Search

Airway inflammation has a pathophysiological role in asthma. Eosinophils, which are commonly increased in asthmatic airways, express eosinophil peroxidase and thereby produce hypobromite and bromotyrosine. Bromotyrosine is believed to be a specific marker for eosinophil activity, but developing an antibody against monobromotyrosine, the predominant brominated tyrosine residue found in vivo has proven difficult. We evaluated whether a 3-bromobenozoic acid hapten antigen produced antibodies that recognized halogenated tyrosine residues. Studies with small-molecule inhibitors or brominated or chlorinated protein suggested that a mouse monoclonal antibody (BTK-94C) selectively bound free and protein mono- and dibromotyrosine and, to a lesser degree, chlorotyrosine, and thus was designated a general halotyrosine antibody. We evaluated if this antibody had potential for characterizing human asthma using an enzyme-linked immunosorbent assay (ELISA) microarray platform to examine the halogenation of 23 proteins in three independent sets of sputum samples (52 samples total). In 15 healthy control or asthmatic subjects, ICAM, PDGF and RANTES had greater proportional amounts of halogenation in asthmatic subjects and the halogenation signal was associated with the severity of exercise-induced airway hyperresponsiveness. In 17 severe asthma patients treated with placebo or mepolizumab to suppress eosinophils, drug-related decreases in halogenation were observed with p values ranging from 0.006 to 0.11 for these 3 proteins. Analysis of 20 subjects that either had neutrophilic asthma or were healthy controls demonstrated a broad increase in halotyrosine (possibly chlorotyrosine) in neutrophilic asthmatics. Overall, these results suggest that an ELISA utilizing BTK-94C could prove useful for assessing airway inflammation in asthma patients., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Background: Post-translational protein modifications (PTMs) are increased in breast tumors., Objective: We explored whether PTMs on proteins secreted by the breast could be detected in plasma and potentially used for the early detection of breast cancer., Methods: We used a custom ELISA microarray platform to measure 4-hydroxynonenal (HNE), glutathione (GSH), nitrotyrosine and halotyrosine adducts in 27 secreted proteins, for a total of 108 candidate biomarkers. Two independent sets of human plasma samples were measured, for a total of 160 samples. The results were analyzed for consistent cancer-associated changes across the two sample sets. Plasma samples for both cases and benign controls were collected at the time of tissue diagnosis after referral from a positive screen (such as mammography). The results from both studies were evaluated using ANOVA and t-tests or receiver operator curves (ROC)., Results: Levels of GSH-modified ceruloplasmin and HNE-modified PDGF were significantly altered in plasma samples from cancer patients relative to benign controls. Healthy controls, which were only included in the first set of samples, were similar to the benign controls for both of these markers. A combination of three glutathionylated proteins produced the best area under the ROC curve, with a value of 76%., Conclusions: Specific PTMs in individual proteins may be useful for distinguishing between women with breast cancer and those with benign breast disease. These oxidative changes in plasma proteins may reflect redox changes in breast cancer. Additional studies on oxidative modifications in individual proteins are warranted.

Background: Current biomarkers for breast cancer have little potential for detection. We determined whether breast cancer subtypes influence circulating protein biomarkers., Methods: A sandwich ELISA microarray platform was used to evaluate 23 candidate biomarkers in plasma samples that were obtained from subjects with either benign breast disease or invasive breast cancer. All plasma samples were collected at the time of biopsy, after a referral due to a suspicious screen (e.g., mammography). Cancer samples were evaluated on the basis of breast cancer subtypes, as defined by the HER2 and estrogen receptor statuses., Results: Ten proteins were statistically altered in at least one breast cancer subtype, including four epidermal growth factor receptor ligands, two matrix metalloproteases, two cytokines, and two angiogenic factors. Only one cytokine, RANTES, was significantly increased (P < 0.01 for each analysis) in all four subtypes, with areas under the curve (AUC) for receiver operating characteristic values that ranged from 0.76 to 0.82, depending on cancer subtype. The best AUC values were observed for analyses that combined data from multiple biomarkers, with values ranging from 0.70 to 0.99, depending on the cancer subtype. Although the results for RANTES are consistent with previous publications, the multi-assay results need to be validated in independent sample sets., Conclusions: Different breast cancer subtypes produce distinct biomarker profiles, and circulating protein biomarkers have potential to differentiate between true- and false-positive screens for breast cancer., Impact: Subtype-specific biomarker panels may be useful for detecting breast cancer or as an adjunct assay to improve the accuracy of current screening methods., (©2011 AACR)

Our research group develops analytical methods and software for the high-throughput analysis of quantitative enzyme-linked immunosorbent assay (ELISA) microarrays. ELISA microarrays differ from DNA microarrays in several fundamental aspects and most algorithms for analysis of DNA microarray data are not applicable to ELISA microarrays. In this review, we provide an overview of the steps involved in ELISA microarray data analysis and how the statistically sound algorithms we have developed provide an integrated software suite to address the needs of each data-processing step. The algorithms discussed are available in a set of open-source software tools (http://www.pnl.gov/statistics/ProMAT).

