Serum and plasma contain abundant biological information that reflect the body's physiological and pathological conditions and are therefore a valuable sample type for disease biomarkers. However, comprehensive profiling of the serological proteome is challenging due to the wide range of protein concentrations in serum. Methods : To address this challenge, we developed a novel in-depth serum proteomics platform capable of analyzing the serum proteome across ~10 orders or magnitude by combining data obtained from Data Independent Acquisition Mass Spectrometry (DIA-MS) and customizable antibody microarrays. Results : Using psoriasis as a proof-of-concept disease model, we screened 50 serum proteomes from healthy controls and psoriasis patients before and after treatment with traditional Chinese medicine (YinXieLing) on our in-depth serum proteomics platform. We identified 106 differentially-expressed proteins in psoriasis patients involved in psoriasis-relevant biological processes, such as blood coagulation, inflammation, apoptosis and angiogenesis signaling pathways. In addition, unbiased clustering and principle component analysis revealed 58 proteins discriminating healthy volunteers from psoriasis patients and 12 proteins distinguishing responders from non-responders to YinXieLing. To further demonstrate the clinical utility of our platform, we performed correlation analyses between serum proteomes and psoriasis activity and found a positive association between the psoriasis area and severity index (PASI) score with three serum proteins (PI3, CCL22, IL-12B). Conclusion : Taken together, these results demonstrate the clinical utility of our in-depth serum proteomics platform to identify specific diagnostic and predictive biomarkers of psoriasis and other immune-mediated diseases.
SummaryCompared with the numerous software tools developed for identification and quantification of -omics data, there remains a lack of suitable tools for both downstream analysis and data visualization. To help researchers better understand the biological meanings in their -omics data, we present an easy-to-use tool, named PANDA-view, for both statistical analysis and visualization of quantitative proteomics data and other -omics data. PANDA-view contains various kinds of analysis methods such as normalization, missing value imputation, statistical tests, clustering and principal component analysis, as well as the most commonly-used data visualization methods including an interactive volcano plot. Additionally, it provides user-friendly interfaces for protein-peptide-spectrum representation of the quantitative proteomics data.Availability and implementationPANDA-view is freely available at https://sourceforge.net/projects/panda-view/.Supplementary information Supplementary data are available at Bioinformatics online.
Background Dysfunctional p53 signaling is one of the major causes of hepatocellular carcinoma (HCC) tumorigenesis and development, but the mechanisms underlying p53 inactivation in HCC have not been fully clarified. The role of Krüppel-associated box (KRAB)-type zinc-finger protein ZNF498 in tumorigenesis and the underlying mechanisms are poorly understood. Methods Clinical HCC samples were used to assess the association of ZNF498 expression with clinicopathological characteristics and patient outcomes. A mouse model in which HCC was induced by diethylnitrosamine (DEN) was used to explore the role of ZNF498 in HCC initiation and progression. ZNF498 overexpression and knockdown HCC cell lines were employed to examine the effects of ZNF498 on cellular proliferation, apoptosis, ferroptosis and tumor growth. Western blotting, immunoprecipitation, qPCR, luciferase assays and flow cytometry were also conducted to determine the underlying mechanisms related to ZNF498 function. Results ZNF498 was found to be highly expressed in HCC, and increased ZNF498 expression was positively correlated with advanced pathological grade and poor survival in HCC patients. Furthermore, ZNF498 promoted DEN-induced hepatocarcinogenesis and progression in mice. Mechanistically, ZNF498 directly interacted with p53 and suppressed p53 transcriptional activation by inhibiting p53 Ser46 phosphorylation. ZNF498 competed with p53INP1 for p53 binding and suppressed PKCδ- and p53INP1-mediated p53 Ser46 phosphorylation. In addition, functional assays revealed that ZNF498 promoted liver cancer cell growth in vivo and in vitro in a p53-dependent manner. Moreover, ZNF498 inhibited p53-mediated apoptosis and ferroptosis by attenuating p53 Ser46 phosphorylation. Conclusions Our results strongly suggest that ZNF498 suppresses apoptosis and ferroptosis by attenuating p53 Ser46 phosphorylation in hepatocellular carcinogenesis, revealing a novel ZNF498-PKCδ-p53INP1-p53 axis in HCC cells that would enrich the non-mutation p53-inactivating mechanisms in HCC.
Summary As the experiment techniques and strategies in quantitative proteomics are improving rapidly, the corresponding algorithms and tools for protein quantification with high accuracy and precision are continuously required to be proposed. Here, we present a comprehensive and flexible tool named PANDA for proteomics data quantification. PANDA, which supports both label-free and labeled quantifications, is compatible with existing peptide identification tools and pipelines with considerable flexibility. Compared with MaxQuant on several complex datasets, PANDA was proved to be more accurate and precise with less computation time. Additionally, PANDA is an easy-to-use desktop application tool with user-friendly interfaces. Availability and implementation PANDA is freely available for download at https://sourceforge.net/projects/panda-tools/ . Supplementary information Supplementary data are available at Bioinformatics online
In the era of life-omics, huge amounts of multi-omics data have been generated and widely used in biomedical research. It is challenging for biologists with limited programming skills to obtain biological insights from multi-omics data. Thus, a biologist-oriented platform containing visualization functions is needed to make complex omics data digestible. Here, we propose an easy-to-use, interactive web server named ExpressVis. In ExpressVis, users can prepare datasets; perform differential expression analysis, clustering analysis, and survival analysis; and integrate expression data with protein–protein interaction networks and pathway maps. These analyses are organized into six modules. Users can use each module independently or use several modules interactively. ExpressVis displays analysis results in interactive figures and tables, and provides comprehensive interactive operations in each figure and table, between figures or tables in each module, and among different modules. It is freely accessible at https://omicsmining.ncpsb.org.cn/ExpressVis and does not require login. To test the performance of ExpressVis for multi-omics studies of clinical cohorts, we re-analyzed a published hepatocellular carcinoma dataset and reproduced their main findings, suggesting that ExpressVis is convenient enough to analyze multi-omics data. Based on its complete analysis processes and unique interactive operations, ExpressVis provides an easy-to-use solution for exploring multi-omics data.
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