bead Array (LeMBA) for Translational Serum Glycoprotein Biomarker Discovery and Validation.

Aberrant protein glycosylation is a characteristic of diverse diseases which has been explored as biomarkers. To support translational serum glycoprotein biomarker discovery and validation, we developed a semi-automated workflow using individual lectin-coupled magnetic beads to conduct lectin pulldowns in a high-throughput format. Lectins are naturally occurring glycoprotein binding proteins widely used in glycobiology. While lectin-affinity isolation has been coupled to mass spectrometry-based proteomics, the lectin magnetic bead array (LeMBA) platform allows technically robust screening and measurement of clinical cohorts. This chapter describes detailed lectin-magnetic bead coupling, serum denaturation, lectin magnetic bead pulldown, and on-bead trypsin digest. The resulting tryptic peptides are analyzed by untargeted or targeted liquid chromatography-mass spectrometry (LC-MS), for biomarker discovery, or qualification/validation, respectively. LeMBA-MS generates quantitative data for glycoforms based on lectin affinity of the glycoprotein coupled with MS measurement of one or more prototypic peptides and has successfully been used to discover and validate novel serum cancer glycoprotein biomarkers. This chapter includes detailed protocols for two different liquid handlers, along with recommendations on quality control measures for clinical biomarker studies.

Comparative sub-cellular proteome analyses reveals metabolic differentiation and production of effector-like molecules in the dieback phytopathogen Phytophthora cinnamomi.

Phytopathogenic oomycetes pose a significant threat to global biodiversity and food security. The proteomes of these oomycetes likely contain important factors that contribute to their pathogenic success, making their discovery crucial for elucidating pathogenicity. Phytophthora cinnamomi is a root pathogen that causes dieback in a wide variety of crops and native vegetation world-wide. Virulence proteins produced by P. cinnamomi are not well defined and a large-scale approach to understand the biochemistry of this pathogen has not been documented. Soluble mycelial, zoospore and secreted proteomes were obtained and label-free quantitative proteomics was used to compare the composition of the three sub-proteomes. A total of 4635 proteins were identified, validating 17.7% of the predicted gene set. The mycelia were abundant in transporters for nutrient acquisition, metabolism and cellular proliferation. The zoospores had less metabolic related ontologies but were abundant in energy generating, motility and signalling associated proteins. Virulence-associated proteins were identified in the secretome such as candidate effector and effector-like proteins, which interfere with the host immune system. These include hydrolases, cell wall degrading enzymes, putative necrosis-inducing proteins and elicitins. The secretome elicited a hypersensitive response on the roots of a model host and thus suggests evidence of effector activity. SIGNIFICANCE: Phytophthora cinnamomi is a phytopathogenic oomycete that causes dieback disease in native vegetation and several horticultural crops such as avocado, pineapple and macadamia. Whilst this pathogen has significance world-wide, its pathogenicity and virulence have not been described in depth. We carried out comparative label-free proteomics of the mycelia, zoospores and secretome of P. cinnamomi. This study highlights the differential metabolism and cellular processes between the sub-proteomes. Proteins associated with metabolism, nutrient transport and cellular proliferation were over represented in the mycelia. The zoospores have a specialised proteome showing increased energy generation geared towards motility. Candidate effectors and effector-like secreted proteins were also identified, which can be exploited for genetic resistance. This demonstrates a better understanding of the biology and pathogenicity of P. cinnamomi infection that can subsequently be used to develop effective methods of disease management.

Gene Validation and Remodelling Using Proteogenomics of Phytophthora cinnamomi, the Causal Agent of Dieback.

