Relative label-free quantification (LFQ) of shotgun proteomics data using precursor (MS1) signal intensities is one of the most commonly used applications to comprehensively and globally quantify proteins across biological samples and conditions. Due to the popularity of this technique, several software packages, such as the popular software suite MaxQuant, have been developed to extract, analyze, and compare spectral features and to report quantitative information of peptides, proteins, and even post-translationally modified sites. However, there is still a lack of accessible tools for the interpretation and downstream statistical analysis of these complex data sets, in particular for researchers and biologists with no or only limited experience in proteomics, bioinformatics, and statistics. We have therefore created LFQ-Analyst, which is an easy-to-use, interactive web application developed to perform differential expression analysis with "one click" and to visualize label-free quantitative proteomic data sets preprocessed with MaxQuant. LFQ-Analyst provides a wealth of user-analytic features and offers numerous publication-quality result graphics to facilitate statistical and exploratory analysis of label-free quantitative data sets. LFQ-Analyst, including an in-depth user manual, is freely available at https:// bioinformatics.erc.monash.edu/apps/LFQ-Analyst.

Cerebral adenosine A2A receptors (A2ARs) are attractive therapeutic targets for the treatment of neurodegenerative and psychiatric disorders. We developed high affinity and selective compound 8 (SCH442416) analogs as in vivo probes for A2ARs using PET. We observed the A2AR-mediated accumulation of [18F]fluoropropyl ([18F]-10b) and [18F]fluoroethyl ([18F]-10a) derivatives of 8 in the brain. The striatum was clearly visualized in PET and in vitro autoradiography images of control animals and was no longer visible after pretreatment with the A2AR subtype-selective antagonist KW6002. In vitro and in vivo metabolite analyses indicated the presence of hydrophilic (radio)metabolite(s), which are not expected to cross the blood-brain-barrier. [18F]-10b and [18F]-10a showed comparable striatum-to- cerebellum ratios (4.6 at 25 and 37 min post injection, respectively) and reversible binding in rat brains. We concluded that these compounds performed equally well, but their kinetics were slightly different. These molecules are potential tools for mapping cerebral A2ARs with PET.

Obesity and metabolic syndrome are associated with several cancers, however, the molecular mechanisms remain to be fully elucidated. Recent studies suggest that hypercholesterolemia increases intratumoral androgen signaling in prostate cancer, but it is unclear whether androgen-independent mechanisms also exist. Since hypercholesterolemia is associated with advanced, castrate-resistant prostate cancer, in this study, we aimed to determine whether and how hypercholesterolemia affects prostate cancer progression in the absence of androgen signaling. We demonstrate that diet-induced hypercholesterolemia promotes orthotopic xenograft PC-3 cell metastasis, concomitant with elevated expression of caveolin-1 and IQGAP1 in xenograft tumor tissues. In vitro cholesterol treatment of PC-3 cells stimulated migration and increased IQGAP1 and caveolin-1 protein level and localization to a detergent-resistant fraction. Down-regulation of caveolin-1 or IQGAP1 in PC-3 cells reduced migration and invasion in vitro, and hypercholesterolemia-induced metastasis in vivo. Double knock-down of caveolin-1 and IQGAP1 showed no additive effect, suggesting that caveolin-1 and IQGAP1 act via the same pathway. Taken together, our data show that hypercholesterolemia promotes prostate cancer metastasis independent of the androgen pathway, in part by increasing IQGAP1 and caveolin-1. These results have broader implications for managing metastasis of cancers in general as IQGAP1 and hypercholesterolemia are implicated in the progression of several cancers.

RaftProt (http://lipid-raft-database.di.uq.edu.au/) is a database of mammalian lipid raft-associated proteins as reported in high-throughput mass spectrometry studies. Lipid rafts are specialized membrane microdomains enriched in cholesterol and sphingolipids thought to act as dynamic signalling and sorting platforms. Given their fundamental roles in cellular regulation, there is a plethora of information on the size, composition and regulation of these membrane microdomains, including a large number of proteomics studies. To facilitate the mining and analysis of published lipid raft proteomics studies, we have developed a searchable database RaftProt. In addition to browsing the studies, performing basic queries by protein and gene names, searching experiments by cell, tissue and organisms; we have implemented several advanced features to facilitate data mining. To address the issue of potential bias due to biochemical preparation procedures used, we have captured the lipid raft preparation methods and implemented advanced search option for methodology and sample treatment conditions, such as cholesterol depletion. Furthermore, we have identified a list of high confidence proteins, and enabled searching only from this list of likely bona fide lipid raft proteins. Given the apparent biological importance of lipid raft and their associated proteins, this database would constitute a key resource for the scientific community.

