Integrated platform for detection of DNA sequence variants using capillary array electrophoresis

We have developed a highly versatile platform that performs temperature gradient capillary electrophoresis (TGCE) for mutation/single-nucleotide polymorphism (SNP) detection, sequencing and mutation/SNP genotyping for identification of sequence variants on an automated 24-, 96- or 192-capillary array instrument. In the first mode, multiple DNA samples consisting of homoduplexes and heteroduplexes are separated by CE, during which a temperature gradient is applied that covers all possible temperatures of 50% melting equilibrium (Tms) for the samples. The differences in Tms result in separation of homoduplexes from heteroduplexes, thereby identifying the presence of DNA variants. The sequencing mode is then used to determine the exact location of the mutation/SNPs in the DNA variants. The first two modes allow the rapid identification of variants from the screening of a large number of samples. Only the variants need to be sequenced. The third mode utilizes multiplexed single-base extensions (SBEs) to survey mutations and SNPs at the known sites of DNA sequence. The TGCE approach combined with sequencing and SBE is fast and cost-effective for high-throughput mutation/SNP detection.     

Optimal peeling order for pedigrees with incomplete genotypic information

The likelihood approach is common in linkage analysis of large extended pedigrees. Various peeling procedures, based on the conditional independence of separate parts of a pedigree, are typically used for likelihood calculations. A peeling order may significantly affect the complexity of such calculations, particularly for pedigrees with loops or when many pedigrees members have unknown genotypes. Several algorithms have been proposed to address this problem for pedigrees with loops. However, the problem has not been solved for pedigrees without loops until now. In this paper, we suggest a new graph theoretic algorithm for optimal selection of peeling order in zero-loop pedigrees with incomplete genotypic information. It is especially useful when multiple likelihood calculation is needed, for example, when genetic parameters are estimated or linkage with multiple marker loci is tested. The algorithm can be easily introduced into the existing software packages for linkage analysis based on the Elston-Stewart algorithm for likelihood calculation. The algorithm was implemented in a software package PedPeel, which is freely available at http://mga.bionet.nsc.ru/nlru/.     

Direct haplotype detection of adjacent polymorphic sites in the regulatory region of the dopamine D4 receptor (DRD4) gene

A novel method of haplotype identification is discussed allowing simultaneous detection of two adjacent polymorphic sites, a single nucleotide polymorphism (SNP) and a length polymorphism within 1-2 kilobase distance. The method combines allele specific-amplification with high-throughput, automated ultrathin-layer gel electrophoresis analysis of fragment size polymorphism. A typical application is shown for genotyping the -521 C/T single nucleotide polymorphism and the 120 bp duplication in the 5"-upstream region of the dopamine D4 receptor (DRD4) gene. We have also demonstrated that the haplotypes of double heterozygotes for -521 C/T and for the 120 bp duplication can be clearly distinguished, that has only been possible previously by extensive pedigree analysis.     

A new strategy of outlier detection for QSAR/QSPR

The crucial step of building a high performance QSAR/QSPR model is the detection of outliers in the model. Detecting outliers in a multivariate point cloud is not trivial, especially when several outliers coexist in the model. The classical identification methods do not always identify them, because they are based on the sample mean and covariance matrix influenced by the outliers. Moreover, existing methods only lay stress on some type of outliers but not all the outliers. To avoid these problems and detect all kinds of outliers simultaneously, we provide a new strategy based on Monte‐Carlo cross‐validation, which was termed as the MC method. The MC method inherently provides a feasible way to detect different kinds of outliers by establishment of many cross‐predictive models. With the help of the distribution of predictive residuals such obtained, it seems to be able to reduce the risk caused by the masking effect. In addition, a new display is proposed, in which the absolute values of mean value of predictive residuals are plotted versus standard deviations of predictive residuals. The plot divides the data into normal samples, y direction outliers and X direction outliers. Several examples are used to demonstrate the detection ability of MC method through the comparison of different diagnostic methods. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

