Phosphoproteomic Data Research Articles

Abstract B178: Proteomics platform identifies vulnerabilities for specific DNA damage repair drugs

Abstract While poly-ADP-ribosylation (PAR) polymerase inhibitors (PARPi) are the only DNA damage repair (DDR) agent currently approved for clinical use, there are numerous ongoing clinical and preclinical drug development efforts of many other DDR-related targets, including ATR, ATM, CHK1 and others. One of the major challenges of these efforts is the ability to identify responsive populations and assign the right population for the right drug or drug combination. Patients are routinely assigned to PARPi or to on-going DDRi clinical trials based on the presence of BRCA mutation, or BRCAness profile encompassing various homologous recombination deficiencies (HRD). However, these biomarkers only partially represent the responsive populations, resulting in low response rates as well as misidentification of potential patient populations outside the genomic label. Since DNA replication and DNA repair mechanisms are dominated by signaling events, we hypothesized that HRD extends beyond the genomic status, and is manifested also in the level of protein expression and protein modification.In the current work, we compare the differences between cell lines that are either sensitive or resistant to a variety of DDR agents and highlight potential predictive biomarkers that are independent of HRD mutation status. We combined publicly available proteomic profiles of unperturbed cell lines, with both existing and newly generated proteomic and phosphoproteomic data of multiple cell lines before and after treatment of multiple DDR drugs.Analysis of HRD mutations showed that drug sensitivity is only partially explained by mutations. In addition, we found DNA replication and spliceosome as pathways that are consistently related to response regardless of drug and mutation status. While there is a general agreement between drugs in the pathway level, we identify smaller groups of proteins that are either drug or target-specific. We present here a proteogenomic approach to tackle the challenge of predictive biomarkers to DDR drugs. While DNA repair vulnerability is essential for DDR drug response, it is often not defined by mutations alone. We provide a proteomics-based framework that would enable future patient selection in DDR clinical trials. Citation Format: Gali Arad, Avital Hay-Koren, David Futorian, Oren Fischman, Dina Daitchman, Ofer Givton, Iris Alchanati, Yonatan Katzenelenbogen, Eran Seger, Kirill Pevzner. Proteomics platform identifies vulnerabilities for specific DNA damage repair drugs [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr B178.

Functional Connectivity Alterations and Molecular Characterization of the Anterior Cingulate Cortex in Tinnitus Pathology without Hearing Loss.

Compared with individuals with hearing loss, tinnitus patients without hearing loss have more psychological or emotional problems. Tinnitus is closely associated to abnormal metabolism and function of the limbic system, a key brain region for emotion experience, but the underlying molecular mechanism remains unknown. Using whole-brain microvasculature dynamics imaging, the anterior cingulate cortex (ACC) is identified as a key brain region of limbic system involve in the onset of salicylate-induced tinnitus in mice. In the tinnitus group, there is enhanced purine metabolism, oxidative phosphorylation, and a distinct pattern of phosphorylation in glutamatergic synaptic pathway according to the metabolome profiles, quantitative proteomic, and phosphoproteomic data of mice ACC tissue. Electroencephalogram in tinnitus patients with normal hearing thresholds show that the functional connectivity between pregenual anterior cingulate cortex and the primary auditory cortex is significantly increased for high-gamma frequency band, which is positively correlated with the serum glutamate level. These findings indicate that ACC plays an important role in the pathophysiology of tinnitus by interacting with the primary auditory cortex and provide potential molecular targets in the ACC for tinnitus treatment.

BIOM-57. UNRAVELING THE DYSREGULATION OF MIR-17/92 CLUSTER IN AGGRESSIVE PEDIATRIC BRAIN TUMORS: IMPLICATIONS FOR NOVEL THERAPEUTIC STRATEGIES

