Identification of selective dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) inhibitors and their effects on tau and microtubule.

The overexpression of dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A), commonly observed in neurodegenerative diseases like Alzheimer's disease (AD) and Down syndrome (DS), can induce the formation of neurofibrillary tangles (NFTs) and amyloid plaques. Hence, designing a selective DYRK1A inhibitor would result in a promising small molecule for treating neurodegenerative diseases. Developing selective inhibitors for DYRK1A has been a difficult challenge due to the highly preserved ATP-binding site of protein kinases. In this study, we employed a structure-based virtual screening (SBVS) campaign targeting DYRK1A from a database containing 1.6 million compounds. Enzymatic assays were utilized to verify inhibitory properties, confirming that Y020-3945 and Y020-3957 showed inhibitory activity towards DYRK1A. In particular, the compounds exhibited high selectivity for DYRK1A over a panel of 120 kinases, reduced the phosphorylation of tau, and reversed the tubulin polymerization for microtubule stability. Additionally, treatment with the compounds significantly reduced the secretion of inflammatory cytokines IL-6 and TNF-α activated by DYRK1A-assisted NFTs and Aß oligomers. These identified inhibitors possess promising therapeutic potential for conditions associated with DYRK1A in neurodegenerative diseases. The results showed that Y020-3945 and Y020-3957 demonstrated structural novelty compared to known DYRK1A inhibitors, making them a valuable addition to developing potential treatments for neurodegenerative diseases.

Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter.

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms; its executable files, user manual, and sample data sets are freely available at https://ms.iis.sinica.edu.tw/comics/Software_FPF.html . Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline.

The study of a novel CDK8 inhibitor E966-0530-45418 that inhibits prostate cancer metastasis in vitro and in vivo.

Prostate cancer is a prevalent malignancy among men globally, and androgen deprivation therapy is the conventional first-line treatment for metastatic prostate cancer. While androgen deprivation therapy is efficacious in castration-sensitive prostate cancer, it remains less effective in castration-resistant cases. Transcriptional dysregulation is a well-established hallmark of cancer, and targeting proteins involved in transcriptional regulation, such as cyclin-dependent kinase 8 (CDK8), has become an attractive therapeutic strategy. CDK8, a nuclear serine-threonine kinase, is a key component of the mediator complex and plays a critical role in transcriptional regulation. Recent studies have highlighted the promising role of CDK8 as a target in the treatment of metastatic prostate cancer. Our study assessed the efficacy of a novel CDK8 inhibitor, E966-0530-45418, which exhibited potent CDK8 inhibition (IC50 of 129 nM) and high CDK8 selectivity. Treatment with E966-0530-45418 significantly inhibited prostate cancer cell migration and epithelial-to-mesenchymal transition (EMT) at both the RNA and protein levels. Further mechanistic analysis indicated that E966-0530-45418 suppresses prostate cancer metastasis by decreasing CDK8 activity and inhibiting TGF-ß1-mediated Smad3/RNA polymerase II linker phosphorylation and Akt/GSK3ß/ß-catenin signaling. The results in animal model also showed that E966-0530-45418 exhibited anti-metastatic properties in vivo. Our study demonstrated that E966-0530-45418 has great therapeutic potential in the treatment of metastatic prostate cancer.

Multiaspect Examinations of Possible Alternative Mappings of Identified Variant Peptides: A Case Study on the HEK293 Cell Line.

Adopting proteogenomics approach to validate single nucleotide variation events by identifying corresponding single amino acid variant peptides from mass spectrometry (MS)-based proteomics data facilitates translational and clinical research. Although variant peptides are usually identified from MS data with a stringent false discovery rate (FDR), FDR control could fail to eliminate dubious results caused by several issues; thus, postexamination to eliminate dubious results is required. However, comprehensive postexaminations of identification results are still lacking. Therefore, we propose a framework of three bottom-up levels, peptide-spectrum match, peptide, and variant event levels, that consists of rigorous 11-aspect examinations from the MS perspective to further confirm the reliability of variant events. As a proof of concept and showing feasibility, we demonstrate 11 examinations on the identified variant peptides from an HEK293 cell line data set, where various database search strategies were applied to maximize the number of identified variant PSMs with an FDR <1% for postexaminations. The results showed that only FDR criterion is insufficient to validate identified variant peptides and the 11 postexaminations can reveal low-confidence variant events detected by shotgun proteomics experiments. Therefore, we suggest that postexaminations of identified variant events based on the proposed framework are necessary for proteogenomics studies.