New approach for rapid detection of known hemoglobin variants using LC-MS/MS combined with a peptide database

The identification of hemoglobin (Hb) variants is usually performed by means of different analytical steps and methodologies. Phenotypic methods, such as gel electrophoresis and high performance liquid chromatography, are used to detect the different electrophoretic or chromatographic behaviors of hemoglobin variants in comparison to HbA0 used as a control. These data often need to be combined with mass spectrometry analyses of intact globins and their tryptic peptide mixtures. As an alternative to a 'step-by-step' procedure, we have developed a 'single step' approach for the identification of Hb variants present in biological samples. This is based on the microHPLC-ESI-MS/MS analysis of the peptide mixture generated by a tryptic digestion of diluted Hb samples and an in-house new database containing solely the variant tryptic peptide of known human Hb variants. The experimental results (full MS and MS/MS spectra) are correlated with theoretical mass spectra generated from our in-house-built variant peptide database (Hbp) using the SEQUEST algorithm. Simple preparation of samples and an automated identification of the variant peptide are the main characteristics of this approach, making it an attractive method for the detection of Hb variants at the routine clinical level. We have analyzed 16 different samples, each containing a different known variant of hemoglobin.     

Nuclear forensics education at the University of Texas at Austin

Nuclear forensics continues to be an integral part of the Domestic Nuclear Detection Office, the Defense Threat Reduction Agency and the National Nuclear Security Administration. As with our previous three-year Nuclear Forensics Education Award Program we will continue to offer a comprehensive educational program and closely collaborate with national laboratories to pursue common research. Our research will primarily focus on analysis of radioactive debris following a nuclear or radiological dispersive device event or the investigation of the pedigree of nuclear materials in nonproliferation. This research will include using Compton suppression and gamma coincidence low-level gamma ray counting, investigation of nuclear fuel cycles for nonproliferation, on-site inspection within the context of the Comprehensive Nuclear-Test-Ban Treaty and radioxenon detection physics. We also offer a graduate program in nuclear robotics, an interdisciplinary program in the automation of handling special nuclear materials. To better equip our students who are entering the workforce at the national laboratories and government agencies we are also proposing the development of several new laboratory modules for non-destructive identification of fission products in environmental samples and irradiated uranium specimens at various enrichments and characterizing naturally occurring radioactive material. Collaboration with Florida Memorial University a Historically Black Colleges and Universities will continue for training and collaborative research.     

Locally centred Mahalanobis distance: A new distance measure with salient features towards outlier detection

Outlier detection is a prerequisite to identify the presence of aberrant samples in a given set of data. The identification of such diverse data samples is significant particularly for multivariate data analysis where increasing data dimensionality can easily hinder the data exploration and such outliers often go undetected. This paper is aimed to introduce a novel Mahalanobis distance measure (namely, a pseudo-distance) termed as locally centred Mahalanobis distance, derived by centering the covariance matrix at each data sample rather than at the data centroid as in the classical covariance matrix. Two parameters, called as Remoteness and Isolation degree, were derived from the resulting pairwise distance matrix and their salient features facilitated a better identification of atypical samples isolated from the rest of the data, thus reflecting their potential application towards outlier detection. The Isolation degree demonstrated to be able to detect a new kind of outliers, that is, isolated samples within the data domain, thus resulting in a useful diagnostic tool to evaluate the reliability of predictions obtained by local models (e.g. k-NN models).     

Decision protocol for checking robustness with previous outlier detection in the validation of analytical methods

A general procedure based on variation of experimental design for checking robustness in the validation of analytical methods is presented. This procedure, which is easy to apply, consists in estimating the main total effects, in detecting outliers, checking the curvature and in determining the main side effects. Two methodologies based on the analysis of a) the residuals from the reduced model and b) the replicates from the reconstructed design were employed for the detection of outliers. In further studies, general experimental design principles were applied using two- and three-level factorial designs. In some cases, a dummy variable was introduced in order not to modify the structure of the designs utilized. Received: 30 October 1998 / Revised: 19 April 1999 / Accepted: 6 May 1999     

