Machine learning applications in proteomics research: How the past can boost the future

Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis.acceptedVersio

Triggering secondary metabolite biosynthesis: exploring the effects of ionic liquids in fungal metabolism

Filamentous fungi are able to synthesise an array of small molecules (secondary metabolites), which are usually not essential for fungal growth but confer competitiveness. As a consequence, numerous secondary metabolites remain cryptic at the artificial conditions of cultivation in a research laboratory. Even in Aspergillus nidulans, one of the most well studied fungi, numerous metabolites remain unseen. Several strategies have been used to solve this knowledge gap, some of which require prior knowledge of genomic sequences, relying on manipulation of targeted genes encoding components of either secondary metabolism or regulatory pathways. Other approaches may be applied also in less well characterised strains, such as cultivation with other species/organisms or modification of the growth media composition. (...

Needles in a haystack of protein diversity: Interrogation of complex biological samples through specialized strategies in bottom-up proteomics uncover peptides of interest for diverse applications

Peptide identification is at the core of bottom-up proteomics measurements. However, even with state-of the-art mass spectrometric instrumentation, peptide level information is still lost or missing in these types of experiments. Reasons behind missing peptide identifications in bottom-up proteomics include variable peptide ionization efficiencies, ion suppression effects, as well as the occurrence of chimeric spectra that can lower the efficacy of database search strategies. Peptides derived from naturally abundant proteins in a biological system also have better chances of being identified in comparison to the ones produced from less abundant proteins, at least in regular discovery-based proteomics experiments. This dissertation focused on the recovery of the “missing or hidden proteome” information in complex biological matrices by approaching this challenge under a peptide-centric view and implementing different liquid chromatography tandem mass spectrometry (LC-MS/MS) experimental workflows. In particular, the projects presented here covered: (1) The feasibility of applying a liquid chromatography-multiple reaction monitoring MS methodology for the targeted identification of peptides serving as surrogates of protein biomarkers in environmental matrices with unknown microbial diversities; (2) the evaluation of selecting unique tryptic peptides in-silico that can distinguish groups of proteins, instead of individual proteins, for targeted proteomics workflows; (3) maximizing peptide identification in spectral data collected from different LC-MS/MS setups by applying a multi-peptide-spectrum-match algorithm, and (4) showing that LC-MS/MS combined with de novo assisted-database searches is a feasible strategy for the comprehensive identification of peptides derived from native proteolytic mechanisms in biological systems

p63 isoforms regulate metabolism of cancer stem cells

p63 is an important regulator of epithelial development expressed in different variants containing (TA) or lacking (\u394N) the N-terminal transactivation domain. The different isoforms regulate stem-cell renewal and differentiation as well as cell senescence. Several studies indicate that p63 isoforms also play a role in cancer development; however, very little is known about the role played by p63 in regulating the cancer stem phenotype. Here we investigate the cellular signals regulated by TAp63 and \u394Np63 in a model of epithelial cancer stem cells. To this end, we used colon cancer stem cells, overexpressing either TAp63 or \u394Np63 isoforms, to carry out a proteomic study by chemical-labeling approach coupled to network analysis. Our results indicate that p63 is implicated in a wide range of biological processes, including metabolism. This was further investigated by a targeted strategy at both protein and metabolite levels. The overall data show that TAp63 overexpressing cells are more glycolytic-active than \u394Np63 cells, indicating that the two isoforms may regulate the key steps of glycolysis in an opposite manner. The mass-spectrometry proteomics data of the study have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange.org) via the PRIDE partner repository with data set identifiers PXD000769 and PXD000768

Power and limitations of electrophoretic separations in proteomics strategies

Proteomics can be defined as the large-scale analysis of proteins. Due to the complexity of biological systems, it is required to concatenate various separation techniques prior to mass spectrometry. These techniques, dealing with proteins or peptides, can rely on chromatography or electrophoresis. In this review, the electrophoretic techniques are under scrutiny. Their principles are recalled, and their applications for peptide and protein separations are presented and critically discussed. In addition, the features that are specific to gel electrophoresis and that interplay with mass spectrometry (i.e., protein detection after electrophoresis, and the process leading from a gel piece to a solution of peptides) are also discussed