Phytophthora cinnamomi is a pathogenic oomycete that causes plant dieback disease across a range of natural ecosystems and in many agriculturally important crops on a global scale. An annotated draft genome sequence is publicly available (JGI Mycocosm) and suggests 26,131 gene models. In this study, soluble mycelial, extracellular (secretome), and zoospore proteins of P. cinnamomi were exploited to refine the genome by correcting gene annotations and discovering novel genes. By implementing the diverse set of sub-proteomes into a generated proteogenomics pipeline, we were able to improve the P. cinnamomi genome annotation. Liquid chromatography mass spectrometry was used to obtain high confidence peptides with spectral matching to both the annotated genome and a generated 6-frame translation. Two thousand seven hundred sixty-four annotations from the draft genome were confirmed by spectral matching. Using a proteogenomic pipeline, mass spectra were used to edit the P. cinnamomi genome and allowed identification of 23 new gene models and 60 edited gene features using high confidence peptides obtained by mass spectrometry, suggesting a rate of incorrect annotations of 3% of the detectable proteome. The novel features were further validated by total peptide support, alongside functional analysis including the use of Gene Ontology and functional domain identification. We demonstrated the use of spectral data in combination with our proteogenomics pipeline can be used to improve the genome annotation of important plant diseases and identify missed genes. This study presents the first use of spectral data to edit and manually annotate an oomycete pathogen.

Assessment of biomarkers associated with rapid renal decline in the detection of retinopathy and its progression in type 2 diabetes: The Fremantle Diabetes Study Phase II.

AIMS: To determine whether biomarkers for diabetic kidney disease (DKD) can be used to determine the prevalence, progression and/or incidence of diabetic retinopathy (DR) complicating type 2 diabetes. METHODS: Proteomic biomarkers were measured in baseline fasting plasma from 958 Fremantle Diabetes Study Phase II participants whose baseline and, in those returning for follow-up (n = 764), Year 4 fundus photographs were graded for DR presence/severity. The performance of PromarkerD (three biomarkers and readily available clinical variables which identify prevalent DKD and predict incident DKD and estimated glomerular filtration rate decline ≥30% over four years) for detecting DR prevalence, progression and incidence was assessed using the area under the receiver operating curve (AUC). Logistic regression determined whether individual proteins were associated with DR outcomes after adjusting for the most parsimonious model. RESULTS: Plasma apolipoprotein A-IV (APOA4) was independently associated with moderate non-proliferative DR at baseline (OR (95% CI): 1.64 (1.01, 2.67), P = 0.047). Model discrimination was poor for all PromarkerD predicted probabilities against all DR outcomes (AUC ≤0.681). CONCLUSIONS: PromarkerD and its constituent biomarkers were not consistently associated with DR prevalence or temporal change. APOA4 was associated with prevalent DR, but not DR incidence or progression. Distinct pathophysiological mechanisms may underlie DKD and DR.

PromarkerD Predicts Renal Function Decline in Type 2 Diabetes in the Canagliflozin Cardiovascular Assessment Study (CANVAS).

The ability of current tests to predict chronic kidney disease (CKD) complicating diabetes is limited. This study investigated the prognostic utility of a novel blood test, PromarkerD, for predicting future renal function decline in individuals with type 2 diabetes from the CANagliflozin CardioVascular Assessment Study (CANVAS). PromarkerD scores were measured at baseline in 3568 CANVAS participants (n = 1195 placebo arm, n = 2373 canagliflozin arm) and used to predict incident CKD (estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2 during follow-up in those above this threshold at baseline) and eGFR decline ≥30% during the 4 years from randomization. Biomarker concentrations (apolipoprotein A-IV (apoA4), CD5 antigen-like (CD5L/AIM) and insulin-like growth factor-binding protein 3 (IGFBP3) measured by mass spectrometry were combined with clinical data (age, serum high-density lipoprotein (HDL)-cholesterol, eGFR) using a previously defined algorithm to provide PromarkerD scores categorized as low-, moderate- or high-risk. The participants (mean age 63 years, 33% females) had a median PromarkerD score of 2.9%, with 70.5% categorized as low-risk, 13.6% as moderate-risk and 15.9% as high-risk for developing incident CKD. After adjusting for treatment, baseline PromarkerD moderate-risk and high-risk scores were increasingly prognostic for incident CKD (odds ratio 5.29 and 13.52 versus low-risk, respectively; both p < 0.001). Analysis of the PromarkerD test system in CANVAS shows the test can predict clinically significant incident CKD in this multi-center clinical study but had limited utility for predicting eGFR decline ≥30%.