Genome-wide scan of granular corneal dystrophy, type II: confirmation of chromosome 5q31 and identification of new co-segregated loci on chromosome 3q26.3

Granular corneal dystrophy, type II (CGD2; Avellino corneal dystrophy) is the most common corneal dystrophy among Koreans, but its pathophysiology is still poorly understood. Many reports showed that even though the causative mutation is the same TGFBI R124H mutation, there are severe and mild phenotypes of the corneal dystrophy. We also observed the phenotype differences in our samples. For this reason, we focused our effort on the identification of unknown genetic factor related to phenotype variation. A total 551 individuals from 59 families were genotyped with SNP chip and used in genome-wide linkage analysis. From single-point linkage analyses, we confirmed the known 5q31 region for TGFBI gene, and selected novel nine candidate loci for CGD2. In simulation analysis, the only 3q26.3 region including neuroligin 1 gene (NLGN1) was supported by empirical statistic significance. To investigate the effect of genetic heterogeneity in linkage analysis, we classified CGD2 families into two subgroups. Although we could not find a significant evidence for correlation between the 3q26.3 region and CGD2 phenotypes, this first genome-wide analysis with CGD2 families in Korea has a very important value for offering insights in genetics of CGD2. In addition, the co-segregating loci with CGD2 including 3q26.3 would be a good target for further study to understand the pathophysiology of CGD2.     

A simple program for weighted linear regression applied to activation analysis data processing

A simple ALGOL program for activation analysis data handling is presented The program, although designed in principle for processing single-channel spectrometry data, may also be used for multichannel spectrometry, on condition that the peak area is computed separately. The calculations of instrumental error and standard deviation are carried out. The outliers are tested, and the regression line diagram with the related observations are plotted by the program.     

Effective sample size: Quick estimation of the effect of related samples in genetic case-control association analyses

Affected relatives are essential for pedigree linkage analysis, however, they cause a violation of the independent sample assumption in case-control association studies. To avoid the correlation between samples, a common practice is to take only one affected sample per pedigree in association analysis. Although several methods exist in handling correlated samples, they are still not widely used in part because these are not easily implemented, or because they are not widely known. We advocate the effective sample size method as a simple and accessible approach for case-control association analysis with correlated samples. This method modifies the chi-square test statistic, p-value, and 95% confidence interval of the odds-ratio by replacing the apparent number of allele or genotype counts with the effective ones in the standard formula, without the need for specialized computer programs. We present a simple formula for calculating effective sample size for many types of relative pairs and relative sets. For allele frequency estimation, the effective sample size method captures the variance inflation exactly. For genotype frequency, simulations showed that effective sample size provides a satisfactory approximation. A gene which is previously identified as a type 1 diabetes susceptibility locus, the interferon-induced helicase gene (IFIH1), is shown to be significantly associated with rheumatoid arthritis when the effective sample size method is applied. This significant association is not established if only one affected sib per pedigree were used in the association analysis. Relationship between the effective sample size method and other methods - the generalized estimation equation, variance of eigenvalues for correlation matrices, and genomic controls - are discussed.     

Sensitivity enhancement in tandem liquid chromatographic mass spectrometric assays by summation of two transition ion pairs – perspectives

The introduction of tandem liquid chromatographic mass spectrometric assays has boosted the sensitivity of the bioanalytical determination for the scores of analytes as compared to other conventional modes of detection [1–6]. Additionally, many intelligent variants such as improved ionization potential, suitable adduct formation, and/or identification of a precursor ion, switching of modality of detection (+ve to –ve and vice versa), and derivatization steps (to aid fragmentation and/or impart ionization property) within tandem mass spectrometric assays have further aided in improving the sensitivity. Some researchers have also expressed thoughts of using matrix effects in a beneficial manner so that there may be ion enhancement for improving the sensitivity of the assay.     