Elucidate the biosy nthesis and the functional role of a new class of antimicrobial peptaibiotics in Neurospora crassa

"Fungi are generally abundant producers of secondary metabolites. In particular, these organisms are involved in the production of a subclass of nonribosomal peptides - peptaibiotics - which display interesting antibiotic activities. Previous studies demonstrated that some fungal strains are able to grow in media supplemented with ionic liquid and this supplementation leads to alterations in their metabolic footprint. Moreover, in Neurospora crassa, growth media supplementation with either 1-ethyl-3-methylimidazolium chloride or cholinium chloride, led to increased levels of 1-aminocyclopropane-1-carboxylate deaminase, an enzyme involved in the production of the rare amino acid 1-aminocyclopropane-1-carboxylic acid (ACC).(...)"N/

Ethanol Induced Disordering of Pancreatic Acinar Cell Endoplasmic Reticulum: An ER Stress/Defective Unfolded Protein Response Model.

Background & aimsHeavy alcohol drinking is associated with pancreatitis, whereas moderate intake lowers the risk. Mice fed ethanol long term show no pancreas damage unless adaptive/protective responses mediating proteostasis are disrupted. Pancreatic acini synthesize digestive enzymes (largely serine hydrolases) in the endoplasmic reticulum (ER), where perturbations (eg, alcohol consumption) activate adaptive unfolded protein responses orchestrated by spliced X-box binding protein 1 (XBP1). Here, we examined ethanol-induced early structural changes in pancreatic ER proteins.MethodsWild-type and Xbp1+/- mice were fed control and ethanol diets, then tissues were homogenized and fractionated. ER proteins were labeled with a cysteine-reactive probe, isotope-coded affinity tag to obtain a novel pancreatic redox ER proteome. Specific labeling of active serine hydrolases in ER with fluorophosphonate desthiobiotin also was characterized proteomically. Protein structural perturbation by redox changes was evaluated further in molecular dynamic simulations.ResultsEthanol feeding and Xbp1 genetic inhibition altered ER redox balance and destabilized key proteins. Proteomic data and molecular dynamic simulations of Carboxyl ester lipase (Cel), a unique serine hydrolase active within ER, showed an uncoupled disulfide bond involving Cel Cys266, Cel dimerization, ER retention, and complex formation in ethanol-fed, XBP1-deficient mice.ConclusionsResults documented in ethanol-fed mice lacking sufficient spliced XBP1 illustrate consequences of ER stress extended by preventing unfolded protein response from fully restoring pancreatic acinar cell proteostasis during ethanol-induced redox challenge. In this model, orderly protein folding and transport to the secretory pathway were disrupted, and abundant molecules including Cel with perturbed structures were retained in ER, promoting ER stress-related pancreas pathology

MRM screening/biomarker discovery with linear ion trap MS: a library of human cancer-specific peptides

