Alexey Chernobrovkin

DeMix Workflow for Efficient Identification of Cofragmented Peptides in High Resolution Data-dependent Tandem Mass Spectrometry

Based on conventional data-dependent acquisition strategy of shotgun proteomics, we present a new workflow DeMix, which significantly increases the efficiency of peptide identification for in-depth shotgun analysis of complex proteomes. Capitalizing on the high resolution and mass accuracy of Orbitrap-based tandem mass spectrometry, we developed a simple deconvolution method of “cloning” chimeric tandem spectra for cofragmented peptides. Additional to a database search, a simple rescoring scheme utilizes mass accuracy and converts the unwanted cofragmenting events into a surprising advantage of multiplexing. With the combination of cloning and rescoring, we obtained on average nine peptide-spectrum matches per second on a Q-Exactive workbench, whereas the actual MS/MS acquisition rate was close to seven spectra per second. This efficiency boost to 1.24 identified peptides per MS/MS spectrum enabled analysis of over 5000 human proteins in single-dimensional LC-MS/MS shotgun experiments with an only two-hour gradient. These findings suggest a change in the dominant “one MS/MS spectrum - one peptide” paradigm for data acquisition and analysis in shotgun data-dependent proteomics. DeMix also demonstrated higher robustness than conventional approaches in terms of lower variation among the results of consecutive LC-MS/MS runs.

Cytotoxic and Proinflammatory Effects of Metal-Based Nanoparticles on THP-1 Monocytes Characterized by Combined Proteomics Approaches.

Thorough characterization of toxic effects of nanoparticles (NP) is desirable due to the increasing risk of potential environmental contamination by NP. In the current study, we combined three recently developed proteomics approaches to assess the effects of Au, CuO, and CdTe NP on the innate immune system. The human monocyte cell line THP-1 was employed as a model. The anticancer drugs camptothecin and doxorubicin were used as positive controls for cell death, and lipopolysaccharide was chosen as a positive control for proinflammatory activation. Despite equivalent overall toxicity effect (50 ± 10% dead cells), the three NP induced distinctly different proteomics signatures, with the strongest effect being induced by CdTe NP, followed by CuO and gold NP. The CdTe toxicity mechanism involves down-regulation of topoisomerases. The effect of CuO NP is most reminiscent of oxidative stress and involves up-regulation of proteins involved in heat response. The gold NP induced up-regulation of the inflammatory mediator, NF-κB, and its inhibitor TIPE2 was identified as a direct target of gold NP. Furthermore, gold NP triggered activation of NF-κB as evidenced by phosphorylation of the p65 subunit. Overall, the combined proteomics approach described here can be used to characterize the effects of NP on immune cells.

Functional Identification of Target by Expression Proteomics (FITExP) reveals protein targets and highlights mechanisms of action of small molecule drugs

Phenomenological screening of small molecule libraries for anticancer activity yields potentially interesting candidate molecules, with a bottleneck in the determination of drug targets and the mechanism of anticancer action. We have found that, for the protein target of a small-molecule drug, the abundance change in late apoptosis is exceptional compared to the expectations based on the abundances of co-regulated proteins. Based on this finding, a novel method to drug target deconvolution is proposed. In a proof of principle experiment, the method yielded known targets of several common anticancer agents among a few (often, just one) likely candidates identified in an unbiased way from cellular proteome comprising more than 4,000 proteins. A validation experiment with a different set of cells and drugs confirmed the findings. As an additional benefit, mapping most specifically regulated proteins on known protein networks highlighted the mechanism of drug action. The new method, if proven to be general, can significantly shorten drug target identification, and thus facilitate the emergence of novel anticancer treatments.Phenomenological screening of small molecule libraries for anticancer activity yields potentially interesting candidate molecules, with a bottleneck in the determination of drug targets and the mechanism of anticancer action. A novel approach to drug target deconvolution compares the abundance profiles of proteins expressed in a panel of cells treated with different drugs, and identifies proteins with cell-type independent and drug-specific regulation that is exceptionally strong in relation to the other proteins. Mapping top candidates on known protein networks reveals the mechanism of drug action, while abundant proteins provide a signature of cellular death/survival pathways. The above approach can significantly shorten drug target identification, and thus facilitate the emergence of novel anticancer treatments.