Calibr improves spectral library search for spectrum-centric analysis of data independent acquisition proteomics.

Identifying peptides and proteins from mass spectrometry (MS) data, spectral library searching has emerged as a complementary approach to the conventional database searching. However, for the spectrum-centric analysis of data-independent acquisition (DIA) data, spectral library searching has not been widely exploited because existing spectral library search tools are mainly designed and optimized for the analysis of data-dependent acquisition (DDA) data. We present Calibr, a spectral library search tool for spectrum-centric DIA data analysis. Calibr optimizes spectrum preprocessing for pseudo MS2 spectra, generating an 8.11% increase in spectrum-spectrum match (SSM) number and a 7.49% increase in peptide number over the traditional preprocessing approach. When searching against the DDA-based spectral library, Calibr improves SSM number by 17.6-26.65% and peptide number by 18.45-37.31% over two state-of-the-art tools on three different data sets. Searching against the public spectral library from MassIVE, Calibr improves state-of-the-art tools in SSM and peptide numbers by more than 31.49% and 25.24%, respectively, for two data sets. Our analyses indicate higher sensitivity of Calibr results from the use of various spectral similarity measures and statistical scores, coupled with machine learning-based statistical validation for FDR control. Calibr executable files including a graphical user-interface application are available at https://ms.iis.sinica.edu.tw/COmics/Software_CalibrWizard.html and https://sourceforge.net/projects/comics-calibr .

Somatic mutation subtypes of lung adenocarcinoma in East Asian reveal divergent biological characteristics and therapeutic vulnerabilities.

Lung adenocarcinoma (LUAD) patients in East Asia predominantly harbor oncogenic EGFR mutations. However, there remains a limited understanding of the biological characteristics and therapeutic vulnerabilities of the concurrent mutations of EGFR and other genes in LUAD. Here, we performed comprehensive bioinformatics analyses on 88 treatment-naïve East Asian LUAD patients. Based on somatic mutation clustering, we identified three somatic mutation subtypes: EGFR + TP53 co-mutation, EGFR mutation, and multiple-gene mutation. A proteogenomic analysis among subtypes revealed varying degrees of dysregulation in cell-cycle-related and immune-related processes. An immune-characteristic analysis revealed higher PDL1 protein expression in the EGFR + TP53 co-mutation subtype than in the EGFR mutation subtype, which may affect the therapeutic efficacy of anti-PD-L1 therapy. Moreover, integrating known and potential therapeutic target analysis reveals therapeutic vulnerabilities of specific subtypes and nominates candidate biomarkers for therapeutic intervention. This study provides new biological insight and therapeutic opportunities with respect to EGFR-mutant LUAD subtypes.

A data-independent acquisition-based global phosphoproteomics system enables deep profiling.

Phosphoproteomics can provide insights into cellular signaling dynamics. To achieve deep and robust quantitative phosphoproteomics profiling for minute amounts of sample, we here develop a global phosphoproteomics strategy based on data-independent acquisition (DIA) mass spectrometry and hybrid spectral libraries derived from data-dependent acquisition (DDA) and DIA data. Benchmarking the method using 166 synthetic phosphopeptides shows high sensitivity (<0.1 ng), accurate site localization and reproducible quantification (~5% median coefficient of variation). As a proof-of-concept, we use lung cancer cell lines and patient-derived tissue to construct a hybrid phosphoproteome spectral library covering 159,524 phosphopeptides (88,107 phosphosites). Based on this library, our single-shot streamlined DIA workflow quantifies 36,350 phosphosites (19,755 class 1) in cell line samples within two hours. Application to drug-resistant cells and patient-derived lung cancer tissues delineates site-specific phosphorylation events associated with resistance and tumor progression, showing that our workflow enables the characterization of phosphorylation signaling with deep coverage, high sensitivity and low between-run missing values.

EGFL6 promotes colorectal cancer cell growth and mobility and the anti-cancer property of anti-EGFL6 antibody.

