Integrative Proteomic Characterization of Human Lung Adenocarcinoma.

Genomic studies of lung adenocarcinoma (LUAD) have advanced our understanding of the disease's biology and accelerated targeted therapy. However, the proteomic characteristics of LUAD remain poorly understood. We carried out a comprehensive proteomics analysis of 103 cases of LUAD in Chinese patients. Integrative analysis of proteome, phosphoproteome, transcriptome, and whole-exome sequencing data revealed cancer-associated characteristics, such as tumor-associated protein variants, distinct proteomics features, and clinical outcomes in patients at an early stage or with EGFR and TP53 mutations. Proteome-based stratification of LUAD revealed three subtypes (S-I, S-II, and S-III) related to different clinical and molecular features. Further, we nominated potential drug targets and validated the plasma protein level of HSP 90ß as a potential prognostic biomarker for LUAD in an independent cohort. Our integrative proteomics analysis enables a more comprehensive understanding of the molecular landscape of LUAD and offers an opportunity for more precise diagnosis and treatment.

Posttranscriptional regulation of de novo lipogenesis by glucose-induced O-GlcNAcylation.

O-linked ß-N-acetyl glucosamine (O-GlcNAc) is attached to proteins under glucose-replete conditions; this posttranslational modification results in molecular and physiological changes that affect cell fate. Here we show that posttranslational modification of serine/arginine-rich protein kinase 2 (SRPK2) by O-GlcNAc regulates de novo lipogenesis by regulating pre-mRNA splicing. We found that O-GlcNAc transferase O-GlcNAcylated SRPK2 at a nuclear localization signal (NLS), which triggers binding of SRPK2 to importin α. Consequently, O-GlcNAcylated SRPK2 was imported into the nucleus, where it phosphorylated serine/arginine-rich proteins and promoted splicing of lipogenic pre-mRNAs. We determined that protein nuclear import by O-GlcNAcylation-dependent binding of cargo protein to importin α might be a general mechanism in cells. This work reveals a role of O-GlcNAc in posttranscriptional regulation of de novo lipogenesis, and our findings indicate that importin α is a "reader" of an O-GlcNAcylated NLS.

A proteomic and phosphoproteomic landscape of KRAS mutant cancers identifies combination therapies.

KRAS mutant cancer, characterized by the activation of a plethora of phosphorylation signaling pathways, remains a major challenge for cancer therapy. Despite recent advancements, a comprehensive profile of the proteome and phosphoproteome is lacking. This study provides a proteomic and phosphoproteomic landscape of 43 KRAS mutant cancer cell lines across different tissue origins. By integrating transcriptomics, proteomics, and phosphoproteomics, we identify three subsets with distinct biological, clinical, and therapeutic characteristics. The integrative analysis of phosphoproteome and drug sensitivity information facilitates the identification of a set of drug combinations with therapeutic potentials. Among them, we demonstrate that the combination of DOT1L and SHP2 inhibitors is an effective treatment specific for subset 2 of KRAS mutant cancers, corresponding to a set of TCGA clinical tumors with the poorest prognosis. Together, this study provides a resource to better understand KRAS mutant cancer heterogeneity and identify new therapeutic possibilities.

OTULIN allies with LUBAC to govern angiogenesis by editing ALK1 linear polyubiquitin.

OTULIN coordinates with LUBAC to edit linear polyubiquitin chains in embryonic development, autoimmunity, and inflammatory diseases. However, the mechanism by which angiogenesis, especially that of endothelial cells (ECs), is regulated by linear ubiquitination remains unclear. Here, we reveal that constitutive or EC-specific deletion of Otulin resulted in arteriovenous malformations and embryonic lethality. LUBAC conjugates linear ubiquitin chains onto Activin receptor-like kinase 1 (ALK1), which is responsible for angiogenesis defects, inhibiting ALK1 enzyme activity and Smad1/5 activation. Conversely, OTULIN deubiquitinates ALK1 to promote Smad1/5 activation. Consistently, embryonic survival of Otulin-deficient mice was prolonged by BMP9 pretreatment or EC-specific ALK1Q200D (constitutively active) knockin. Moreover, mutant ALK1 from type 2 hereditary hemorrhagic telangiectasia (HHT2) patients exhibited excessive linear ubiquitination and increased HOIP binding. As such, a HOIP inhibitor restricted the excessive angiogenesis of ECs derived from ALK1G309S-expressing HHT2 patients. These results show that OTULIN and LUBAC govern ALK1 activity to balance EC angiogenesis.