Cellular membranes feature dynamic submicrometer-scale lateral domains termed lipid rafts, membrane rafts or glycosphingolipid-enriched microdomains (GEM). Numerous proteomics studies have been conducted on the lipid raft proteome, however, interpretation of individual studies is limited by potential undefined contaminant proteins. To enable integrated analyses, we previously developed RaftProt (http://lipid-raft-database.di.uq.edu.au/), a searchable database of mammalian lipid raft-associated proteins. Despite being a highly used resource, further developments in annotation and utilities were required. Here, we present RaftProt V2 (http://raftprot.org), an improved update of RaftProt. Besides the addition of new datasets and re-mapping of all entries to both UniProt and UniRef IDs, we have implemented a stringent annotation based on experimental evidence level to assist in identification of possible contaminant proteins. RaftProt V2 allows for simultaneous search of multiple proteins/experiments at the cell/tissue type and UniRef/Gene level, where correlations, interactions or overlaps can be investigated. The web-interface has been completely re-designed to enable interactive data and subset selection, correlation analysis and network visualization. Overall, RaftProt aims to advance our understanding of lipid raft function through integrative analysis of datasets collected from diverse tissue and conditions. Database URL: http://raftprot.org.

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative ‘Connect to Decode’ (C2D) to generate the first and largest manually curated interactome of Mtb termed ‘interactome pathway’ (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

The utility of high-throughput quantitative proteomics to identify differentially abundant proteins en-masse relies on suitable and accessible statistical methodology, which remains mostly an unmet need. We present a free web-based tool, called Quantitative Proteomics p-value Calculator (QPPC), designed for accessibility and usability by proteomics scientists and biologists. Being an online tool, there is no requirement for software installation. Furthermore, QPPC accepts generic peptide ratio data generated by any mass spectrometer and database search engine. Importantly, QPPC utilizes the permutation test that we recently found to be superior to other methods for analysis of peptide ratios because it does not assume normal distributions.1 QPPC assists the user in selecting significantly altered proteins based on numerical fold change, or standard deviation from the mean or median, together with the permutation p-value. Output is in the form of comma separated values files, along with graphical visualization using volcano plots and histograms. We evaluate the optimal parameters for use of QPPC, including the permutation level and the effect of outlier and contaminant peptides on p-value variability. The optimal parameters defined are deployed as default for the web-tool at http://qppc.di.uq.edu.au/ .

Background Plasmodium falciparum causes the majority of malaria mortality worldwide, and the disease occurs during the asexual red blood cell (RBC) stage of infection. In the absence of an effective and available vaccine, and with increasing drug resistance, asexual RBC stage parasites are an important research focus. In recent years, mass spectrometry–based proteomics using data-dependent acquisition has been extensively used to understand the biochemical processes within the parasite. However, data-dependent acquisition is problematic for the detection of low-abundance proteins and proteome coverage and has poor run-to-run reproducibility. Results Here, we present a comprehensive P. falciparum–infected RBC (iRBC) spectral library to measure the abundance of 44,449 peptides from 3,113 P. falciparum and 1,617 RBC proteins using a data-independent acquisition mass spectrometric approach. The spectral library includes proteins expressed in the 3 morphologically distinct RBC stages (ring, trophozoite, schizont), the RBC compartment of trophozoite-iRBCs, and the cytosolic fraction from uninfected RBCs. This spectral library contains 87% of all P. falciparum proteins that have previously been reported with protein-level evidence in blood stages, as well as 692 previously unidentified proteins. The P. falciparum spectral library was successfully applied to generate semi-quantitative proteomics datasets that characterize the 3 distinct asexual parasite stages in RBCs, and compared artemisinin-resistant (Cam3.IIR539T) and artemisinin-sensitive (Cam3.IIrev) parasites. Conclusion A reproducible, high-coverage proteomics spectral library and analysis method has been generated for investigating sets of proteins expressed in the iRBC stage of P. falciparum malaria. This will provide a foundation for an improved understanding of parasite biology, pathogenesis, drug mechanisms, and vaccine candidate discovery for malaria.