BACKGROUND: The availability of a reliable tumor target for advanced colorectal cancer (CRC) therapeutic approaches is critical since current treatments are limited. Epidermal growth factor-like domain 6 (EGFL6) has been reported to be associated with cancer development. Here, we focused on the role of EGFL6 in CRC progression and its clinical relevance. In addition, an anti-EGFL6 antibody was generated by phage display technology to investigate its potential therapeutic efficacy in CRC. RESULTS: EGFL6 expression significantly increased in the colon tissues from CRC patients and mice showing spontaneous tumorigenesis, but not in normal tissue. Under hypoxic condition, EGFL6 expression was enhanced at both protein and transcript levels. Moreover, EGFL6 could promote cancer cell migration invasion, and proliferation of CRC cells via up-regulation of the ERK/ AKT pathway. EGFL6 also regulated cell migration, invasion, proliferation, and self-renewal through EGFR/αvß3 integrin receptors. Treatment with the anti-EGFL6 antibody EGFL6-E5-IgG showed tumor-inhibition and anti-metastasis abilities in the xenograft and syngeneic mouse models, respectively. Moreover, EGFL6-E5-IgG treatment had no adverse effect on angiogenesis and wound healing CONCLUSIONS: We demonstrated that EGFL6 plays a role in CRC tumorigenesis and tumor progression, indicating that EGFL6 is a potential therapeutic target worth further investigation.

Multi-Q 2 software facilitates isobaric labeling quantitation analysis with improved accuracy and coverage.

Mass spectrometry-based proteomics using isobaric labeling for multiplex quantitation has become a popular approach for proteomic studies. We present Multi-Q 2, an isobaric-labeling quantitation tool which can yield the largest quantitation coverage and improved quantitation accuracy compared to three state-of-the-art methods. Multi-Q 2 supports identification results from several popular proteomic data analysis platforms for quantitation, offering up to 12% improvement in quantitation coverage for accepting identification results from multiple search engines when compared with MaxQuant and PatternLab. It is equipped with various quantitation algorithms, including a ratio compression correction algorithm, and results in up to 336 algorithmic combinations. Systematic evaluation shows different algorithmic combinations have different strengths and are suitable for different situations. We also demonstrate that the flexibility of Multi-Q 2 in customizing algorithmic combination can lead to improved quantitation accuracy over existing tools. Moreover, the use of complementary algorithmic combinations can be an effective strategy to enhance sensitivity when searching for biomarkers from differentially expressed proteins in proteomic experiments. Multi-Q 2 provides interactive graphical interfaces to process quantitation and to display ratios at protein, peptide, and spectrum levels. It also supports a heatmap module, enabling users to cluster proteins based on their abundance ratios and to visualize the clustering results. Multi-Q 2 executable files, sample data sets, and user manual are freely available at http://ms.iis.sinica.edu.tw/COmics/Software_Multi-Q2.html .

Erratum: TIMP3 expression associates with prognosis in colorectal cancer and its novel arylsulfonamide inducer, MPT0B390, inhibits tumor growth, metastasis and angiogenesis: Erratum.

[This corrects the article DOI: 10.7150/thno.34020.].

Comparison of different variant sequence types coupled with decoy generation methods used in concatenated target-decoy database searches for proteogenomic research.

Concatenated target-decoy database searches are commonly used in proteogenomic research for variant peptide identification. Currently, protein-based and peptide-based sequence databases are applied to store variant sequences for database searches. The protein-based database records a full-length wild-type protein sequence but using the given variant events to replace the original amino acids, whereas the peptide-based database retains only the in silico digested peptides containing the variants. However, the performance of applying various decoy generation methods on the peptide-based variant sequence database is still unclear, compared to the protein-based database. In this paper, we conduct a thorough comparison on target-decoy databases constructed by the above two types of databases coupled with various decoy generation methods for proteogenomic analyses. The results show that for the protein-based variant sequence database, using the reverse or the pseudo reverse method achieves similar performance for variant peptide identification. Furthermore, for the peptide-based database, the pseudo reverse method is more suitable than the widely used reverse method, as shown by identifying 6% more variant PSMs in a HEK293 cell line data set. SIGNIFICANCE: In our survey of publications on proteogenomic studies, 57% of the studies adopt the peptide-based variant sequence database coupled with the reverse method for decoy generation to construct a target-decoy database for searches. However, our results show that when using the peptide-based variant sequence database, it is better to adopt the pseudo reverse method for generating decoy sequences, to avoid leading to fewer variant peptides being identified.

Proteogenomics of Non-smoking Lung Cancer in East Asia Delineates Molecular Signatures of Pathogenesis and Progression.

