SUMO Proteomics Analyses Identify Protein Inhibitor of Activated STAT-Mediated Regulatory Networks Involved in Cell Cycle and Cell Proliferation.

Protein inhibitor of activated STAT (PIAS) proteins are E3 SUMO ligases playing important roles in protein stability and signaling transduction pathways. PIAS proteins are overexpressed in the triple-negative breast cancer cell line MDA-MB-231, and PIAS knockout (KO) results in a reduction in cell proliferation and cell arrest in the S phase. However, the molecular mechanisms underlying PIAS functions in cell proliferation and cell cycle remain largely unknown. Here, we used quantitative SUMO proteomics to explore the regulatory role of PIAS SUMO E3 ligases upon CRISPR/Cas9 KO of individual PIAS. A total of 1422 sites were identified, and around 10% of SUMO sites were regulated following KO of one or more PIAS genes. We identified protein substrates that were either specific to individual PIAS ligase or regulated by several PIAS ligases. Ki-67 and TOP2A, which are involved in cell proliferation and epithelial-to-mesenchymal transition, are SUMOylated at several lysine residues by all PIAS ligases, suggesting a level of redundancy between these proteins. Confocal microscopy and biochemical experiments revealed that SUMOylation regulated TOP2A protein stability, while this modification is involved in the recruitment of Ki-67 nucleolar proteins containing the SUMO interacting motif. These results provide novel insights into both the redundant and specific regulatory mechanisms of cell proliferation and cell cycle mediated by PIAS SUMO E3 ligases.

Comparison of the MHC I Immunopeptidome Repertoire of B-Cell Lymphoblasts Using Two Isolation Methods.

Significant technological advances in both affinity chromatography and mass spectrometry have facilitated the identification of peptides associated with the major histocompatibility complex class I (MHC I) molecules, and enabled a greater understanding of the dynamic nature of the immunopeptidome of normal and neoplastic cells. While the isolation of MHC I-associated peptides (MIPs) typically used mild acid elution (MAE) or immunoprecipitation (IP), limited information currently exists regarding their respective analytical merits. Here, a comparison of these approaches for the isolation of two different B-cell lymphoblast cell models is presented, and it is reported on the recovery, reproducibility, scalability, and complementarity of identification from each method. Both approaches yielded reproducible datasets for peptide extracts obtained from 2 to 100 million cells, with 2016 to 5093 MIPs, respectively. The IP typically provides up to 6.4-fold increase in MIPs compared to the MAE. The comprehensiveness of these immunopeptidome analyses is extended using personalized genomic database of B-cell lymphoblasts, and it is discovered that 0.4% of their respective MIP repertoire harbored nonsynonymous single nucleotide variations (also known as minor histocompatibility antigens, MiHAs).

Immunopeptidomic Data Integration to Artificial Neural Networks Enhances Protein-Drug Immunogenicity Prediction.

Recombinant DNA technology has, in the last decades, contributed to a vast expansion of the use of protein drugs as pharmaceutical agents. However, such biological drugs can lead to the formation of anti-drug antibodies (ADAs) that may result in adverse effects, including allergic reactions and compromised therapeutic efficacy. Production of ADAs is most often associated with activation of CD4 T cell responses resulting from proteolysis of the biotherapeutic and loading of drug-specific peptides into major histocompatibility complex (MHC) class II on professional antigen-presenting cells. Recently, readouts from MHC-associated peptide proteomics (MAPPs) assays have been shown to correlate with the presence of CD4 T cell epitopes. However, the limited sensitivity of MAPPs challenges its use as an immunogenicity biomarker. In this work, MAPPs data was used to construct an artificial neural network (ANN) model for MHC class II antigen presentation. Using Infliximab and Rituximab as showcase stories, the model demonstrated an unprecedented performance for predicting MAPPs and CD4 T cell epitopes in the context of protein-drug immunogenicity, complementing results from MAPPs assays and outperforming conventional prediction models trained on binding affinity data.

Analysis of emerging contaminants in water and solid samples using high resolution mass spectrometry with a Q Exactive orbital ion trap and estrogenic activity with YES-assay.

Trace emerging contaminants (ECs) occur in both waste and surface waters that are rich in particulates that have been found to sorb several organic contaminants. An analytical method based on off-line solid-phase extraction (SPE) followed by liquid chromatography-mass spectrometry (LC-MS) analysis was developed for the detection and quantification of 31 ECs from surface water, wastewater, suspended particulate matter (SPM) as well as sediments. Lyophilized sediments and air-dried SPM were subjected to ultrasonic extraction. Water samples and extracts were then concentrated and cleaned-up by off-line SPE. Quantification was realized using a Q Exactive mass spectrometer in both full scan (FS) and MS2 modes. These two modes were optimized and compared to determine which one was the most suitable for each matrix studied. Yeast estrogen screen assay (YES-assay) adapted from the direct measurement of estrogenic activity without sample extraction was tested on filtered wastewater samples. An endocrine disrupting effect was detected in all effluent samples analyzed with estradiol equivalent concentrations ranging from 4.4 to 720 ng eq E2 L-1 for the WWTP-1 and 6.5-42 ng eq E2 L-1 for the WWTP-2. The analytical methods were also applied on six samples of surface water, the corresponding SPM, the sediments and thirty-nine effluent samples from two wastewater treatment plants (WWTPs) sampled over a period of five months (February to June 2014).