Biomedical subjects

Martin Mehlmann

Publications and source records attributed to Martin Mehlmann.

7 recordsLinked to original sources

PubMed2007

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%.

Animals↗

PubMed2006

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%.

Automation↗

PubMed2006

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.

Animals↗

PubMed2006

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/.

Computational Biology↗

PubMed2005

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.

Algorithms↗

PubMed2005

Optimization of fragmentation conditions for microarray analysis of viral RNA.

An important consideration in microarray analysis of nucleic acids is the efficiency with which the target molecule is captured by, or hybridized to, surface-immobilized oligos. For RNA, secondary and tertiary structure of the target strand can significantly decrease capture efficiency. To overcome this limitation, RNA is often fragmented to reduce structural effects. In this study, the metal ion-catalyzed base hydrolysis fragmentation conditions for viral RNA extracted from influenza viruses were evaluated and the hybridization efficiency of the resulting fragments was determined as a function of fragment length. The amount of RNA captured was evaluated qualitatively by fluorescence intensity normalized to an internal standard. Optimized conditions for influenza RNA were determined to include a fragmentation time of 20-30 min at 75 degrees C. These conditions resulted in a maximum concentration of fragments between 38 and 150 nt in length and a maximum in the capture and label efficiency.

Base Sequence↗

PubMed2005

Reflectometric interference spectroscopy combined with MALDI-TOF mass spectrometry to determine quantitative and qualitative binding of mixtures of vancomycin derivatives.

This paper describes the combination of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry with label free bio-interaction analysis based on reflectometric interference spectroscopy (RIfS). The potential of this concerted approach is demonstrated by measuring the binding properties of different vancomycin-type glycopeptide antibiotic mixtures. Although RIfS is sensitive and does not require use of a label, it cannot determine which components of a mixture have bound to the surface after incubation. Fortunately, each bound species has a unique mass that can, afterwards, be determined by mass spectrometry. Thus, the screening capability of RIfS is combined with the identification capability of mass spectrometry.

Anti-Bacterial Agents↗