Nuisance factors in a protein-array study add obfuscating variation to spot intensity measurements, diminishing the accuracy and precision of protein concentration predictions. The effects of nuisance factors may be reduced by design of experiments, and by estimating and then subtracting nuisance effects. Estimated nuisance effects also inform about the quality of the study and suggest refinements for future studies.We demonstrate a method to reduce nuisance effects by incorporating a non-interfering internal calibration in the study design and its complemental analysis of variance. We illustrate this method by applying a chip-level internal calibration in a biomarker discovery study. The variability of sample intensity estimates was reduced 16% to 92% with a median of 58%; confidence interval widths were reduced 8% to 70% with a median of 35%. Calibration diagnostics revealed processing nuisance trends potentially related to spot print order and chip location on a slide. The accuracy and precision of a protein-array study may be increased by incorporating a non-interfering internal calibration. Internal calibration modeling diagnostics improve confidence in study results and suggest process steps that may need refinement. Though developed for our protein-array studies, this internal calibration method is applicable to other targeted array-based studies.

Our research group has been developing enzyme-linked immunosorbent assays (ELISA) microarray technology for the rapid and quantitative evaluation of biomarker panels. Studies using antibody microarrays are susceptible to systematic bias from the various steps in the experimental process, and these biases can mask biologically significant differences. For this reason, we have developed a calibration system that can identify and reduce systematic bias due to processing factors. Specifically, we developed a sandwich ELISA for green fluorescent protein (GFP) that is included on each chip. The GFP antigen is spiked into each biological sample or standard mixture and the resulting signal is used for calibration between chips. We developed ProMAT Calibrator, an open-source bioinformatics tool, for the rapid visualization and interpretation of the calibrator data and, if desired, data normalization. We demonstrate that data normalization using this system markedly reduces bias from processing factors. Equally useful, this calibrator system can help reveal the source of the bias, thereby facilitating the elimination of the underlying problem. ProMAT Calibrator can be downloaded at http://www.pnl.gov/statistics/ProMAT .

Summary: ELISA-BASE is an open source database for capturing, organizing and analyzing enzyme-linked immunosorbent assay (ELISA) microarray data. ELISA-BASE is an extension of the BioArray Software Environment (BASE) database system., Availability: http://www.pnl.gov/statistics/ProMAT/ELISA-BASE.stm.

Human platelets play a key role in hemostasis and thrombosis and have recently emerged as key regulators of inflammation. Platelets stored for transfusion produce pro-thrombotic and pro-inflammatory mediators implicated in adverse transfusion reactions. Correspondingly, these mediators are central players in pathological conditions including cardiovascular disease, the major cause of death in diabetics. In view of this, a mass spectrometry based proteomics study was performed on platelets collected from healthy and type-2 diabetics stored for transfusion. Strikingly, our innovative and sensitive proteomic approach identified 122 proteins that were either up- or down-regulated in type-2 diabetics relative to nondiabetic controls and 117 proteins whose abundances changed during a 5-day storage period. Notably, our studies are the first to characterize the proteome of platelets from diabetics before and after storage for transfusion. These identified differences allow us to formulate new hypotheses and experimentation to improve clinical outcomes by targeting "high risk platelets" that render platelet transfusion less effective or even unsafe.

We present a platform for the reconstruction of protein-protein interaction networks inferred from Mass Spectrometry (MS) bait-prey data. The Software Environment for Biological Network Inference (SEBINI), an environment for the deployment of network inference algorithms that use high-throughput data, forms the platform core. Among the many algorithms available in SEBINI is the Bayesian Estimator of Probabilities of Protein-Protein Associations (BEPro3) algorithm, which is used to infer interaction networks from such MS affinity isolation data. Also, the pipeline incorporates the Collective Analysis of Biological Interaction Networks (CABIN) software. We have thus created a structured workflow for protein-protein network inference and supplemental analysis.