The New and the Old: Platform Cross-Validation of Immunoaffinity MASS Spectrometry versus ELISA for PromarkerD, a Predictive Test for Diabetic Kidney Disease.

PromarkerD is a proteomics derived test for predicting diabetic kidney disease that measures the concentrations of three plasma protein biomarkers, APOA4, CD5L and IBP3. Antibodies against these proteins were developed and applied to a multiplexed immunoaffinity capture mass spectrometry assay. In parallel, and facilitating current clinical laboratory workflows, a standard ELISA was also developed to measure each protein. The performance characteristics of the two technology platforms were compared using a cohort of 100 samples, with PromarkerD test scores demonstrating a high correlation (R = 0.97). These technologies illustrate the potential for large scale, high throughput clinical applications of proteomics now and into the future.

A robust multiplex immunoaffinity mass spectrometry assay (PromarkerD) for clinical prediction of diabetic kidney disease.

BACKGROUND: PromarkerD is a novel proteomics derived blood test for predicting diabetic kidney disease (DKD). The test is based on an algorithm that combines the measurement of three plasma protein biomarkers (CD5L, APOA4, and IBP3) with three clinical variables (age, HDL-cholesterol, and eGFR). The initial format of the assay used immunodepletion of plasma samples followed by targeted mass spectrometry (MRM-LCMS). The aim of this study was to convert the existing assay into an immunoaffinity approach compatible with higher throughput and robust clinical application. METHODS: A newly optimised immunoaffinity-based assay was developed in a 96 well format with MRM measurements made using a low-flow LCMS method. The stability, reproducibility and precision of the assay was evaluated. A direct comparison between the immunoaffinity method and the original immunodepletion method was conducted on a 100-person cohort. Subsequently, an inter-lab study was performed of the optimised immunoaffinity method in two independent laboratories. RESULTS: Processing of plasma samples was greatly simplified by switching to an immunoaffinity bead capture method, coupled to a faster and more robust microflow LCMS system. Processing time was reduced from seven to two days and the chromatography reduced from 90 to 8 min. Biomarker stability by temperature and time difference treatments passed acceptance criteria. Intra/Inter-day test reproducibility and precision were within 11% CV for all biomarkers. PromarkerD test results from the new immunoaffinity method demonstrated excellent correlation (R = 0.96) to the original immunodepletion method. The immunoaffinity assay was successfully transferred to a second laboratory (R = 0.98) demonstrating the robustness of the methodology and ease of method transfer. CONCLUSIONS: An immunoaffinity capture targeted mass spectrometry assay was developed and optimised. It showed statistically comparable results to those obtained from the original immunodepletion method and was also able to provide comparable results when deployed to an independent laboratory. Taking a research grade assay and optimising to a clinical grade workflow provides insights into the future of multiplex biomarker measurement with an immunoaffinity mass spectrometry foundation. In the current format the PromarkerD immunoaffinity assay has the potential to make a significant impact on prediction of diabetic kidney disease with consequent benefit to patients.

Proteomic profiling of developing wheat heads under water-stress.

