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.

High NRBP1 expression in prostate cancer is linked with poor clinical outcomes and increased cancer cell growth

We recently established the rationale that NRBP1 (nuclear receptor binding protein 1) has a potential growth-promoting role in cell biology. NRBP1 interacts directly with TSC-22, a potential tumor suppressor gene that is differently expressed in prostate cancer. Consequently, we analyzed the role of NRBP1 expression in prostate cancer cell lines and its expression on prostate cancer tissue microarrays (TMA).

Characterisation and clinical features of Enterobacter cloacae bloodstream infections occurring at a tertiary care university hospital in Switzerland: is cefepime adequate therapy?

Despite many years of clinical experience with cefepime, data regarding the outcome of patients suffering from bloodstream infections (BSIs) due to Enterobacter cloacae (Ecl) are scarce. To address the gap in our knowledge, 57 Ecl responsible for 51 BSIs were analysed implementing phenotypic and molecular methods (microarrays, PCRs for bla and other genes, rep-PCR to analyse clonality). Only two E. cloacae (3.5%) were ESBL-producers, whereas 34 (59.6%) and 18 (31.6%) possessed inducible (Ind-Ecl) or derepressed (Der-Ecl) AmpC enzymes, respectively. All isolates were susceptible to imipenem, meropenem, gentamicin and ciprofloxacin. Der-Ecl were highly resistant to ceftazidime and piperacillin/tazobactam (both MIC₉₀≥256 μg/mL), whereas cefepime retained its activity (MIC₉₀ of 3 μg/mL). rep-PCR indicated that the isolates were sporadic, but Ecl collected from the same patients were indistinguishable. In particular, three BSIs initially due to Ind-Ecl evolved (under ceftriaxone or piperacillin/tazobactam treatment) into Der-Ecl because of mutations or a deletion in ampD or insertion of IS4321 in the promoter. These last two mechanisms have never been described in Ecl. Mortality was higher for BSIs due to Der-Ecl than Ind-Ecl (3.8% vs. 29.4%; P=0.028) and was associated with the Charlson co-morbidity index (P=0.046). Using the following directed treatments, patients with BSI showed a favourable treatment outcome: cefepime (16/18; 88.9%); carbapenems (12/13; 92.3%); ceftriaxone (4/7; 57.1%); piperacillin/tazobactam (5/7; 71.4%); and ciprofloxacin (6/6; 100%). Cefepime represents a safe therapeutic option and an alternative to carbapenems to treat BSIs due to Ecl when the prevalence of ESBL-producers is low.

Hint2, a mitochondrial apoptotic sensitizer down-regulated in hepatocellular carcinoma

BACKGROUND ; AIMS: Hints, histidine triad nucleotide-binding proteins, are adenosine monophosphate-lysine hydrolases of uncertain biological function. Here we report the characterization of human Hint2. METHODS: Tissue distribution was determined by real-time quantitative polymerase chain reaction and immunoblotting, cellular localization by immunocytochemistry, and transfection with green fluorescent protein constructs. Enzymatic activities for protein kinase C and adenosine phosphoramidase in the presence of Hint2 were measured. HepG2 cell lines with Hint2 overexpressed or knocked down were established. Apoptosis was assessed by immunoblotting for caspases and by flow cytometry. Tumor growth was measured in SCID mice. Expression in human tumors was investigated by microarrays. RESULTS: Hint2 was predominantly expressed in liver and pancreas. Hint2 was localized in mitochondria. Hint2 hydrolyzed adenosine monophosphate linked to an amino group (AMP-pNA; k(cat):0.0223 s(-1); Km:128 micromol/L). Exposed to apoptotic stress, fewer HepG2 cells overexpressing Hint2 remained viable (32.2 +/- 0.6% vs 57.7 +/- 4.6%), and more cells displayed changes of the mitochondrial membrane potential (87.8 +/- 2.35 vs 49.7 +/- 1.6%) with more cleaved caspases than control cells. The opposite was observed in HepG2 cells with knockdown expression of Hint2. Subcutaneous injection of HepG2 cells overexpressing Hint2 in SCID mice resulted in smaller tumors (0.32 +/- 0.13 g vs 0.85 +/- 0.35 g). Microarray analyses revealed that HINT2 messenger RNA is downregulated in hepatocellular carcinomas (-0.42 +/- 0.58 log2 vs -0.11 +/- 0.28 log2). Low abundance of HINT2 messenger RNA was associated with poor survival. CONCLUSION: Hint2 defines a novel class of mitochondrial apoptotic sensitizers down-regulated in hepatocellular carcinoma.