Abstract The miR-17/92 cluster, commonly referred to as “OncoMir-1,” is extensively studied in cancer due to its oncogenic role, but it has yet to be fully characterized in pediatric brain tumors. In this study, we conduct a comprehensive investigation involving miRNA profiling of 262 pediatric brain tumors, along with sample-matched data from the Pediatric Brain Tumor Atlas, including WGS, RNA-sequencing, proteomics, and phospho-proteomics data. Through miRNA-based unsupervised clustering, we identified four distinct miRNA signatures that transcend histological boundaries. An aggressive cluster (consisting of Medulloblastoma, ATRT, High-Grade Astrocytic Tumors, and some Ependymomas) exhibited the shortest survival and displayed an enriched network of co-expressed miR-17/92 cluster family members and paralogues, specifically miR-106b/25 and miR-106a/363 (p< 0.001). These tumors exhibited significant upregulation of the MIR17HG gene, which encodes the miR-17/92 cluster of miRNAs (p< 0.001). Further exploration of the upstream regulatory mechanisms of MIR17HG identified n-MYC and E2F1/2/3 significantly upregulated (p< 0.01). Additionally, utilizing sample-matched phospho-proteomics data, we found hyperphosphorylation of RB1, the upstream regulator of E2F, at three sites, providing additional evidence of RB1-E2F regulation upstream of MIR17HG. RB1 hyperphosphorylation and subsequent complete inactivation are mediated by the CDK2/Cyclin E complex, which is inhibited by the tumor suppressor p21. Interestingly, p21 (CDKN1A) gene expression was entirely depleted in the aggressive brain tumors group. In summary, our findings reveal the aberrant upregulation of the oncogenic miR-17/92 cluster in a group of aggressive brain tumors, potentially resulting from inactivating hyperphosphorylation of RB1 caused by p21 loss. This event frees E2F transcription factors and enables transcription of MIR17HG. Additionally, our study highlights E2F1/2/3 and CDK2 as potential therapeutic targets not only for aggressive brain tumors but also for other cancers overexpressing the miR-17/92 cluster. Future research will focus on molecular validation and targeting of the miR-17/92 regulatory pathway using available brain tumor cell line models.

Functional and spatial proteomics profiling reveals intra- and intercellular signaling crosstalk in colorectal cancer

Precision oncology approaches for patients with colorectal cancer (CRC) continue to lag behind other solid cancers. Functional precision oncology-a strategy that is based on perturbing primary tumor cells from cancer patients-could provide a road forward to personalize treatment. We extend this paradigm to measuring proteome activity landscapes by acquiring quantitative phosphoproteomic data from patient-derived organoids (PDOs). We show that kinase inhibitors induce inhibitor- and patient-specific off-target effects and pathway crosstalk. Reconstruction of the kinase networks revealed that the signaling rewiring is modestly affected by mutations. We show non-genetic heterogeneity of the PDOs and upregulation of stemness and differentiation genes by kinase inhibitors. Using imaging mass-cytometry-based profiling of the primary tumors, we characterize the tumor microenvironment (TME) and determine spatial heterocellular crosstalk and tumor-immune cell interactions. Collectively, we provide a framework for inferring tumor cell intrinsic signaling and external signaling from the TME to inform precision (immuno-) oncology in CRC.

Phosphatase specificity principles uncovered by MRBLE:Dephos and global substrate identification.

Phosphoprotein phosphatases (PPPs) regulate major signaling pathways, but the determinants of phosphatase specificity are poorly understood. This is because methods to investigate this at scale are lacking. Here, we develop a novel in vitro assay, MRBLE:Dephos, that allows multiplexing of dephosphorylation reactions to determine phosphatase preferences. Using MRBLE:Dephos, we establish amino acid preferences of the residues surrounding the dephosphorylation site for PP1 and PP2A-B55, which reveals common and unique preferences. To compare the MRBLE:Dephos results to cellular substrates, we focused on mitotic exit that requires extensive dephosphorylation by PP1 and PP2A-B55. We use specific inhibition of PP1 and PP2A-B55 in mitotic exit lysates coupled with phosphoproteomics to identify more than 2,000 regulated sites. Importantly, the sites dephosphorylated during mitotic exit reveal key signatures that are consistent with MRBLE:Dephos. Furthermore, integration of our phosphoproteomic data with mitotic interactomes of PP1 and PP2A-B55 provides insight into how binding of phosphatases to substrates shapes dephosphorylation. Collectively, we develop novel approaches to investigate protein phosphatases that provide insight into mitotic exit regulation.

SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data.