Proteomics Links Ubiquitin Chain Topology Change to Transcription Factor Activation.

A surprising complexity of ubiquitin signaling has emerged with identification of different ubiquitin chain topologies. However, mechanisms of how the diverse ubiquitin codes control biological processes remain poorly understood. Here, we use quantitative whole-proteome mass spectrometry to identify yeast proteins that are regulated by lysine 11 (K11)-linked ubiquitin chains. The entire Met4 pathway, which links cell proliferation with sulfur amino acid metabolism, was significantly affected by K11 chains and selected for mechanistic studies. Previously, we demonstrated that a K48-linked ubiquitin chain represses the transcription factor Met4. Here, we show that efficient Met4 activation requires a K11-linked topology. Mechanistically, our results propose that the K48 chain binds to a topology-selective tandem ubiquitin binding region in Met4 and competes with binding of the basal transcription machinery to the same region. The change to K11-enriched chain architecture releases this competition and permits binding of the basal transcription complex to activate transcription.

Introducing π-HelixNovo for practical large-scale de novo peptide sequencing.

De novo peptide sequencing is a promising approach for novel peptide discovery, highlighting the performance improvements for the state-of-the-art models. The quality of mass spectra often varies due to unexpected missing of certain ions, presenting a significant challenge in de novo peptide sequencing. Here, we use a novel concept of complementary spectra to enhance ion information of the experimental spectrum and demonstrate it through conceptual and practical analyses. Afterward, we design suitable encoders to encode the experimental spectrum and the corresponding complementary spectrum and propose a de novo sequencing model $\pi$-HelixNovo based on the Transformer architecture. We first demonstrated that $\pi$-HelixNovo outperforms other state-of-the-art models using a series of comparative experiments. Then, we utilized $\pi$-HelixNovo to de novo gut metaproteome peptides for the first time. The results show $\pi$-HelixNovo increases the identification coverage and accuracy of gut metaproteome and enhances the taxonomic resolution of gut metaproteome. We finally trained a powerful $\pi$-HelixNovo utilizing a larger training dataset, and as expected, $\pi$-HelixNovo achieves unprecedented performance, even for peptide-spectrum matches with never-before-seen peptide sequences. We also use the powerful $\pi$-HelixNovo to identify antibody peptides and multi-enzyme cleavage peptides, and $\pi$-HelixNovo is highly robust in these applications. Our results demonstrate the effectivity of the complementary spectrum and take a significant step forward in de novo peptide sequencing.

ZASP: A Highly Compatible and Sensitive ZnCl2 Precipitation-Assisted Sample Preparation Method for Proteomic Analysis.

Universal sample preparation for proteomic analysis that enables unbiased protein manipulation, flexible reagent use, and low protein loss is required to ensure the highest sensitivity of downstream liquid chromatography-mass spectrometry (LC-MS) analysis. To address these needs, we developed a ZnCl2 precipitation-assisted sample preparation method (ZASP) that depletes harsh detergents and impurities in protein solutions prior to trypsin digestion via 10 min of ZnCl2 and methanol-induced protein precipitation at room temperature (RT). ZASP can remove trypsin digestion and LC-MS incompatible detergents such as SDS, Triton X-100, and urea at high concentrations in solution and unbiasedly recover proteins independent of the amount of protein input. We demonstrated the sensitivity and reproducibility of ZASP in an analysis of samples with 1 µg to 1000 µg of proteins. Compared to commonly used sample preparation methods such as SDC-based in-solution digestion, acetone precipitation, FASP, and SP3, ZASP has proven to be an efficient approach. Here, we present ZASP, a practical, robust, and cost-effective proteomic sample preparation method that can be applied to profile different types of samples.

CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment.

Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating. However, a lack of systematic collection makes data access, integration, and reuse challenging. We therefore present the Cancer Drug-induced gene expression Signature DataBase (CDS-DB). CDS-DB has collected 78 patient-derived, paired pre- and post-treatment transcriptomic source datasets with uniformly reprocessed expression profiles and manually curated metadata such as drug administration dosage, sampling time and location, and intrinsic drug response status. From these source datasets, 2012 patient-level gene perturbation signatures were obtained, covering 85 therapeutic regimens, 39 cancer subtypes and 3628 patient samples. Besides data browsing, download and search, CDS-DB also supports single signature analysis (including differential gene expression, functional enrichment, tumor microenvironment and correlation analyses), signature comparative analysis and signature connectivity analysis. This provides insights into drug MOA and its heterogeneity in patients, drug resistance mechanisms, drug repositioning and drug (combination) discovery, etc. CDS-DB is available at http://cdsdb.ncpsb.org.cn/.

Aberrant LYZ expression in tumor cells serves as the potential biomarker and target for HCC and promotes tumor progression via csGRP78.

Hepatocellular carcinoma (HCC) takes the predominant malignancy of hepatocytes with bleak outcomes owing to high heterogeneity among patients. Personalized treatments based on molecular profiles will better improve patients' prognosis. Lysozyme (LYZ), a secretory protein with antibacterial function generally expressed in monocytes/macrophages, has been observed for the prognostic implications in different types of tumors. However, studies about the explicit applicative scenarios and mechanisms for tumor progression are still quite limited, especially for HCC. Here, based on the proteomic molecular classification data of early-stage HCC, we revealed that the LYZ level was elevated significantly in the most malignant HCC subtype and could serve as an independent prognostic predictor for HCC patients. Molecular profiles of LYZ-high HCCs were typical of those for the most malignant HCC subtype, with impaired metabolism, along with promoted proliferation and metastasis characteristics. Further studies demonstrated that LYZ tended to be aberrantly expressed in poorly differentiated HCC cells, which was regulated by STAT3 activation. LYZ promoted HCC proliferation and migration in both autocrine and paracrine manners independent of the muramidase activity through the activation of downstream protumoral signaling pathways via cell surface GRP78. Subcutaneous and orthotopic xenograft tumor models indicated that targeting LYZ inhibited HCC growth markedly in NOD/SCID mice. These results propose LYZ as a prognostic biomarker and therapeutic target for the subclass of HCC with an aggressive phenotype.

Proteomic Characterization Identifies Clinically Relevant Subgroups of Gastrointestinal Stromal Tumors.

BACKGROUND & AIMS: Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor of the gastrointestinal tract, and it has high metastatic and recurrence rates. We aimed to characterize the proteomic features of GIST to understand biological processes and treatment vulnerabilities. METHODS: Quantitative proteomics and phosphoproteomics analyses were performed on 193 patients with GIST to reveal the biological characteristics of GIST. Data-driven hypotheses were tested by performing functional experiments using both GIST cell lines and xenograft mouse models. RESULTS: Proteomic analysis revealed differences in the molecular features of GISTs from different locations or with different histological grades. MAPK7 was identified and functionally proved to be associated with tumor cell proliferation in GIST. Integrative analysis revealed that increased SQSTM1 expression inhibited the patient response to imatinib mesylate. Proteomics subtyping identified 4 clusters of tumors with different clinical and molecular attributes. Functional experiments confirmed the role of SRSF3 in promoting tumor cell proliferation and leading to poor prognosis. CONCLUSIONS: Our study provides a valuable data resource and highlights potential therapeutic approaches for GIST.

Nucleophilic covalent ligand discovery for the cysteine redoxome.

With an eye toward expanding chemistries used for covalent ligand discovery, we elaborated an umpolung strategy that exploits the 'polarity reversal' of sulfur when cysteine is oxidized to sulfenic acid, a widespread post-translational modification, for selective bioconjugation with C-nucleophiles. Here we present a global map of a human sulfenome that is susceptible to covalent modification by members of a nucleophilic fragment library. More than 500 liganded sulfenic acids were identified on proteins across diverse functional classes, and, of these, more than 80% were not targeted by electrophilic fragment analogs. We further show that members of our nucleophilic fragment library can impair functional protein-protein interactions involved in nuclear oncoprotein transport and DNA damage repair. Our findings reveal a vast expanse of ligandable sulfenic acids in the human proteome and highlight the utility of nucleophilic small molecules in the fragment-based covalent ligand discovery pipeline, presaging further opportunities using non-traditional chemistries for targeting proteins.

Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma.

Hepatocellular carcinoma is the third leading cause of deaths from cancer worldwide. Infection with the hepatitis B virus is one of the leading risk factors for developing hepatocellular carcinoma, particularly in East Asia1. Although surgical treatment may be effective in the early stages, the five-year overall rate of survival after developing this cancer is only 50-70%2. Here, using proteomic and phospho-proteomic profiling, we characterize 110 paired tumour and non-tumour tissues of clinical early-stage hepatocellular carcinoma related to hepatitis B virus infection. Our quantitative proteomic data highlight heterogeneity in early-stage hepatocellular carcinoma: we used this to stratify the cohort into the subtypes S-I, S-II and S-III, each of which has a different clinical outcome. S-III, which is characterized by disrupted cholesterol homeostasis, is associated with the lowest overall rate of survival and the greatest risk of a poor prognosis after first-line surgery. The knockdown of sterol O-acyltransferase 1 (SOAT1)-high expression of which is a signature specific to the S-III subtype-alters the distribution of cellular cholesterol, and effectively suppresses the proliferation and migration of hepatocellular carcinoma. Finally, on the basis of a patient-derived tumour xenograft mouse model of hepatocellular carcinoma, we found that treatment with avasimibe, an inhibitor of SOAT1, markedly reduced the size of tumours that had high levels of SOAT1 expression. The proteomic stratification of early-stage hepatocellular carcinoma presented in this study provides insight into the tumour biology of this cancer, and suggests opportunities for personalized therapies that target it.

Proteomics Identifies LUC7L3 as a Prognostic Biomarker for Hepatocellular Carcinoma.

Alternative splicing has been shown to participate in tumor progression, including hepatocellular carcinoma. The poor prognosis of patients with HCC calls for molecular classification and biomarker identification to facilitate precision medicine. We performed ssGSEA analysis to quantify the pathway activity of RNA splicing in three HCC cohorts. Kaplan-Meier and Cox methods were used for survival analysis. GO and GSEA were performed to analyze pathway enrichment. We confirmed that RNA splicing is significantly correlated with prognosis, and identified an alternative splicing-associated protein LUC7L3 as a potential HCC prognostic biomarker. Further bioinformatics analysis revealed that high LUC7L3 expression indicated a more progressive HCC subtype and worse clinical features. Cell proliferation-related pathways were enriched in HCC patients with high LUC7L3 expression. Consistently, we proved that LUC7L3 knockdown could significantly inhibit cell proliferation and suppress the activation of associated signaling pathways in vitro. In this research, the relevance between RNA splicing and HCC patient prognosis was outlined. Our newly identified biomarker LUC7L3 could provide stratification for patient survival and recurrence risk, facilitating early medical intervention before recurrence or disease progression.

Macrophage-Derived Cathepsin S Remodels the Extracellular Matrix to Promote Liver Fibrogenesis.

BACKGROUND & AIMS: Liver fibrosis is an intrinsic wound-healing response to chronic injury and the major cause of liver-related morbidity and mortality worldwide. However, no effective diagnostic or therapeutic strategies are available, owing to its poorly characterized molecular etiology. We aimed to elucidate the mechanisms underlying liver fibrogenesis. METHODS: We performed a quantitative proteomic analysis of clinical fibrotic liver samples to identify dysregulated proteins. Further analyses were performed on the sera of 164 patients with liver fibrosis. Two fibrosis mouse models and several biochemical experiments were used to elucidate liver fibrogenesis. RESULTS: We identified cathepsin S (CTSS) up-regulation as a central node for extracellular matrix remodeling in the human fibrotic liver by proteomic screening. Increased serum CTSS levels efficiently predicted liver fibrosis, even at an early stage. Secreted CTSS cleaved collagen 18A1 at its C-terminus, releasing endostatin peptide, which directly bound to and activated hepatic stellate cells via integrin α5ß1 signaling, whereas genetic ablation of Ctss remarkably suppressed liver fibrogenesis via endostatin reduction in vivo. Further studies identified macrophages as the main source of hepatic CTSS, and splenectomy effectively attenuated macrophage infiltration and CTSS expression in the fibrotic liver. Pharmacologic inhibition of CTSS ameliorated liver fibrosis progression in the mouse models. CONCLUSIONS: CTSS functions as a novel profibrotic factor by remodeling extracellular matrix proteins and may represent a promising target for the diagnosis and treatment of liver fibrosis.