Gene expression in cortex and hippocampus during acute pneumococcal meningitis

BACKGROUND: Pneumococcal meningitis is associated with high mortality (approximately 30%) and morbidity. Up to 50% of survivors are affected by neurological sequelae due to a wide spectrum of brain injury mainly affecting the cortex and hippocampus. Despite this significant disease burden, the genetic program that regulates the host response leading to brain damage as a consequence of bacterial meningitis is largely unknown.We used an infant rat model of pneumococcal meningitis to assess gene expression profiles in cortex and hippocampus at 22 and 44 hours after infection and in controls at 22 h after mock-infection with saline. To analyze the biological significance of the data generated by Affymetrix DNA microarrays, a bioinformatics pipeline was used combining (i) a literature-profiling algorithm to cluster genes based on the vocabulary of abstracts indexed in MEDLINE (NCBI) and (ii) the self-organizing map (SOM), a clustering technique based on covariance in gene expression kinetics. RESULTS: Among 598 genes differentially regulated (change factor > or = 1.5; p < or = 0.05), 77% were automatically assigned to one of 11 functional groups with 94% accuracy. SOM disclosed six patterns of expression kinetics. Genes associated with growth control/neuroplasticity, signal transduction, cell death/survival, cytoskeleton, and immunity were generally upregulated. In contrast, genes related to neurotransmission and lipid metabolism were transiently downregulated on the whole. The majority of the genes associated with ionic homeostasis, neurotransmission, signal transduction and lipid metabolism were differentially regulated specifically in the hippocampus. Of the cell death/survival genes found to be continuously upregulated only in hippocampus, the majority are pro-apoptotic, while those continuously upregulated only in cortex are anti-apoptotic. CONCLUSION: Temporal and spatial analysis of gene expression in experimental pneumococcal meningitis identified potential targets for therapy.

Multiple Lab Comparison of Microarray Platforms

Microarray technology is a powerful tool able to measure RNA expression for thousands of genes at once. Various studies have been published comparing competing platforms with mixed results: some find agreement, others do not. As the number of researchers starting to use microarrays and the number of crossplatform meta-analysis studies rapidly increase, appropriate platform assessments become more important. Here we present results from a comparison study that offers important improvements over those previously described in the literature. In particular, we notice that none of the previously published papers consider differences between labs. For this paper, a consortium of ten labs from the Washington DC/Baltimore (USA) area was formed to compare three heavily used platforms using identical RNA samples: Appropriate statistical analysis demonstrates that relatively large differences exist between labs using the same platform, but that the results from the best performing labs agree rather well. Supplemental material is available from http://www.biostat.jhsph.edu/~ririzarr/techcomp/

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.

On the Synthesis of Microarray Experiments

With many different investigators studying the same disease and with a strong commitment to publish supporting data in the scientific community, there are often many different datasets available for any given disease. Hence there is substantial interest in finding methods for combining these datasets to provide better and more detailed understanding of the underlying biology. We consider the synthesis of different microarray data sets using a random effects paradigm and demonstrate how relatively standard statistical approaches yield good results. We identify a number of important and substantive areas which require further investigation.

An introduction to low-level analysis methods of DNA microarray data

This article gives an overview over the methods used in the low--level analysis of gene expression data generated using DNA microarrays. This type of experiment allows to determine relative levels of nucleic acid abundance in a set of tissues or cell populations for thousands of transcripts or loci simultaneously. Careful statistical design and analysis are essential to improve the efficiency and reliability of microarray experiments throughout the data acquisition and analysis process. This includes the design of probes, the experimental design, the image analysis of microarray scanned images, the normalization of fluorescence intensities, the assessment of the quality of microarray data and incorporation of quality information in subsequent analyses, the combination of information across arrays and across sets of experiments, the discovery and recognition of patterns in expression at the single gene and multiple gene levels, and the assessment of significance of these findings, considering the fact that there is a lot of noise and thus random features in the data. For all of these components, access to a flexible and efficient statistical computing environment is an essential aspect.

Classification Using Generalized Partial Least Squares

The advances in computational biology have made simultaneous monitoring of thousands of features possible. The high throughput technologies not only bring about a much richer information context in which to study various aspects of gene functions but they also present challenge of analyzing data with large number of covariates and few samples. As an integral part of machine learning, classification of samples into two or more categories is almost always of interest to scientists. In this paper, we address the question of classification in this setting by extending partial least squares (PLS), a popular dimension reduction tool in chemometrics, in the context of generalized linear regression based on a previous approach, Iteratively ReWeighted Partial Least Squares, i.e. IRWPLS (Marx, 1996). We compare our results with two-stage PLS (Nguyen and Rocke, 2002A; Nguyen and Rocke, 2002B) and other classifiers. We show that by phrasing the problem in a generalized linear model setting and by applying bias correction to the likelihood to avoid (quasi)separation, we often get lower classification error rates.

Error models for microarray intensities

We derive the additive-multiplicative error model for microarray intensities, and describe two applications. For the detection of differentially expressed genes, we obtain a statistic whose variance is approximately independent of the mean intensity. For the post hoc calibration (normalization) of data with respect to experimental factors, we describe a method for parameter estimation.

Differential Expression with the Bioconductor Project

A basic, yet challenging task in the analysis of microarray gene expression data is the identification of changes in gene expression that are associated with particular biological conditions. We discuss different approaches to this task and illustrate how they can be applied using software from the Bioconductor Project. A central problem is the high dimensionality of gene expression space, which prohibits a comprehensive statistical analysis without focusing on particular aspects of the joint distribution of the genes expression levels. Possible strategies are to do univariate gene-by-gene analysis, and to perform data-driven nonspecific filtering of genes before the actual statistical analysis. However, more focused strategies that make use of biologically relevant knowledge are more likely to increase our understanding of the data.