A replicated iTRAQ (isobaric tags for relative and absolute quantification) study on developing wheat heads from two doubled haploid (DH) lines identified from a cross between cv Westonia x cv Kauz characterized the proteome changes influenced by reproductive stage water-stress. All lines were exposed to 10 days of water-stress from early booting (Zadok 40), with sample sets taken from five head developmental stages. Two sample groups (water-stressed and control) account for 120 samples that required 18 eight-plex iTRAQ runs. Based on the IWGSC RefSeq v1 wheat assembly, among the 4592 identified proteins, a total of 132 proteins showed a significant response to water-stress, including the down-regulation of a mitochondrial Rho GTPase, a regulator of intercellular fundamental biological processes (7.5 fold) and cell division protein FtsZ at anthesis (6.0 fold). Up-regulated proteins included inosine-5'-monophosphate dehydrogenase (3.83 fold) and glycerophosphodiester phosphodiesterase (4.05 fold). The Pre-FHE and FHE stages (full head emerged) of head development were differentiated by 391 proteins and 270 proteins differentiated the FHE and Post-FHE stages. Water-stress during meiosis affected seed setting with 27% and 6% reduction in the progeny DH105 and DH299 respectively. Among the 77 proteins that differentiated between the two DH lines, 7 proteins were significantly influenced by water-stress and correlated with the seed set phenotype response of the DH lines to water-stress (e.g. the up-regulation of a subtilisin-like protease in DH 299 relative to DH 105). This study provided unique insights into the biological changes in developing wheat head that occur during water-stress.

Protein Network Analysis Identifies Changes in the Level of Proteins Involved in Platelet Degranulation, Proteolysis and Cholesterol Metabolism Pathways in AECOPD Patients.

Chronic obstructive pulmonary disease (COPD) is characterised by a progressive pulmonary and systemic inflammation. Acute exacerbations of COPD (AECOPD) are associated with acute inflammation and infection, increase in the rates of morbidity and mortality. Previous proteomic studies have focussed on identifying proteins involved in COPD pathogenesis in samples collected from the lung (e.g. lung tissue biopsies, bronchoalveolar lavage and sputum) but not from blood of patients who experienced AECOPD. In this study, plasma was analysed by two independent quantitative proteomics techniques; isobaric tag for relative and absolute quantitation (iTRAQ) and multiple reaction monitoring (MRM) to identify differential expression of circulating proteins in patients with stable COPD (sCOPD) and AECOPD. Firstly, iTRAQ performed on pooled plasma samples from AECOPD, sCOPD, and healthy non-smoking controls (HC) revealed 15 differentially expressed proteins between the 3 groups. MRM subsequently performed on a separate cohort of AECOPD, sCOPD, and HC patients confirmed 9 proteins to be differentially expressed by AECOPD compared to HC (Afamin, alpha-1-antichymotrypsin, Apolipoprotein E, Beta-2-glycoprotein 1, Complement component C9, Fibronectin, Immunoglobulin lambda like polypeptide 5, Inter-alpha-trypsin inhibitor heavy chain H3, Leucine rich alpha-2-glycoprotein 1). Network analysis demonstrates that most of these proteins are involved in proteolysis regulation, platelet degranulation and cholesterol metabolism. In conclusion, several potential plasma biomarkers for AECOPD were identified in this study. Further validation studies of these proteins may elucidate their roles in the development of AECOPD.

Validation of a protein biomarker test for predicting renal decline in type 2 diabetes: The Fremantle Diabetes Study Phase II.

AIMS: To validate the prognostic utility of a novel plasma biomarker panel, PromarkerD, for predicting renal decline in an independent cohort of people with type 2 diabetes. METHODS: Models for predicting rapid estimated glomerular filtration rate (eGFR) decline defined as i) incident diabetic kidney disease (DKD), ii) eGFR decline ≥30% over four years, and iii) annual eGFR decline ≥5 mL/min/1.73 m2 were applied to 447 participants from the longitudinal observational Fremantle Diabetes Study Phase II. Model performance was assessed using discrimination and calibration. RESULTS: During 4.2 ±â€¯0.3 years of follow-up, 5-10% of participants experienced a rapid decline in eGFR. A consensus model comprising apolipoprotein A-IV (apoA4), CD5 antigen-like (CD5L), insulin-like growth factor-binding protein 3 (IGFBP3), age, serum HDL-cholesterol and eGFR showed the best performance for predicting incident DKD (AUC = 0.88 (95% CI 0.84-0.93)); calibration Chi-squared = 5.6, P = 0.78). At the optimal score cut-off, this model provided 86% sensitivity, 78% specificity, 30% positive predictive value and 98% negative predictive value for four-year risk of developing DKD. CONCLUSIONS: The combination of readily available clinical and laboratory features and the PromarkerD biomarkers (apoA4, CD5L, IGFBP3) proved an accurate prognostic test for future renal decline in an independent validation cohort of people with type 2 diabetes.