A modification-centric assessment tool for the performance of chemoproteomic probes.

Chemoproteomics has emerged as a key technology to expand the functional space in complex proteomes for probing fundamental biology and for discovering new small-molecule-based therapies. Here we report a modification-centric computational tool termed pChem to provide a streamlined pipeline for unbiased performance assessment of chemoproteomic probes. The pipeline starts with an experimental setting for isotopically coding probe-derived modifications that can be automatically recognized by pChem, with masses accurately calculated and sites precisely localized. pChem exports on-demand reports by scoring the profiling efficiency, modification homogeneity and proteome-wide residue selectivity of a tested probe. The performance and robustness of pChem were benchmarked by applying it to eighteen bioorthogonal probes. These analyses reveal that the formation of unexpected probe-derived modifications can be driven by endogenous reactive metabolites (for example, bioactive aldehydes and glutathione). pChem is a powerful and user-friendly tool that aims to facilitate the development of probes for the ever-growing field of chemoproteomics.

iProX in 2021: connecting proteomics data sharing with big data.

The rapid development of proteomics studies has resulted in large volumes of experimental data. The emergence of big data platform provides the opportunity to handle these large amounts of data. The integrated proteome resource, iProX (https://www.iprox.cn), which was initiated in 2017, has been greatly improved with an up-to-date big data platform implemented in 2021. Here, we describe the main iProX developments since its first publication in Nucleic Acids Research in 2019. First, a hyper-converged architecture with high scalability supports the submission process. A hadoop cluster can store large amounts of proteomics datasets, and a distributed, RESTful-styled Elastic Search engine can query millions of records within one second. Also, several new features, including the Universal Spectrum Identifier (USI) mechanism proposed by ProteomeXchange, RESTful Web Service API, and a high-efficiency reanalysis pipeline, have been added to iProX for better open data sharing. By the end of August 2021, 1526 datasets had been submitted to iProX, reaching a total data volume of 92.42TB. With the implementation of the big data platform, iProX can support PB-level data storage, hundreds of billions of spectra records, and second-level latency service capabilities that meet the requirements of the fast growing field of proteomics.

CTR-DB, an omnibus for patient-derived gene expression signatures correlated with cancer drug response.

To date, only some cancer patients can benefit from chemotherapy and targeted therapy. Drug resistance continues to be a major and challenging problem facing current cancer research. Rapidly accumulated patient-derived clinical transcriptomic data with cancer drug response bring opportunities for exploring molecular determinants of drug response, but meanwhile pose challenges for data management, integration, and reuse. Here we present the Cancer Treatment Response gene signature DataBase (CTR-DB, http://ctrdb.ncpsb.org.cn/), a unique database for basic and clinical researchers to access, integrate, and reuse clinical transcriptomes with cancer drug response. CTR-DB has collected and uniformly reprocessed 83 patient-derived pre-treatment transcriptomic source datasets with manually curated cancer drug response information, involving 28 histological cancer types, 123 drugs, and 5139 patient samples. These data are browsable, searchable, and downloadable. Moreover, CTR-DB supports single-dataset exploration (including differential gene expression, receiver operating characteristic curve, functional enrichment, sensitizing drug search, and tumor microenvironment analyses), and multiple-dataset combination and comparison, as well as biomarker validation function, which provide insights into the drug resistance mechanism, predictive biomarker discovery and validation, drug combination, and resistance mechanism heterogeneity.

