Molecular detection of Streptococcus pyogenes and Streptococcus dysgalactiae subsp. equisimilis.

We developed molecular diagnostic assays for the detection of Streptococcus pyogenes (GAS) and Streptococcus dysgalactiae subsp. equisimilis (SDSE), two streptococcal pathogens known to cause both pharyngitis and more invasive forms of disease in humans. Two real-time PCR assays coupled with an internal control were designed to be performed in parallel. One assay utilizes a gene target specific to GAS, and the other utilizes a gene target common to the two species. Both assays showed 2-3 orders of magnitude improved analytical sensitivity when compared to a commercially available rapid antigen test. In addition, when compared to standard culture in an analysis of 96 throat swabs, the real-time PCR assays resulted in clinical sensitivity and specificity of 91.7 and 100%, respectively. As capital equipment costs for real-time PCR can be prohibitive in smaller laboratories, the real-time PCR assays were converted to a low-density microarray format designed to function with an inexpensive photopolymerization-based non-enzymatic signal amplification (NESA) method. S. pyogenes was successfully detected on the low-density microarray in less than 4 h from sample extraction through detection.

Detection of adamantane-resistant influenza on a microarray.

BACKGROUND: Influenza A has the ability to rapidly mutate and become resistant to the commonly prescribed influenza therapeutics, thereby complicating treatment decisions. OBJECTIVE: To design a cost-effective low-density microarray for use in detection of influenza resistance to the adamantanes. STUDY DESIGN: We have taken advantage of functional genomics and microarray technology to design a DNA microarray that can detect the two most common mutations in the M2 protein associated with adamantane resistance, V27A and S31N. RESULTS: In a blind study of 22 influenza isolates, the antiviral resistance-chip (AVR-Chip) had a success rate of 95% for detecting these mutations. Microarray data from a larger set of samples were further analyzed using an artificial neural network and resulted in a correct identification rate of 94% for influenza virus samples that had V27A and S31N mutations. CONCLUSIONS: The AVR-Chip provided a method for rapidly screening influenza viruses for adamantane sensitivity, and the general approach could be easily extended to detect resistance to other chemotherapeutics.

Neuraminidase inhibitor susceptibility testing in human influenza viruses: a laboratory surveillance perspective.

Neuraminidase inhibitors (NAIs) are vital in managing seasonal and pandemic influenza infections. NAI susceptibilities of virus isolates (n = 5540) collected during the 2008-2009 influenza season were assessed in the chemiluminescent neuraminidase inhibition (NI) assay. Box-and-whisker plot analyses of log-transformed IC(50)s were performed for each virus type/subtype and NAI to identify outliers which were characterized based on a statistical cutoff of IC(50) >3 interquartile ranges (IQR) from the 75(th) percentile. Among 1533 seasonal H1N1 viruses tested, 1431 (93.3%) were outliers for oseltamivir; they all harbored the H275Y mutation in the neuraminidase (NA) and were reported as oseltamivir-resistant. Only 15 (0.7%) of pandemic 2009 H1N1 viruses tested (n = 2259) were resistant to oseltamivir. All influenza A(H3N2) (n = 834) and B (n = 914) viruses were sensitive to oseltamivir, except for one A(H3N2) and one B virus, with D151V and D197E (D198E in N2 numbering) mutations in the NA, respectively. All viruses tested were sensitive to zanamivir, except for six seasonal A(H1N1) and several A(H3N2) outliers (n = 22) which exhibited cell culture induced mutations at residue D151 of the NA. A subset of viruses (n = 1058) tested for peramivir were sensitive to the drug, with exception of H275Y variants that exhibited reduced susceptibility to this NAI. This study summarizes baseline susceptibility patterns of seasonal and pandemic influenza viruses, and seeks to contribute towards criteria for defining NAI resistance.

Antigenic and genetic characteristics of swine-origin 2009 A(H1N1) influenza viruses circulating in humans.

Since its identification in April 2009, an A(H1N1) virus containing a unique combination of gene segments from both North American and Eurasian swine lineages has continued to circulate in humans. The lack of similarity between the 2009 A(H1N1) virus and its nearest relatives indicates that its gene segments have been circulating undetected for an extended period. Its low genetic diversity suggests that the introduction into humans was a single event or multiple events of similar viruses. Molecular markers predictive of adaptation to humans are not currently present in 2009 A(H1N1) viruses, suggesting that previously unrecognized molecular determinants could be responsible for the transmission among humans. Antigenically the viruses are homogeneous and similar to North American swine A(H1N1) viruses but distinct from seasonal human A(H1N1).

Evaluation of MChip with historic subtype H1N1 influenza A viruses, including the 1918 "Spanish Flu" strain.

The robustness of a recently developed diagnostic microarray for influenza, the MChip, was evaluated with 16 historic subtype H1N1 influenza A viruses (A/H1N1), including A/Brevig Mission/1/1918. The matrix gene segments from all 16 viruses were successfully detected on the array. An artificial neural network trained with temporally related A/H1N1 viruses identified A/Brevig Mission/1/1918 as influenza virus A/H1N1 with 94% probability.

Identification of A/H5N1 influenza viruses using a single gene diagnostic microarray.

