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Background Gold nanoparticles (AuNPs) are a popular choice for use in medical and biomedical research applications. With suitable functionalisation AuNPs can be applied in drug delivery systems, or can aid in disease diagnosis. One such functionalisation is with chitosan, which enables efficient interaction and permeation of cellular membranes, providing an effective adjuvant. As both AuNPs and chitosan have been shown to have low toxicity and high biocompatibility their proposed use in nanomedicine, either individually or combined, is expanding. However, further toxicological and immunological assessments of AuNP-chitosan conjugates are still needed. Therefore, we have evaluated how AuNP functionalisation with chitosan can affect uptake, cytotoxicity, and immunological responses within mononuclear cells, and influence the interaction of AuNPs with biomolecules within a complex biofluid. The AuNPs used were negatively charged through citrate-coating, or presented either low or high positive charge through chitosan-functionalisation. Uptake by THP-1 cells was assessed via transmission electron microscopy and electron energy loss spectroscopy, pro-inflammatory responses by ELISA and qRT-PCR, and cell death and viability via lactate dehydrogenase release and mitochondrial activity, respectively. Interactions of AuNPs with protein components of a frequently used in vitro cell culture medium supplement, foetal calf serum, were investigated using mass spectrometry.ResultsAlthough cells internalised all AuNPs, uptake rates and specific routes of intracellular trafficking were dependent upon chitosan-functionalisation. Accordingly, an enhanced immune response was found to be chitosan-functionalisation-dependent, in the form of CCL2, IL-1β, TNF-α and IL-6 secretion, and expression of IL-1β and NLRP3 mRNA. A corresponding increase in cytotoxicity was found in response to chitosan-coated AuNPs. Furthermore, chitosan-functionalisation was shown to induce an increase in unique proteins associating with these highly charged AuNPs.ConclusionsIt can be concluded that functionalisation of AuNPs with the perceived non-toxic biocompatible molecule chitosan at a high density can elicit functionalisation-dependent intracellular trafficking mechanisms and provoke strong pro-inflammatory conditions, and that a high affinity of these NP-conjugates for biomolecules may be implicit in these cellular responses.Electronic supplementary materialThe online version of this article (doi:10.1186/s12951-015-0146-9) contains supplementary material, which is available to authorized users.

The present study was performed in an attempt to develop an in vitro integrated testing strategy (ITS) to evaluate drug-induced neurotoxicity. A number of endpoints were analyzed using two complementary brain cell culture models and an in vitro blood-brain barrier (BBB) model after single and repeated exposure treatments with selected drugs that covered the major biological, pharmacological and neuro-toxicological responses. Furthermore, four drugs (diazepam, cyclosporine A, chlorpromazine and amiodarone) were tested more in depth as representatives of different classes of neurotoxicants, inducing toxicity through different pathways of toxicity. The developed in vitro BBB model allowed detection of toxic effects at the level of BBB and evaluation of drug transport through the barrier for predicting free brain concentrations of the studied drugs. The measurement of neuronal electrical activity was found to be a sensitive tool to predict the neuroactivity and neurotoxicity of drugs after acute exposure. The histotypic 3D re-aggregating brain cell cultures, containing all brain cell types, were found to be well suited for OMICs analyses after both acute and long term treatment. The obtained data suggest that an in vitro ITS based on the information obtained from BBB studies and combined with metabolomics, proteomics and neuronal electrical activity measurements performed in stable in vitro neuronal cell culture systems, has high potential to improve current in vitro drug-induced neurotoxicity evaluation.

One pressure and three chemical mobilization strategies have been optimized and tested for two-step capillary isoelectric focusing with ultraviolet detection with simultaneous refining of the composition of carrier ampholytes as well as of anodic and cathodic spacers. The comparison of individual mobilization strategies was performed on basis of model proteins and peptides covering a pI range of 4.1-10.0, finally targeting an acidic major food allergen, that is, ovalbumin. Resolution was improved by combining Pharmalyte 3-10 with Pharmalyte 5-6 with concentration adjustment of carrier ampholytes and the anodic and cathodic spacer, respectively. Analytes within pI 5-6 but not ovalbumin were prone to artificial peak duplication under selected capillary isoelectric focusing conditions due to retardation during focusing. l-Arginine and iminodiacetic acid were included as spacer to prevent drifts of the pH gradient and optionally block the distal capillary part. l-Arginine affected the baseline in the acidic regime in some instances by introducing irregularities that interfered with ovalbumin. Cathodic mobilization with an acidic zwitterion provided the best selectivity for ovalbumin and was successfully applied for the characterization of three commercial products of ovalbumin, revealing differences between the respective profiles. Up to 12 different fractions situated between pI 4.51 and 4.72 could be addressed.