Lung cancer in East Asia is characterized by a high percentage of never-smokers, early onset and predominant EGFR mutations. To illuminate the molecular phenotype of this demographically distinct disease, we performed a deep comprehensive proteogenomic study on a prospectively collected cohort in Taiwan, representing early stage, predominantly female, non-smoking lung adenocarcinoma. Integrated genomic, proteomic, and phosphoproteomic analysis delineated the demographically distinct molecular attributes and hallmarks of tumor progression. Mutational signature analysis revealed age- and gender-related mutagenesis mechanisms, characterized by high prevalence of APOBEC mutational signature in younger females and over-representation of environmental carcinogen-like mutational signatures in older females. A proteomics-informed classification distinguished the clinical characteristics of early stage patients with EGFR mutations. Furthermore, integrated protein network analysis revealed the cellular remodeling underpinning clinical trajectories and nominated candidate biomarkers for patient stratification and therapeutic intervention. This multi-omic molecular architecture may help develop strategies for management of early stage never-smoker lung adenocarcinoma.

MinProtMaxVP: Generating a minimized number of protein variant sequences containing all possible variant peptides for proteogenomic analysis.

Identifying single-amino-acid variants (SAVs) from mass spectrometry-based experiments is critical for validating single-nucleotide variants (SNVs) at the protein level to facilitate biomedical research. Currently, two approaches are usually applied to convert SNV annotations into SAV-harboring protein sequences. One approach generates one sequence containing exactly one SAV, and the other all SAVs. However, they may neglect the possibility of SAV combinations, e.g., haplotypes, existing in bio-samples. Therefore, it is necessary to consider all SAV combinations of a protein when generating SAV-harboring protein sequences. In this paper, we propose MinProtMaxVP, a novel approach which selects a minimized number of SAV-harboring protein sequences generated from the exhaustive approach, while still accommodating all possible variant peptides, by solving a classic set covering problem. Our study on known haplotype variations of TAS2R38 justifies the necessity for MinProtMaxVP to consider all combinations of SAVs. The performance of MinProtMaxVP is demonstrated by an in silico study on OR2T27 with five SAVs and real experimental data of the HEK293 cell line. Furthermore, assuming simulated somatic and germline variants of OR2T27 in tumor and normal tissues demonstrates that when adopting the appropriate somatic and germline SAV integration strategy, MinProtMaxVP is adaptable to labeling and label-free mass spectrometry-based experiments. SIGNIFICANCE: We present MinProtMaxVP, a novel approach to generate SAV-harboring protein sequences for constructing a customized protein sequence database, which is used in database searching for variant peptide identification. This approach outperforms the existing approaches in generating all possible variant peptides to be included in protein sequences and possibly leading to identification of more variant peptides in proteogenomic analysis.

N-GlyDE: a two-stage N-linked glycosylation site prediction incorporating gapped dipeptides and pattern-based encoding.

N-linked glycosylation is one of the predominant post-translational modifications involved in a number of biological functions. Since experimental characterization of glycosites is challenging, glycosite prediction is crucial. Several predictors have been made available and report high performance. Most of them evaluate their performance at every asparagine in protein sequences, not confined to asparagine in the N-X-S/T sequon. In this paper, we present N-GlyDE, a two-stage prediction tool trained on rigorously-constructed non-redundant datasets to predict N-linked glycosites in the human proteome. The first stage uses a protein similarity voting algorithm trained  on both glycoproteins and non-glycoproteins to predict a score for a protein to improve glycosite prediction. The second stage uses a support vector machine to predict N-linked glycosites by utilizing features of gapped dipeptides, pattern-based predicted surface accessibility, and predicted secondary structure. N-GlyDE's final predictions are derived from a weight adjustment of the second-stage prediction results based on the first-stage prediction score. Evaluated on N-X-S/T sequons of an independent dataset comprised of 53 glycoproteins and 33 non-glycoproteins, N-GlyDE achieves an accuracy and MCC of 0.740 and 0.499, respectively, outperforming the compared tools. The N-GlyDE web server is available at http://bioapp.iis.sinica.edu.tw/N-GlyDE/ .

TIMP3 expression associates with prognosis in colorectal cancer and its novel arylsulfonamide inducer, MPT0B390, inhibits tumor growth, metastasis and angiogenesis.

