Mass spectrometry-based discovery proteomics is an essential tool for the proximal read-out of cellular drug action. Here, we used a robust proteomic workflow to rapidly and systematically profile the proteomes of five cell lines in response to > 50 drugs. We found that aggregating millions of quantitative protein-drug associations substantially improved the mechanism of action (MoA) deconvolution of single compounds. For example, MoA specificity increased after removal of proteins which frequently responded to drugs and the aggregation of proteome changes across multiple cell lines resolved compound effects on proteostasis. These characteristics were further leveraged to demonstrate efficient target identification of protein degraders. Moreover, we followed up on selected proteomic findings and showed that the inhibition of mitochondrial function is an off-target mechanism of the clinical MEK inhibitor PD184352 and that Ceritinib, an FDA approved drug in lung cancer, modulates autophagy. Overall, this study demonstrates that large-scale proteome perturbation profiling can be a useful addition to the drug discovery toolbox.

The sudden global emergence of SARS-CoV-2 urgently requires an in-depth understanding of 39 molecular functions of viral proteins and their interactions with the host proteome. Several omics studies have extended our knowledge of COVID-19 pathophysiology, including some focused on proteomic aspects1–3. To understand how SARS-CoV-2 and related coronaviruses manipulate the host we here characterized interactome, proteome and signaling processes in a systems-wide manner. This identified connections between the corresponding cellular events, revealed functional effects of the individual viral proteins and put these findings into the context of host signaling pathways. We investigated the closely related SARS-CoV-2 and SARS-CoV viruses as well as the influence of SARS-CoV-2 on transcriptome, proteome, ubiquitinome and phosphoproteome of a lung-derived human cell line. Projecting these data onto the global network of cellular interactions revealed 48 relationships between the perturbations taking place upon SARS-CoV-2 infection at different layers and identified unique and common molecular mechanisms of SARS coronaviruses. The results highlight the functionality of individual proteins as well as vulnerability hotspots of SARS-CoV-2, which we targeted with clinically approved drugs. We exemplify this by identification of kinase inhibitors as well as MMPase inhibitors with significant antiviral effects against SARS-CoV-2

This dataset contains key characteristics about the data described in the Data Descriptor Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

We used decoupleR to evaluate the performance of individual methods by recovering perturbed transcription factors (TFs) from a curation of single-gene perturbation experiments (Holland et al., 2020). As a resource we used DoRothEA, a gene regulatory network linking TFs to target genes by their mode of regulation (Garcia-Alonso et al., 2019). Perturbation experiments where the targeted regulator was not in DoRothEA were removed. After filtering, this dataset is composed of gene expression data from 92 knockdown and overexpression experiments of 40 unique TFs in human cells. Additionally, we tested the performance of decoupleR on phospho-proteomic data. For this, we filtered in a similar fashion a curated set of knockdown and overexpression single-kinase perturbation experiments, obtaining 63 experiments including 14 unique kinases, and applied a weighted resource from the same publication that links kinases to their target phosphosites (Hernandez-Armenta et al., 2017). For the transcriptomic dataset, differential expression analysis was performed with limma (Ritchie et al., 2015) and the resulting t-values were used as input. For the phospho-proteomics, the quantile-normalized log2-fold changes from different studies were used to make them comparable.

Anabaena sp. PCC 7120 (Anabaena 7120) is a photoautotrophic filamentous cyanobacterium capable of fixing atmospheric nitrogen. It is a model organism used for studying cell differentiation and nitrogen fixation. Under nitrogen deficiency, Anabaena 7120 forms specialized heterocysts capable of nitrogen fixation. However, the molecular mechanisms involved in the cyanobacterial adaptation to nitrogen deficiency are not well understood. Here, we employed a label-free quantitative proteomic strategy to systematically investigate the nitrogen deficiency response of Anabaena 7120 at different time points. In total, 363, 603, and 669 proteins showed significant changes in protein abundance under nitrogen deficiency for 3, 12, and 24 h, respectively. With mapping onto metabolic pathways, we revealed proteomic perturbation and regulation of carbon and nitrogen metabolism in response to nitrogen deficiency. Functional analysis confirmed the involvement of nitrogen stress-responsive proteins in biological processes, including nitrogen fixation, photosynthesis, energy and carbon metabolism, and heterocyst development. The expression of 10 proteins at different time points was further validated by using multiple reaction monitoring assays. In particular, many dysregulated proteins were found to be time-specific and involved in heterocyst development, providing new candidates for future functional studies in this model cyanobacterium. These results provide novel insights into the molecular mechanisms of nitrogen stress responses and heterocyst development in Anabaena 7120.