UbiBrowser 2.0: a comprehensive resource for proteome-wide known and predicted ubiquitin ligase/deubiquitinase-substrate interactions in eukaryotic species.

As an important post-translational modification, ubiquitination mediates ∼80% of protein degradation in eukaryotes. The degree of protein ubiquitination is tightly determined by the delicate balance between specific ubiquitin ligase (E3)-mediated ubiquitination and deubiquitinase-mediated deubiquitination. In 2017, we developed UbiBrowser 1.0, which is an integrated database for predicted human proteome-wide E3-substrate interactions. Here, to meet the urgent requirement of proteome-wide E3/deubiquitinase-substrate interactions (ESIs/DSIs) in multiple organisms, we updated UbiBrowser to version 2.0 (http://ubibrowser.ncpsb.org.cn). Using an improved protocol, we collected 4068/967 known ESIs/DSIs by manual curation, and we predicted about 2.2 million highly confident ESIs/DSIs in 39 organisms, with >210-fold increase in total data volume. In addition, we made several new features in the updated version: (i) it allows exploring proteins' upstream E3 ligases and deubiquitinases simultaneously; (ii) it has significantly increased species coverage; (iii) it presents a uniform confidence scoring system to rank predicted ESIs/DSIs. To facilitate the usage of UbiBrowser 2.0, we also redesigned the web interface for exploring these known and predicted ESIs/DSIs, and added functions of 'Browse', 'Download' and 'Application Programming Interface'. We believe that UbiBrowser 2.0, as a discovery tool, will contribute to the study of protein ubiquitination and the development of drug targets for complex diseases.

RUPE-phospho: Rapid Ultrasound-Assisted Peptide-Identification-Enhanced Phosphoproteomics Workflow for Microscale Samples.

Global phosphoproteome profiling can provide insights into cellular signaling and disease pathogenesis. To achieve comprehensive phosphoproteomic analyses with minute quantities of material, we developed a rapid and sensitive phosphoproteomics sample preparation strategy based on ultrasound. We found that ultrasonication-assisted digestion can significantly improve peptide identification by 20% due to the generation of longer peptides that can be detected by mass spectrometry. By integrating this rapid ultrasound-assisted peptide-identification-enhanced proteomic method (RUPE) with streamlined phosphopeptide enrichment steps, we established RUPE-phospho, a fast and efficient strategy to characterize protein phosphorylation in mass-limited samples. This approach dramatically reduces the sample loss and processing time: 24 samples can be processed in 3 h; 5325 phosphosites, 4549 phosphopeptides, and 1888 phosphoproteins were quantified from 5 µg of human embryonic kidney (HEK) 293T cell lysate. In addition, 9219 phosphosites were quantified from 1-2 mg of OCT-embedded mouse brain with 120 min streamlined RUPE-phospho workflow. RUPE-phospho facilitates phosphoproteome profiling for microscale samples and will provide a powerful tool for proteomics-driven precision medicine research.

TSMiner: a novel framework for generating time-specific gene regulatory networks from time-series expression profiles.

Time-series gene expression profiles are the primary source of information on complicated biological processes; however, capturing dynamic regulatory events from such data is challenging. Herein, we present a novel analytic tool, time-series miner (TSMiner), that can construct time-specific regulatory networks from time-series expression profiles using two groups of genes: (i) genes encoding transcription factors (TFs) that are activated or repressed at a specific time and (ii) genes associated with biological pathways showing significant mutual interactions with these TFs. Compared with existing methods, TSMiner demonstrated superior sensitivity and accuracy. Additionally, the application of TSMiner to a time-course RNA-seq dataset associated with mouse liver regeneration (LR) identified 389 transcriptional activators and 49 transcriptional repressors that were either activated or repressed across the LR process. TSMiner also predicted 109 and 47 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significantly interacting with the transcriptional activators and repressors, respectively. These findings revealed the temporal dynamics of multiple critical LR-related biological processes, including cell proliferation, metabolism and the immune response. The series of evaluations and experiments demonstrated that TSMiner provides highly reliable predictions and increases the understanding of rapidly accumulating time-series omics data.