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We have developed a new method for the identification of bacterial species causing Urinary Tract Infections. The first training step used DIA analysis on multiple replicates of bacterial inoculates to define a peptide signature by machine learning classifiers. In a second identification step, the signature is monitored by targeted proteomics on unknown samples. This fast, culture-free and accurate method paves the way of the development of new diagnostic approaches limiting the emergence of antimicrobial resistances., Graphical Abstract Highlights Fast and culture-free method for the identification of the 15 bacterial species causing UTIs. Combination of DIA analysis and machine learning algorithms to define a peptide signature. High accuracy, good linearity and reproducibility, sensitivity below standard threshold. Transferability to other laboratories and other mass spectrometers., Fast identification of microbial species in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials leading to antibioresistances. MALDI-TOF-MS technology has become a tool of choice for microbial identification but has several drawbacks: it requires a long step of bacterial culture before analysis (≥24 h), has a low specificity and is not quantitative. We developed a new strategy for identifying bacterial species in urine using specific LC-MS/MS peptidic signatures. In the first training step, libraries of peptides are obtained on pure bacterial colonies in DDA mode, their detection in urine is then verified in DIA mode, followed by the use of machine learning classifiers (NaiveBayes, BayesNet and Hoeffding tree) to define a peptidic signature to distinguish each bacterial species from the others. Then, in the second step, this signature is monitored in unknown urine samples using targeted proteomics. This method, allowing bacterial identification in less than 4 h, has been applied to fifteen species representing 84% of all Urinary Tract Infections. More than 31,000 peptides in 190 samples were quantified by DIA and classified by machine learning to determine an 82 peptides signature and build a prediction model. This signature was validated for its use in routine using Parallel Reaction Monitoring on two different instruments. Linearity and reproducibility of the method were demonstrated as well as its accuracy on donor specimens. Within 4h and without bacterial culture, our method was able to predict the predominant bacteria infecting a sample in 97% of cases and 100% above the standard threshold. This work demonstrates the efficiency of our method for the rapid and specific identification of the bacterial species causing UTI and could be extended in the future to other biological specimens and to bacteria having specific virulence or resistance factors.

Exercise limitation comes from a close interaction between cardiovascular and skeletal muscle impairments. To better understand the implication of possible peripheral oxidative metabolism dysfunction, we studied the proteomic signature of skeletal muscle in pulmonary arterial hypertension (PAH). Eight idiopathic PAH patients and eight matched healthy sedentary subjects were evaluated for exercise capacity, skeletal muscle proteomic profile, metabolism, and mitochondrial function. Skeletal muscle proteins were extracted, and fractioned peptides were tagged using an iTRAQ protocol. Proteomic analyses have documented a total of 9 downregulated proteins in PAH skeletal muscles and 10 upregulated proteins compared to healthy subjects. Most of the downregulated proteins were related to mitochondrial structure and function. Focusing on skeletal muscle metabolism and mitochondrial health, PAH patients presented a decreased expression of oxidative enzymes (pyruvate dehydrogenase, p

Male factors account for 40% of infertility cases. The identification of differentially expressed proteins on spermatozoa from fertile and infertile men can help in the elucidation of the molecular basis of male infertility. The aim of this study was to compare sperm proteomes from 3 different groups: fertile men, normozoospermic men consulting for infertility, and normozoospermic men with an impaired capacity for fertilization (IVF-failure). We used differential proteomics with isobaric tags for relative and absolute quantitation (iTRAQ) labeling, and LC-MS analysis to identify proteins that are differentially expressed. A total of 348 unique proteins were identified and quantified. The analysis identified 33 proteins that were differentially expressed in the IVF-failure group vs the fertile group. Comparison of the infertile and fertile groups revealed that 18 proteins appeared to be differentially expressed. Four proteins were similarly altered in the IVF-failure and infertile groups: semenogelin 1 (SEMG1), prolactin-induced protein (PIP), glyceraldehyde-3-phosphate dehydrogenase (GAPDHS), and phosphoglycerate kinase 2 (PGK2). These protein markers were selected for validation using multiple reactions monitoring mass spectrometry (MRM-MS) and further confirmed by Western blot analysis. Overall, these results suggest that a panel of proteins may be used as biomarkers for future studies of infertility.

