Serum-autoantibodies for discovery of prostate cancer specific biomarkers.

BACKGROUND: The currently used prostate cancer serum marker has a low cancer specificity and improved diagnostics are needed. Here we evaluated whether autoantibodies are present in sera of prostate cancer patients and whether they are useful diagnostic markers for prostate cancer. METHODS: Sera from 20 prostate cancer patients and 20 healthy controls were incubated on expression clone arrays containing more than 37,000 recombinant human proteins. Functional annotation clustering of the identified autoantigens was performed using the DAVID database. Autoantigens identified in the prostate cancer group were validated on microarrays using sera of 40 prostate cancer patients, 40 patients with elevated PSA levels but prostate cancer negative biopsies (benign disease), and 40 healthy controls. RESULTS: We detected autoantibodies against 408 different antigens in sera of prostate cancer patients. One hundred seventy-four of these were exclusively detected in the cancer group compared to the healthy control group. Functional annotation clustering revealed an enrichment of RNA-associated, cytoskeleton, and nuclear proteins. The autoantibody panel was validated in serum samples of independent prostate cancer patients. Autoantibody profiles discriminated between prostate cancer patients and benign disease patients with an ROC curve AUC of 0.71. TTLL12, a protein recently described to be over-expressed in prostate cancer, was the highest ranked discrimination autoantigen. CONCLUSION: A variety of autoantibodies were identified in sera of prostate cancer patients and provide a first step towards autoantibody diagnostics. Serum autoantibodies reflect the disease and represent valuable tools not only for prostate cancer, but also for other diseases affecting the immune response.

A fluorescent two-hybrid assay for direct visualization of protein interactions in living cells.

Genetic high throughput screens have yielded large sets of potential protein-protein interactions now to be verified and further investigated. Here we present a simple assay to directly visualize protein-protein interactions in single living cells. Using a modified lac repressor system, we tethered a fluorescent bait at a chromosomal lac operator array and assayed for co-localization of fluorescent prey fusion proteins. With this fluorescent two-hybrid assay we successfully investigated the interaction of proteins from different subcellular compartments including nucleus, cytoplasm, and mitochondria. In combination with an S phase marker we also studied the cell cycle dependence of protein-protein interactions. These results indicate that the fluorescent two-hybrid assay is a powerful tool to investigate protein-protein interactions within their cellular environment and to monitor the response to external stimuli in real time.

FragIdent--automatic identification and characterisation of cDNA-fragments.

BACKGROUND: Many genetic studies and functional assays are based on cDNA fragments. After the generation of cDNA fragments from an mRNA sample, their content is at first unknown and must be assigned by sequencing reactions or hybridisation experiments. Even in characterised libraries, a considerable number of clones are wrongly annotated. Furthermore, mix-ups can happen in the laboratory. It is therefore essential to the relevance of experimental results to confirm or determine the identity of the employed cDNA fragments. However, the manual approach for the characterisation of these fragments using BLAST web interfaces is not suited for larger number of sequences and so far, no user-friendly software is publicly available. RESULTS: Here we present the development of FragIdent, an application for the automatic identification of open reading frames (ORFs) within cDNA-fragments. The software performs BLAST analyses to identify the genes represented by the sequences and suggests primers to complete the sequencing of the whole insert. Gene-specific information as well as the protein domains encoded by the cDNA fragment are retrieved from Internet-based databases and included in the output. The application features an intuitive graphical interface and is designed for researchers without any bioinformatics skills. It is suited for projects comprising up to several hundred different clones. CONCLUSION: We used FragIdent to identify 84 cDNA clones from a yeast two-hybrid experiment. Furthermore, we identified 131 protein domains within our analysed clones. The source code is freely available from our homepage at http://compbio.charite.de/genetik/FragIdent/.

Sequential high-content profiling of the IgG-autoantibody repertoire reveals novel antigens in rheumatoid arthritis.

BACKGROUND: The aim was to identify novel diagnostic autoantibody candidates for rheumatoid arthritis (RA) by comprehensive screening for autoreactivity. METHOD: We incubated 5892 recombinant proteins coupled to fluorescent beads, with patients' sera for the detection of IgG-autoantibodies in three independent patient cohorts: A (n = 72 patients with established RA); B/B- (n = 116 patients with early RA (B) and n = 51 CCP-negative patients with early RA from B (B-)); and C (n = 184 patients with early seronegative RA), in comparison to matched healthy controls. Intersects of significantly increased autoantibodies as determined by the Mann-Whitney test were sought. RESULT: Screening of 5892 antigens in RA cohorts A and B, or the seronegative cohorts B- and C revealed intersects of 23 and 13 significantly increased autoantibodies, respectively. Reactivity to three antigens was increased in all cohorts tested: N-acetylglucosamine-1-phosphate transferase, gamma subunit (GNPTG), heterogeneous nuclear ribonucleoprotein A1-like 2 (HNRNPA1), and insulin-like growth factor binding protein 2 (IGFBP2). CONCLUSIONS: Comprehensive sequential screening for autoantibodies reveals novel candidates for diagnostic markers in both seropositive and seronegative RA and suggests new fields of research into the pathogenesis of RA.