<p>Abstract</p> <p>Background</p> <p>The discovery of novel protein biomarkers is essential in the clinical setting to enable early disease diagnosis and increase survivability rates. To facilitate differential expression analysis and biomarker discovery, a variety of tandem mass spectrometry (MS/MS)-based protein profiling techniques have been developed. For achieving sensitive detection and accurate quantitation, targeted MS screening approaches, such as multiple reaction monitoring (MRM), have been implemented.</p> <p>Methods</p> <p>MCF-7 breast cancer protein cellular extracts were analyzed by 2D-strong cation exchange (SCX)/reversed phase liquid chromatography (RPLC) separations interfaced to linear ion trap MS detection. MS data were interpreted with the Sequest-based Bioworks software (Thermo Electron). In-house developed Perl-scripts were used to calculate the spectral counts and the representative fragment ions for each peptide.</p> <p>Results</p> <p>In this work, we report on the generation of a library of 9,677 peptides (p < 0.001), representing ~1,572 proteins from human breast cancer cells, that can be used for MRM/MS-based biomarker screening studies. For each protein, the library provides the number and sequence of detectable peptides, the charge state, the spectral count, the molecular weight, the parameters that characterize the quality of the tandem mass spectrum (p-value, DeltaM, Xcorr, DeltaCn, Sp, no. of matching <b><it>a</it></b>, <b><it>b</it></b>, <b><it>y </it></b>ions in the spectrum), the retention time, and the top 10 most intense product ions that correspond to a given peptide. Only proteins identified by at least two spectral counts are listed. The experimental distribution of protein frequencies, as a function of molecular weight, closely matched the theoretical distribution of proteins in the human proteome, as provided in the SwissProt database. The amino acid sequence coverage of the identified proteins ranged from 0.04% to 98.3%. The highest-abundance proteins in the cellular extract had a molecular weight (MW)<50,000.</p> <p>Conclusion</p> <p>Preliminary experiments have demonstrated that putative biomarkers, that are not detectable by conventional data dependent MS acquisition methods in complex un-fractionated samples, can be reliable identified with the information provided in this library. Based on the spectral count, the quality of a tandem mass spectrum and the m/z values for a parent peptide and its most abundant daughter ions, MRM conditions can be selected to enable the detection of target peptides and proteins.</p

Plasma proteomic profiles from disease-discordant monozygotic twins suggest that molecular pathways are shared in multiple systemic autoimmune diseases*

Introduction: Although systemic autoimmune diseases (SAID) share many clinical and laboratory features, whether they also share some common features of pathogenesis remains unclear. We assessed plasma proteomic profiles among different SAID for evidence of common molecular pathways that could provide insights into pathogenic mechanisms shared by these diseases. Methods: Differential quantitative proteomic analyses (one-dimensional reverse-phase liquid chromatography-mass spectrometry) were performed to assess patterns of plasma protein expression. Monozygotic twins (four pairs discordant for systemic lupus erythematosus, four pairs discordant for juvenile idiopathic arthritis and two pairs discordant for juvenile dermatomyositis) were studied to minimize polymorphic gene effects. Comparisons were also made to 10 unrelated, matched controls. Results: Multiple plasma proteins, including acute phase reactants, structural proteins, immune response proteins, coagulation and transcriptional factors, were differentially expressed similarly among the different SAID studied. Multivariate Random Forest modeling identified seven proteins whose combined altered expression levels effectively segregated affected vs. unaffected twins. Among these seven proteins, four were also identified in univariate analyses of proteomic data (syntaxin 17, a-glucosidase, paraoxonase 1, and the sixth component of complement). Molecular pathway modeling indicated that these factors may be integrated through interactions with a candidate plasma biomarker, PON1 and the pro-inflammatory cytokine IL-6. Conclusions: Together, these data suggest that different SAID may share common alterations of plasma protein expression and molecular pathways. An understanding of the mechanisms leading to the altered plasma proteomes common among these SAID may provide useful insights into their pathogeneses

Bioinformatic analysis of proteomics data

Most biochemical reactions in a cell are regulated by highly specialized proteins, which are the prime mediators of the cellular phenotype. Therefore the identification, quantitation and characterization of all proteins in a cell are of utmost importance to understand the molecular processes that mediate cellular physiology. With the advent of robust and reliable mass spectrometers that are able to analyze complex protein mixtures within a reasonable timeframe, the systematic analysis of all proteins in a cell becomes feasible. Besides the ongoing improvements of analytical hardware, standardized methods to analyze and study all proteins have to be developed that allow the generation of testable new hypothesis based on the enormous pre-existing amount of biological information. Here we discuss current strategies on how to gather, filter and analyze proteomic data sates using available software packages