ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data.

This report presents the results from the 2016 Association of Biomolecular Resource Facilities Proteome Informatics Research Group (iPRG) study on proteoform inference and false discovery rate (FDR) estimation from bottom-up proteomics data. For this study, 3 replicate Q Exactive Orbitrap liquid chromatography-tandom mass spectrometry datasets were generated from each of

Comparison of Extensive Protein Fractionation and Repetitive LC-MS/MS Analyses on Depth of Analysis for Complex Proteomes

In-depth, reproducible coverage of complex proteomes is challenging because the complexity of tryptic digests subjected to LC-MS/MS analysis frequently exceeds mass spectrometer analytical capacity, which results in undersampling of data. In this study, we used cancer cell lysates to systematically compare the commonly used GeLC-MS/MS (1-D protein + 1-D peptide separation) method using four repetitive injections (2-D/repetitive) with a 3-D method that included solution isoelectric focusing and involved an equal number of LC-MS/MS runs. The 3-D method detected substantially more unique peptides and proteins, including higher numbers of unique peptides from low-abundance proteins, demonstrating that additional fractionation at the protein level is more effective than repetitive analyses at overcoming LC-MS/MS undersampling. Importantly, more than 90 % of the 2-D/repetitive protein identifications were found in the 3-D method data in a direct protein level comparison, and the reproducibility between data sets increased to greater than 96 % when factors such as database redundancy and use of rigid scoring thresholds were considered. Hence, high reproducibility of complex proteomes, such as human cancer cell lysates, readily can be achieved when using multidimensional separation methods with good depth of analysis

Quantitative Proteomic and Interaction Network Analysis of Cisplatin Resistance in HeLa Cells

Cisplatin along with other platinum based drugs are some of the most widely used chemotherapeutic agents. However drug resistance is a major problem for the successful chemotherapeutic treatment of cancer. Current evidence suggests that drug resistance is a multifactorial problem due to changes in the expression levels and activity of a wide number of proteins. A majority of the studies to date have quantified mRNA levels between drug resistant and drug sensitive cell lines. Unfortunately mRNA levels do not always correlate with protein expression levels due to post-transcriptional changes in protein abundance. Therefore global quantitative proteomics screens are needed to identify the protein targets that are differentially expressed in drug resistant cell lines. Here we employ a quantitative proteomics technique using stable isotope labeling with amino acids in cell culture (SILAC) coupled with mass spectrometry to quantify changes in protein levels between cisplatin resistant (HeLa/CDDP) and sensitive HeLa cells in an unbiased fashion. A total of 856 proteins were identified and quantified, with 374 displaying significantly altered expression levels between the cell lines. Expression level data was then integrated with a network of protein-protein interactions, and biological pathways to obtain a systems level view of proteome changes which occur with cisplatin resistance. Several of these proteins have been previously implicated in resistance towards platinum-based and other drugs, while many represent new potential markers or therapeutic targets

Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance.

The development of reliable, mixed-culture biotechnological processes hinges on understanding how microbial ecosystems respond to disturbances. Here we reveal extensive phenotypic plasticity and niche complementarity in oleaginous microbial populations from a biological wastewater treatment plant. We perform meta-omics analyses (metagenomics, metatranscriptomics, metaproteomics and metabolomics) on in situ samples over 14 months at weekly intervals. Based on 1,364 de novo metagenome-assembled genomes, we uncover four distinct fundamental niche types. Throughout the time-series, we observe a major, transient shift in community structure, coinciding with substrate availability changes. Functional omics data reveals extensive variation in gene expression and substrate usage amongst community members. Ex situ bioreactor experiments confirm that responses occur within five hours of a pulse disturbance, demonstrating rapid adaptation by specific populations. Our results show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity, and set the foundation for future ecological engineering efforts

Identification of ejaculated proteins in the house mouse (Mus domesticus) via isotopic labeling

