Pitfalls and Solutions in Mass Spectrometry-Based Identification of Protein Glycation.

Emerging evidence suggests that advanced glycation end-products (AGEs) such as Nε-(carboxymethyl)lysine (CML) and Nε-(carboxymethyl)lysine (CEL) may play important roles in certain human diseases. Reliable analytical methods are needed for their characterizations and measurements. Pitfalls have been reported for applications of LC-MS/MS to identify various types of post-translational modifications, but not yet for the case of AGEs. Here, we showed that in the absence of manual inspection, cysteine alkylation with 2-iodoacetamide (IAA) can result in false-positive/ambiguous identifications of CML >20%. They were attributed to offsite alkylation together with incorrect monoisotopic peak assignment (pitfall 1) or together with deamidation (pitfall 2). For pitfall 1, false-positive identifications can be alleviated using a peptide mass error tolerance ≤5 ppm during the database search. Pitfall 2 results in ambiguous modification assignments, which may be overcome by using other alkylation reagents. According to calculations of theoretical mass shifts, the use of other common alkylation reagents (iodoacetic acid, 2-chloroacetamide, and acrylamide) should face similar pitfalls. The use of acrylamide can result in false-positive identifications of CEL instead of CML. Subsequently, we showed that compared to IAA, the use of N-isopropylacrylamide (NIPAM) as an alkylation reagent achieved similar levels of proteome coverage, while reducing the offsite alkylation reactions at lysine by more than five times. Furthermore, false-positive/ambiguous identifications of CML due to the two types of pitfalls were absent when using NIPAM. NIPAM alkylation results in a unique mass shift that allows reliable identifications of CML and most likely other AGEs, such as CEL.

A Small RNA Transforms the Multidrug Resistance of Pseudomonas aeruginosa to Drug Susceptibility.

Bacteria with multiple drug resistance (MDR) have become a global issue worldwide, and hundreds of thousands of people's lives are threatened every year. The emergence of novel MDR strains and insufficient development of new antimicrobial agents are the major reasons that limit the choice of antibiotics for the treatment of bacterial infection. Thus, preserving the clinical value of current antibiotics could be one of the effective approaches to resolve this problem. Here we identified numerous novel small RNAs that were downregulated in the MDR clinical isolates of Pseudomonas aeruginosa (P. aeru), and we demonstrated that overexpression of one of these small RNAs (sRNAs), AS1974, was able to transform the MDR clinical strain to drug hypersusceptibility. AS1974 is the master regulator to moderate the expression of several drug resistance pathways, including membrane transporters and biofilm-associated antibiotic-resistant genes, and its expression is regulated by the methylation sites located at the 5' UTR of the gene. Our findings unravel the sRNA that regulates the MDR pathways in clinical isolates of P. aeru. Moreover, transforming bacterial drug resistance to hypersusceptibility using sRNA could be the potential approach for tackling MDR bacteria in the future.

Host-response biomarkers for diagnosis of late-onset septicemia and necrotizing enterocolitis in preterm infants.

Preterm infants are highly susceptible to life-threatening infections that are clinically difficult to detect, such as late-onset septicemia and necrotizing enterocolitis (NEC). Here, we used a proteomic approach to identify biomarkers for diagnosis of these devastating conditions. In a case-control study comprising 77 sepsis/NEC and 77 nonsepsis cases (10 in each group being monitored longitudinally), plasma samples collected at clinical presentation were assessed in the biomarker discovery and independent validation phases. We validated the discovered biomarkers in a prospective cohort study with 104 consecutively suspected sepsis/NEC episodes. Proapolipoprotein CII (Pro-apoC2) and a des-arginine variant of serum amyloid A (SAA) were identified as the most promising biomarkers. The ApoSAA score computed from plasma apoC2 and SAA concentrations was effective in identifying sepsis/NEC cases in the case-control and cohort studies. Stratification of infants into different risk categories by the ApoSAA score enabled neonatologists to withhold treatment in 45% and enact early stoppage of antibiotics in 16% of nonsepsis infants. The negative predictive value of this antibiotic policy was 100%. The ApoSAA score could potentially allow early and accurate diagnosis of sepsis/NEC. Upon confirmation by further multicenter trials, the score would facilitate rational prescription of antibiotics and target infants who require urgent treatment.

Prediction of liver fibrosis and cirrhosis in chronic hepatitis B infection by serum proteomic fingerprinting: a pilot study.

BACKGROUND: Most noninvasive predictive models of liver fibrosis are complicated and have suboptimal sensitivity. This study was designed to identify serum proteomic signatures associated with liver fibrosis and to develop a proteome-based fingerprinting model for prediction of liver fibrosis. METHODS: Serum proteins from 46 patients with chronic hepatitis B (CHB) were profiled quantitatively on surface-enhanced laser desorption/ionization (SELDI) ProteinChip arrays. The identified liver fibrosis-associated proteomic fingerprint was used to construct an artificial neural network (ANN) model that produced a fibrosis index with a range of 0-6. The clinical value of this index was evaluated by leave-one-out cross-validation. RESULTS: Thirty SELDI proteomic features were significantly associated with the degree of fibrosis. Cross-validation showed that the ANN fibrosis indices derived from the proteomic fingerprint strongly correlated with Ishak scores (r = 0.831) and were significantly different among stages of fibrosis. ROC curve areas in predicting significant fibrosis (Ishak score >or=3) and cirrhosis (Ishak score >or=5) were 0.906 and 0.921, respectively. At 89% specificity, the sensitivity of the ANN fibrosis index in predicting fibrosis was 89%. The sensitivity for prediction increased with degree of fibrosis, achieving 100% for patients with Ishak scores >4. The accuracy for prediction of cirrhosis was also 89%. Inclusion of International Normalized Ratio, total protein, bilirubin, alanine transaminase, and hemoglobin in the ANN model improved the predictive power, giving accuracies >90% for the prediction of fibrosis and cirrhosis. CONCLUSIONS: A unique serum proteomic fingerprint is present in the sera of patients with fibrosis. An ANN fibrosis index derived from this fingerprint could differentiate between different stages of fibrosis and predict fibrosis and cirrhosis in CHB infection.