Epithelial ovarian cancer (EOC) is a disease responsible for more deaths among women in the Western world than all other gynecologic malignancies. There is urgent need for new therapeutic targets and a better understanding of EOC initiation and progression. We have previously identified the polypeptide N-acetylgalactosaminyltransferase 3 ( GALNT3 ) gene, a member of the GalNAc-transferases (GalNAc-Ts) gene family, as hypomethylated and overexpressed in high-grade serous EOC tumors, compared to low malignant potential EOC tumors and normal ovarian tissues. This data also suggested for a role of GALNT3 in aberrant EOC glycosylation, possibly implicated in disease progression. To evaluate differential glycosylation in EOC caused by modulations in GALNT3 expression, we used a metabolic labeling strategy for enrichment and mass spectrometry-based characterization of glycoproteins following GALNT3 gene knockdown (KD) in A2780s EOC cells. A total of 589 differentially expressed glycoproteins were identified upon GALNT3 KD. Most identified proteins were involved in mechanisms of cellular metabolic functions, post-translational modifications, and some have been reported to be implicated in EOC etiology. The GALNT3 -dependent glycoproteins identified by this metabolic labeling approach support the oncogenic role of GALNT3 in EOC dissemination and may be pursued as novel EOC biomarkers and/or therapeutic targets. Biological significance Knowledge of the O-glycoproteome has been relatively elusive, and the functions of the individual polypeptide GalNAc-Ts have been poorly characterized. Alterations in GalNAc-Ts expression were shown to provide huge variability in the O-glycoproteome in various pathologies, including cancer. The application of a chemical biology approach for the metabolic labeling and subsequent characterization of O-glycoproteins in EOC using the Ac 4 GalNAz metabolite has provided a strategy allowing for proteomic discovery of GalNAc-Ts specific functions. Our study supports an essential role of one of the GalNAc-Ts — GALNT3, in EOC dissemination, including its implication in modulating PTMs and EOC metabolism. Our approach validates the use of the applied metabolic strategy to identify important functions of GalNAc-Ts in normal and pathological conditions.

Summary: rTANDEM is an R/Bioconductor package that interfaces the X!Tandem protein identification algorithm. The package can run the multi-threaded algorithm on proteomic data files directly from R. It also provides functions to convert search parameters and results to/from R as well as functions to manipulate parameters and automate searches. An associated R package, shinyTANDEM, provides a web-based graphical interface to visualize and interpret the results. Together, those two packages form an entry point for a general MS/MS-based proteomic pipeline in R/Bioconductor. Availability and implementation: rTANDEM and shinyTANDEM are distributed in R/Bioconductor, http://bioconductor.org/packages/release/bioc/ . The packages are under open licenses (GPL-3 and Artistice-1.0). Contact: frederic.fournier@crchuq.ulaval.ca or arnaud.droit@crchuq.ulaval.ca Supplementary information: Supplementary data are available at Bioinformatics online.

In the last 10 years, several forms of two-dimensional infrared (2DIR) spectroscopy have been developed, such as IR pump−probe spectroscopy and photon-echo techniques. In this Account, we describe a doubly vibrationally enhanced four-wave mixing method, in which a third-order nonlinear signal is generated from the interaction of two independently tunable IR beams and an electron-polarizing visible beam at 790 nm. When the IR beams are independently in resonance with coupled vibrational transitions, the signal is enhanced and cross-peaks appear in the spectrum. This method is known as either DOVE (doubly vibrationally enhanced) four-wave mixing or EVV (electron−vibration−vibration) 2DIR spectroscopy. We begin by discussing the basis and properties of EVV 2DIR. We then discuss several biological and potential biomedical applications. These include protein identification and quantification, as well as the potential of this label-free spectroscopy for protein and peptide structural analysis. In proteomics, we...