Protein-Protein, Genetic, and Chemical Interactions for Stukalov A (2021):Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV. curated by BioGRID (https://thebiogrid.org); ABSTRACT: The global emergence of SARS-CoV-2 urgently requires an in-depth understanding of molecular functions of viral proteins and their interactions with the host proteome. Several individual omics studies have extended our knowledge of COVID-19 pathophysiology1-10. Integration of such datasets to obtain a holistic view of virus-host interactions and to define the pathogenic properties of SARS-CoV-2 is limited by the heterogeneity of the experimental systems. We therefore conducted a concurrent multi-omics study of SARS-CoV-2 and SARS-CoV. Using state-of-the-art proteomics, we profiled the interactome of both viruses, as well as their influence on transcriptome, proteome, ubiquitinome and phosphoproteome in a lung-derived human cell line. Projecting these data onto the global network of cellular interactions revealed crosstalk between the perturbations taking place upon SARS-CoV-2 and SARS-CoV infections at different layers and identified unique and common molecular mechanisms of these closely related coronaviruses. The TGF-? pathway, known for its involvement in tissue fibrosis, was specifically dysregulated by SARS-CoV-2 ORF8 and autophagy by SARS-CoV-2 ORF3. The extensive dataset (available at https://covinet.innatelab.org ) highlights many hotspots that can be targeted by existing drugs and it can guide rational design of virus- and host-directed therapies, which we exemplify by identifying kinase and MMPs inhibitors with potent antiviral effects against SARS-CoV-2.

this project carried out a global mass spectrometry analysis in a panel of HGSOC cell lines analyzing proteomic perturbations following HDACi treatment

In situ profiling of subcellular proteomic networks in primary and living systems, such as primary cells from native tissues or clinic samples, is crucial for the understanding of life processes and diseases, yet challenging for the current proximity labeling methods (e.g., BioID, APEX) due to their necessity of genetic engineering. Here we report CAT-S, a state-of-the-art bioorthogonal photocatalytic chemistry-enabled proximity labeling method, that expands proximity labeling to a wide range of primary living samples for in situ profiling of subcellular proteomes. Powered by the newly introduced thioQM labeling warhead and targeted bioorthogonal photocatalytic decaging chemistry, CAT-S enables labeling of mitochondrial proteins in living cells with high efficiency and specificity (up to 87%). We applied CAT-S to diverse cell cultures, mouse tissues as well as primary T cells from human blood, portraying the native-state mitochondrial proteomic characteristics, and unveiled a set of hidden mitochondrial proteins in human proteome. Furthermore, CAT-S allows quantitative analysis of the in situ proteomic perturbations on dysfunctional tissue samples, exampled by diabetic mouse kidneys, and revealed the alterations of lipid metabolism machinery that drive the disease progression. Given the advantages of non-genetic operation, generality, efficiency as well as spatiotemporal resolution, CAT-S may open new avenues as a proximity labeling strategy for in situ investigation of subcellular proteomic landscape of primary living samples that are otherwise inaccessible.

Advances in mass spectrometry have made the quantitative measurement of proteins across multiple samples a reality, allowing for the study of complex biological systems such as the metabolic syndrome. Although the deregulation of lipid metabolism and increased hepatic storage of triacylglycerides are known to play a part in the onset of the metabolic syndrome, its molecular basis and dependency on dietary and genotypic factors are poorly characterized. Here, we used an experimental design with two different mouse strains and dietary and metabolic perturbations to generate a compendium of quantitative proteome data using three mass spectrometric techniques. The data reproduce known properties of the metabolic system and indicate differential molecular adaptation of the two mouse strains to perturbations, contributing to a better understanding of the metabolic syndrome. We show that high-quality, high-throughput proteomic data sets provide an unbiased broad overview of the behavior of complex systems after perturbation.

To gain insight into the function of both OCIAD paralogs, we used untargeted quantitative mass spectrometry to compare the whole-cell proteomes of control U2OS cells, U2OS cells with individual or double OCIAD1/OCIAD2 knockdown, and OCIAD1 knockdown cells in which OCIAD1 expression was reintroduced by lentiviral delivery.

Aim of the project was to investigate drug induced proteomic changes in T. brucei within 6 h of treatment with NEU-4438 or SCYX-7158