One of the most promising methods for large-scale studies of protein interactions is isolation of an affinity-tagged protein with its in vivo interaction partners, followed by mass spectrometric identification of the copurified proteins. Previous studies have generated affinity-tagged proteins using genetic tools or cloning systems that are specific to a particular organism. To enable protein-protein interaction studies across a wider range of Gram-negative bacteria, we have developed a methodology based on expression of affinity-tagged "bait" proteins from a medium copy-number plasmid. This construct is based on a broad-host-range vector backbone (pBBR1MCS5). The vector has been modified to incorporate the Gateway DEST vector recombination region, to facilitate cloning and expression of fusion proteins bearing a variety of affinity, fluorescent, or other tags. We demonstrate this methodology by characterizing interactions among subunits of the DNA-dependent RNA polymerase complex in two metabolically versatile Gram-negative microbial species of environmental interest, Rhodopseudomonas palustris CGA010 and Shewanella oneidensis MR-1. Results compared favorably with those for both plasmid and chromosomally encoded affinity-tagged fusion proteins expressed in a model organism, Escherichia coli.

Unlabelled: The Bayesian Estimator of Protein-Protein Association Probabilities (BEPro aff3) is a software tool for estimating probabilities of protein-protein association between bait and prey protein pairs using data from multiple-bait, multiple-replicate, protein liquid chromatography tandem mass spectrometry LC-MS/MS affinity isolation experiments., Availability: BEPro (3) is public domain software, has been tested on WIndows XP, Linux and Mac OS, and is freely available from http://www.pnl.gov/statistics/BEPro3., Supplementary Information: A user guide, example dataset with analysis and additional documentation are included with the BEPro (3) download.

Quantitative analysis of liquid chromatography (LC)-mass spectrometry (MS) and tandem mass spectrometry (MS/MS) data is essential to many proteomics studies. We have developed MASIC(2) to accurately measure peptide abundances and LC elution times in LC-MS/MS analyses. This software program uses an efficient processing algorithm to quickly generate mass specific selected ion chromatograms from a dataset and provides an interactive browser that allows users to examine individual chromatograms with a variety of options.

Comparing a protein's concentrations across two or more treatments is the focus of many proteomics studies. A frequent source of measurements for these comparisons is a mass spectrometry (MS) analysis of a protein's peptide ions separated by liquid chromatography (LC) following its enzymatic digestion. Alas, LC-MS identification and quantification of equimolar peptides can vary significantly due to their unequal digestion, separation, and ionization. This unequal measurability of peptides, the largest source of LC-MS nuisance variation, stymies confident comparison of a protein's concentration across treatments. Our objective is to introduce a mixed-effects statistical model for comparative LC-MS proteomics studies. We describe LC-MS peptide abundance with a linear model featuring pivotal terms that account for unequal peptide LC-MS measurability. We advance fitting this model to an often incomplete LC-MS data set with REstricted Maximum Likelihood (REML) estimation, producing estimates of model goodness-of-fit, treatment effects, standard errors, confidence intervals, and protein relative concentrations. We illustrate the model with an experiment featuring a known dilution series of a filamentous ascomycete fungus Trichoderma reesei protein mixture. For 781 of the 1546 T. reesei proteins with sufficient data coverage, the fitted mixed-effects models capably described the LC-MS measurements. The LC-MS measurability terms effectively accounted for this major source of uncertainty. Ninety percent of the relative concentration estimates were within 0.5-fold of the true relative concentrations. Akin to the common ratio method, this model also produced biased estimates, albeit less biased. Bias decreased significantly, both absolutely and relative to the ratio method, as the number of observed peptides per protein increased. Mixed-effects statistical modeling offers a flexible, well-established methodology for comparative proteomics studies integrating common experimental designs with LC-MS sample processing plans. It favorably accounts for the unequal LC-MS measurability of peptides and produces informative quantitative comparisons of a protein's concentration across treatments with objective measures of uncertainties.

Making sound proteomic inferences using ELISA microarray assay requires both an accurate prediction of protein concentration and a credible estimate of its error. We present a method using monotonic spline statistical models (MS), penalized constrained least squares fitting (PCLS) and Monte Carlo simulation (MC) to predict ELISA microarray protein concentrations and estimate their prediction errors. We contrast the MSMC (monotone spline Monte Carlo) method with a LNLS (logistic nonlinear least squares) method using simulated and real ELISA microarray data sets.MSMC rendered good fits in almost all tests, including those with left and/or right clipped standard curves. MS predictions were nominally more accurate; especially at the extremes of the prediction curve. MC provided credible asymmetric prediction intervals for both MS and LN fits that were superior to LNLS propagation-of-error intervals in achieving the target statistical confidence. MSMC was more reliable when automated prediction across simultaneous assays was applied routinely with minimal user guidance.

