Deciphering Multifactorial Resistance Phenotypes in Acinetobacter baumannii by Genomics and Targeted Label-free Proteomics.

Resistance to ß-lactams in Acinetobacter baumannii involves various mechanisms. To decipher them, whole genome sequencing (WGS) and real-time quantitative polymerase chain reaction (RT-qPCR) were complemented by mass spectrometry (MS) in selected reaction monitoring mode (SRM) in 39 clinical isolates. The targeted label-free proteomic approach enabled, in one hour and using a single method, the quantitative detection of 16 proteins associated with antibiotic resistance: eight acquired ß-lactamases (i.e. GES, NDM-1, OXA-23, OXA-24, OXA-58, PER, TEM-1, and VEB), two resident ß-lactamases (i.e. ADC and OXA-51-like) and six components of the two major efflux systems (i.e. AdeABC and AdeIJK). Results were normalized using "bacterial quantotypic peptides," i.e. peptide markers of the bacterial quantity, to obtain precise protein quantitation (on average 8.93% coefficient of variation for three biological replicates). This allowed to correlate the levels of resistance to ß-lactam with those of the production of acquired as well as resident ß-lactamases or of efflux systems. SRM detected enhanced ADC or OXA-51-like production and absence or increased efflux pump production. Precise protein quantitation was particularly valuable to detect resistance mechanisms mediated by regulated genes or by overexpression of chromosomal genes. Combination of WGS and MS, two orthogonal and complementary techniques, allows thereby interpretation of the resistance phenotypes at the molecular level.

Variance component analysis to assess protein quantification in biomarker validation: application to selected reaction monitoring-mass spectrometry.

BACKGROUND: In the field of biomarker validation with mass spectrometry, controlling the technical variability is a critical issue. In selected reaction monitoring (SRM) measurements, this issue provides the opportunity of using variance component analysis to distinguish various sources of variability. However, in case of unbalanced data (unequal number of observations in all factor combinations), the classical methods cannot correctly estimate the various sources of variability, particularly in presence of interaction. The present paper proposes an extension of the variance component analysis to estimate the various components of the variance, including an interaction component in case of unbalanced data. RESULTS: We applied an experimental design that uses a serial dilution to generate known relative protein concentrations and estimated these concentrations by two processing algorithms, a classical and a more recent one. The extended method allowed estimating the variances explained by the dilution and the technical process by each algorithm in an experiment with 9 proteins: L-FABP, 14.3.3 sigma, Calgi, Def.A6, Villin, Calmo, I-FABP, Peroxi-5, and S100A14. Whereas, the recent algorithm gave a higher dilution variance and a lower technical variance than the classical one in two proteins with three peptides (L-FABP and Villin), there were no significant difference between the two algorithms on all proteins. CONCLUSIONS: The extension of the variance component analysis was able to estimate correctly the variance components of protein concentration measurement in case of unbalanced design.

Impact of Serum and Plasma Matrices on the Titration of Human Inflammatory Biomarkers Using Analytically Validated SRM Assays.

Protein biomarker discovery has inherent challenges linked to the validation of the analytical method used or to the impact of biological matrices. Matrix influences must be mastered to guarantee the reliability of the identified biomarkers to monitor human diseases. In this study, multiplexed mass spectrometry assays in selected reaction monitoring (SRM) mode have been developed to measure 107 inflammatory putative proteins in matched serum and plasma from 36 ICU trauma patients. The assays' validation directly in clinical samples was shown to be valuable to manage intersample variability. Using the validation process developed here, assays were validated for 58 biomarkers in serum, 57 in plasma, and 55 in both matrices. Correlation analyses demonstrated that the quantitation using SRM of most of the validated biomarkers (45/55) was impacted by the biological matrix and that the matrix impact was biomarker-dependent. Among the 45 impacted biomarkers, 23 were nevertheless correlated between serum and plasma, whereas the quantitation was shown to be equivalent in both for the 10 last proteins. Matrix selection using SRM is therefore suggested to be suitable prior to clinical evaluation of biomarkers in a large cohort of patients.

Rapid Bacterial Identification, Resistance, Virulence and Type Profiling using Selected Reaction Monitoring Mass Spectrometry.

Mass spectrometry (MS) in Selected Reaction Monitoring (SRM) mode is proposed for in-depth characterisation of microorganisms in a multiplexed analysis. Within 60-80 minutes, the SRM method performs microbial identification (I), antibiotic-resistance detection (R), virulence assessment (V) and it provides epidemiological typing information (T). This SRM application is illustrated by the analysis of the human pathogen Staphylococcus aureus, demonstrating its promise for rapid characterisation of bacteria from positive blood cultures of sepsis patients.

Identification and verification of heat shock protein 60 as a potential serum marker for colorectal cancer.

Colorectal cancer (CRC) is a major public health issue worldwide, and novel tumor markers may contribute to its efficient management by helping in early detection, prognosis or surveillance of disease. The aim of our study was to identify new serum biomarkers for CRC, and we followed a phased biomarker discovery and validation process to obtain an accurate preliminary assessment of potential clinical utility. We compared colonic tumors and matched normal tissue from 15 CRC patients, using two-dimensional difference gel electrophoresis (2D-DIGE), and identified 17 proteins that had significant differential expression. These results were further confirmed by western blotting for heat shock protein (HSP) 60, glutathione-S-transferase Pi, α-enolase, T-complex protein 1 subunit ß, and leukocyte elastase inhibitor, and by immunohistochemistry for HSP60. Using mAbs raised against HSP60, we developed a reliable (precision of 5-15%) and sensitive (0.3 ng·mL(-1)) immunoassay for the detection of HSP60 in serum. Elevated levels of HSP60 were found in serum from CRC patients in two independent cohorts; the receiver-operating characteristic curve obtained in 112 patients with CRC and 90 healthy controls had an area under the curve (AUC) of 0.70, which was identical to the AUC of carcinoembryonic antigen. Combination of serum markers improved clinical performance: the AUC of a three-marker logistic regression model combining HSP60, carcinoembryonic antigen and carbohydrate antigen 19-9 reached 0.77. Serum HSP60 appeared to be more specific for late-stage CRC; therefore, future studies should evaluate its utility for determining prognosis or monitoring therapy rather than early detection.

Analysis of high molecular mass proteins larger than 150 kDa using cyanogen bromide cleavage and conventional 2-DE.

Proteomic approaches including high-resolution 2-DE are providing the tools needed to discover disease-associated biomarkers in complex biological samples. Although 2-DE is an extremely powerful approach to analyze the proteome, the separation of proteins with extreme molecular masses still remains an issue requiring improvement. Because high molecular mass (HMM) proteins larger than 150 kDa have already been observed to be differentially expressed in several pathologies such as cancer, we developed an original strategy to analyze this part of the proteome that is not easily separated by 2-DE in polyacrylamide gels. This strategy is based on the 2-DE separation of cyanogen bromide (CNBr) fragments of purified HMM protein fractions, and combines techniques including SEC fractionation, TCA precipitation, CNBr cleavage, 2-DE and MS analysis. The method was first tested on a model protein, the BSA. Preliminary results obtained using colonic tissues led to the identification of six HMM proteins with M(r) comprised between 163 and 533 kDa in their reduced state. These results demonstrated that our CNBr/2-DE approach should provide a powerful tool for identification of new biomarkers larger than 150 kDa.