Discovery of circulating proteins associated to knee radiographic osteoarthritis.

Currently there are no sufficiently sensitive biomarkers able to reflect changes in joint remodelling during osteoarthritis (OA). In this work, we took an affinity proteomic approach to profile serum samples for proteins that could serve as indicators for the diagnosis of radiographic knee OA. Antibody suspension bead arrays were applied to analyze serum samples from patients with OA (n = 273), control subjects (n = 76) and patients with rheumatoid arthritis (RA, n = 244).

Identification of a Novel Autoimmune Peptide Epitope of Prostein in Prostate Cancer.

There is a demand for novel targets and approaches to diagnose and treat prostate cancer (PCA). In this context, serum and plasma samples from a total of 609 individuals from two independent patient cohorts were screened for IgG reactivity against a sum of 3833 human protein fragments. Starting from planar protein arrays with 3786 protein fragments to screen 80 patients with and without PCA diagnosis, 161 fragments (4%) were chosen for further analysis based on their reactivity profiles.

Circulating carnosine dipeptidase 1 associates with weight loss and poor prognosis in gastrointestinal cancer.

Background: Cancer cachexia (CC) is linked to poor prognosis. Although the mechanisms promoting this condition are not known, several circulating proteins have been proposed to contribute. We analyzed the plasma proteome in cancer subjects in order to identify factors associated with cachexia.

Analysis of autoantibody profiles in osteoarthritis using comprehensive protein array concepts.

Osteoarthritis (OA) is the most common rheumatic disease and one of the most disabling pathologies worldwide. To date, the diagnostic methods of OA are very limited, and there are no available medications capable of halting its characteristic cartilage degeneration. Therefore, there is a significant interest in new biomarkers useful for the early diagnosis, prognosis, and therapeutic monitoring.

Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines.

Background: Despite promising progress in targeted breast cancer therapy, drug resistance remains challenging. The monoclonal antibody drugs trastuzumab and pertuzumab as well as the small molecule inhibitor erlotinib were designed to prevent ErbB-2 and ErbB-1 receptor induced deregulated protein signalling, contributing to tumour progression. The oncogenic potential of ErbB receptors unfolds in case of overexpression or mutations.

A Systems Biology Approach to Deciphering the Etiology of Steatosis Employing Patient-Derived Dermal Fibroblasts and iPS Cells.

Non-alcoholic fatty liver disease comprises a broad spectrum of disease states ranging from simple steatosis to non-alcoholic steatohepatitis. As a result of increases in the prevalences of obesity, insulin resistance, and hyperlipidemia, the number of people with hepatic steatosis continues to increase. Differences in susceptibility to steatohepatitis and its progression to cirrhosis have been attributed to a complex interplay of genetic and external factors all addressing the intracellular network.

Global microRNA level regulation of EGFR-driven cell-cycle protein network in breast cancer.

The EGFR-driven cell-cycle pathway has been extensively studied due to its pivotal role in breast cancer proliferation and pathogenesis. Although several studies reported regulation of individual pathway components by microRNAs (miRNAs), little is known about how miRNAs coordinate the EGFR protein network on a global miRNA (miRNome) level. Here, we combined a large-scale miRNA screening approach with a high-throughput proteomic readout and network-based data analysis to identify which miRNAs are involved, and to uncover potential regulatory patterns.

Quantitative analysis of phosphoproteins using microspot immunoassays.

Protein microarrays are an ideal technology platform which allow for a robust and standardized profiling of the cellular proteome. Many cellular functions are not simply controlled by the presence of certain proteins, especially the propagation of external stimuli, which depend on transient post-translational modifications that determine whether a protein is in its active or inactive state. Thus, complex biological processes require the availability of a sound set of quantitative and time-resolved measurements to be understood.

Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'.

Background: Network inference from high-throughput data has become an important means of current analysis of biological systems. For instance, in cancer research, the functional relationships of cancer related proteins, summarised into signalling networks are of central interest for the identification of pathways that influence tumour development. Cancer cell lines can be used as model systems to study the cellular response to drug treatments in a time-resolved way.

Dynamic deterministic effects propagation networks: learning signalling pathways from longitudinal protein array data.

Motivation: Network modelling in systems biology has become an important tool to study molecular interactions in cancer research, because understanding the interplay of proteins is necessary for developing novel drugs and therapies. De novo reconstruction of signalling pathways from data allows to unravel interactions between proteins and make qualitative statements on possible aberrations of the cellular regulatory program. We present a new method for reconstructing signalling networks from time course experiments after external perturbation and show an application of the method to data measuring abundance of phosphorylated proteins in a human breast cancer cell line, generated on reverse phase protein arrays.

QuantProReloaded: quantitative analysis of microspot immunoassays.

Unlabelled: Protein microarrays are well-established as sensitive tools for proteomics. Particularly, the microspot immunoassay (MIA) platform enables a quantitative analysis of (phospho-) proteins in complex solutions (e.g.

RPPanalyzer: Analysis of reverse-phase protein array data.

Summary: RPPanalyzer is a statistical tool developed to read reverse-phase protein array data, to perform the basic data analysis and to visualize the resulting biological information. The R-package provides different functions to compare protein expression levels of different samples and to normalize the data. Implemented plotting functions permit a quality control by monitoring data distribution and signal validity.

Quantitative protein microarrays for time-resolved measurements of protein phosphorylation.

The quantitative analysis of signaling networks requires highly sensitive methods for the time-resolved determination of protein phosphorylation. For this reason, we developed a quantitative protein microarray that monitors the activation of multiple signaling pathways in parallel, and at high temporal resolution. A label-free sandwich approach was combined with near infrared detection, thus permitting the accurate quantification of low-level phosphoproteins in limited biological samples corresponding to less than 50,000 cells, and with a very low standard deviation of approximately 5%.

Antibody microarrays as an experimental platform for the analysis of signal transduction networks.

A significant bottleneck for the time-resolved and quantitative description of signaling networks is the limited sample capacity and sensitivity of existing methods. Recently, antibody microarrays have emerged as a promising experimental platform for the quantitative and comprehensive determination of protein abundance and protein phosphorylation. This review summarizes the development of microarray applications involving antibody-based capture of target proteins with a focus on quantitative applications.

Infrared-based protein detection arrays for quantitative proteomics.

The advancement of efficient technologies to comply with the needs of systems biology and drug discovery has so far not received adequate attention. A substantial bottleneck for the time-resolved quantitative description of signaling networks is the limited throughput and the inadequate sensitivity of currently established methods. Here, we present an improved protein microarray-based approach towards the sensitive detection of proteins in the fg-range which is based on signal detection in the near-infrared range.