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Novel autoantibodies identified in ACPA-negative rheumatoid arthritis

OBJECTIVES

Lack of effective biomarkers in anti-citrullinated protein antibody (ACPA)-negative rheumatoid arthritis (RA) impedes early diagnosis and treatment. This study aimed to identify novel autoantibodies in RA and verify their diagnostic values in ACPA-negative RA based on protein microarray technology.

METHODS

A total of 1011 sera from 559 RA (276 ACPA-positive and 283 ACPA-negative), 239 disease controls (DCs) and 213 healthy controls (HCs) were collected and sampled on two sequential microarrays and ELISA and western blot (WB) detection, for novel autoantibodies discovery, validation and verification, respectively. The high-density protein microarray printed with a broad spectrum of recombinant human proteins was first employed to screen candidate autoantibodies, then focused microarrays composed of candidate autoantigens were used for validation, followed by ELISA and WB to verify the presence of the most promising candidates in ACPA-negative RA.

RESULTS

Nine novel autoantibodies were identified by two sequential microarrays with positivity 17.93%-27.59% and specificities >90% in ACPA-negative RA. Among these, anti-PTX3 and anti-DUSP11 autoantibodies presented with the highest sensitivity and were consistently verified by ELISA and WB. Pooling samples of all cohorts, the positivities of anti-PTX3 and anti-DUSP11 in ACPA-negative RA were 27.56% and 31.80%, respectively, similar to those in ACPA-positive RA, and significantly higher than in HCs (4.69% and 2.35%) and DCs (10.04% and 8.49%) (p<0.0001). Combination of anti-PTX3 with anti-DUSP11 significantly increased the diagnostic sensitivity (38.00%) with specificity of 88.72%, regardless of ACPA status.

CONCLUSION

Anti-PTX3 and anti-DUSP11 autoantibodies are newly identified biomarkers for diagnosis of ACPA-negative RA.

High-Throughput Chip Assay for Investigating Escherichia coli Interaction with the Blood-Brain Barrier Using Microbial and Human Proteome Microarrays (Dual-Microarray Technology)

Bacterial meningitis in neonates and infants is an acute lethal disease and occurs in response to microbial exploitation of the blood-brain barrier (BBB), resulting in the intracranial inflammation. Several pathogens, such as Escherichia coli ( E. coli), can cause this devastating disease; however, the underlying molecular mechanisms by which these pathogens exploit the BBB remain incompletely understood. To identify important players on both the pathogen and host sides that govern the E. coli-BBB cell interactions, we took advantage of the E. coli and human proteome microarrays (i.e., HuProt) as an unbiased, proteome-wide tool for identification of important players on both sides. Using the E. coli proteome microarrays, we developed a unique high throughput chip-based cell probing assay to probe with fluorescent live human brain microvascular endothelial cells (HBMEC, which constitute the BBB). We identified several transmembrane proteins, which effectively bound to live HBMEC. We focused on YojI protein for further study. By probing the HuProt arrays with YojI, interferon-alpha receptor (IFNAR2) was identified as one of its binding proteins. The importance of YojI and IFNAR2 involved in E. coli-HBMEC interactions was characterized using the YojI knockout bacteria and IFNAR2-knock down HBMEC and further confirmed by E. coli binding assay in HBMEC. This study represents a new paradigm (dual-microarray technology) that enables rapid, unbiased discovery of both pathogen and host players that are involved in pathogen-host interactions for human infectious diseases in a high throughput manner.

Systematic identification of molecular links between core and candidate genes in breast cancer

Despite the remarkable progress achieved in the identification of specific genes involved in breast cancer (BC), our understanding of their complex functioning is still limited. In this manuscript, we systematically explore the existence of direct physical interactions between the products of BC core and associated genes. Our aim is to generate a protein interaction network of BC-associated gene products and suggest potential molecular mechanisms to unveil their role in the disease. In total, we report 599 novel high-confidence interactions among 44 BC core, 54 BC candidate/associated and 96 newly identified proteins. Our findings indicate that this network-based approach is indeed a robust inference tool to pinpoint new potential players and gain insight into the underlying mechanisms of those proteins with previously unknown roles in BC. To illustrate the power of our approach, we provide initial validation of two BC-associated proteins on the alteration of DNA damage response as a result of specific re-wiring interactions. Overall, our BC-related network may serve as a framework to integrate clinical and molecular data and foster novel global therapeutic strategies.