In previous work, a simple diagnostic DNA microarray that targeted only the matrix gene segment of influenza A (MChip) was developed and evaluated with patient samples. In this work, the analytical utility of the MChip for detection and subtyping of an emerging virus was evaluated with a diverse set of A/H5N1 influenza viruses. A total of 43 different highly pathogenic A/H5N1 viral isolates that were collected from diverse geographic locations, including Vietnam, Nigeria, Indonesia, and Kazakhstan, representing human, feline, and a variety of avian infections spanning the time period 2003-2006 were used in this study. A probabilistic artificial neural network was developed for automated microarray image interpretation through pattern recognition. The microarray assay and subsequent subtype assignment by the artificial neural network resulted in correct identification of 24 "unknown" A/H5N1 positive samples with no false positives. Analysis of a data set composed of A/H5N1, A/H3N2, and A/H1N1 positive samples and negative controls resulted in a clinical sensitivity of 97% and a clinical specificity of 100%.

Diagnostic microarray for influenza B viruses.

The importance of global influenza surveillance using simple and rapid diagnostics has been frequently highlighted. For influenza type B, the need exists for discrimination between the two currently circulating major lineages, represented by virus strains B/Victoria/2/87 and B/Yamagata/16/88, as only one of these lineages is represented in seasonal influenza vaccines. Here, the development and characterization of a low-density DNA microarray (designated BChip) designed to detect and identify the two influenza B lineages is presented. The assay involved multiplex nucleic acid amplification and microarray hybridization of viral RNA. Detection and lineage identification was achieved in less than 8 h. In a study of 62 influenza B virus samples from 19 countries, dating from 1945 to 2005, as well as 5 negative control samples, the assay exhibited 97% sensitivity and 100% specificity. Furthermore, application of a trained artificial neural network to the pattern of relative fluorescence signals resulted in correct lineage assignment for 94% of 50 applicable influenza B viruses, with no false assignments.

Robust sequence selection method used to develop the FluChip diagnostic microarray for influenza virus.

DNA microarrays have proven to be powerful tools for gene expression analyses and are becoming increasingly attractive for diagnostic applications, e.g., for virus identification and subtyping. The selection of appropriate sequences for use on a microarray poses a challenge, particularly for highly mutable organisms such as influenza viruses, human immunodeficiency viruses, and hepatitis C viruses. The goal of this work was to develop an efficient method for mining large databases in order to identify regions of conservation in the influenza virus genome. From these regions of conservation, capture and label sequences capable of discriminating between different viral types and subtypes were selected. The salient features of the method were the use of phylogenetic trees for data reduction and the selection of a relatively small number of capture and label sequences capable of identifying a broad spectrum of influenza viruses. A detailed experimental evaluation of the selected sequences is described in a companion paper. The software is freely available under the General Public License at http://www.colorado.edu/chemistry/RGHP/software/.

MChip: a tool for influenza surveillance.

The design and characterization of a low-density microarray for subtyping influenza A is presented. The microarray consisted of 15 distinct oligonucleotides designed to target only the matrix gene segment of influenza A. An artificial neural network was utilized to automate microarray image interpretation. The neural network was trained to recognize fluorescence image patterns for 68 known influenza viruses and subsequently used to identify 53 unknowns in a blind study that included 39 human patient samples and 14 negative control samples. The assay exhibited a clinical sensitivity of 95% and clinical specificity of 92%.

Experimental evaluation of the FluChip diagnostic microarray for influenza virus surveillance.

Global surveillance of influenza is critical for improvements in disease management and is especially important for early detection, rapid intervention, and a possible reduction of the impact of an influenza pandemic. Enhanced surveillance requires rapid, robust, and inexpensive analytical techniques capable of providing a detailed analysis of influenza virus strains. Low-density oligonucleotide microarrays with highly multiplexed "signatures" for influenza viruses offer many of the desired characteristics. However, the high mutability of the influenza virus represents a design challenge. In order for an influenza virus microarray to be of utility, it must provide information for a wide range of viral strains and lineages. The design and characterization of an influenza microarray, the FluChip-55 microarray, for the relatively rapid identification of influenza A virus subtypes H1N1, H3N2, and H5N1 are described here. In this work, a small set of sequences was carefully selected to exhibit broad coverage for the influenza A and B viruses currently circulating in the human population as well as the avian A/H5N1 virus that has become enzootic in poultry in Southeast Asia and that has recently spread to Europe. A complete assay involving extraction and amplification of the viral RNA was developed and tested. In a blind study of 72 influenza virus isolates, RNA from a wide range of influenza A and B viruses was amplified, hybridized, labeled with a fluorophore, and imaged. The entire analysis time was less than 12 h. The combined results for two assays provided the absolutely correct types and subtypes for an average of 72% of the isolates, the correct type and partially correct subtype information for 13% of the isolates, the correct type only for 10% of the isolates, false-negative signals for 4% of the isolates, and false-positive signals for 1% of the isolates. In the overwhelming majority of cases in which incomplete subtyping was observed, the failure was due to the nucleic acid amplification step rather than limitations in the microarray.

ConFind: a robust tool for conserved sequence identification.

SUMMARY: ConFind (conserved region finder) identifies regions of conservation in multiple sequence alignments that can serve as diagnostic targets. Designed to work with a large number of closely related, highly variable sequences, ConFind provides robust handling of alignments containing partial sequences and ambiguous characters. Conserved regions are defined in terms of minimum region length, maximum informational entropy (variability) per position, number of exceptions allowed to the maximum entropy criterion and the minimum number of sequences that must contain a non-ambiguous character at a position to be considered for inclusion in a conserved region. Comparison of the calculated entropy for an alignment of 95 influenza A hemagglutinin sequences with random deletions results in a 98% reduction in the average error in ConFind relative to the 'Find Conserved Regions' option in BioEdit. REQUIREMENTS: ConFind requires Python 2.3, but Python 2.4 or an upgrade of the optparse module to Optik 1.5 is suggested. The program is known to run under Linux and DOS.