Assessment of differential protein abundance from the observed properties of detected peptides is an essential part of protein profiling based on shotgun proteomics. However, the abundance observed for shared peptides may be due to contributions from multiple proteins that are affected differently by a given treatment. Excluding shared peptides eliminates this ambiguity but may significantly decrease the number of proteins for which abundance estimates can be obtained. Peptide sharing within a family of biologically related proteins does not cause ambiguity if family members have a common response to treatment. On the basis of this concept, we have developed an approach for including shared peptides in the analysis of differential protein abundance in protein profiling. Data from a recent proteomics study of lung tissue from mice exposed to lipopolysaccharide, cigarette smoke, and a combination of these agents are used to illustrate our method. Starting from data where about half of the implicated database protein involved shared peptides, 82% of the affected proteins were grouped into families, based on FASTA annotation, with closure on peptide sharing. In many cases, a common abundance relative to control was sufficient to explain ion-current peak areas for peptides, both unique and shared, that identified biologically related proteins in a peptide-sharing closure group. On the basis of these results, we propose that peptide-sharing closure groups provide a way to include abundance data for shared peptides in quantitative protein profiling by high-throughput mass spectrometry.

Affinity isolation of protein complexes followed by protein identification by LC-MS/MS is an increasingly popular approach for mapping protein interactions. However, systematic and random assay errors from multiple sources must be considered to confidently infer authentic protein-protein interactions. To address this issue, we developed a general, robust statistical method for inferring authentic interactions from protein prey-by-bait frequency tables using a binomial-based likelihood ratio test (LRT) coupled with Bayes' Odds estimation. We then applied our LRT-Bayes' algorithm experimentally using data from protein complexes isolated from Rhodopseudomonas palustris. Our algorithm, in conjunction with the experimental protocol, inferred with high confidence authentic interacting proteins from abundant, stable complexes, but few or no authentic interactions for lower-abundance complexes. The algorithm can discriminate against a background of prey proteins that are detected in association with a large number of baits as an artifact of the measurement. We conclude that the experimental protocol including the LRT-Bayes' algorithm produces results with high confidence but moderate sensitivity. We also found that Monte Carlo simulation is a feasible tool for checking modeling assumptions, estimating parameters, and evaluating the significance of results in protein association studies.

Noninvasive measurements over a biofilm, a three-dimensional (3-D) community of microorganisms immobilized at a substratum, were made using an acoustic microscope operating at frequencies up to 70 MHz. The microscope scanned a 2.5-mm by 2.5-mm region of a living biofilm having a nominal thickness of 100 microm. Spatial variation of surface heterogeneity, thickness, interior structure, and biomass were estimated. Thickness was estimated as the product of the speed of sound of the medium and the interim between the highest signal peak and that of the substratum plane without biofilm. The thickest portions of biofilm were 145 microm; however, slender structures attributed as streamers extended above, with one obtaining a 274-microm height above the substratum. Three-dimensional iso-contours of amplitude were used to estimate the internal structure of the biofilm. Backscatter amplitude was examined at five zones of increasing height from the substratum to examine biomass distribution. Ultrasound-based estimates of thickness were corroborated with optical microscopy. The experimental acoustic and optical systems, methods used to estimate biofilm properties, and potential applications for the resulting data are discussed.

Summary: ProMAT is a software tool for statistically analyzing data from enzyme-linked immunosorbent assay microarray experiments. The software estimates standard curves, sample protein concentrations and their uncertainties for multiple assays. ProMAT generates a set of comprehensive figures for assessing results and diagnosing process quality. The tool is available for Windows or Mac, and is distributed as open-source Java and R code., Availability: ProMAT is available at http://www.pnl.gov/statistics/ProMAT. ProMAT requires Java version 1.5.0 and R version 1.9.1 (or more recent versions). ProMAT requires either Windows XP or Mac OS 10.4 or newer versions.

Background: The field of proteomics involves the characterization of the peptides and proteins expressed in a cell under specific conditions. Proteomics has made rapid advances in recent years following the sequencing of the genomes of an increasing number of organisms. A prominent technology for high throughput proteomics analysis is the use of liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR-MS). Meaningful biological conclusions can best be made when the peptide identities returned by this technique are accompanied by measures of accuracy and confidence., Methods: After a tryptically digested protein mixture is analyzed by LC-FTICR-MS, the observed masses and normalized elution times of the detected features are statistically matched to the theoretical masses and elution times of known peptides listed in a large database. The probability of matching is estimated for each peptide in the reference database using statistical classification methods assuming bivariate Gaussian probability distributions on the uncertainties in the masses and the normalized elution times., Results: A database of 69,220 features from 32 LC-FTICR-MS analyses of a tryptically digested bovine serum albumin (BSA) sample was matched to a database populated with 97% false positive peptides. The percentage of high confidence identifications was found to be consistent with other database search procedures. BSA database peptides were identified with high confidence on average in 14.1 of the 32 analyses. False positives were identified on average in just 2.7 analyses., Conclusion: Using a priori probabilities that contrast peptides from expected and unexpected proteins was shown to perform better in identifying target peptides than using equally likely a priori probabilities. This is because a large percentage of the target peptides were similar to unexpected peptides which were included to be false positives. The use of triplicate analyses with a "2 out of 3" reporting rule was shown to have excellent rejection of false positives.