Breast cancer genes PSMC3IP and EPSTI1 play a role in apoptosis regulation

A key element to delineate the biology of individual tumors is the regulation of apoptosis. In this work, we functionally characterize two breast cancer associated genes, the proteasome 26S subunit ATPase 3 interacting protein (PSMC3IP) and the epithelial-stromal interaction 1 (EPSTI1), to explore their potential apoptotic role in breast cancer. We first explore the existence of direct physical interactions with annotated BC-apoptotic genes. Based on the generated interaction network, we examine several apoptotic markers to determine the effect of PSMC3IP and EPSTI1 gene expression modulation in two different human breast cancer cell lines to suggest potential molecular mechanisms to unveil their role in the disease. Our results show that PSMC3IP and EPSTI1 are able to modulate the extrinsic apoptotic pathway in estrogen receptor positive and triple negative breast cancer cell lines, highlighting them as potential therapeutic targets.

RXR partial agonist produced by side chain repositioning of alkoxy RXR full agonist retains antitype 2 diabetes activity without the adverse effects

We previously reported RXR partial agonist CBt-PMN (1-(3,5,5,8,8-pentamethyl-5,6,7,8-tetrahydro-2-naphthyl)-1H-benzotriazole-5-carboxylic acid: 5, EC50 = 143 nM, Emax = 75%), which showed a potent glucose-lowering effect without causing serious adverse effects. However, it remains important to elucidate the structural requirements for RXR efficacy and the glucose-lowering effect because RXR-permissive heterodimers such as PPAR/RXR or LXR/RXR are reported to be activated differently depending upon the chemical structure of RXR agonists. In this work, we show that an RXR partial agonist, NEt-4IB (6-[ethyl-(4-isobutoxy-3-isopropylphenyl)amino]pyridine-3-carboxylic acid: 8b, EC50 = 169 nM, Emax = 55%), can be obtained simply by repositioning the side chains (interchanging the isobutoxy and isopropoxy groups) at the hydrophobic moiety of the RXR full agonist NEt-3IB (6-[ethyl-(3-isobutoxy-4-isopropylphenyl)amino]pyridine-3-carboxylic acid: 7b, EC50 = 19 nM). NEt-4IB (8b) showed antitype 2 diabetes activity without the above side effects upon repeated oral administration to mice at 10 mg/kg/day, similarly to 5.

Rapid identification of monospecific monoclonal antibodies using a human proteome microarray

To broaden the range of tools available for proteomic research, we generated a library of 16,368 unique full-length human ORFs that are expressible as N-terminal GST-His₆ fusion proteins. Following expression in yeast, these proteins were then individually purified and used to construct a human proteome microarray. To demonstrate the usefulness of this reagent, we developed a streamlined strategy for the production of monospecific monoclonal antibodies that used immunization with live human cells and microarray-based analysis of antibody specificity as its central components. We showed that microarray-based analysis of antibody specificity can be performed efficiently using a two-dimensional pooling strategy. We also demonstrated that our immunization and selection strategies result in a large fraction of monospecific monoclonal antibodies that are both immunoblot and immunoprecipitation grade. Our data indicate that the pipeline provides a robust platform for the generation of monoclonal antibodies of exceptional specificity.

Profiling the human protein-DNA interactome reveals ERK2 as a transcriptional repressor of interferon signaling

Protein-DNA interactions (PDIs) mediate a broad range of functions essential for cellular differentiation, function, and survival. However, it is still a daunting task to comprehensively identify and profile sequence-specific PDIs in complex genomes. Here, we have used a combined bioinformatics and protein microarray-based strategy to systematically characterize the human protein-DNA interactome. We identified 17,718 PDIs between 460 DNA motifs predicted to regulate transcription and 4,191 human proteins of various functional classes. Among them, we recovered many known PDIs for transcription factors (TFs). We identified a large number of unanticipated PDIs for known TFs, as well as for previously uncharacterized TFs. We also found that over three hundred unconventional DNA-binding proteins (uDBPs)--which include RNA-binding proteins, mitochondrial proteins, and protein kinases--showed sequence-specific PDIs. One such uDBP, ERK2, acts as a transcriptional repressor for interferon gamma-induced genes, suggesting important biological roles for such proteins.

A HUPO test sample study reveals common problems in mass spectrometry-based proteomics

We carried out a test sample study to try to identify errors leading to irreproducibility, including incompleteness of peptide sampling, in LC-MS-based proteomics. We distributed a test sample consisting of an equimolar mix of 20 highly purified recombinant human proteins, to 27 laboratories for identification. Each protein contained one or more unique tryptic peptides of 1250 Da to also test for ion selection and sampling in the mass spectrometer. Of the 27 labs, initially only 7 labs reported all 20 proteins correctly, and only 1 lab reported all the tryptic peptides of 1250 Da. Nevertheless, a subsequent centralized analysis of the raw data revealed that all 20 proteins and most of the 1250 Da peptides had in fact been detected by all 27 labs. The centralized analysis allowed us to determine sources of problems encountered in the study, which include missed identifications (false negatives), environmental contamination, database matching, and curation of protein identifications. Improved search engines and databases are likely to increase the fidelity of mass spectrometry-based proteomics.