Yeast remains an important model for systems biology and for evaluating proteomics strategies. In-depth shotgun proteomics studies have reached nearly comprehensive coverage, and rapid, targeted approaches have been developed for this organism. Recently, we demonstrated that single LC-MS/MS analysis using long columns and gradients coupled to a linear ion trap Orbitrap instrument had an unexpectedly large dynamic range of protein identification (Thakur, S. S., Geiger, T., Chatterjee, B., Bandilla, P., Frohlich, F., Cox, J., and Mann, M. (2011) Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation. Mol. Cell Proteomics 10, 10.1074/mcp.M110.003699). Here we couple an ultra high pressure liquid chromatography system to a novel bench top Orbitrap mass spectrometer (Q Exactive) with the goal of nearly complete, rapid, and robust analysis of the yeast proteome. Single runs of filter-aided sample preparation (FASP)-prepared and LysC-digested yeast cell lysates identified an average of 3923 proteins. Combined analysis of six single runs improved these values to more than 4000 identified proteins/run, close to the total number of proteins expressed under standard conditions, with median sequence coverage of 23%. Because of the absence of fractionation steps, only minuscule amounts of sample are required. Thus the yeast model proteome can now largely be covered within a few hours of measurement time and at high sensitivity. Median coverage of proteins in Kyoto Encyclopedia of Genes and Genomes pathways with at least 10 members was 88%, and pathways not covered were not expected to be active under the conditions used. To study perturbations of the yeast proteome, we developed an external, heavy lysine-labeled SILAC yeast standard representing different proteome states. This spike-in standard was employed to measure the heat shock response of the yeast proteome. Bioinformatic analysis of the heat shock response revealed that translation-related functions were down-regulated prominently, including nucleolar processes. Conversely, stress-related pathways were up-regulated. The proteomic technology described here is straightforward, rapid, and robust, potentially enabling widespread use in the yeast and other biological research communities. Data analysis: The raw files were processed using MaxQuant version 1.2.0.34. The fragmentation spectra were searched against the yeast ORF database (release date of February 3, 2011; 6752 entries) using the Andromeda search engine with the initial precursor and fragment mass tolerances set to 7 and 20 ppm, respectively, and with up to two missed cleavages. Carabamidomethlyation of cysteine was set as a fixed modification, and oxidation of methionine and protein N-terminal acetylation were chosen as variable modifications for database searching. Both peptide and protein identifications were filtered at 1% false discovery rate and thus were not dependent on the peptide score. Bioinformatics analysis was performed using the Perseus tools available in the MaxQuant environment.

Hematological disorders result in perturbed homeostasis of the blood system. However, a comprehensive understanding of how physiological and genetic mechanisms regulate blood cell precursor maintenance and differentiation is lacking. Owing to simplicity and ease of genetic analysis, the Drosophila melanogaster lymph gland (LG) is an excellent model to study hematopoiesis. The LG is a multi-lobed structure compartmentalized into precursor and differentiation zones whose geography and identity is regulated by multiple signalling pathways. While additional molecular and functional subtypes are expected there is a paucity of information on gene expression and regulation of hemocyte homeostasis. Hence, we quantitatively analyzed the LG proteome under conditions that maintain precursors or promote their differentiation in vivo, by perturbing expression of Asrij, a conserved endosomal regulator of hematopoiesis. Although technically demanding, we pooled samples obtained from 1500 larval dissections per genotype and using iTRAQ quantitative proteomics, determined the relative expression levels of polypeptides in Asrij knockout (KO) and overexpressing (OV) LGs in comparison to wild type (control). Mass spectrometry data analysis showed that at least 6.5% of the Drosophila proteome is expressed in wild type LGs.Of 2,133 proteins identified, 780 and 208 proteins were common to the previously reported cardiac tube and hemolymph proteomes, respectively, resulting in the identification of 1238 proteins exclusive to the LG. Perturbation of Asrij levels led to differential expression of 619 proteins, of which 23% have human homologs implicated in various diseases. Proteins regulating metabolism, immune system, signal transduction and vesicle-mediated transport were significantly enriched. Immunostaining of representative candidates from the enriched categories and previous reports confirmed 75% of our results and validated the LG proteome. Our study provides, for the first time, an in vivo proteomics resource for identifying novel regulators of hematopoiesis that will also be applicable to understanding vertebrate blood cell development.

The global spread of Monkeypox virus (MPXV) has resulted in the urgent need for an in-depth molecular characterization of the virus infection. Multiple omics studies on Vaccinia virus have extended our knowledge of poxvirus pathophysiology (REFs). Nevertheless, there are no comparative studies that would include an in-depth comparison of MPXV with other poxviruses in the context of molecular biology. Here, we report a comparative time-resolved proteomic study of MPXV and vaccinia viruses and a concurrent multi-omics study of MPXV infection in primary human cells. Using state-of-the-art proteomics, we profiled the virus-host interplay on the transcriptome, proteome and phosphoproteome levels in primary human foreskin fibroblasts. Pathway analysis in combination with projecting the gathered data onto the global network of host-signaling interactions revealed crosstalk between the perturbations at different levels, enabling identification of distinct and common molecular mechanisms of poxviruses. The NF-κB pathway, a signaling pathway governing immunomodulation and inflammation, was unexpectedly activated by MPXV but not VacV, while MPXV exhibited an astounding resistance to the effects of antiviral interferon response, potentially explaining the unique pathogenicity hallmarks of the monkeypox. Our extensive dataset highlights many signaling events and hotspots that influence the MPXV life cycle and may be used to guide rational design of both virus- and host-directed therapies, which we exemplify by identifying inhibitors of XXX, YYY, and ZZZ with potent antiviral efficacy against MPXV and/or VacV.