A large gap currently exists between the ability to discover potential biomarkers and the ability to assess the real value of these proteins for cancer screening. One major challenge in biomarker validation is the inherent variability in biomarker levels. This variability stems from the diversity across the human population and the considerable molecular heterogeneity between individual tumors, even those that originate from a single tissue. An additional challenge with cancer screening is that most cancers are rare in the general population, meaning that assay specificity must be very high. Otherwise, the number of false positives will be much greater than the number of true positives. Due to these challenges associated with biomarker validation, it is necessary to analyze thousands of samples in order to obtain a clear idea of the utility of a screening assay. Enzyme-linked immunosorbent assay (ELISA) microarray technology can simultaneously quantify levels of multiple proteins and, thus, has the potential to accelerate validation of protein biomarkers for clinical use. This review will discuss current ELISA microarray technology and potential advances that could help to achieve the reproducibility and throughput that are required to evaluate cancer biomarkers.

A genome-independent microarray and new statistical techniques were used to genotype Bacillus strains and quantitatively compare DNA fingerprints with the known taxonomy of the genus. A synthetic DNA standard was used to understand process level variability and lead to recommended standard operating procedures for microbial forensics and clinical diagnostics.

Unlabelled: The Automated Microarray Image Analysis (AMIA) Toolbox for MATLAB is a flexible, open-source, microarray image analysis tool that allows the user to customize analyses of microarray image sets. This tool provides several methods to identify and quantify spot statistics, as well as extensive diagnostic statistics and images to evaluate data quality and array processing. The open, modular nature of AMIA provides access to implementation details and encourages modification and extension of AMIA's capabilities., Availability: The AMIA Toolbox is freely available at http://www.pnl.gov/statistics/amia. The AMIA Toolbox requires MATLAB 6.5 (R13) (MathWorks, Inc. Natick, MA), as well as the Statistics Toolbox 4.1 and Image Processing Toolbox 4.1 for MATLAB or more recent versions., Contact: amanda.white@pnl.gov

The combination of mass and normalized elution time (NET) of a peptide identified by liquid chromatography-mass spectrometry (LC-MS) measurements can serve as a unique signature for that peptide. However, the specificity of an LC-MS measurement depends upon the complexity of the proteome (i.e., the number of possible peptides) and the accuracy of the LC-MS measurements. In this work, theoretical tryptic digests of all predicted proteins from the genomes of three organisms of varying complexity were evaluated for specificity. Accuracy of the LC-MS measurement of mass-NET pairs (on a 0 to 1.0 NET scale) was described by bivariate normal sampling distributions centered on the peptide signatures. Measurement accuracy (i.e., mass and NET standard deviations of +/-0.1, 1, 5, and 10 ppm, and +/-0.01 and 0.05, respectively) was varied to evaluate improvements in process quality. The spatially localized confidence score, a conditional probability of peptide uniqueness, formed the basis for the peptide identification. Application of this approach to organisms with comparatively small proteomes, such as Deinococcus radiodurans, shows that modest mass and elution time accuracies are generally adequate for confidently identifying most peptides. For more complex proteomes, more accurate measurements are required. However, the study suggests that the majority of proteins for even the human proteome should be identifiable with reasonable confidence by using LC-MS measurements with mass accuracies within +/-1 ppm and high efficiency separations having elution time measurements within +/-0.01 NET.

Background: Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to estimate a protein's concentration in a sample. Deploying ELISA in a microarray format permits simultaneous estimation of the concentrations of numerous proteins in a small sample. These estimates, however, are uncertain due to processing error and biological variability. Evaluating estimation error is critical to interpreting biological significance and improving the ELISA microarray process. Estimation error evaluation must be automated to realize a reliable high-throughput ELISA microarray system. In this paper, we present a statistical method based on propagation of error to evaluate concentration estimation errors in the ELISA microarray process. Although propagation of error is central to this method and the focus of this paper, it is most effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization, and statistical diagnostics when evaluating ELISA microarray concentration estimation errors., Results: We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of concentration estimation errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error. We summarize the results with a simple, three-panel diagnostic visualization featuring a scatterplot of the standard data with logistic standard curve and 95% confidence intervals, an annotated histogram of sample measurements, and a plot of the 95% concentration coefficient of variation, or relative error, as a function of concentration., Conclusions: This statistical method should be of value in the rapid evaluation and quality control of high-throughput ELISA microarray analyses. Applying propagation of error to a variety of ELISA microarray concentration estimation models is straightforward. Displaying the results in the three-panel layout succinctly summarizes both the standard and sample data while providing an informative critique of applicability of the fitted model, the uncertainty in concentration estimates, and the quality of both the experiment and the ELISA microarray process.

