Pro-inflammatory Cytokines Alter the Immunopeptidome Landscape by Modulation of HLA-B Expression

Antigen presentation on HLA molecules is a major mechanism by which the immune system monitors self and non-self-recognition. Importantly, HLA-I presentation has gained much attention through its role in eliciting anti-tumor immunity. Several determinants controlling the peptides presented on HLA have been uncovered, mainly through the study of model substrates and large-scale immunopeptidome analyses. These determinants include the relative abundances of proteins in the cell, the stability or turnover rate of these proteins and the binding affinities of a given peptide to the HLA haplotypes found in a cell. However, the regulatory principles involved in selection and regulation of specific antigens in response to tumor pro-inflammatory signals remain largely unknown. Here, we chose to examine the effect that TNFα and IFNγ stimulation may exert on the immunopeptidome landscape of lung cancer cells. We show that the expression of many of the proteins involved in the class I antigen presentation pathway are changed by pro-inflammatory cytokines. Further, we could show that increased expression of the HLA-B allomorph drives a significant change in HLA-bound antigens, independently of the significant changes observed in the cellular proteome. Finally, we observed increased HLA-B levels in correlation with tumor infiltration across the TCGA lung cancer cohorts. Taken together, our results suggest that the immunopeptidome landscape should be examined in the context of anti-tumor immunity whereby signals in the microenvironment may be critical in shaping and modulating this important aspect of host-tumor interactions

Regulatory module involving FGF13, miR-504, and p53 regulates ribosomal biogenesis and supports cancer cell survival

The microRNA miR-504 targets TP53 mRNA encoding the p53 tumor suppressor. miR-504 resides within the fibroblast growth factor 13 (FGF13) gene, which is overexpressed in various cancers. We report that the FGF13 locus, comprising FGF13 and miR-504, is transcriptionally repressed by p53, defining an additional negative feedback loop in the p53 network. Furthermore, we show that FGF13 1A is a nucleolar protein that represses ribosomal RNA transcription and attenuates protein synthesis. Importantly, in cancer cells expressing high levels of FGF13, the depletion of FGF13 elicits increased proteostasis stress, associated with the accumulation of reactive oxygen species and apoptosis. Notably, stepwise neoplastic transformation is accompanied by a gradual increase in FGF13 expression and increased dependence on FGF13 for survival ("nononcogene addiction"). Moreover, FGF13 overexpression enables cells to cope more effectively with the stress elicited by oncogenic Ras protein. We propose that, in cells in which activated oncogenes drive excessive protein synthesis, FGF13 may favor survival by maintaining translation rates at a level compatible with the protein quality- control capacity of the cell. Thus, FGF13 may serve as an enabler, allowing cancer cells to evade proteostasis stress triggered by oncogene activation

Validation of a Blood-Based Laboratory Test to Aid in the Confirmation of a Diagnosis of Schizophrenia

We describe the validation of a serum-based test developed by Rules-Based Medicine which can be used to help confirm the diagnosis of schizophrenia. In preliminary studies using multiplex immunoassay profiling technology, we identified a disease signature comprised of 51 analytes which could distinguish schizophrenia (n = 250) from control (n = 230) subjects. In the next stage, these analytes were developed as a refined 51-plex immunoassay panel for validation using a large independent cohort of schizophrenia (n = 577) and control (n = 229) subjects. The resulting test yielded an overall sensitivity of 83% and specificity of 83% with a receiver operating characteristic area under the curve (ROC-AUC) of 89%. These 51 immunoassays and the associated decision rule delivered a sensitive and specific prediction for the presence of schizophrenia in patients compared to matched healthy controls

Proteome dataset of pre-ovulatory follicular fluids from less fertile dairy cows

This article contains raw and processed data related to research published in Zachut et al. (2016) [1]. Proteomics data from preovulatory follicles in cows was obtained by liquid chromatography-mass spectrometry following protein extraction. Differential expression between controls and less fertile cows (LFC) was quantified using MS1 intensity based label-free. The only previous proteomic analysis of bovine FF detected merely 40 proteins in follicular cysts obtained from the slaughterhouse (Maniwa et al., 2005) [2], and the abundance of proteins in the bovine preovulatory FF remains unknown. Therefore, the objectives were to establish the first dataset of FF proteome in preovulatory follicles of cows, and to examine differentially expressed proteins in FF obtained in-vivo from preovulatory follicles of less fertile cows (also termed “repeat breeder”) and control (CTL) cows. The proteome of FF from 10 preovulatory follicles that were aspirated in vivo (estradiol/progesterone>1) was analyzed. This novel dataset contains 219 identified and quantified proteins in FF, consisting mainly of binding proteins, proteases, receptor ligands, enzymes and transporters. In addition, differential abundance of 8 proteins relevant to follicular function was found in LFC compared to CTL; these findings are discussed in our recent research article Zachut et al. (2016) [1]. The present dataset of bovine FF proteome can be used as a reference for any study involving disorders of follicular development in dairy cows or in comparative studies between species. Keywords: Proteomics, Follicular fluid, Reproductive disorders, Co

Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics

We present a data-independent acquisition mass spectrometry method, ultradefinition (UD) MS(E). This approach utilizes ion mobility drift time-specific collision-energy profiles to enhance precursor fragmentation efficiency over current MS(E) and high-definition (HD) MS(E) data-independent acquisition techniques. UDMS(E) provided high reproducibility and substantially improved proteome coverage of the HeLa cell proteome compared to previous implementations of MS(E), and it also outperformed a state-of-the-art data-dependent acquisition workflow. Additionally, we report a software tool, ISOQuant, for processing label-free quantitative UDMS(E) data

Identification of a biomarker in cerebrospinal fluid for neuronopathic forms of Gaucher disease.

Gaucher disease, a recessive inherited metabolic disorder caused by defects in the gene encoding glucosylceramidase (GlcCerase), can be divided into three subtypes according to the appearance of symptoms associated with central nervous system involvement. We now identify a protein, glycoprotein non-metastatic B (GPNMB), that acts as an authentic marker of brain pathology in neurological forms of Gaucher disease. Using three independent techniques, including quantitative global proteomic analysis of cerebrospinal fluid (CSF) in samples from Gaucher disease patients that display neurological symptoms, we demonstrate a correlation between the severity of symptoms and GPNMB levels. Moreover, GPNMB levels in the CSF correlate with disease severity in a mouse model of Gaucher disease. GPNMB was also elevated in brain samples from patients with type 2 and 3 Gaucher disease. Our data suggest that GPNMB can be used as a marker to quantify neuropathology in Gaucher disease patients and as a marker of treatment efficacy once suitable treatments towards the neurological symptoms of Gaucher disease become available

Characterization of the endocannabinoid system in subcutaneous adipose tissue in periparturient dairy cows and its association to metabolic profiles.

Adipose tissue (AT) plays a major role in metabolic adaptations in postpartum (PP) dairy cows. The endocannabinoid (eCB) system is a key regulator of metabolism and energy homeostasis; however, information about this system in ruminants is scarce. Therefore, this work aimed to assess the eCB system in subcutaneous AT, and to determine its relation to the metabolic profile in peripartum cows. Biopsies of AT were performed at 14 d prepartum, and 4 and 30 d PP from 18 multiparous peripartum cows. Cows were categorized retrospectively according to those with high body weight (BW) loss (HWL, 8.5 ± 1.7% BW loss) or low body weight loss (LWL, 2.9 ± 2.5% BW loss) during the first month PP. The HWL had higher plasma non-esterified fatty acids and a lower insulin/glucagon ratio PP than did LWL. Two-fold elevated AT levels of the main eCBs, N-arachidonoylethanolamine (AEA) and 2-arachidonoylglycerol (2-AG), were found 4 d PP compared with prepartum in HWL, but not in LWL cows. AT levels of the eCB-like molecules oleoylethanolamide, palmitoylethanolamide, and of arachidonic acid were elevated PP compared with prepartum in all cows. The abundance of monoglyceride lipase (MGLL), the 2-AG degrading enzyme, was lower in HWL vs. LWL AT PP. The relative gene expression of the cannabinoid receptors CNR1 and CNR2 in AT tended to be higher in HWL vs. LWL PP. Proteomic analysis of AT showed an enrichment of the inflammatory pathways' acute phase signaling and complement system in HWL vs. LWL cows PP. In summary, eCB levels in AT were elevated at the onset of lactation as part of the metabolic adaptations in PP dairy cows. Furthermore, activating the eCB system in AT is most likely associated with a metabolic response of greater BW loss, lipolysis, and AT inflammation in PP dairy cows

MS1-Based Label-Free Proteomics Using a Quadrupole Orbitrap Mass Spectrometer

Presented is a data set for benchmarking MS1-based label-free quantitative proteomics using a quadrupole orbitrap mass spectrometer. Escherichia coli digest was spiked into a HeLa digest in four different concentrations, simulating protein expression differences in a background of an unchanged complex proteome. The data set provides a unique opportunity to evaluate the proteomic platform (instrumentation and software) in its ability to perform MS1-intensity-based label-free quantification. We show that the presented combination of informatics and instrumentation produces high precision and quantification accuracy. The data were also used to compare different quantitative protein inference methods such as iBAQ and Hi-<i>N</i>. The data can also be used as a resource for development and optimization of proteomics informatics tools, thus the raw data have been deposited to ProteomeXchange with identifier PXD001385