Analysis by proteomics reveals unique circulatory proteins in idiopathic pulmonary fibrosis.

BACKGROUND AND OBJECTIVE: Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic disease that has a poor 3-year median survival rate with unclear pathophysiology. Radiological features include bibasal, subpleural fibrosis and honeycombing while its pathology is characterized by fibroblastic foci and honeycombing. Proteomic analysis of circulating molecules in plasma may identify factors that characterize IPF and may assist in the diagnosis, prognostication and determination of pathogenic pathways in this condition. METHODS: Two independent quantitative proteomic techniques were used, isobaric tags for relative and absolute quantitation (iTRAQ) and multiple reaction monitoring (MRM), to identify differentially expressed plasma proteins in a group of IPF patients in comparison to healthy controls with normal lung function matched for age and gender. RESULTS: Five proteins were identified to be differentially expressed in IPF compared to healthy controls (upregulation of platelet basic protein and downregulation of actin, cytoplasmic 2, antithrombin-III, extracellular matrix protein-1 and fibronectin). CONCLUSION: This study further validates the combinational use of non-targeted discovery proteomics (iTRAQ) with targeted quantitation by mass spectrometry (MRM) of soluble biomarkers to identify potentially important molecules and pathways for pulmonary diseases such as IPF.

Identification of Novel Circulating Biomarkers Predicting Rapid Decline in Renal Function in Type 2 Diabetes: The Fremantle Diabetes Study Phase II.

OBJECTIVE: To assess the ability of plasma apolipoprotein (apo) A-IV (apoA4), apo C-III, CD5 antigen-like (CD5L), complement C1q subcomponent subunit B (C1QB), complement factor H-related protein 2, and insulin-like growth factor binding protein 3 (IBP3) to predict rapid decline in estimated glomerular filtration rate (eGFR) in type 2 diabetes. RESEARCH DESIGN AND METHODS: Mass spectrometry was used to measure baseline biomarkers in 345 community-based patients (mean age 67.0 years, 51.9% males) from the Fremantle Diabetes Study Phase II (FDS2). Multiple logistic regression was used to determine clinical predictors of rapid eGFR decline trajectory defined by semiparametric group-based modeling over a 4-year follow-up period. The incremental benefit of each biomarker was then assessed. Similar analyses were performed for a ≥30% eGFR fall, incident chronic kidney disease (eGFR <60 mL/min/1.73 m2), and eGFR decline of ≥5 mL/min/1.73 m2/year. RESULTS: Based on eGFR trajectory analysis, 35 participants (10.1%) were defined as "rapid decliners" (mean decrease 2.9 mL/min/1.73 m2/year). After adjustment for clinical predictors, apoA4, CD5L, and C1QB independently predicted rapid decline (odds ratio 2.40 [95% CI 1.24-4.61], 0.52 [0.29-0.93], and 2.41 [1.14-5.11], respectively) and improved model performance and fit (P < 0.001), discrimination (area under the curve 0.75-0.82, P = 0.039), and reclassification (net reclassification index 0.76 [0.63-0.89]; integrated discrimination improvement 6.3% [2.1-10.4%]). These biomarkers and IBP3 contributed to improved model performance in predicting other indices of rapid eGFR decline. CONCLUSIONS: The current study has identified novel plasma biomarkers (apoA4, CD5L, C1QB, and IBP3) that may improve the prediction of rapid decline in renal function independently of recognized clinical risk factors in type 2 diabetes.

Limiting the Hydrolysis and Oxidation of Maleimide-Peptide Adducts Improves Detection of Protein Thiol Oxidation.