Epidemiologic and forensic investigations often require assays to detect subtle genetic differences between closely related microorganisms. Typically, gel electrophoresis is used to compare randomly amplified DNA fragments between microbial samples, where the patterns of DNA fragment sizes are viewed as genotype 'fingerprints'. The limited genomic sample captured on a gel, however, is not always sufficient to discriminate closely related strains. This paper examines the application of microarray technology to DNA fingerprinting as a high-resolution alternative to gel-based methods. The so-called universal microarray, which uses short oligonucleotide probes that do not target specific genes or species, is intended to be applicable to all microorganisms because it does not require prior knowledge of genomic sequence. In principle, closely related strains can be distinguished if enough independent oligonucleotide probes are used on the microarray, i.e., if the genome is sufficiently sampled. In practice, we confront noisy data, imperfectly matched hybridizations, and a high-dimensional inference problem. We describe the statistical problems of microarray fingerprinting, outline similarities with and differences from more conventional microarray applications, and illustrate a statistical measurement error model to fingerprint 10 closely related strains from three Bacillus species, and 3 strains from non-Bacillus species.

We report on a genome-independent microbial fingerprinting method using nucleic acid microarrays for microbial forensics and epidemiology applications and demonstrate that the microarray method provides high resolution differentiation between closely related microorganisms, using Salmonella enterica strains as the test case. In replicate trials we used a simple 192 probe nonamer array to construct a fingerprint library of 25 closely related Salmonella isolates. Controlling false discovery rate for multiple testing at alpha = 0.05, at least 295 of 300 pairs of S.enterica isolate fingerprints were found to be statistically distinct using a modified Hotelling T2 test. Although most pairs of Salmonella fingerprints are found to be distinct, forensic applications will also require a protocol for library construction and reliable microbial classification against a fingerprint library. We outline additional steps required to produce such a protocol.

A two-probe proximal chaperone detection system consisting of a species-specific capture probe for the microarray and a labeled, proximal chaperone probe for detection was recently described for direct detection of intact rRNAs from environmental samples on oligonucleotide arrays. In this study, we investigated the physical spacing and nucleotide mismatch tolerance between capture and proximal chaperone detector probes that are required to achieve species-specific 16S rRNA detection for the dissimilatory metal and sulfate reducer 16S rRNAs. Microarray specificity was deduced by analyzing signal intensities across replicate microarrays with a statistical analysis-of-variance model that accommodates well-to-well and slide-to-slide variations in microarray signal intensity. Chaperone detector probes located in immediate proximity to the capture probe resulted in detectable, nonspecific binding of nontarget rRNA, presumably due to base-stacking effects. Species-specific rRNA detection was achieved by using a 22-nt capture probe and a 15-nt detector probe separated by 10 to 14 nt along the primary sequence. Chaperone detector probes with up to three mismatched nucleotides still resulted in species-specific capture of 16S rRNAs. There was no obvious relationship between position or number of mismatches and within- or between-genus hybridization specificity. From these results, we conclude that relieving secondary structure is of principal concern for the successful capture and detection of 16S rRNAs on planar surfaces but that the sequence of the capture probe is more important than relieving secondary structure for achieving specific hybridization.

Current bacterial DNA-typing methods are typically based on gel-based fingerprinting methods. As such, they access a limited complement of genetic information and many independent restriction enzymes or probes are required to achieve statistical rigor and confidence in the resulting pattern of DNA fragments. Furthermore, statistical comparison of gel-based fingerprints is complex and nonstandardized. To overcome these limitations of gel-based microbial DNA fingerprinting, we developed a prototype, 47-probe microarray consisting of randomly selected nonamer oligonucleotides. Custom image analysis algorithms and statistical tools were developed to automatically extract fingerprint profiles from microarray images. The prototype array and new image analysis algorithms were used to analyze 14 closely related Xanthomonas pathovars. Of the 47 probes on the prototype array, 10 had diagnostic value (based on a chi-squared test) and were used to construct statistically robust microarray fingerprints. Analysis of the microarray fingerprints showed clear differences between the 14 test organisms, including the separation of X. oryzae strains 43836 and 49072, which could not be resolved by traditional gel electrophoresis of REP-PCR amplification products. The proof-of-application study described here represents an important first step to high-resolution bacterial DNA fingerprinting with microarrays. The universal nature of the nonamer fingerprinting microarray and data analysis methods developed here also forms a basis for method standardization and application to the forensic identification of other closely related bacteria.

