Simplified microarray system for simultaneously detecting rifampin, isoniazid, ethambutol, and streptomycin resistance markers in Mycobacterium tuberculosis.

We developed a simplified microarray test for detecting and identifying mutations in rpoB, katG, inhA, embB, and rpsL and compared the analytical performance of the test to that of phenotypic drug susceptibility testing (DST). The analytical sensitivity was estimated to be at least 110 genome copies per amplification reaction. The microarray test correctly detected 95.2% of mutations for which there was a sequence-specific probe on the microarray and 100% of 96 wild-type sequences. In a blinded analysis of 153 clinical isolates, microarray sensitivity for first-line drugs relative to phenotypic DST (true resistance) was 100% for rifampin (RIF) (14/14), 90.0% for isoniazid (INH) (36/40), 70% for ethambutol (EMB) (7/10), and 89.1% (57/64) combined. Microarray specificity (true susceptibility) for first-line agents was 95.0% for RIF (132/139), 98.2% for INH (111/113), and 98.6% for EMB (141/143). Overall microarray specificity for RIF, INH, and EMB combined was 97.2% (384/395). The overall positive and negative predictive values for RIF, INH, and EMB combined were 84.9% and 98.3%, respectively. For the second-line drug streptomycin (STR), overall concordance between the agar proportion method and microarray analysis was 89.5% (137/153). Sensitivity was 34.8% (8/23) because of limited microarray coverage for STR-conferring mutations, and specificity was 99.2% (129/130). All false-susceptible discrepant results were a consequence of DNA mutations that are not represented by a specific microarray probe. There were zero invalid results from 220 total tests. The simplified microarray system is suitable for detecting resistance-conferring mutations in clinical M. tuberculosis isolates and can now be used for prospective trials or integrated into an all-in-one, closed-amplicon consumable.

PoPS: a computational tool for modeling and predicting protease specificity.

Proteases play a fundamental role in the control of intra- and extra-cellular processes by binding and cleaving specific amino acid sequences. Identifying these targets is extremely challenging. Current computational attempts to predict cleavage sites are limited, representing these amino acid sequences as patterns or frequency matrices. Here we present PoPS, a publicly accessible bioinformatics tool (http://pops.csse.monash.edu.au/) that provides a novel method for building computational models of protease specificity, which while still being based on these amino acid sequences, can be built from any experimental data or expert knowledge available to the user. PoPS specificity models can be used to predict and rank likely cleavages within a single substrate, and within entire proteomes. Other factors, such as the secondary or tertiary structure of the substrate, can be used to screen unlikely sites. Furthermore, the tool also provides facilities to infer, compare and test models, and to store them in a publicly accessible database.

Development and clinical testing of a simple, low-density gel element array for influenza identification, subtyping, and H275Y detection.

The objectives of this study were to develop a user-friendly, gel element microarray test for influenza virus detection, subtyping, and neuraminidase inhibitor resistance detection, assess the performance characteristics of the assay, and perform a clinical evaluation on retrospective nasopharyngeal swab specimens. A streamlined microarray workflow enabled a single user to run up to 24 tests in an 8h shift. The most sensitive components of the test were the primers and probes targeting the A/H1 pdm09 HA gene with an analytical limit of detection (LoD) <100 gene copies (gc) per reaction. LoDs for all targets in nasopharyngeal swab samples were ≤1000 gc, with the exception of one target in the seasonal A/H1N1 subtype. Seasonal H275Y variants were detectable in a mixed population when present at >5% with wild type virus, while the 2009 pandemic H1N1 H275Y variant was detectable at ≤1% in a mixture with pandemic wild type virus. Influenza typing and subtyping results concurred with those obtained with real-time RT-PCR assays on more than 97% of the samples tested. The results demonstrate that a large panel of single-plex, real-time RT-PCR tests can be translated to an easy-to-use, sensitive, and specific microarray test for potential diagnostic use.