Tissue inhibitors of metalloproteinase 3 (TIMP3) are a major endogenous inhibitor of matrix metalloproteinase (MMPs) that inhibit tumor growth, invasion, metastasis and angiogenesis. In this study, we found that TIMP3 expression is associated with positive prognosis of colorectal cancer (CRC) clinicopathologically. Therefore, we developed a series of arylsulfonamide derivatives as TIMP3 inducers in order to define potential colorectal cancer therapeutic agent. Among these, MPT0B390 was selected for anti-tumor, anti-metastasis, and anti-angiogenesis property determination. Methods: The relationship between TIMP3 expression and clinical pathological features in colorectal patients and cell lines were determined by immunohistochemistry, bioinformatics analysis and western blotting. The anti-tumor function was validated by using MTT, apoptosis pathway detection and in vivo xenograft model for tumor growth inhibition determination. The anti-metastatic function was validated using a transwell migration assay, and using in vivo lung metastasis and liver metastasis models. The mechanism of MPT0B390-induced TIMP3 expression was further tested using qPCR and Chromatin IP assay. The anti-angiogenesis function was examined by using transwell migration assay, and in vivo Matrigel plug assay. Results: After screening candidate compounds, we identified MPT0B390 as an effective inducer of TIMP3. We showed that MPT0B390 induces TIMP3 expression significantly and inhibits CRC cell growth in vitro and in vivo. By inducing TIMP3 expression, MPT0B390 can also exert its anti-metastasis effect to inhibit CRC cell migration and invasion and downregulates migration markers such as uPA, uPAR, and c-Met. Subsequent Chromatin immunoprecipitation assay revealed that MPT0B390 can significantly inhibit EZH2 expression as well as its binding to TIMP3 promoter region to regulate TIMP3 induction. In addition to the anti-tumor and anti-metastasis capability, MPT0B390 can also induce TIMP3 expression in endothelial cells to inhibit tumor angiogenesis. Conclusion: These data suggest the potential therapeutic applications of the TIMP3 inducer, MPT0B390, for colorectal cancer treatment.

iHPDM: In Silico Human Proteome Digestion Map with Proteolytic Peptide Analysis and Graphical Visualizations.

When conducting proteomics experiments to detect missing proteins and protein isoforms in the human proteome, it is desirable to use a protease that can yield more unique peptides with properties amenable for mass spectrometry analysis. Though trypsin is currently the most widely used protease, some proteins can yield only a limited number of unique peptides by trypsin digestion. Other proteases and multiple proteases have been applied in reported studies to increase the number of identified proteins and protein sequence coverage. To facilitate the selection of proteases, we developed a web-based resource, called in silico Human Proteome Digestion Map (iHPDM), which contains a comprehensive proteolytic peptide database constructed from human proteins, including isoforms, in neXtProt digested by 15 protease combinations of one or two proteases. iHPDM provides convenient functions and graphical visualizations for users to examine and compare the digestion results of different proteases. Notably, it also supports users to input filtering criteria on digested peptides, e.g., peptide length and uniqueness, to select suitable proteases. iHPDM can facilitate protease selection for shotgun proteomics experiments to identify missing proteins, protein isoforms, and single amino acid variant peptides.

WinProphet: A User-Friendly Pipeline Management System for Proteomics Data Analysis Based on Trans-Proteomic Pipeline.

Protein and peptide identification and quantitation are essential tasks in proteomics research and involve a series of steps in analyzing mass spectrometry data. Trans-Proteomic Pipeline (TPP) provides a wide range of useful tools through its web interfaces for analyses such as sequence database search, statistical validation, and quantitation. To utilize the powerful functionality of TPP without the need for manual intervention to launch each step, we developed a software tool, called WinProphet, to create and automatically execute a pipeline for proteomic analyses. It seamlessly integrates with TPP and other external command-line programs, supporting various functionalities, including database search for protein and peptide identification, spectral library construction and search, data-independent acquisition (DIA) data analysis, and isobaric labeling and label-free quantitation. WinProphet is a standalone, installation-free tool with graphical interfaces for users to configure, manage, and automatically execute pipelines. The constructed pipelines can be exported as XML files with all of the parameter settings for reusability and portability. The executable files, user manual, and sample data sets of WinProphet are freely available at  http://ms.iis.sinica.edu.tw/COmics/Software_WinProphet.html .

MPT0G413, A Novel HDAC6-Selective Inhibitor, and Bortezomib Synergistically Exert Anti-tumor Activity in Multiple Myeloma Cells.