A major challenge in mass spectrometry-based phosphoproteomics lies in identifying the substrates of kinases, as currently only a small fraction of substrates identified can be confidently linked with a known kinase. Machine learning techniques are promising approaches for leveraging large-scale phosphoproteomics data to computationally predict substrates of kinases. However, the small number of experimentally validated kinase substrates (true positive) and the high data noise in many phosphoproteomics datasets together limit their applicability and utility. Here, we aim to develop advanced kinase-substrate prediction methods to address these challenges. Using a collection of seven large phosphoproteomics datasets, and both traditional and deep learning models, we first demonstrate that a 'pseudo-positive' learning strategy for alleviating small sample size is effective at improving model predictive performance. We next show that a data resampling-based ensemble learning strategy is useful for improving model stability while further enhancing prediction. Lastly, we introduce an ensemble deep learning model ('SnapKin') by incorporating the above two learning strategies into a 'snapshot' ensemble learning algorithm. We propose SnapKin, an ensemble deep learning method, for predicting substrates of kinases from large-scale phosphoproteomics data. We demonstratethat SnapKin consistently outperforms existing methods in kinase-substrate prediction. SnapKin is freely available at https://github.com/PYangLab/SnapKin.

Phosphoproteomic Analysis Reveals the Predominating Cellular Processes and the Involved Key Phosphoproteins Essential for the Proliferation of Toxoplasma gondii.

To explore the essential roles of phosphorylation in mediating the proliferation of T. gondii in its cell lytic life. We profiled the phosphoproteome data of T. gondii residing in HFF cells for 2h and 6h, representing the early- and late-stages of proliferation (ESP and LSP) within its first generation of division. We identified 70 phosphoproteins, among which 8 phosphoproteins were quantified with the phosphorylation level significantly regulated. While only two of the eight phosphoproteins, GRA7 and TGGT1_242070, were significantly down-regulated at the transcriptional level in the group of LSP vs. ESP. Moreover, GO terms correlated with host membrane component were significantly enriched in the category of cellular component, suggesting phosphoprotein played important roles in acquiring essential substance from host cell via manipulating host membrane. Further GO analysis in the categories of molecular function and biological process and pathway analysis revealed that the cellular processes of glucose and lipid metabolism were regulated by T. gondii phosphoproteins such as PMCAA1, LIPIN, Pyk1 and ALD. Additionally, several phosphoproteins were enriched at the central nodes in the protein-protein interaction network, which may have essential roles in T. gondii proliferation including GAP45, MLC1, fructose-1,6-bisphosphate aldolase, GRAs and so on. This study revealed the main cellular processes and key phosphoproteins crucial for the intracellular proliferation of T. gondii, which would provide clues to explore the roles of phosphorylation in regulating the development of tachyzoites and new insight into the mechanism of T. gondii development in vitro.

Abstract 131: Phosphoproteomics Analysis Reveals That Angiotensin-(1-5) Inhibits MTOR Signalling Pathway In Endothelial Cells

Introduction: Our group demonstrated that Angiotensin-(1-5) [Ang-(1-5)] is a biologically active component of the protective arm of the Renin-Angiotensin System (RAS), displaying high efficacy towards the AT 2 R. However, signaling mechanisms for Ang-(1-5) in vitro are still unknown. Objective: To characterize Ang-(1-5) signaling signature in human aortic endothelial cells. Methods: Human aortic endothelial cells (HAEC) were stimulated with Ang-(1-5) (1μM) for 1, 3, 5 or 20 minutes. Proteins were isolated, digested and labeled with TMTpro TM 16-plex. Phosphorylation changes of proteins in response to Ang-(1-5) treatment were analyzed by mass spectrometry. Raw data were processed in Proteome Discoverer. General bioinformatic analysis was performed in R Studios and functional analysis was performed with DAVID and Cytoscape. Validation of phosphoproteomics data was performed by western blot in HAEC treated with Ang-(1-5) (1μM) for 1, 3, 5 or 20 minutes. Results: Treatment of HAEC with Ang-(1-5) significantly modified the phosphorylation status of 831 proteins at 1799 residues, majority of these being dephosphorylated (1079). Functional bioinformatic analysis revealed a cluster of proteins involved in cell cycle and cell division regulation. Upon further analysis, we observed that Ang-(1-5) led to dephosphorylation of residues of CLIP-170 (Ser 195 , Ser 200 , Ser 204 ), 4EBP1 (Ser 65 ), S6K1 (Ser 447 ) and ribosomal S6 protein (Ser 244 , Ser 247 ), suggesting an inhibitory effect on mTOR activity. Western blot analysis confirmed the inhibitory effect of Ang-(1-5) on mTOR signaling, as a significant dephosphorylation of Ser 65 -4EBP1 [% vs. CT = 69.9%] and Thr 389 -S6K1 [% vs. CT = 43.7%] was observed at all timepoints tested. These effects were AT 2 R-mediated, since PD123319 prevented dephosphorylation of Ser 65 -4EBP1 and Thr 389 -S6K1 driven by Ang-(1-5). Conclusion: This study provides an in-depth analysis of Ang-(1-5) signaling patterns in HAEC. The overall similarity of Ang-(1-5) and C21 intracellular signaling further supports that Ang-(1-5) is a hormone of the protective RAS. Since inhibition of mTOR in endothelial cells is known as an anti-senescence mechanism, further investigations are needed to confirm an anti-senescence effect of Ang-(1-5).