Pathogenic polyglutamine tracts are potent inducers of spontaneous Sup35 and Rnq1 amyloidogenesis.

The glutamine/asparagine (Q/N)-rich yeast prion protein Sup35 has a low intrinsic propensity to spontaneously self-assemble into ordered, beta-sheet-rich amyloid fibrils. In yeast cells, de novo formation of Sup35 aggregates is greatly facilitated by high protein concentrations and the presence of preformed Q/N-rich protein aggregates that template Sup35 polymerization. Here, we have investigated whether aggregation-promoting polyglutamine (polyQ) tracts can stimulate the de novo formation of ordered Sup35 protein aggregates in the absence of Q/N-rich yeast prions. Fusion proteins with polyQ tracts of different lengths were produced and their ability to spontaneously self-assemble into amlyloid structures was analyzed using in vitro and in vivo model systems. We found that Sup35 fusions with pathogenic (>or=54 glutamines), as opposed to non-pathogenic (19 glutamines) polyQ tracts efficiently form seeding-competent protein aggregates. Strikingly, polyQ-mediated de novo assembly of Sup35 protein aggregates in yeast cells was independent of pre-existing Q/N-rich protein aggregates. This indicates that increasing the content of aggregation-promoting sequences enhances the tendency of Sup35 to spontaneously self-assemble into insoluble protein aggregates. A similar result was obtained when pathogenic polyQ tracts were linked to the yeast prion protein Rnq1, demonstrating that polyQ sequences are generic inducers of amyloidogenesis. In conclusion, long polyQ sequences are powerful molecular tools that allow the efficient production of seeding-competent amyloid structures.

Toward a Successful Clinical Neuroproteomics The 11th HUPO Brain Proteome Project Workshop 3 March, 2009, Kolymbari, Greece.

The HUPO Brain Proteome Project (HUPO BPP) held its 11th workshop in Kolymbari on March 3, 2009. The principal aim of this project is to obtain a better understanding of neurodiseases and ageing, with the ultimate objective of discovering prognostic and diagnostic biomarkers, in addition to the development of novel diagnostic techniques and new medications. The attendees came together to discuss sub-project progress in the clinical neuroproteomics of human or mouse models of Alzheimer's and Parkinson's disease, and to define the needs and guidelines required for more advanced proteomics approaches. With the election of new steering committees, the members of the HUPO BPP elaborated an actual plan promoting activities, outcomes, and future directions of the HUPO BPP to acquire new funding and new participants.

A human protein-protein interaction network: a resource for annotating the proteome.

Protein-protein interaction maps provide a valuable framework for a better understanding of the functional organization of the proteome. To detect interacting pairs of human proteins systematically, a protein matrix of 4456 baits and 5632 preys was screened by automated yeast two-hybrid (Y2H) interaction mating. We identified 3186 mostly novel interactions among 1705 proteins, resulting in a large, highly connected network. Independent pull-down and co-immunoprecipitation assays validated the overall quality of the Y2H interactions. Using topological and GO criteria, a scoring system was developed to define 911 high-confidence interactions among 401 proteins. Furthermore, the network was searched for interactions linking uncharacterized gene products and human disease proteins to regulatory cellular pathways. Two novel Axin-1 interactions were validated experimentally, characterizing ANP32A and CRMP1 as modulators of Wnt signaling. Systematic human protein interaction screens can lead to a more comprehensive understanding of protein function and cellular processes.

A protein interaction network links GIT1, an enhancer of huntingtin aggregation, to Huntington's disease.

Analysis of protein-protein interactions (PPIs) is a valuable approach for characterizing proteins of unknown function. Here, we have developed a strategy combining library and matrix yeast two-hybrid screens to generate a highly connected PPI network for Huntington's disease (HD). The network contains 186 PPIs among 35 bait and 51 prey proteins. It revealed 165 new potential interactions, 32 of which were confirmed by independent binding experiments. The network also permitted the functional annotation of 16 uncharacterized proteins and facilitated the discovery of GIT1, a G protein-coupled receptor kinase-interacting protein, which enhances huntingtin aggregation by recruitment of the protein into membranous vesicles. Coimmunoprecipitations and immunofluorescence studies revealed that GIT1 and huntingtin associate in mammalian cells under physiological conditions. Moreover, GIT1 localizes to neuronal inclusions, and is selectively cleaved in HD brains, indicating that its distribution and function is altered during disease pathogenesis.