In the last 10 years, several forms of two-dimensional infrared (2DIR) spectroscopy have been developed, such as IR pump-probe spectroscopy and photon-echo techniques. In this Account, we describe a doubly vibrationally enhanced four-wave mixing method, in which a third-order nonlinear signal is generated from the interaction of two independently tunable IR beams and an electron-polarizing visible beam at 790 nm. When the IR beams are independently in resonance with coupled vibrational transitions, the signal is enhanced and cross-peaks appear in the spectrum. This method is known as either DOVE (doubly vibrationally enhanced) four-wave mixing or EVV (electron-vibration-vibration) 2DIR spectroscopy. We begin by discussing the basis and properties of EVV 2DIR. We then discuss several biological and potential biomedical applications. These include protein identification and quantification, as well as the potential of this label-free spectroscopy for protein and peptide structural analysis. In proteomics, we also show how post-translational modifications in peptides (tyrosine phosphorylation) can be detected by EVV 2DIR spectroscopy. The feasibility of EVV 2DIR spectroscopy for tissue imaging is also evaluated. Preliminary results were obtained on a mouse kidney histological section that was stained with hematoxylin (a small organic molecule). We obtained images by setting the IR frequencies to a specific cross-peak (the strongest for hematoxylin was obtained from its analysis in isolation; a general CH(3) cross-peak for proteins was also used) and then spatially mapping as a function of the beam position relative to the sample. Protein and hematoxylin distribution in the tissue were measured and show differential contrast, which can be entirely explained by the different tissue structures and their functions. The possibility of triply resonant EVV 2DIR spectroscopy was investigated on the retinal chromophore at the centre of the photosynthetic protein bacteriorhodopsin (bR). By putting the visible third beam in resonance with an electronic transition, we were able to enhance the signal and increase the sensitivity of the method by several orders of magnitude. This increase in sensitivity is of great importance for biological applications, in which the number of proteins, metabolites, or drug molecules to be detected is low (typically pico- to femtomoles). Finally, we present theoretical investigations for using EVV 2DIR spectroscopy as a structural analysis tool for inter- and intramolecular interaction geometries.

Electron-vibration-vibration two-dimensional coherent spectroscopy, a variant of 2DIR, is shown to be a useful tool to differentiate a set of 10 proteins based on their amino acid content. Two-dimensional vibrational signatures of amino acid side chains are identified and the corresponding signal strengths used to quantify their levels by using a methyl vibrational feature as an internal reference. With the current apparatus, effective differentiation can be achieved in four to five minutes per protein, and our results suggest that this can be reduced to

Insulin resistance (IR) is associated with increased production of triglyceride-rich lipoproteins of intestinal origin. In order to assess whether insulin resistance affects the proteins involved in lipid metabolism, we used two mass spectrometry based quantitative proteomics techniques to compare the intestinal proteome of 14 IR patients to that of 15 insulin sensitive (IS) control patients matched for age and waist circumference. A total of 3886 proteins were identified by the iTRAQ (Isobaric Tags for Relative and Absolute Quantitation) mass spectrometry approach and 2290 by the SWATH-MS strategy (Serial Window Acquisition of Theoretical Spectra). Using these two methods, 208 common proteins were identified with a confidence corresponding to FDR < 1%, and quantified with p-value < 0.05. The quantification of those 208 proteins has a Pearson correlation coefficient (r2) of 0.728 across the two techniques. Gene Ontology analyses of the differentially expressed proteins revealed that annotations related to lipid metabolic process and oxidation reduction process are overly represented in the set of under-expressed proteins in IR subjects. Furthermore, both methods quantified proteins of relevance to IR. These data also showed that SWATH-MS is a promising and compelling alternative to iTRAQ for protein quantitation of complex mixtures.