Serodiagnosis of Echinococcus spp. infection: explorative selection of diagnostic antigens by peptide microarray

BACKGROUND: Production of native antigens for serodiagnosis of helminthic infections is laborious and hampered by batch-to-batch variation. For serodiagnosis of echinococcosis, especially cystic disease, most screening tests rely on crude or purified Echinococcus granulosus hydatid cyst fluid. To resolve limitations associated with native antigens in serological tests, the use of standardized and highly pure antigens produced by chemical synthesis offers considerable advantages, provided appropriate diagnostic sensitivity and specificity is achieved. METHODOLOGY/PRINCIPAL FINDINGS: Making use of the growing collection of genomic and proteomic data, we applied a set of bioinformatic selection criteria to a collection of protein sequences including conceptually translated nucleotide sequence data of two related tapeworms, Echinococcus multilocularis and Echinococcus granulosus. Our approach targeted alpha-helical coiled-coils and intrinsically unstructured regions of parasite proteins potentially exposed to the host immune system. From 6 proteins of E. multilocularis and 5 proteins of E. granulosus, 45 peptides between 24 and 30 amino acids in length were designed. These peptides were chemically synthesized, spotted on microarrays and screened for reactivity with sera from infected humans. Peptides reacting above the cut-off were validated in enzyme-linked immunosorbent assays (ELISA). Peptides identified failed to differentiate between E. multilocularis and E. granulosus infection. The peptide performing best reached 57% sensitivity and 94% specificity. This candidate derived from Echinococcus multilocularis antigen B8/1 and showed strong reactivity to sera from patients infected either with E. multilocularis or E. granulosus. CONCLUSIONS/SIGNIFICANCE: This study provides proof of principle for the discovery of diagnostically relevant peptides by bioinformatic selection complemented with screening on a high-throughput microarray platform. Our data showed that a single peptide cannot provide sufficient diagnostic sensitivity whereas pooling several peptide antigens improved sensitivity; thus combinations of several peptides may lead the way to new diagnostic tests that replace, or at least complement conventional immunodiagnosis of echinococcosis. Our strategy could prove useful for diagnostic developments in other pathogens.

A global ”imaging” view on systems approaches in immunology

The immune system exhibits an enormous complexity. High throughput methods such as the "-omic'' technologies generate vast amounts of data that facilitate dissection of immunological processes at ever finer resolution. Using high-resolution data-driven systems analysis, causal relationships between complex molecular processes and particular immunological phenotypes can be constructed. However, processes in tissues, organs, and the organism itself (so-called higher level processes) also control and regulate the molecular (lower level) processes. Reverse systems engineering approaches, which focus on the examination of the structure, dynamics and control of the immune system, can help to understand the construction principles of the immune system. Such integrative mechanistic models can properly describe, explain, and predict the behavior of the immune system in health and disease by combining both higher and lower level processes. Moving from molecular and cellular levels to a multiscale systems understanding requires the development of methodologies that integrate data from different biological levels into multiscale mechanistic models. In particular, 3D imaging techniques and 4D modeling of the spatiotemporal dynamics of immune processes within lymphoid tissues are central for such integrative approaches. Both dynamic and global organ imaging technologies will be instrumental in facilitating comprehensive multiscale systems immunology analyses as discussed in this review.

Measuring substrate binding and affinity of purified membrane transport proteins using the scintillation proximity assay

The scintillation proximity assay (SPA) is a rapid radioligand binding assay. Upon binding of radioactively labeled ligands (here L-[(3)H]arginine or D-[(3)H]glucose) to acceptor proteins immobilized on fluoromicrospheres (containing the scintillant), a light signal is stimulated and measured. The application of SPA to purified, detergent-solubilized membrane transport proteins allows substrate-binding properties to be assessed (e.g., substrate specificity and affinity), usually within 1 d. Notably, the SPA makes it possible to study specific transporters without interference from other cellular components, such as endogenous transporters. Reconstitution of the target transporter into proteoliposomes is not required. The SPA procedure allows high sample throughput and simple sample handling without the need for washing or separation steps: components are mixed in one well and the signal is measured directly after incubation. Therefore, the SPA is an excellent tool for high-throughput screening experiments, e.g., to search for substrates and inhibitors, and it has also recently become an attractive tool for drug discovery.