Detection of viral proteins in human cells lines by xeno-proteomics: elimination of the last valid excuse for not testing every cellular proteome dataset for viral proteins.

Cell cultures used routinely in proteomic experiments may contain proteins from other species because of infection, transfection or just contamination. Since infection or contamination may affect the results of a biological experiment, it is important to test the samples for the presence of “alien” proteins. Usually cells are tested only for the most common infections, and most of the existing tests are targeting specific contaminations. Here we describe a three-step procedure for reliable untargeted detection of viral proteins using proteomics data, and recommend this or similar procedure to be applied to every proteomics dataset submitted for publication.

Expression proteomics study to determine metallodrug targets and optimal drug combinations

The emerging technique termed functional identification of target by expression proteomics (FITExP) has been shown to identify the key protein targets of anti-cancer drugs. Here, we use this approach to elucidate the proteins involved in the mechanism of action of two ruthenium(II)-based anti-cancer compounds, RAPTA-T and RAPTA-EA in breast cancer cells, revealing significant differences in the proteins upregulated. RAPTA-T causes upregulation of multiple proteins suggesting a broad mechanism of action involving suppression of both metastasis and tumorigenicity. RAPTA-EA bearing a GST inhibiting ethacrynic acid moiety, causes upregulation of mainly oxidative stress related proteins. The approach used in this work could be applied to the prediction of effective drug combinations to test in cancer chemotherapy clinical trials.

ProTargetMiner: A proteome signature library of anticancer molecules for functional discovery

We present a publicly available, expandable proteome signature library of anticancer molecules in A549 adenocarcinoma cells. Based on 287 proteomes affected by 56 drugs, the main dataset contains 7,328 proteins and 1,307,859 refined protein-drug pairs. By employing the specificity concept in partial least square modeling, deconvolution of drug targets and mechanistic proteins is achieved for most compounds, including some kinase inhibitors. We built the first protein co-regulation database that takes into account both protein expression and degradation. A surprising number of strong anti-correlations is found, underscoring the importance of protein repression in cell regulation. Our analysis uncovered a group of proteins with extremely steady expression which are likely essential for core cellular functions. These findings bring about deeper understanding of cell mechanics. Extension of the dataset to novel compounds will facilitate drug design. The introduced specificity concept and modeling scheme are beneficial in other analysis types as well. Statement of Significance ProTargetMiner is the first of its kind library of proteome responses of human cancer cells to anticancer molecules. This expandable resource facilitates the deconvolution of drug targets, action mechanisms, and cellular effects. It reveals death modalities, uncovers protein co-regulation and anti-correlation networks and defines the “untouchable” proteome essential for core cellular functionalities.

System-wide Identification of Enzyme Substrates by Thermal Analysis (SIESTA)

Despite the immense importance of enzyme-substrate reactions, there is a lack of generic and unbiased tools for identifying and prioritizing substrate proteins which are modulated in the structural and functional levels through modification. Here we describe a high-throughput unbiased proteomic method called System-wide Identification and prioritization of Enzyme Substrates by Thermal Analysis (SIESTA). The approach assumes that enzymatic post-translational modification of substrate proteins might change their thermal stability. SIESTA successfully identifies several known and novel substrate candidates for selenoprotein thioredoxin reductase 1, protein kinase B (AKT1) and poly-(ADP-ribose) polymerase-10 systems in up to a depth of 7179 proteins. Wider application of SIESTA can enhance our understanding of the role of enzymes in homeostasis and disease, open new opportunities in investigating the effect of PTMs on signal transduction, and facilitate drug discovery.