Human Gene and Protein Database (HGPD): a novel database presenting a large quantity of experiment-based results in human proteomics

Completion of human genome sequencing has greatly accelerated functional genomic research. Full-length cDNA clones are essential experimental tools for functional analysis of human genes. In one of the projects of the New Energy and Industrial Technology Development Organization (NEDO) in Japan, the full-length human cDNA sequencing project (FLJ project), nucleotide sequences of approximately 30 000 human cDNA clones have been analyzed. The Gateway system is a versatile framework to construct a variety of expression clones for various experiments. We have constructed 33 275 human Gateway entry clones from full-length cDNAs, representing to our knowledge the largest collection in the world. Utilizing these clones with a highly efficient cell-free protein synthesis system based on wheat germ extract, we have systematically and comprehensively produced and analyzed human proteins in vitro. Sequence information for both amino acids and nucleotides of open reading frames of cDNAs cloned into Gateway entry clones and in vitro expression data using those clones can be retrieved from the Human Gene and Protein Database (HGPD, http://www.HGPD.jp). HGPD is a unique database that stores the information of a set of human Gateway entry clones and protein expression data and helps the user to search the Gateway entry clones.

A concentration-dependent analysis method for high density protein microarrays

Protein microarray technology is rapidly growing and has the potential to accelerate the discovery of targets of serum antibody responses in cancer, autoimmunity and infectious disease. Analytical tools for interpreting this high-throughput array data, however, are not well-established. We developed a concentration-dependent analysis (CDA) method which normalizes protein microarray data based on the concentration of spotted probes. We show that this analysis samples a data space that is complementary to other commonly employed analyses, and demonstrate experimental validation of 92% of hits identified by the intersection of CDA with other tools. These data support the use of CDA either as a preprocessing step for a more complete proteomic microarray data analysis or as a stand-alone analysis method.

ProCKSI: a decision support system for Protein (structure) Comparison, Knowledge, Similarity and Information

Background

We introduce the decision support system for Protein (Structure) Comparison, Knowledge, Similarity and Information (ProCKSI). ProCKSI integrates various protein similarity measures through an easy to use interface that allows the comparison of multiple proteins simultaneously. It employs the Universal Similarity Metric (USM), the Maximum Contact Map Overlap (MaxCMO) of protein structures and other external methods such as the DaliLite and the TM-align methods, the Combinatorial Extension (CE) of the optimal path, and the FAST Align and Search Tool (FAST). Additionally, ProCKSI allows the user to upload a user-defined similarity matrix supplementing the methods mentioned, and computes a similarity consensus in order to provide a rich, integrated, multicriteria view of large datasets of protein structures.

Results

We present ProCKSI's architecture and workflow describing its intuitive user interface, and show its potential on three distinct test-cases. In the first case, ProCKSI is used to evaluate the results of a previous CASP competition, assessing the similarity of proposed models for given targets where the structures could have a large deviation from one another. To perform this type of comparison reliably, we introduce a new consensus method. The second study deals with the verification of a classification scheme for protein kinases, originally derived by sequence comparison by Hanks and Hunter, but here we use a consensus similarity measure based on structures. In the third experiment using the Rost and Sander dataset (RS126), we investigate how a combination of different sets of similarity measures influences the quality and performance of ProCKSI's new consensus measure. ProCKSI performs well with all three datasets, showing its potential for complex, simultaneous multi-method assessment of structural similarity in large protein datasets. Furthermore, combining different similarity measures is usually more robust than relying on one single, unique measure.

Conclusion

Based on a diverse set of similarity measures, ProCKSI computes a consensus similarity profile for the entire protein set. All results can be clustered, visualised, analysed and easily compared with each other through a simple and intuitive interface.

ProCKSI is publicly available at for academic and non-commercial use.

The Molecular Biology Database Collection: 2007 update

The NAR online Molecular Biology Database Collection is a public resource that contains links to the databases described in this issue of Nucleic Acids Research, previous NAR database issues, as well as a selection of other molecular biology databases that are freely available on the web and might be useful to the molecular biologist. The 2007 update includes 968 databases, 110 more than the previous one. Many databases that have been described in earlier issues of NAR come with updated summaries, which reflect recent progress and, in some instances, an expanded scope of these databases. The complete database list and summaries are available online on the Nucleic Acids Research web site http://nar.oxfordjournals.org/.