Multilayer omics landscape analyses reveal the regulatory responses of tea plants to drought stress

Adverse environments, especially drought conditions, deeply influence plant development and growth in all aspects, and the yield and quality of tea plants are largely dependent on favorable growth conditions. Although tea plant responses to drought stress (DS) have been studied, a comprehensive multilayer epigenetic, transcriptomic, and proteomic investigation of how tea responds to DS is lacking. In this study, we generated DNA methylome, transcriptome, proteome, and phosphoproteome data to explore multiple regulatory landscapes in the tea plant response to DS. An integrated multiomics analysis revealed the response of tea plants to DS at multiple regulatory levels. Furthermore, a set of DS-responsive genes involved in photosynthesis, transmembrane transportation, phytohormone metabolism and signaling, secondary metabolite pathways, transcription factors, protein kinases, posttranslational and epigenetic modification, and other key stress-responsive genes were identified for further functional investigation. These results reveal the multilayer regulatory landscape of the tea plant response to DS and provide insight into the mechanisms of these DS responses.

Phospho-Analyst: An Interactive, Easy-to-Use Web Platform To Analyze Quantitative Phosphoproteomics Data.

Phosphoproteomics is nowadays the method of choice to comprehensively identify and quantify thousands of phosphorylated peptides and their associated proteins with the goal of interrogating changes in signal transduction pathways and other cellular processes. One of the most popular software suites to analyze phosphoproteomic data sets is MaxQuant, which converts mass spectrometric raw data into quantitative information on phosphopeptides and proteins. However, despite the increased utilization of phosphoproteomics in biomedical research, simple and user-friendly tools supporting downstream statistical analysis and interpretation of these highly complex outputs are still lacking. We have therefore developed Phospho-Analyst, which─similar to its sibling LFQ-Analyst─is an easy-to-use, interactive web application specifically designed to reproducibly perform differential expression analyses with "one click" and to visualize phosphoproteomic results in a meaningful and practical manner. Furthermore, if quantitative total proteomic information is available for the same samples, Phospho-Analyst automatically normalizes all phosphoproteomic results to underlying protein abundance levels, thereby ensuring that only genuine changes in phosphorylation events are considered. As such, Phospho-Analyst can not only be used by experienced proteomic veterans but also by researchers without any prior knowledge in (phospho)proteomics, statistics, or bioinformatics. Phospho-Analyst, including a detailed manual, is freely available at https://analyst-suites.org/apps/phospho-analyst/.

A phospho-dawn of protein modification anticipates light onset in the picoeukaryote Ostreococcus tauri.

Diel regulation of protein levels and protein modification had been less studied than transcript rhythms. Here, we compare transcriptome data under light-dark cycles with partial proteome and phosphoproteome data, assayed using shotgun MS, from the alga Ostreococcus tauri, the smallest free-living eukaryote. A total of 10% of quantified proteins but two-thirds of phosphoproteins were rhythmic. Mathematical modelling showed that light-stimulated protein synthesis can account for the observed clustering of protein peaks in the daytime. Prompted by night-peaking and apparently dark-stable proteins, we also tested cultures under prolonged darkness, where the proteome changed less than under the diel cycle. Among the dark-stable proteins were prasinophyte-specific sequences that were also reported to accumulate when O. tauri formed lipid droplets. In the phosphoproteome, 39% of rhythmic phospho-sites reached peak levels just before dawn. This anticipatory phosphorylation suggests that a clock-regulated phospho-dawn prepares green cells for daytime functions. Acid-directed and proline-directed protein phosphorylation sites were regulated in antiphase, implicating the clock-related casein kinases 1 and 2 in phase-specific regulation, alternating with the CMGC protein kinase family. Understanding the dynamic phosphoprotein network should be facilitated by the minimal kinome and proteome of O. tauri. The data are available from ProteomeXchange, with identifiers PXD001734, PXD001735, and PXD002909.