Proteogenomics of Malignant Melanoma Cell Lines: The Effect of Stringency of Exome Data Filtering on Variant Peptide Identification in Shotgun Proteomics

The identification of genetically encoded variants at the proteome level is an important problem in cancer proteogenomics. The generation of customized protein databases from DNA or RNA sequencing data is a crucial stage of the identification workflow. Genomic data filtering applied at this stage may significantly modify variant search results, yet its effect is generally left out of the scope of proteogenomic studies. In this work, we focused on this impact using data of exome sequencing and LC-MS/MS analyses of six replicates for eight melanoma cell lines processed by a proteogenomics workflow. The main objectives were identifying variant peptides and revealing the role of the genomic data filtering in the variant identification. A series of six confidence thresholds for single nucleotide polymorphisms and indels from the exome data were applied to generate customized sequence databases of different stringency. In the searches against unfiltered databases, between 100 and 160 variant peptides were identified for each of the cell lines using X!Tandem and MS-GF+ search engines. The recovery rate for variant peptides was ∼1%, which is approximately three times lower than that of the wild-type peptides. Using unfiltered genomic databases for variant searches resulted in higher sensitivity and selectivity of the proteogenomic workflow and positively affected the ability to distinguish the cell lines based on variant peptide signatures.

Proteogenomics of Adenosine-to-Inosine RNA Editing in the Fruit Fly

Adenosine-to-inosine RNA editing is one of the most common types of RNA editing, a posttranscriptional modification made by special enzymes. We present a proteomic study on this phenomenon for Drosophila melanogaster. Three proteome data sets were used in the study: two taken from public repository and the third one obtained here. A customized protein sequence database was generated using results of genome-wide adenosine-to-inosine RNA studies and applied for identifying the edited proteins. The total number of 68 edited peptides belonging to 59 proteins was identified in all data sets. Eight of them being shared between the whole insect, head, and brain proteomes. Seven edited sites belonging to synaptic vesicle and membrane trafficking proteins were selected for validation by orthogonal analysis by Multiple Reaction Monitoring. Five editing events in cpx, Syx1A, Cadps, CG4587, and EndoA were validated in fruit fly brain tissue at the proteome level using isotopically labeled standards. Ratios of unedited-to-edited proteoforms varied from 35:1 ( Syx1A) to 1:2 ( EndoA). Lys-137 to Glu editing of endophilin A may have functional consequences for its interaction to membrane. The work demonstrates the feasibility to identify the RNA editing event at the proteome level using shotgun proteomics and customized edited protein database.

Proteomic Identification of Heat Shock-Induced Danger Signals in a Melanoma Cell Lysate Used in Dendritic Cell-Based Cancer Immunotherapy

Autologous dendritic cells (DCs) loaded with cancer cell-derived lysates have become a promising tool in cancer immunotherapy. During the last decade, we demonstrated that vaccination of advanced melanoma patients with autologous tumor antigen presenting cells (TAPCells) loaded with an allogeneic heat shock- (HS-) conditioned melanoma cell-derived lysate (called TRIMEL) is able to induce an antitumor immune response associated with a prolonged patient survival. TRIMEL provides not only a broad spectrum of potential melanoma-associated antigens but also danger signals that are crucial in the induction of a committed mature DC phenotype. However, potential changes induced by heat conditioning on the proteome of TRIMEL are still unknown. The identification of newly or differentially expressed proteins under defined stress conditions is relevant for understanding the lysate immunogenicity. Here, we characterized the proteomic profile of TRIMEL in response to HS treatment. A quantitative label-free proteome analysis of over 2800 proteins was performed, with 91 proteins that were found to be regulated by HS treatment: 18 proteins were overexpressed and 73 underexpressed. Additionally, 32 proteins were only identified in the HS-treated TRIMEL and 26 in non HS-conditioned samples. One protein from the overexpressed group and two proteins from the HS-exclusive group were previously described as potential damage-associated molecular patterns (DAMPs). Some of the HS-induced proteins, such as haptoglobin, could be also considered as DAMPs and candidates for further immunological analysis in the establishment of new putative danger signals with immunostimulatory functions.