Modulation of L-α-lysophosphatidylinositol/GPR55 mitogen-activated protein kinase (MAPK) signaling by cannabinoids

GPR55 is activated by l-α-lysophosphatidylinositol (LPI) but also by certain cannabinoids. In this study, we investigated the GPR55 pharmacology of various cannabinoids, including analogues of the CB1 receptor antagonist Rimonabant®, CB2 receptor agonists, and Cannabis sativa constituents. To test ERK1/2 phosphorylation, a primary downstream signaling pathway that conveys LPI-induced activation of GPR55, a high throughput system, was established using the AlphaScreen® SureFire® assay. Here, we show that CB1 receptor antagonists can act both as agonists alone and as inhibitors of LPI signaling under the same assay conditions. This study clarifies the controversy surrounding the GPR55-mediated actions of SR141716A; some reports indicate the compound to be an agonist and some report antagonism. In contrast, we report that the CB2 ligand GW405833 behaves as a partial agonist of GPR55 alone and enhances LPI signaling. GPR55 has been implicated in pain transmission, and thus our results suggest that this receptor may be responsible for some of the antinociceptive actions of certain CB2 receptor ligands. The phytocannabinoids Δ9-tetrahydrocannabivarin, cannabidivarin, and cannabigerovarin are also potent inhibitors of LPI. These Cannabis sativa constituents may represent novel therapeutics targeting GPR55.

Performance Modeling Of Reconfigurable Distributed Systems Based On The Opensparc Fpga Board And The Sirc Communication Framewor

This thesis explores system performance for reconfigurable distributed systems and provides an analytical model for determining throughput of theoretical systems based on the OpenSPARC FPGA Board and the SIRC Communication Framework. This model was developed by studying a small set of variables that together determine a system¿s throughput. The importance of this model is in assisting system designers to make decisions as to whether or not to commit to designing a reconfigurable distributed system based on the estimated performance and hardware costs. Because custom hardware design and distributed system design are both time consuming and costly, it is important for designers to make decisions regarding system feasibility early in the development cycle. Based on experimental data the model presented in this paper shows a close fit with less than 10% experimental error on average. The model is limited to a certain range of problems, but it can still be used given those limitations and also provides a foundation for further development of modeling reconfigurable distributed systems.

An integrated nano-scale approach to profile miRNAs in limited clinical samples

Profiling miRNA expression in cells that directly contribute to human disease pathogenesis is likely to aid the discovery of novel drug targets and biomarkers. However, tissue heterogeneity and the limited amount of human diseased tissue available for research purposes present fundamental difficulties that often constrain the scope and potential of such studies. We established a flow cytometry-based method for isolating pure populations of pathogenic T cells from bronchial biopsy samples of asthma patients, and optimized a high-throughput nano-scale qRT-PCR method capable of accurately measuring 96 miRNAs in as little as 100 cells. Comparison of circulating and airway T cells from healthy and asthmatic subjects revealed asthma-associated and tissue-specific miRNA expression patterns. These results establish the feasibility and utility of investigating miRNA expression in small populations of cells involved in asthma pathogenesis, and set a precedent for application of our nano-scale approach in other human diseases. The microarray data from this study (Figure 7) has been submitted to the NCBI Gene Expression Omnibus (GEO; http://ncbi.nlm.nih.gov/geo) under accession no. GSE31030.