PhosphoDisco: A Toolkit for Co-regulated Phosphorylation Module Discovery in Phosphoproteomic Data

Kinases are key players in cancer-relevant pathways and are the targets of many successful precision cancer therapies. Phosphoproteomics is a powerful approach to study kinase activity and has been used increasingly for the characterization of tumor samples leading to the identification of novel chemotherapeutic targets and biomarkers. Finding co-regulated phosphorylation sites which represent potential kinase-substrate sets or members of the same signaling pathway allows us to harness these data to identify clinically relevant and targetable alterations in signaling cascades. Unfortunately, studies have found that databases of co-regulated phosphorylation sites are only experimentally supported in a small number of substrate sets. To address the inherent challenge of defining co-regulated phosphorylation modules relevant to a given dataset, we developed PhosphoDisco, a toolkit for determining co-regulated phosphorylation modules. We applied this approach to tandem mass spectrometry based phosphoproteomic data for breast and non-small cell lung cancer and identified canonical as well as putative new phosphorylation site modules. Our analysis identified several interesting modules in each cohort. Among these was a new cell cycle checkpoint module enriched in basal breast cancer samples and a module of PRKC isozymes putatively co-regulated by CDK12 in lung cancer. We demonstrate that modules defined by PhosphoDisco can be used to further personalized cancer treatment strategies by establishing active signaling pathways in a given patient tumor or set of tumors, and in providing new ways to classify tumors based on signaling activity.

Community detection in empirical kinase networks identifies new potential members of signalling pathways.

Phosphoproteomics allows one to measure the activity of kinases that drive the fluxes of signal transduction pathways involved in biological processes such as immune function, senescence and cell growth. However, deriving knowledge of signalling network circuitry from these data is challenging due to a scarcity of phosphorylation sites that define kinase-kinase relationships. To address this issue, we previously identified around 6,000 phosphorylation sites as markers of kinase-kinase relationships (that may be conceptualised as network edges), from which empirical cell-model-specific weighted kinase networks may be reconstructed. Here, we assess whether the application of community detection algorithms to such networks can identify new components linked to canonical signalling pathways. Phosphoproteomics data from acute myeloid leukaemia (AML) cells treated separately with PI3K, AKT, MEK and ERK inhibitors were used to reconstruct individual kinase networks. We used modularity maximisation to detect communities in each network, and selected the community containing the main target of the inhibitor used to treat cells. These analyses returned communities that contained known canonical signalling components. Interestingly, in addition to canonical PI3K/AKT/mTOR members, the community assignments returned TTK (also known as MPS1) as a likely component of PI3K/AKT/mTOR signalling. We drew similar insights from an external phosphoproteomics dataset from breast cancer cells treated with rapamycin and oestrogen. We confirmed this observation with wet-lab laboratory experiments showing that TTK phosphorylation was decreased in AML cells treated with AKT and MTOR inhibitors. This study illustrates the application of community detection algorithms to the analysis of empirical kinase networks to uncover new members linked to canonical signalling pathways.

The soybean immune receptor GmBIR1 regulates host transcriptome, spliceome, and immunity during cyst nematode infection.

BAK1-INTERACTING RECEPTOR LIKE KINASE1 (BIR1) is a negative regulator of various aspects of disease resistance and immune responses. Here, we investigated the functional role of soybean (Glycine max) BIR1 (GmBIR1) during soybean interaction with soybean cyst nematode (SCN, Heterodera glycines) and the molecular mechanism through which GmBIR1 regulates plant immunity. Overexpression of wild-type variant of GmBIR1 (WT-GmBIR1) using transgenic soybean hairy roots significantly increased soybean susceptibility to SCN, whereas overexpression of kinase-dead variant (KD-GmBIR1) significantly increased plant resistance. Transcriptome analysis revealed that genes oppositely regulated in WT-GmBIR1 and KD-GmBIR1 upon SCN infection were enriched primarily in defense and immunity-related functions. Quantitative phosphoproteomic analysis identified 208 proteins as putative substrates of the GmBIR1 signaling pathway, 114 of which were differentially phosphorylated upon SCN infection. In addition, the phosphoproteomic data pointed to a role of the GmBIR1 signaling pathway in regulating alternative pre-mRNA splicing. Genome-wide analysis of splicing events provided compelling evidence supporting a role of the GmBIR1 signaling pathway in establishing alternative splicing during SCN infection. Our results provide novel mechanistic insights into the function of the GmBIR1 signaling pathway in regulating soybean transcriptome and spliceome via differential phosphorylation of splicing factors and regulation of splicing events of pre-mRNA decay- and spliceosome-related genes.