We investigated the application of an oligonucleotide microarray to (i) specifically detect Cryptosporidium spp., (ii) differentiate between closely related C. parvum isolates and Cryptosporidium species, and (iii) differentiate between principle genotypes known to infect humans. A microarray of 68 capture probes targeting seven single-nucleotide polymorphisms (SNPs) within a 190-bp region of the hsp70 gene of Cryptosporidium parvum was constructed. Labeled hsp70 targets were generated by PCR with biotin- or Cy3-labeled primers. Hybridization conditions were optimized for hybridization time, temperature, and salt concentration. Two genotype I C. parvum isolates (TU502 and UG502), two C. parvum genotype II isolates (Iowa and GCH1), and DNAs from 22 non-Cryptosporidium sp. organisms were used to test method specificity. Only DNAs from C. parvum isolates produced labeled amplicons that could be hybridized to and detected on the array. Hybridization patterns between genotypes were visually distinct, but identification of SNPs required statistical analysis of the signal intensity data. The results indicated that correct mismatch discrimination could be achieved for all seven SNPs for the UG502 isolate, five of seven SNPs for the TU502 isolate, and six of seven SNPs for both the Iowa and GCH1 isolates. Even without perfect mismatch discrimination, the microarray method unambiguously distinguished between genotype I and genotype II isolates and demonstrated the potential to differentiate between other isolates and species on a single microarray. This method may provide a powerful new tool for water utilities and public health officials for assessing point and nonpoint source contamination of water supplies.

Complementary data acquired with different microscopy techniques provide a basis for establishing a more comprehensive understanding of health and disease at a cellular level, particularly when data acquired with different methodologies can be correlated in both time and space. In this Communication, a brief description of a novel instrument capable of simultaneously performing confocal optical and magnetic resonance microscopy is presented, and the first combined images of live Xenopus laevis oocytes are shown. Also, the potential benefits of combined microscopy are discussed, and it is shown that the a priori knowledge of the high-resolution optical images can be used to enhance the boundary resolution and contrast of the MR images., (Copyright 2000 Academic Press.)

We have developed a method for constructing and extracting matrix-assisted laser desorption/ionization (MALDI) fingerprints. This method is fully automated and statistically based, allowing a large number of spectra to be analyzed at a time in an objective manner. This method can be used to extract the fingerprint of a particular analyte from a spectrum containing multiple analytes. Therefore, this method lends itself well to real-world applications where samples to be analyzed are likely to be impure. We illustrate this method on experimental results from a series of studies of E. coli and B. atrophaeus MALDI time-of-flight mass spectrometry (TOFMS) fingerprints., (Copyright 1999 John Wiley & Sons, Ltd.)

The authors studied the efficacy and cost of substituting sedation using midazolam and alfentanil for the existing regimen of diazepam and meperidine in patients requiring upper gastrointestinal endoscopy. Sixty consenting subjects were randomized to receive either meperidine 50 mg with diazepam approximately 90 micrograms.kg-1 (Group D) or alfentanil 250 micrograms with midazolam approximately 50 micrograms.kg-1 (Group M). Endoscope insertion time, patient acceptance, apnoeic or desaturation episodes were noted by a physician observer. Pulse oximetry was used to monitor heart rate and oxygen saturation (SpO2) during endoscopy. Subjects performed four-choice reaction time (4CRT) tests before, 30 and 60 min after endoscopy, and were assessed for nausea or dizziness and their ability to stand and walk. During endoscopy, insertion time was shorter (84 +/- 45 sec vs 122 +/- 83 sec, P < 0.03) and fewer aversive movements occurred (0.4 +/- 0.6 vs. 1.7 +/- 2.4, P < 0.005) in Group M than Group D. No subject in either group suffered any apnoea or prolonged desaturation requiring supplemental oxygen. Irrespective of treatment group, greater decreases in SpO2 (6.1 +/- 3.4% vs 3.6 +/- 2.2% P < 0.001) occurred in subjects > 45 yr of age than in subjects < or = 45 yr. During recovery 4CRT values at 30 min after endoscopy were longer (723 +/- 226 msec vs 594 +/- 139 msec, P < 0.005) in Group M than in Group D but not after 60 min. It was concluded that the small differences in endoscopy conditions and greater sedation during the first 30 min of recovery did not justify the additional cost of using midazolam and alfentnil.(ABSTRACT TRUNCATED AT 250 WORDS)

