Biology subjects

Lobas, A. A.

Publications and source records attributed to Lobas, A. A..

4 recordsLinked to original sources

bioRxiv2023

Chemical proteomics reveals that the anticancer drug everolimus affects ubiquitin-proteasome system

Rapamycin is a natural antifungal, immunosuppressive, and antiproliferative compound allosterically inhibiting mTOR complex 1. The ubiquitin-proteasome system (UPS) responsible for protein turnover is usually not listed among the pathways affected by mTOR signaling; however, a number of reports indicated the interplay between UPS and mTOR. It is also reported that rapamycin and analogs can allosterically inhibit the proteasome itself. We studied the molecular effect of rapamycin and its analogs (rapalogs), everolimus, and temsirolimus by expression chemical proteomics on A549 cell line. The analysis revealed that the cellular response to everolimus differs dramatically from that of rapamycin and temsirolimus. In cluster analysis the effect of everolimus was similar to that of bortezomib, a well-established proteasome inhibitor. UPS-related pathways were enriched in the cluster of proteins specifically up-regulated upon everolimus and bortezomib treatments, suggesting that both compounds have similar proteasome inhibition effects. In particular, the total amount of ubiquitin was significantly elevated in the samples treated with everolimus and bortezomib, and the analysis of the polyubiquitination patterns revealed elevated intensities of the ubiquitin peptide with a GG modification at K48 residue, consistent with a bottleneck in the proteasomal protein degradation. Moreover, everolimus treatment resulted in both ubiquitin phosphorylation and a significant amount of semi-tryptic peptides illustrating the induction of protease activity. These observations suggest that everolimus affects the ubiquitin-proteasome system in a unique way and its mechanism of action is strikingly different from that of its close chemical analogs, rapamycin and temsirolimus. Data are available via ProteomeXchange with identifier PXD045774.

cancer biology↗

bioRxiv2022

Massive proteogenomic reanalysis of publicly available proteomic datasets of human tissues in search for protein recoding via adenosine-to-inosine RNA editing

The proteogenomic search pipeline developed in this work has been applied for re-analysis of 40 publicly available shotgun proteomic datasets from various human tissues comprising more than 8,000 individual LC-MS/MS runs, of which 5442 .raw data files were processed in total. The scope of this re-analysis was focused on searching for ADAR-mediated RNA editing events, their clustering across samples of different origin, and classification. In total, 33 recoded protein sites were identified in 21 datasets. Of those, 18 sites were detected in at least two datasets representing the core human protein editome. In agreement with prior art works, neural and cancer tissues were found being enriched with recoded proteins. Quantitative analysis indicated that recoding of specific sites did not directly depend on the levels of ADAR enzymes or targeted proteins themselves, rather it was provided by differential and yet undescribed regulation of interaction of enzymes with mRNA. Nine recoding sites conservative between human and rodents were validated by targeted proteomics using stable isotope standards in murine brain cortex and cerebellum, and an additional one was validated in human cerebrospinal fluid. In addition to previous data of the same type from cancer proteomes, we provide a comprehensive catalog of recoding events caused by ADAR RNA editing in the human proteome.

bioinformatics↗

bioRxiv2022

Validating amino acid variants in proteogenomics using sequence coverage by multiple reads

Mass spectrometry-based proteome analysis usually implies matching mass spectra of proteolytic peptides to amino acid sequences predicted from nucleic acid sequences. At the same time, due to the stochastic nature of the method when it comes to proteome-wide analysis, in which only a fraction of peptides are selected for sequencing, the completeness of protein sequence identification is undermined. Likewise, the reliability of peptide variant identification in proteogenomic studies is suffering. We propose a way to interpret shotgun proteomics results, specifically in data-dependent acquisition mode, as protein sequence coverage by multiple reads, just as it is done in the field of nucleic acid sequencing for the calling of single nucleotide variants. Multiple reads for each position in a sequence could be provided by overlapping distinct peptides, thus, confirming the presence of certain amino acid residues in the overlapping stretch with much lower false discovery rate than conventional 1%. The source of overlapping distinct peptides are, first, miscleaved tryptic peptides in combination with their properly cleaved counterparts, and, second, peptides generated by several proteases with different specificities after the same specimen is subject to parallel digestion and analyzed separately. We illustrate this approach using publicly available multiprotease proteomic datasets and our own data generated for HEK-293 cell line digests obtained using trypsin, LysC and GluC proteases. From 5000 to 8000 protein groups are identified for each digest corresponding to up to 30% of the whole proteome coverage. Most of this coverage was provided by a single read, while up to 7% of the observed protein sequences were covered two-fold and more. The proteogenomic analysis of HEK-293 cell line revealed 36 peptide variants associated with SNP, seven of which were supported by multiple reads. The efficiency of the multiple reads approach depends strongly on the depth of proteome analysis, the digesting features such as the level of miscleavages, and will increase with the number of different proteases used in parallel proteome digestion. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/475497v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@1d6ee2org.highwire.dtl.DTLVardef@5ae8baorg.highwire.dtl.DTLVardef@652216org.highwire.dtl.DTLVardef@1a0d49b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2019

DirectMS1: MS/MS-free identification of 1000 proteins of cellular proteomes in 5 minutes

Proteome characterization relies heavily on tandem mass spectrometry (MS/MS) and is thus associated with instrumentation complexity, lengthy analysis time, and limited duty-cycle. It was always tempting to implement approaches which do not require MS/MS, yet, they were constantly failing in achieving meaningful depth of quantitative proteome coverage within short experimental times, which is particular important for clinical or biomarker discovery applications. Here, we report on the first successful attempt to develop a truly MS/MS-free and label-free method for bottom-up proteomics. We demonstrate identification of 1000 protein groups for a standard HeLa cell line digest using 5-minute LC gradients. The amount of loaded sample was varied in a range from 1 ng to 500 ng, and the method demonstrated 10-fold higher sensitivity compared with the standard MS/MS-based approach. Due to significantly higher sequence coverage obtained by the developed method, it outperforms all popular MS/MS-based label-free quantitation approaches.

biochemistry↗