The purpose of this experiment was to investigate structural alterations in proteins involved in central carbon metabolism and photosynthetic electron transfer pathways in Synechococcus elongatus PCC 7942. Sample data was obtained from S. elongatus cell lysates using three complementary mass spectrometry (MS) techniques using limited proteolysis (LiP-MS), thermal proteome profiling (TPP-MS), and redox enrichment (Redox-MS) in evaluating alterations solvent accessibility and structural stability caused by light perturbation at the molecular level. Experimentally processed sample data for LiP and TPP proteomic datasets were derived from the same cell culture stock, prepared simultaneously in parallel, and acquired by mass spectrometry. Processed datasets are openly accessible from the download button and contain secondary processed proteomic results files, computed outputs, and supporting metadata materials. Experimental samples processed for LiP-MS label-free quantification (LFQ) or TPP-MS tandem mass tag (TMT) 10-plex were acquired using a Q-Exactive HF-X mass spectrometer and processed/compiled using either MSGF+ (v2024.03.26) or ​​​​PlexedPiper for proteome evaluation. Additional software supporting downstream proteomic analysis include FragPipe (v.4.0), MSFragger (v.22.1), and an adapted Microbial Isolate LiP Analysis Workflow (located at Zenodo). Processed proteomic data downloads include a sample naming key, normalized quantification results files, and processed protein annotated abundance files.

Proteins regulate biological processes by changing their structure or abundance to accomplish a specific function. In response to any perturbation or stimulus, protein structure may be altered by a variety of molecular events, such as post translational modifications, protein-protein interactions, aggregation, allostery, or binding to other molecules. The ability to probe these structural changes in thousands of proteins simultaneously in cells or tissues can provide valuable information about the functional state of a variety of biological processes and pathways. Here we present an updated protocol for LiP-MS, a proteomics technique combining limited proteolysis with mass spectrometry, to detect protein structural alterations in complex backgrounds and on a proteome-wide scale (Cappelletti et al., 2021; Piazza et al., 2020; Schopper et al., 2017). We describe advances in the throughput and robustness of the LiP-MS workflow and implementation of data-independent acquisition (DIA) based mass spectrometry, which together achieve high reproducibility and sensitivity, even on large sample sizes. In addition, we introduce MSstatsLiP, an R package dedicated to the analysis of LiP-MS data for the identification of structurally altered peptides and differentially abundant proteins. Altogether, the newly proposed improvements expand the adaptability of the method and allow for its wide use in systematic functional proteomic studies and translational applications.

The spatial mass spectrometry market is projected to grow from USD 385.5 million in 2025 to USD 1,532.7 million by 2035, at a CAGR of 14.8%. Spatial Proteomics will dominate with a 49.6% market share, while instruments will lead the product type segment with a 52.3% share.

Protein expression profiles of human cancer and NPC cell lines treated with kinase inhibitors, epigenetic and neurodevelopmental compounds.

Chemical proteomics encompasses novel drug target deconvolution methods in which compound modification is not required. Herein we use Thermal Proteome Profiling, Functional Identification of Target by Expression Proteomics and multiplexed redox proteomics for deconvolution of auranofin targets to aid elucidation of its mechanisms of action. Auranofin (Ridaura®) was approved for treatment of rheumatoid arthritis in 1985. Because several clinical trials are currently ongoing to repurpose auranofin for cancer therapy, comprehensive characterization of its targets and effects in cancer cells is important. Together, our chemical proteomics tools confirmed thioredoxin reductase 1 (TXNRD1) as a main auranofin target, with perturbation of oxidoreductase pathways as the top mechanism of drug action. Additional indirect targets included NFKB2 and CHORDC1. Our comprehensive data can be used as a proteomic signature resource for further analyses of the effects of auranofin. Here we also assessed the orthogonality and complementarity of different chemical proteomics methods that can furnish invaluable mechanistic information and thus the approach can facilitate drug discovery efforts in general.

Decitabine (5-aza-dC) is a DNMT1-DPC inducing agent used to treat several hematological cancers. However, response rates vary, relapse is common, and predictive biomarkers are unknown. Here, we develop an adaptation of identification of proteins on nascent DNA (iPOND) by combining EdU with 5-aza-dC incorporation by itself or the presence of either ubiquitin E1 or SUMO E1 inhibitor which we term iPOND-DPC. The purpose of this experiments is to identify proteins that are recruited to DNMT1-DPCs and categorize whether their recruitment is dependent on SUMOylation or ubiquitylation.