The multiple-indicator dilution technique was utilized to examine the hepatic uptake of albumin-bound labeled palmitate from the portal vein blood of the pentobarbital-anesthetized dog, in a fasted state and after infusion of a variety of compounds that were expected to bind to Z protein, the cellular cytosolic protein binding free fatty acids, and their acyl-CoA derivatives. Analysis of the data indicates that after infusion of alpha-bromopalmitate, 16-bromo-9-hexadecenoate, and sulfobromophthalein sodium (which also bind to albumin), the palmitate label influx, efflux, and metabolic sequestration (removal of label from the pool of free fatty acids able to leave the cell) all increase and that, after infusion of flavaspidic acid, label efflux and metabolic sequestration increase. In vitro competitive binding studies carried out on the cellular cytosol indicat that the basis for the increase in efflux and metabolic sequestration is displacement of labeled palmitate from high affinity sites on the intracellular Z protein (which are presumably in equilibrium with and may be taken to be representative of other intracellular binding sites). These studies also suggest that increased uptake is due to similar displacement from high affinity sites on serum albumin.

Since the original report by Mallory and Weiss of tears in the lower esophagus or cardia of the stomach following alcoholic debauch, there have been many other cases, associated with sundry other causes, described in the literature. Recently, a Mallory-Weiss tear was reported in a patient as a complication of cancer chemotherapy. This article describes two similar cases and suggests that the Mallory-Weiss syndrome should be included in the differential diagnosis of any patient with epigastric pain, hematemesis, or melena after chemotherapy-induced retching or vomiting.

The results of treatment of 13 consecutive cases of antibiotic-associated pseudomembranous colitis with oral metronidazole are described. The diagnosis was made by typical sigmoidoscopic appearance with a confirming characteristic colonic biopsy specimen and/or stools positive for Clostridium difficile cytotoxin. All patients responded with the disappearance of diarrhea between 1 and 5 days. Two patients experienced relapse when the therapy was discontinued. Our experience with metronidazole shows that it is an effective treatment for antibiotic-associated pseudomembranous colitis. The response to metronidazole treatment compares favorably with that usually obtained with vancomycin.

A case of intramural esophageal hemorrhage in a hemodialysis patient is described. The hemorrhage followed an episode of vomiting and violent retching. Spontaneous resolution occurred with conservative management. The clinical course resembled that of previous case reports of intramural esophageal hemorrhage, whether or not associated with chronic renal failure and intermittent hemodialysis.

The finding, in a patient with celiac sprue, of a characteristic change at endoscopy (scalloping of the valvulae conniventes, event on close inspection, but forming only a mosaic pattern from a distance) led to an endoscopic survey designed to define its incidence. In a series of 28 sequential patients found to have microscopic changes characteristic of sprue on biopsy, distinctive endoscopic changes were found in 22 (in 6 of 9 with sprue in relapse, and 16 of 19 presenting with initial symptoms). The finding of the distinctive appearance provides an endoscopically recognizable pattern that can be associated with sprue. It also provides the potential for early recognition of the process in patients in whom the diagnosis might otherwise have been delayed due to a lack of substantial evolution of the usually associated symptom complex.

We report 3 patients with severe and persistent iron deficiency anemia who were found to have gastric antral vascular ectasia. Endoscopically, the patients presented with a characteristic antral appearance so distinctive as to be diagnostic: longitudinal rugal folds traversing the antrum and converging on the pylorus, each containing a visible convoluted column of vessels, the aggregate resembling the stripes on a watermelon; and, less prominently, evidence of mucosal prolapse. In 2 of these patients, with uncontrollable anemia, antrectomy and Billroth I anastomosis were performed; their hemoglobin levels have subsequently remained normal over the following 2 yr. In the third patient, who was achlorhydric, prednisone therapy substantially reduced the rate of bleeding. In all patients, endoscopic biopsy specimens showed dilatation of mucosal capillaries, with focal thrombosis and fibromuscular hyperplasia of the lamina propria; the resected specimens, additionally, show thickened mucosa with tortuous submucosal venous channels. The importance of the condition lies in its recognition.

A prospective survey of acute upper gastrointestinal hemorrhage in the major government hospital of Kenya was done using fibre-optic esophagogastroduodenoscopy. Of 66 African patients presenting with hematemesis and melena, a precise visual diagnosis was made in 89%. Duodenal ulcer was most common, accounting for 53%, but esophageal varices occurred in 20%. Gastric ulcers and esophagitis were surprisingly infrequent. There was a correlation between hemorrhage from esophageal varices and schistosomiasis distribution. Variceal bleeding occurred in a young age group (mean age 28 yr) and correlated closely with the presence of splenomegaly. These findings have implications for the diagnostic approach and management of patients from areas of endemic schistosomiasis.

An electrical device capable of continuously measuring micronsized changes in stem diameter of woody and herbaceous plants is described. Fumigation of sunflowers with automotive exhaust was used to test the ability of the device to detect short-term plant responses.