Oxidative stress, caused by reactive oxygen and nitrogen species (RONS), is important in the pathophysiology of many diseases. A key target of RONS is the thiol group of protein cysteine residues. Because thiol oxidation can affect protein function, mechanistic information about how oxidative stress affects tissue function can be ascertained by identifying oxidized proteins. The probes used must be specific and sensitive, such as maleimides for the alkylation of reduced cysteine thiols. However, we find that maleimide-alkylated peptides (MAPs) are oxidized and hydrolyzed under sample preparation conditions common for proteomic studies. This can result in up to 90% of the MAP signal being converted to oxidized or hydrolyzed MAPs, decreasing the sensitivity of the analysis. A substantial portion of these modifications were accounted for by Coomassie "blue silver" staining (∼14%) of gels and proteolytic digestion buffers (∼20%). More than 40% of the MAP signal can be retained with the use of thioglycolic acid during gel electrophoresis, trichloroethanol-UV protein visualization in gels, and proteolytic digestion buffer of pH 7.0 TRIS. This work demonstrates that it is possible to decrease modifications to MAPs through changes to the sample preparation workflow, enhancing the potential usefulness of maleimide in identifying oxidized peptides.

Analysis of Reproducibility of Proteome Coverage and Quantitation Using Isobaric Mass Tags (iTRAQ and TMT).

This study aimed to compare the depth and reproducibility of total proteome and differentially expressed protein coverage in technical duplicates and triplicates using iTRAQ 4-plex, iTRAQ 8-plex, and TMT 6-plex reagents. The analysis was undertaken because comprehensive comparisons of isobaric mass tag reproducibility have not been widely reported in the literature. The highest number of proteins was identified with 4-plex, followed by 8-plex and then 6-plex reagents. Quantitative analyses revealed that more differentially expressed proteins were identified with 4-plex reagents than 8-plex reagents and 6-plex reagents. Replicate reproducibility was determined to be ≥69% for technical duplicates and ≥57% for technical triplicates. The results indicate that running an 8-plex or 6-plex experiment instead of a 4-plex experiment resulted in 26 or 39% fewer protein identifications, respectively. When 4-plex spectra were searched with three software tools-ProteinPilot, Mascot, and Proteome Discoverer-the highest number of protein identifications were obtained with Mascot. The analysis of negative controls demonstrated the importance of running experiments as replicates. Overall, this study demonstrates the advantages of using iTRAQ 4-plex reagents over iTRAQ 8-plex and TMT 6-plex reagents, provides estimates of technical duplicate and triplicate reproducibility, and emphasizes the value of running replicate samples.

Comprehensive mass spectrometry based biomarker discovery and validation platform as applied to diabetic kidney disease.

A protein biomarker discovery workflow was applied to plasma samples from patients at different stages of diabetic kidney disease. The proteomics platform produced a panel of significant plasma biomarkers that were statistically scrutinised against the current gold standard tests on an analysis of 572 patients. Five proteins were significantly associated with diabetic kidney disease defined by albuminuria, renal impairment (eGFR) and chronic kidney disease staging (CKD Stage ≥1, ROC curve of 0.77). The results prove the suitability and efficacy of the process used, and introduce a biomarker panel with the potential to improve diagnosis of diabetic kidney disease.

Classification of fish samples via an integrated proteomics and bioinformatics approach.

There is an increasing demand to develop cost-effective and accurate approaches to analyzing biological tissue samples. This is especially relevant in the fishing industry where closely related fish samples can be mislabeled, and the high market value of certain fish leads to the use of alternative species as substitutes, for example, Barramundi and Nile Perch (belonging to the same genus, Lates). There is a need to combine selective proteomic datasets with sophisticated computational analysis to devise a robust classification approach. This paper describes an integrated MS-based proteomics and bioinformatics approach to classifying a range of fish samples. A classifier is developed using training data that successfully discriminates between Barramundi and Nile Perch samples using a selected protein subset of the proteome. Additionally, the classifier is shown to successfully discriminate between test samples not used to develop the classifier, including samples that have been cooked, and to classify other fish species as neither Barramundi nor Nile Perch. This approach has applications to truth in labeling for fishmongers and restaurants, monitoring fish catches, and for scientific research into distances between species.