A comprehensive mechanistic model of adipocyte signaling with layers of confidence

Adipocyte signaling, normally and in type 2 diabetes, is far from fully understood. We have earlier developed detailed dynamic mathematical models for several well-studied, partially overlapping, signaling pathways in adipocytes. Still, these models only cover a fraction of the total cellular response. For a broader coverage of the response, large-scale phosphoproteomic data and systems level knowledge on protein interactions are key. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. We have developed a method to first establish a core model by connecting existing models of adipocyte cellular signaling for: (1) lipolysis and fatty acid release, (2) glucose uptake, and (3) the release of adiponectin. Next, we use publicly available phosphoproteome data for the insulin response in adipocytes together with prior knowledge on protein interactions, to identify phosphosites downstream of the core model. In a parallel pairwise approach with low computation time, we test whether identified phosphosites can be added to the model. We iteratively collect accepted additions into layers and continue the search for phosphosites downstream of these added layers. For the first 30 layers with the highest confidence (311 added phosphosites), the model predicts independent data well (70–90% correct), and the predictive capability gradually decreases when we add layers of decreasing confidence. In total, 57 layers (3059 phosphosites) can be added to the model with predictive ability kept. Finally, our large-scale, layered model enables dynamic simulations of systems-wide alterations in adipocytes in type 2 diabetes.

Integrated proteomics and phosphoproteomics profiling reveals the cardioprotective mechanism of bioactive compounds derived from Salvia miltiorrhiza Burge

BackgroundNatural products are an important source for discovering novel drugs due to their various pharmacological activities. Salvia miltiorrhiza Burge (Danshen) has been shown to have promising therapeutic potential in the management of heart diseases, making it a candidate for cardiovascular drug discovery. Currently, there is limited quantitative analysis of the phosphorylation levels of Danshen-derived natural products on a proteome-wide, which may bias the study of their mechanisms of action. PurposeThis study aimed to evaluate the global signaling perturbation induced by Danshen-derived bioactive compounds and their potential relationship with myocardial ischemia/reperfusion (IR) injury therapy. Study designWe employed quantitative proteome and phosphoproteome analysis to identify dysregulated signaling in IR injury hearts from mice. We compared changes induced by Danshen-derived compounds based on IR-associated phospho-events, using an integrative approach that maps relative abundance of proteins and phosphorylation sites. MethodsIsobaric chemical tandem mass tags (TMT) labeled multiplexing strategy was used to generate unbiased quantitative proteomics and phosphoproteomics data. Highly accurate and precise TMT quantitation was performed using the Orbitrap Fusion Tribrid Mass Spectrometer with synchronous precursor selection MS3 detection mode. Mass spectrometric raw files were analyzed with MaxQuant (2.0.1.0) and statistical and bioinformatics analysis was conducted with Perseus (1.6.15). ResultsWe quantified 3661 proteins and over 11,000 phosphosites in impaired heart tissue of the IR mice model, expanding our knowledge of signaling pathways and other biological processes disrupted in IR injury. Next, 1548 and 5545 differently expressed proteins and phosphosites were identified by quantifying the proteome and phosphoproteome of H9c2 cells treated by five Danshen bioactive compounds respectively. Results revealed the vast differences in abilities of five Danshen-derived bioactive compounds to regulate phosphorylation modifications in cardiomyocytes, with dihydrotanshinone I (DHT) showing potential for protecting against IR injury by modulating the AMPK/mTOR signaling pathway. ConclusionsThis study provides a new strategy for analyzing drug/natural product-regulated phosphorylation modification levels on a proteome-wide scale, leading to a better understanding of cell signaling pathways and downstream phenotypic responses.

L-Ala-L-Gln Suppresses Hypoxic Phenotype and Fibrogenic Activity of Rat Perineurial Fibroblasts.