The Crystallization Kinetics of Polylactic Acid (Pla) Processed Through Solid-State/Melt Extrusion

Polylactic acid (PLA) is a bio-derived, biodegradable polymer with a number of similar mechanical properties to commodity plastics like polyethylene (PE) and polyethylene terephthalate (PETE). There has recently been a great interest in using PLA to replace these typical petroleum-derived polymers because of the developing trend to use more sustainable materials and technologies. However, PLA¿s inherent slow crystallization behavior is not compatible with prototypical polymer processing techniques such as molding and extrusion, and in turn inhibits its widespread use in industrial applications. In order to make PLA into a commercially-viable material, there is a need to process the material in such a way that its tendency to form crystals is enhanced. The industry standard for producing PLA products is via twin screw extrusion (TSE), where polymer pellets are fed into a heated extruder, mixed at a temperature above its melting temperature, and molded into a desired shape. A relatively novel processing technique called solid-state shear pulverization (SSSP) processes the polymer in the solid state so that nucleation sites can develop and fast crystallization can occur. SSSP has also been found to enhance the mechanical properties of a material, but its powder output form is undesirable in industry. A new process called solid-state/melt extrusion (SSME), developed at Bucknell University, combines the TSE and SSSP processes in one instrument. This technique has proven to produce moldable polymer products with increased mechanical strength. This thesis first investigated the effects of the TSE, SSSP, and SSME polymer processing techniques on PLA. The study seeks to determine the process that yields products with the most enhanced thermal and mechanical properties. For characterization, percent crystallinity, crystallization half time, storage modulus, softening temperature, degradation temperature and molecular weight were analyzed for all samples. Through these characterization techniques, it was observed that SSME-processed PLA had enhanced properties relative to TSE- and SSSP-processed PLA. Because of the previous findings, an optimization study for SSME-processed PLA was conducted where throughput and screw design were varied. The optimization study determined PLA processed with a low flow rate and a moderate screw design in an SSME process produced a polymer product with the largest increase in thermal properties and a high retention of polymer structure relative to TSE-, SSSP-, and all other SSME-processed PLA. It was concluded that the SSSP part of processing scissions polymer chains, creating defects within the material, while the TSE part of processing allows these defects to be mixed thoroughly throughout the sample. The study showed that a proper SSME setup allows for both the increase in nucleation sites within the polymer and sufficient mixing, which in turn leads to the development of a large amount of crystals in a short period of time.

16S rRNA terminal restriction fragment length polymorphism for the characterization of the nasopharyngeal microbiota

A novel non-culture based 16S rRNA Terminal Restriction Fragment Length Polymorphism (T-RFLP) method using the restriction enzymes Tsp509I and Hpy166II was developed for the characterization of the nasopharyngeal microbiota and validated using recently published 454 pyrosequencing data. 16S rRNA gene T-RFLP for 153 clinical nasopharyngeal samples from infants with acute otitis media (AOM) revealed 5 Tsp509I and 6 Hpy166II terminal fragments (TFs) with a prevalence of >10%. Cloning and sequencing identified all TFs with a prevalence >6% allowing a sufficient description of bacterial community changes for the most important bacterial taxa. The conjugated 7-valent pneumococcal polysaccharide vaccine (PCV-7) and prior antibiotic exposure had significant effects on the bacterial composition in an additive main effects and multiplicative interaction model (AMMI) in concordance with the 16S rRNA 454 pyrosequencing data. In addition, the presented T-RFLP method is able to discriminate S. pneumoniae from other members of the Mitis group of streptococci, which therefore allows the identification of one of the most important human respiratory tract pathogens. This is usually not achieved by current high throughput sequencing protocols. In conclusion, the presented 16S rRNA gene T-RFLP method is a highly robust, easy to handle and a cheap alternative to the computationally demanding next-generation sequencing analysis. In case a lot of nasopharyngeal samples have to be characterized, it is suggested to first perform 16S rRNA T-RFLP and only use next generation sequencing if the T-RFLP nasopharyngeal patterns differ or show unknown TFs.

Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma

Metabolomics as one of the most rapidly growing technologies in the "-omics" field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput mass-spectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels. Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [Formula: see text] We combined highly standardized samples, a three-class study design, a high-throughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and-despite all its current limitations-can deliver marker panels with high selectivity even in multi-class settings.

Point-of-care testing in the cardiovascular operating theatre

Point-of-care testing (POCT) remains under scrutiny by healthcare professionals because of its ill-tried, young history. POCT methods are being developed by a few major equipment companies based on rapid progress in informatics and nanotechnology. Issues as POCT quality control, comparability with standard laboratory procedures, standardisation, traceability and round robin testing are being left to hospitals. As a result, the clinical and operational benefits of POCT were first evident for patients on the operating table. For the management of cardiovascular surgery patients, POCT technology is an indispensable aid. Improvement of the technology has meant that clinical laboratory pathologists now recognise the need for POCT beyond their high-throughput areas.

Rapid and reliable genotyping of polymorphic loci modifying correct splicing of CFTR pre-mRNA using mass spectrometry

We describe a fast and unambiguous method for haplotyping the (TG)mTn repeat in IVS8 and determining three other single nucleotide polymorphisms (SNPs) in exons 10, 14a and 24 in the cystic fibrosis transmembrane conductance regulator (CFTR) gene affecting correct splicing of the CFTR pre-mRNA using primer extension and mass spectrometry. The diagnostic products are generated by primer extension (PEX) reactions, which require a single detection primer complementary to a region downstream of a target strand's variable site. On addition of a polymerase and an appropriate mixture of dNTP's and 2', 3'-dideoxynucleotide triphosphates (ddNTP's), the primer is extended through the mutation region until the first ddNTP is incorporated and the mass of the extension products determines the composition of the variable site. Analysis of patient DNA assigned the correct and unambiguous haplotype for the (TG)mTn repeat in intron 8 of the CFTR gene. Additional crucial SNPs influencing correct splicing in exon 10, 14 and 24 can easily be detected by biplexing the assay to genotype allelic variants important for correct splicing of the CFTR pre-mRNA. Different PEX reactions with subsequent mass spectrometry generate sufficient data, to enable unambiguous and easy haplotyping of the (TG)mTn repeat in the CFTR gene. The method can be easily extended to the inclusion of additional SNPs of interest by biplexing some of the PEX reactions. All experimental steps required for PEX are amenable to the high degree of automation desirable for a high-throughput diagnostic setting, facilitating the work of clinicians involved in the diagnosis of non-classic cystic fibrosis.

ASSOCIATON TESTS THAT ACCOMMODATE GENOTYPING ERRORS

High-throughput SNP arrays provide estimates of genotypes for up to one million loci, often used in genome-wide association studies. While these estimates are typically very accurate, genotyping errors do occur, which can influence in particular the most extreme test statistics and p-values. Estimates for the genotype uncertainties are also available, although typically ignored. In this manuscript, we develop a framework to incorporate these genotype uncertainties in case-control studies for any genetic model. We verify that using the assumption of a “local alternative” in the score test is very reasonable for effect sizes typically seen in SNP association studies, and show that the power of the score test is simply a function of the correlation of the genotype probabilities with the true genotypes. We demonstrate that the power to detect a true association can be substantially increased for difficult to call genotypes, resulting in improved inference in association studies.

Classification and selection of biomarkers in genomic data using LASSO

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been on assessing univariate associations between gene expression with clinical outcome (variable selection) or on developing classification procedures with gene expression data (supervised learning). We consider a hybrid variable selection/classification approach that is based on linear combinations of the gene expression profiles that maximize an accuracy measure summarized using the receiver operating characteristic curve. Under a specific probability model, this leads to consideration of linear discriminant functions. We incorporate an automated variable selection approach using LASSO. An equivalence between LASSO estimation with support vector machines allows for model fitting using standard software. We apply the proposed method to simulated data as well as data from a recently published prostate cancer study.