<p>Abstract</p> <p>Background</p> <p>Seminal fluid plays an important role in successful fertilization, but knowledge of the full suite of proteins transferred from males to females during copulation is incomplete. The list of ejaculated proteins remains particularly scant in one of the best-studied mammalian systems, the house mouse (<it>Mus domesticus</it>), where artificial ejaculation techniques have proven inadequate. Here we investigate an alternative method for identifying ejaculated proteins, by isotopically labeling females with ¹⁵N and then mating them to unlabeled, vasectomized males. Proteins were then isolated from mated females and identified using mass spectrometry. In addition to gaining insights into possible functions and fates of ejaculated proteins, our study serves as proof of concept that isotopic labeling is a powerful means to study reproductive proteins.</p> <p>Results</p> <p>We identified 69 male-derived proteins from the female reproductive tract following copulation. More than a third of all spectra detected mapped to just seven genes known to be structurally important in the formation of the copulatory plug, a hard coagulum that forms shortly after mating. Seminal fluid is significantly enriched for proteins that function in protection from oxidative stress and endopeptidase inhibition. Females, on the other hand, produce endopeptidases in response to mating. The 69 ejaculated proteins evolve significantly more rapidly than other proteins that we previously identified directly from dissection of the male reproductive tract.</p> <p>Conclusion</p> <p>Our study attempts to comprehensively identify the proteins transferred from males to females during mating, expanding the application of isotopic labeling to mammalian reproductive genomics. This technique opens the way to the targeted monitoring of the fate of ejaculated proteins as they incubate in the female reproductive tract.</p

LC-MSsim – a simulation software for liquid chromatography mass spectrometry data

<p>Abstract</p> <p>Background</p> <p>Mass Spectrometry coupled to Liquid Chromatography (LC-MS) is commonly used to analyze the protein content of biological samples in large scale studies. The data resulting from an LC-MS experiment is huge, highly complex and noisy. Accordingly, it has sparked new developments in Bioinformatics, especially in the fields of algorithm development, statistics and software engineering. In a quantitative label-free mass spectrometry experiment, crucial steps are the detection of peptide features in the mass spectra and the alignment of samples by correcting for shifts in retention time. At the moment, it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists only for peptide identification algorithms but no data that represents a ground truth for the evaluation of feature detection, alignment and filtering algorithms.</p> <p>Results</p> <p>We present <it>LC-MSsim</it>, a simulation software for LC-ESI-MS experiments. It simulates ESI spectra on the MS level. It reads a list of proteins from a FASTA file and digests the protein mixture using a user-defined enzyme. The software creates an LC-MS data set using a predictor for the retention time of the peptides and a model for peak shapes and elution profiles of the mass spectral peaks. Our software also offers the possibility to add contaminants, to change the background noise level and includes a model for the detectability of peptides in mass spectra. After the simulation, <it>LC-MSsim </it>writes the simulated data to mzData, a public XML format. The software also stores the positions (monoisotopic m/z and retention time) and ion counts of the simulated ions in separate files.</p> <p>Conclusion</p> <p><it>LC-MSsim </it>generates simulated LC-MS data sets and incorporates models for peak shapes and contaminations. Algorithm developers can match the results of feature detection and alignment algorithms against the simulated ion lists and meaningful error rates can be computed. We anticipate that <it>LC-MSsim </it>will be useful to the wider community to perform benchmark studies and comparisons between computational tools.</p

Amyloid Precursor Protein Is Trafficked and Secreted via Synaptic Vesicles

A large body of evidence has implicated amyloid precursor protein (APP) and its proteolytic derivatives as key players in the physiological context of neuronal synaptogenesis and synapse maintenance, as well as in the pathology of Alzheimer's Disease (AD). Although APP processing and release are known to occur in response to neuronal stimulation, the exact mechanism by which APP reaches the neuronal surface is unclear. We now demonstrate that a small but relevant number of synaptic vesicles contain APP, which can be released during neuronal activity, and most likely represent the major exocytic pathway of APP. This novel finding leads us to propose a revised model of presynaptic APP trafficking that reconciles existing knowledge on APP with our present understanding of vesicular release and recycling