Twenty million Americans suffer from peripheral nerve injury (PNI) and approximately $150 billion is spent annually in the United States for the treatment of nerve injuries. Moreover, 50,000 cases of PNI repairs are performed annually in the United States, with even less than 42% experiencing satisfactory sensory recovery. Available therapies control painful symptoms but do not treat axonal degeneration or neuronal cell death. Peripheral nerve fibrosis (PNF) associated with chronic inflammation, perineural adhesions, and scarring is often reported in patients with nerve injury. Unfortunately, post-surgical adhesions and fibrosis often lead to aberrated wound healing and impairment of nerve functions. Various treatment strategies have been attempted, including the use of grafts and biomaterials; however, few appear promising. L-Alanyl-L-Glutamine (L-Ala-L-Gln) was reported to protect the lung from sepsisinduced injury and play an immunomodulatory role in stress and fibrosis. This study aimed to examine the potential anti-fibrotic effects of L-Ala-L-Gln in an in vitro model of neural fibrosis. Primary fibroblasts isolated from rat sciatic nerve were exposed to chronic (48 h) and episodic (2 h) hypoxic conditions. Cultures were then treated for 48 h with various concentrations of L-Ala-L-Gln (0, 1, 10, and 100 mM). The expression of hypoxic and pro-fibrotic markers in the different culture conditions was assessed by immunocytochemistry and western blot analyses. Quantitative phosphor-proteomic profiling was performed to investigate mechanistically the impact of L-Ala- L-Gln on collagen biosynthesis and hypoxia-driven tissue fibrosis in vitro. In protein expression assays, L-Ala-L-Gln significantly reduced markers related to the cellular response to hypoxia, in particular HIF-1 signaling. L-Ala-L-Gln also significantly reduced the expression of pro-fibrotic and cell-adhesion-inducing factors. Phospho-proteomic data indicated that L-Ala-L-Gln modulates several pro-fibrotic factors and associated pathways. Altogether, our data demonstrate that L-Ala-L-Gln efficiently suppresses hypoxiamediated fibrotic processes at different concentrations in rat primary fibroblasts. Thus, L-Ala-L-Gln presents a high potential therapeutic value as an antifibrotic pharmaceutical agent for the treatment of neural fibrosis.

Evaluation of DDA Library-Free Strategies for Phosphoproteomics and Ubiquitinomics Data-Independent Acquisition Data.

Phosphoproteomics and ubiquitinomics data-independent acquisition (DIA) mass spectrometry (MS) data is typically analyzed by using a data-dependent acquisition (DDA) spectral library. The performance of various library-free strategies for analyzing phosphoproteomics and ubiquitinomics DIA MS data has not been evaluated. In this study, we systematically compare four commonly used DDA library-free approaches including Spectronaut's directDIA, DIA-Umpire, DIA-MSFragger, and in silico-predicted library for analysis of phosphoproteomics SWATH, DIA, and diaPASEF data as well as ubiquitinomics diaPASEF data. Spectronaut's directDIA shows the highest sensitivity for phosphopeptide detection not only in synthetic phosphopeptide samples but also in phosphoproteomics SWATH-MS and DIA data from real biological samples, when compared to the other three library-free strategies. For phosphoproteomics diaPASEF data, Spectronaut's directDIA and the in silico-predicted library based on DIA-NN identify almost the same number of phosphopeptides as a project-specific DDA spectral library. However, only about 30% of the total phosphopeptides are commonly identified, suggesting that the library-free strategies for phospho-diaPASEF data need further improvement in terms of sensitivity. For ubiquitinomics diaPASEF data, the in silico-predicted library performs the best among the four workflows and detects ∼50% more K-GG peptides than a project-specific DDA spectral library. Our results demonstrate that Spectronaut's directDIA is suitable for the analysis of phosphoproteomics SWATH-MS and DIA MS data, while the in silico-predicted library based on DIA-NN shows substantial advantages for ubiquitinomics diaPASEF MS data.

Enzymatic Phosphorylation of Oxidized Tyrosine Residues.