Background: Pancreatic cancer is a heterogenous disease with a poor prognosis. This study aimed to discover and validate prognostic tissue biomarkers in pancreatic cancer using a mass spectrometry (MS) based proteomics approach. Methods: Global protein sequencing of fresh frozen pancreatic cancer and healthy pancreas tissue samples was conducted by MS to discover potential protein biomarkers. Selected candidate proteins were further verified by targeted proteomics using parallel reaction monitoring (PRM). The expression of biomarker candidates was validated by immunohistochemistry in a large tissue microarray (TMA) cohort of 141 patients with resectable pancreatic cancer. Kaplan-Meier and Cox proportional hazard modelling was used to investigate the prognostic utility of candidate protein markers. Findings: In the initial MS-discovery phase, 165 proteins were identified as potential biomarkers. In the subsequent MS-verification phase, a panel of 45 candidate proteins was verified by the development of a PRM assay. Brain acid soluble protein 1 (BASP1) was identified as a new biomarker candidate for pancreatic cancer possessing largely unknown biological and clinical functions and was selected for further analysis. Importantly, bioinformatic analysis indicated that BASP1 interacts with Wilms tumour protein (WT1) in pancreatic cancer. TMA-based immunohistochemistry analysis showed that BASP1 was an independent predictor of prolonged survival (HR 0.468, 95% CI 0.257-0.852, p = .013) and predicted favourable response to adjuvant chemotherapy, whereas WT1 indicated a worsened survival (HR 1.636, 95% CI 1.083-2.473, p = .019) and resistance to chemotherapy. Interaction analysis showed that patients with negative BASP1 and high WT1 expression had the poorest outcome (HR 3.536, 95% CI 1.336-9.362, p = .011). Interpretation: We here describe an MS-based proteomics platform for developing biomarkers for pancreatic cancer. Bioinformatic analysis and clinical data from our study suggest that BASP1 and its putative interaction partner WT1 can be used as biomarkers for predicting outcomes in pancreatic cancer patients.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Here we show that shotgun and targeted protein sequencing can be used to identify potential prognostic biomarkers in formalin-fixed paraffin-embedded specimens from 9 patients with PDAC with “short” survival (<12 months) and 10 patients with “long” survival (>45 months) undergoing surgical resection. A total of 24 and 147 proteins were significantly upregulated [fold change ≥2 or ≤0.5 and P<0.05; or different detection frequencies (≥5 samples)] in patients with “short” survival (including GLUT1) and “long” survival (including C9orf64, FAM96A, CDH1 and CDH17), respectively. STRING analysis of these proteins indicated a tight protein-protein interaction network centered on TP53. Ingenuity pathway analysis linked proteins representing “activated stroma factors” and “basal tumor factors” to poor prognosis of PDAC. It also highlighted TCF1 and CTNNB1 as possible upstream regulators. Further parallel reaction monitoring verified that seven proteins were upregulated in patients with “short” survival (MMP9, CLIC3, MMP8, PRTN3, P4HA2, THBS1 and FN1), while 18 proteins were upregulated in patients with “long” survival, including EPCAM, LGALS4, VIL1, CLCA1 and TPPP3. Thus, we verified 25 protein biomarker candidates for PDAC prognosis at the tissue level. Furthermore, an activated stroma status and protein-protein interactions with TP53 might be linked to poor prognosis of PDAC.

MS-based proteomics and metabolomics are rapidly evolving research fields driven by the development of novel instruments, experimental approaches, and analysis methods. Monolithic analysis tools perform well on single tasks but lack the flexibility to cope with the constantly changing requirements and experimental setups. Workflow systems, which combine small processing tools into complex analysis pipelines, allow custom-tailored and flexible data-processing workflows that can be published or shared with collaborators. In this article, we present the integration of established tools for computational MS from the open-source software framework OpenMS into the workflow engine Konstanz Information Miner (KNIME) for the analysis of large datasets and production of high-quality visualizations. We provide example workflows to demonstrate combined data processing and visualization for three diverse tasks in computational MS: isobaric mass tag based quantitation in complex experimental setups, label-free quantitation and identification of metabolites, and quality control for proteomics experiments.

Inter-cellular communication with stromal cells is vital for cancer cells. Molecules involved in the communication are potential drug targets. To identify them systematically, we applied a systems level analysis that combined reverse network engineering with causal effect estimation. Using only observational transcriptome profiles we searched for paracrine factors sending messages from activated hepatic stellate cells (HSC) to hepatocellular carcinoma (HCC) cells. We condensed these messages to predict ten proteins that, acting in concert, cause the majority of the gene expression changes observed in HCC cells. Among the 10 paracrine factors were both known and unknown cancer promoting stromal factors, the former including Placental Growth Factor (PGF) and Periostin (POSTN), while Pregnancy-Associated Plasma Protein A (PAPPA) was among the latter. Further support for the predicted effect of PAPPA on HCC cells came from both in vitro studies that showed PAPPA to contribute to the activation of NFκB signaling, and clinical data, which linked higher expression levels of PAPPA to advanced stage HCC. In summary, this study demonstrates the potential of causal modeling in combination with a condensation step borrowed from gene set analysis [Model-based Gene Set Analysis (MGSA)] in the identification of stromal signaling molecules influencing the cancer phenotype.