April 09 , 2021
· Publication 01 2021/4/9
DIA-based Proteomics Identifies IDH2 asa Targetable Regulator of Acquired Drug Resistance in Chronic Myeloid Leukemia
Drug resistance is a critical obstacleto effective treatment in patients with chronic myeloid leukemia (CML). To understand the underlying resistance mechanisms in response to imatinib (IMA)and adriamycin (ADR), the parental K562 cells were treated with low doses of IMA or ADR for two months to generate derivative cells with mild, intermediate and severe resistance to the drugs as defined by their increasing resistance index (RI). PulseDIA-based quantitative proteomics was then employed to reveal the proteome changes in these resistant cells. In total, 7,082 proteotypic proteins from 98,232 peptides were identified and quantified from the dataset using four DIA software tools including OpenSWATH, Spectronaut, DIA-NN, and EncyclopeDIA. Sirtuin Signaling Pathway was found to be significantly enriched in both ADR-and IMA-resistant K562 cells. In particular, IDH2 was identified as a potential drug target correlated with the drug resistance phenotype, and its inhibition by the antagonist AGI-6780 reversed the acquired resistance in K562 cells to either ADR or IMA. Together, our study has implicated IDH2 as a potential target that can be the rapeutically leveraged to alleviate the drug resistance in K562 cells when treated with IMAand ADR.
· Publication 02 2021/4/9
Optimization of Spectral Library SizeImproves DIA-MS Proteome Coverage
Efficient peptide and protein identification from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on an experiment-specific spectral library with a suitable size. Here, we report a computational strategy for optimizing the spectrallibrary for a specific DIA dataset based on a comprehensive spectral library, which is accomplished by a priori analysis of the DIA dataset. This strategy achieved up to 44.7% increase in peptide identification and 38.1% increase in protein identification in the test dataset of six colorectal tumor samples compared with the comprehensive pan-human library strategy. We further applied this strategy to 389 carcinoma samples from 15 tumor datasets and observed up to 39.2% increase in peptide identification and 19.0% increase in protein identification. In summary, we present a computational strategy for spectrallibrary size optimization to achieve deeper proteome coverage of DIA-MS data.
· Publication 03 2021/2/4
Multi-organproteomic landscape ofCOVID-19 autopsies
The molecular pathology of multi-organ injuries in COVID-19 patients remains unclear, preventing effective therapeutics development. Here, we report a proteomic analysis of 144 autopsy samples from seven organs in 19 COVID-19 patients. We quantified 11,394 proteins in the sesamples, in which 5,336 were perturbed in the COVID-19 patients compared to controls. Our data showed that cathepsin L1, rather than ACE2, was significantly upregulated in the lung from the COVID-19 patients.Systemic hyper inflammation and dysregulation of glucose and fatty acidmetabolism were detected in multiple organs. We also observed dysregulation ofkey factors involved in hypoxia, angiogenesis, blood coagulation, and fibrosisin multiple organs from the COVID-19 patients. Evidence for testicular injuries includes reduced Leydig cells, suppressed cholesterol biosynthesis, and spermmobility. In summary, this study depicts a multi-organ proteomic landscape of COVID-19 autopsies that furthers our understanding of the biological basis of COVID-19 pathology.
· Publication 04 2020/12/23
ProteomeExpert: A Docker image based web-server for exploring, modeling, visualizing, and mining quantitative proteomic data sets
The rapid progresses of high-throughput sequencing technology-based omics and mass spectrometry-based proteomics, such as data-independent acquisition and its penetration to clinical studies have generated increasing number of proteomic datasets containing hundreds to thousands of samples. To analyze these quantitative proteomic datasets and other omics (e.g. transcriptomics and metabolomics) datasets more efficiently and conveniently, we present a web server-based software tool ProteomeExpert implemented in Docker, which offers various analysis tools for experimental design, data mining, interpretation and visualization of quantitative proteomic datasets. ProteomeExpert can be deployed on an operating system with Docker installed or with R language environment.
· Publication 05 2020/6/17
Proteomics Uncovers Immunosuppression inCOVID-19 Patients with Long Disease Course
Little is known regarding why a subset of COVID-19 patients exhibited prolonged positivity of SARS-CoV-2 infection. Here, we studied the sera proteomic dynamics in 37 COVID-19 patients over nine weeks, quantifying 2700 proteins with high quality. Remarkably, we found that during the first three weeks since disease onset, while clinical symptoms and outcome were indistinguishable, patients with prolonged disease coursedis played characteristic immunological responses including enhanced Natural Killer cell-mediated innate immunity and regulatory T cell-mediated immunosuppression. We further showed that it is possible to predict the length of disease course using machine learning based on blood protein levels during the first three weeks. Validation in an independent cohort achieved an accuracy of 82%. In summary, this study presents a rich serum proteomic resource tounderst and host responses in COVID-19 patients and identifies characteristic Treg-mediated immunosuppression in patients with prolonged disease course, nominating new the rapeutic target and diagnosis strategy.