Post-translational modifications (PTMs) alter the function and fate of proteins and cells in almost every conceivable way. Protein modifications can occur as a result of specific regulating actions of enzymes, such as tyrosine kinases phosphorylating tyrosine residues or by nonenzymatic reactions, such as oxidation related to oxidative stress and diseases. While many studies have addressed the multisite, dynamic, and network-like properties of PTMs, only little is known of the interplay of the same site modifications. In this work, we studied the enzymatic phosphorylation of oxidized tyrosine (l-DOPA) residues using synthetic insulin receptor peptides, in which tyrosine residues were replaced with l-DOPA. The phosphorylated peptides were identified by liquid chromatography-high-resolution mass spectrometry and the site of phosphorylation by tandem mass spectrometry. The results clearly show that the oxidized tyrosine residues are phosphorylated, displaying a specific immonium ion peak in the MS2 spectra. Furthermore, we detected this modification in our reanalysis (MassIVE ID: MSV000090106) of published bottom-up phosphoproteomics data. The modification, where both oxidation and phosphorylation take place at the same amino acid, has not yet been published in PTM databases. Our data indicate that there can be multiple PTMs that do not exclude each other at the same modification site.

Abstract A007: Proteogenomic landscape of KRASG12C lung adenocarcinomas reveals new subtypes and potential combination therapies

Abstract Despite the presence of the KRASG12C oncogene, not all patients respond to KRASG12C inhibitors (e.g., sotorasib, adagrasib) and duration of response has been variable. We hypothesized that subtypes of KRASG12C lung cancers could be responsible for such differential effects. To define subtypes, we performed targeted exon sequencing, transcriptomics, proteomics, and phosphoproteomics on 46 frozen surgically resected KRASG12C lung adenocarcinomas. We employed unbiased consensus clustering methods, pathway analysis, and PTM-SEA to identify kinase signatures from phosphoproteomics data. Transcriptomics data revealed 4 clusters. Pathway enrichment showed that gene cluster (GC) 1 was enriched for immune pathways including interferon gamma response. Epithelial-mesenchymal transition (EMT), assessed by a TGF-β gene signature, was also enriched in GC1. 100% of GC1 overlapped the Proximal-Inflammatory subtype (Wilkerson et al.) and 53% with the mesenchymal subtype (Daemen et al.). GC2 was enriched for inflammatory response and TNF-alpha signaling. GC3 represented a metabolic phenotype with enriched xenobiotic metabolism and other metabolism pathways. 83% of GC3 overlapped with the Terminal Respiratory Unit subtype (Wilkerson et al.) and 56% with the proliferative subtype (Daemen et al.). GC4 only had 5 samples and did not have any significant enrichment. We next identified four proteomic clusters with distinct biology. Protein Cluster 1 (PC1) was enriched for immune pathways, PC2 was enriched for EMT, and PC3 was enriched for TNF/NF-κβ signaling. PC4 was enriched for xenobiotic and fatty acid metabolism and had significantly more KEAP1 mutations. We observed concordance of protein based clusters with mRNA based clusters, as PC1 largely overlapped with GC1 (70%) and PC4 largely overlapped with GC3 (86%). Cell type inference using xCell largely recapitulated the immune subtypes identified by transcriptomics and proteomics. GC1/PC1 had significantly more CD8+ effector memory T-cells, CD4+ Th2 T-cells, and M1 macrophages, while GC3/PC4 had significantly less of these same cell types. Collectively, GC1/PC1 represents 43% of the cohort and was defined by enrichment of immune pathways, elevated immune populations, and significantly higher PD-L1. Finally, we leveraged the phosphoproteomics data to determine whether signaling activity differs across subtypes. Using PTM-SEA, we identified phosphoproteomic signatures of activity for 50 kinases and pathways. We identified 26% of samples (N=12) with high EGFR activity, which is known to affect sensitivity of cells to KRASG12C inhibitors. The EGFR-high samples were observed across all genomic, transcriptomic, and proteomic subtypes. Our results suggest concordance of some transcriptomic and proteomic subtypes, including an immune subtype, while a metabolic protein subtype and EGFR high signaling subtype may provide additional subclasses with potential relevance for treatment responses. Additional phosphoproteomics signatures such as PLK1 and AKT1 may identify subtypes amenable to combination therapy approaches. Citation Format: Paul A. Stewart, Hitendra Solanki, Eric Welsh, Yaakov Stern, Denis Imbody, Yonghong Zhang, Bruna Pellini, Bin Fang, Sean Yoder, Steven Eschrich, Jamie Teer, John Koomen, Eric B. Haura. Proteogenomic landscape of KRASG12C lung adenocarcinomas reveals new subtypes and potential combination therapies [abstract]. In: Proceedings of the AACR Special Conference: Targeting RAS; 2023 Mar 5-8; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Res 2023;21(5_Suppl):Abstract nr A007.