Development of a simple, low-density array to detect methicillin-resistant Staphylococcus aureus and mecA dropouts in nasal swabs.

Detection of methicillin-resistant Staphylococcus aureus (MRSA) is important for prevention and control of MRSA infections, but the discovery of mecA dropouts and SCCmec junction sequences with homology to coagulase-negative staphylococci (CoNS) has challenged several real-time PCR tests. The objective of this study was to develop a user-friendly, gel element microarray test for MRSA detection, to estimate the analytical performance characteristics of the test on bacterial isolates, and to perform an initial evaluation of the test on nasopharyngeal swabs from patients known to have a high prevalence of S. aureus containing mecA dropouts. The assay limit of detection for the test was 250 fg (or less) of genomic DNA per amplification reaction (approximately 80 cell equivalents) and MRSA was consistently detected at a ratio of 1:12,000 in a non-target background. Of 87 bacterial isolates, the test accurately classified 86 (98.8%) overall, and correctly identified 14 mecA dropout specimens that were falsely positive in the BD GeneOhm MRSA test or BD GeneOhm StaphSR test. A retrospective analysis of 246 nasal swab samples acquired from a high-risk patient population (overall prevalence=10.8% by culture) resulted in 80.5% sensitivity (95% CI=68.4%, 92.6%) and 96.6% specificity. Of these 246 samples, 174 (71%) were positive for mecA, 86 (35%) were positive for S. aureus tufA and 46 (19%) were positive for a SCCmec junction sequence. To estimate method repeatability, 48 samples representing the full range of phenotypes, genotypes and microarray probe SNR values were tested in triplicate, with three discordant results for a concordance rate of 97.9% (141/144 tests). These data demonstrate that a very simple microarray test can identify mecA dropouts with high specificity in either cultured isolates or nasal swabs from a high-prevalence, high-risk patient population. However, the clinical sensitivity of the test will likely depend on local microbial ecology and the prevalence of mecA positive CoNS in any given patient population.

Monitoring microbial community structure and dynamics during in situ U(VI) bioremediation with a field-portable microarray analysis system.

The objective of this study was to develop and validate a simple, field-portable, microarray system for monitoring microbial community structure and dynamics in groundwater and subsurface environments, using samples representing site status before acetate injection, during Fe-reduction, in the transition from Fe- to SO(4)(2-)-reduction, and into the SO(4)(2-)-reduction phase. Limits of detection for the array are approximately 10(2)-10(3) cell equivalents of DNA per reaction. Sample-to-answer results for the field deployment were obtained in 4 h. Retrospective analysis of 50 samples showed the expected progression of microbial signatures from Fe- to SO(4)(2-) -reducers with changes in acetate amendment and in situ field conditions. The microarray response for Geobacter was highly correlated with qPCR for the same target gene (R(2) = 0.84). Microarray results were in concordance with quantitative PCR data, aqueous chemistry, site lithology, and the expected microbial community response, indicating that the field-portable microarray is an accurate indicator of microbial presence and response to in situ remediation of a uranium-contaminated site.

PoPS: a computational tool for modeling and predicting protease specificity.

Proteases play a fundamental role in the control of intra- and extracellular processes by binding and cleaving specific amino acid sequences. Identifying these targets is extremely challenging. Current computational attempts to predict cleavage sites are limited, representing these amino acid sequences as patterns or frequency matrices. Here we present PoPS, a publicly accessible bioinformatics tool (http://pops.csse.monash.edu.au/) which provides a novel method for building computational models of protease specificity that, while still being based on these amino acid sequences, can be built from any experimental data or expert knowledge available to the user. PoPS specificity models can be used to predict and rank likely cleavages within a single substrate, and within entire proteomes. Other factors, such as the secondary or tertiary structure of the substrate, can be used to screen unlikely sites. Furthermore, the tool also provides facilities to infer, compare and test models, and to store them in a publicly accessible database.