In multiple myeloma (MM), homeostasis is largely maintained by misfolded protein clearance via the proteasomal and aggresomal pathways. Histone deacetylase 6 (HDAC6) binds polyubiquitinated proteins and dynein motors and transports this protein cargo to the aggresome for further degradation. Accordingly, a combination of an HDAC6 inhibitor and bortezomib (BTZ) could increase ubiquitinated protein accumulation, leading to further apoptosis. Here we evaluated the anti-MM activity of MPT0G413, a novel specific HDAC6 inhibitor, using in vitro and in vivo models. MPT0G413 treatment more significantly inhibited cell growth in MM cells than in normal bone marrow cells. Furthermore, the combination of MPT0G413 and BTZ enhanced polyubiquitinated protein accumulation and synergistically reduced MM viability, increased caspase-3, caspase-8, caspase-9 levels, and cleaved poly (ADP) ribosome polymerase and also inhibited adherence of MM cells to bone marrow stromal cells (BMSC) and reduced VEGF and IL-6 levels and cell growth in a co-culture system. The combination treatment disturbed the bone marrow microenvironment and induced synergic, caspase-dependent apoptosis. Xenograft tumor growth significantly decreased in combination-treated SCID mice. In conclusion, MPT0G413 and BTZ synergistically inhibit MM viability, providing a framework for the clinical evaluation of combined therapies for MM.

The anticancer effects of MPT0G211, a novel HDAC6 inhibitor, combined with chemotherapeutic agents in human acute leukemia cells.

BACKGROUND: There are some limitations of standard chemotherapy for acute leukemia. Vincristine and doxorubicin are commonly used for acute leukemia, but they may induce serious side effects such as cardiomyopathy and neurotoxicity. Furthermore, chemotherapy resistance occurs more and more frequently. Therefore, effective treatment strategies are needed. Histone deacetylase 6 inhibition is considered as a potential therapeutic strategy for acute leukemia, since it is observed that HDAC6 is overexpressed in acute leukemia and regulates tumor survival. Combination therapy for cancer is used to minimize adverse drug effects, reduce drug dosage, enhance efficacy, and prevent drug resistance. In order to improve efficacy of chemotherapy agents of acute leukemia, this study will investigate the effects of combination MPT0G211, a novel histone deacetylase 6 inhibitor, with doxorubicin or vincristine on human acute leukemia cells. RESULTS: MPT0G211 combined with doxorubicin induces DNA damage response on human acute myeloid leukemia cells. MPT0G211 can additionally increase Ku70 acetylation and release BAX to mitochondria. Ectopic expression of HDAC6 successively reversed the apoptosis triggered by the combined treatment. Moreover, co-treatment of MPT0G211 and vincristine may alter microtubule dynamics, triggering acute lymphoblastic leukemia cells arrest in mitotic phase followed by induction of the apoptotic pathway. Finally, MPT0G211 plus doxorubicin or vincristine can significantly improve the tumor growth delay in a tumor xenograft model. CONCLUSIONS: Collectively, our data highlighted that MPT0G211 in combination with chemotherapy drugs has significant anticancer activity, suggesting a novel strategy for the treatment of acute leukemia.

Subcellular Proteome Landscape of Human Embryonic Stem Cells Revealed Missing Membrane Proteins.

Human embryonic stem cells (hESCs) have the capacity for self-renewal and multilineage differentiation, which are of clinical importance for regeneration medicine. Despite the significant progress of hESC study, the complete hESC proteome atlas, especially the surface protein composition, awaits delineation. According to the latest release of neXtProt database (January 17, 2018; 19 658 PE1, 2, 3, and 4 human proteins), membrane proteins present the major category (1047; 48%) among all 2186 missing proteins (MPs). We conducted a deep subcellular proteomics analysis of hESCs to identify the nuclear, cytoplasmic, and membrane proteins in hESCs and to mine missing membrane proteins in the very early cell status. To our knowledge, our study achieved the largest data set with confident identification of 11 970 unique proteins (1% false discovery rate at peptide, protein, and PSM levels), including the most-comprehensive description of 6 138 annotated membrane proteins in hESCs. Following the HPP guideline, we identified 26 gold (neXtProt PE2, 3, and 4 MPs) and 87 silver (potential MP candidates with a single unique peptide detected) MPs, of which 69 were membrane proteins, and the expression of 21 gold MPs was further verified either by multiple reaction monitoring mass spectrometry or by matching synthetic peptides in the Peptide Atlas database. Functional analysis of the MPs revealed their potential roles in the pluripotency-related pathways and the lineage- and tissue-specific differentiation processes. Our proteome map of hESCs may provide a rich resource not only for the identification of MPs in the human proteome but also for the investigation on self-renewal and differentiation of hESC. All mass spectrometry data were deposited in ProteomeXchange via jPOST with identifier PXD009840.