No abstract

Ion mobility can add a dimension to LC-MS based shotgun proteomics which has the potential to boost proteome coverage, quantification accuracy and dynamic range. Required for this is suitable software that extracts the information contained in the four-dimensional (4D) data space spanned by m/z, retention time, ion mobility and signal intensity. Here we describe the ion mobility enhanced MaxQuant software, which utilizes the added data dimension. It offers an end to end computational workflow for the identification and quantification of peptides and proteins in LC-IMS-MS/MS shotgun proteomics data. We apply it to trapped ion mobility spectrometry (TIMS) coupled to a quadrupole time-of-flight (QTOF) analyzer. A highly parallelizable 4D feature detection algorithm extracts peaks which are assembled to isotope patterns. Masses are recalibrated with a non-linear m/z, retention time, ion mobility and signal intensity dependent model, based on peptides from the sample. A new matching between runs (MBR) algorithm that utilizes collisional cross section (CCS) values of MS1 features in the matching process significantly gains specificity from the extra dimension. Prerequisite for using CCS values in MBR is a relative alignment of the ion mobility values between the runs. The missing value problem in protein quantification over many samples is greatly reduced by CCS aware MBR.MS1 level label-free quantification is also implemented which proves to be highly precise and accurate on a benchmark dataset with known ground truth. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org.

Human and other animal cells deploy three closely related dioxygenases (PHD 1, 2 and 3) to signal oxygen levels by catalysing oxygen regulated prolyl hydroxylation of the transcription factor HIF. The discovery of the HIF prolyl-hydroxylase (PHD) enzymes as oxygen sensors raises a key question as to the existence and nature of non-HIF substrates, potentially transducing other biological responses to hypoxia. Over 20 such substrates are reported. We therefore sought to characterise their reactivity with recombinant PHD enzymes. Unexpectedly, we did not detect prolyl-hydroxylase activity on any reported non-HIF protein or peptide, using conditions supporting robust HIF-α hydroxylation. We cannot exclude PHD-catalysed prolyl hydroxylation occurring under conditions other than those we have examined. However, our findings using recombinant enzymes provide no support for the wide range of non-HIF PHD substrates that have been reported.

Objective: To demonstrate the feasibility of studying exosomes directly from peritoneal fluid, we isolated exosomes from endometriosis patient samples and from controls, and characterized their cargo. Design: Case-control experimental study. Setting: Academic clinical center. Patient (s): Women with and without endometriosis who underwent laparoscopic surgery (n ¼ 28 in total). Intervention (s): None. Main Outcome Measure (s): Concentration of exosomes within peritoneal fluid and protein content of the isolated exosomes. Result (s): Peritoneal fluid samples were pooled according to the cycle phase and disease stage to form six experimental groups, from which the exosomes were isolated. Exosomes were successfully isolated from peritoneal fluid in all the study groups. The concentration varied with cycle phase and disease stage. Proteomic analysis showed specific proteins in the exosomes derived from endometriosis patients that were absent in the controls. Five proteins were found exclusively in the endometriosis groups: PRDX1, H2A type 2-C, ANXA2, ITIH4, and the tubulin a-chain. Conclusion (s):Exosomes are present in peritoneal fluid. The characterization of endometriosis-specific exosomes opens up new avenues for the diagnosis and investigation of endometriosis.

The potency and selectivity of a small molecule inhibitor are key parameters to assess during the early stages of drug discovery. In particular, it is very informative for characterizing compounds in a relevant cellular context in order to reveal potential off-target effects and drug efficacy. Activity-based probes are valuable tools for that purpose, however, obtaining cellular target engagement data in a high-throughput format has been particularly challenging. Here, we describe a new methodology named ABPP-HT (high-throughput-compatible activity-based protein profiling), implementing a semi-automated proteomic sample preparation workflow that increases the throughput capabilities of the classical ABPP workflow approximately ten times while preserving its enzyme profiling characteristics. Using a panel of deubiquitylating enzyme (DUB) inhibitors, we demonstrate the feasibility of ABPP-HT to provide compound selectivity profiles of endogenous DUBs in a cellular context at a fraction of time as compared to previous methodologies.