Robust identification of significant interactions between toxicogenomic biomarkers and their regulatory chemical compounds using logistic moving range chart

Identification of significant interactions between genes and chemical compounds/drugs is an important issue in toxicogenomic studies as well as in drug discovery and development. There are some online and offline computational tools for toxicogenomic data analysis to identify the biomarker genes and their regulatory chemical compounds/drugs. However, none of the researchers has considered yet the identification of significant interactions between genes and compounds. Therefore, in this paper, we have discussed two approaches namely moving range chart (MRC) and logistic moving range chart (LMRC) for the identification of significant up-regulatory (UpR) and down-regulatory (DnR) gene-compound interactions as well as toxicogenomic biomarkers and their regulatory chemical compounds/drugs. We have investigated the performance of both MRC and LMRC approaches using simulated datasets. Simulation results show that both approaches perform almost equally in absence of outliers. However, in presence of outliers, the LMRC shows much better performance than the MRC. In case of real life toxicogenomic data analysis, the proposed LMRC approach detected some important down-regulated biomarker genes those were not detected by other approaches. Therefore, in this paper, our proposal is to use LMRC for robust identification of significant interactions between genes and chemical compounds/drugs.     

Characterization of N-terminal processing of group VIA phospholipase A<Subscript>2</Subscript> and of potential cleavage sites of amyloid precursor protein constructs by automated identification of signature peptides in LC/MS/MS analyses of proteolytic digests

Dysregulation of proteolytic processing of the amyloid precursor protein (APP) contributes to the pathogenesis of Alzheimer's Disease, and the Group VIA phospholipase A(2) (iPLA(2)beta) is the dominant PLA(2) enzyme in the central nervous system and is subject to regulatory proteolytic processing. We have identified novel N-terminal variants of iPLA(2)beta and previously unrecognized proteolysis sites in APP constructs with a C-terminal 6-myc tag by automated identification of signature peptides in LC/MS/MS analyses of proteolytic digests. We have developed a Signature-Discovery (SD) program to characterize protein isoforms by identifying signature peptides that arise from proteolytic processing in vivo. This program analyzes MS/MS data from LC analyses of proteolytic digests of protein mixtures that can include incompletely resolved components in biological samples. This reduces requirements for purification and thereby minimizes artifactual modifications during sample processing. A new algorithm to generate the theoretical signature peptide set and to calculate similarity scores between predicted and observed mass spectra has been tested and optimized with model proteins. The program has been applied to the identification of variants of proteins of biological interest, including APP cleavage products and iPLA(2)beta, and such applications demonstrate the utility of this approach.     

Adaptive Modelling of Mutated FMO3 Enzyme Could Unveil Unexplored Scenarios Linking Variant Haplotypes to TMAU Phenotypes

Background: Trimethylaminuria (TMAU) is a rare genetic disease characterized by the accumulation of trimethylamine (TMA) and its subsequent excretion trough main body fluids, determining the characteristic fish odour in affected patients. We realized an experimental study to investigate the role of several coding variants in the causative gene FMO3, that were only considered as polymorphic or benign, even if the available literature on them did not functionally explain their ineffectiveness on the encoded enzyme. Methods: Mutational analysis of 26 TMAU patients was realized by Sanger sequencing. Detected variants were, subsequently, deeply statistically and in silico characterized to determine their possible effects on the enzyme activity. To achieve this goal, a docking prediction for TMA/FMO3 and an unbinding pathway study were performed. Finally, a TMAO/TMA urine quantification by 1H-NMR spectroscopy was performed to support modelling results. Results: The FMO3 screening of all patients highlighted the presence of 17 variants distributed in 26 different haplotypes. Both non-sense and missense considered variants might impair the enzymatic kinetics of FMO3, probably reducing the interaction time between the protein catalytic site and TMA, or losing the wild-type binding site. Conclusions: Even if further functional assays will confirm our predictive results, considering the possible role of FMO3 variants with still uncertain effects, might be a relevant step towards the detection of novel scenarios in TMAU etiopathogenesis.     

回归分析在稀土元素光度分析中的应用

本文研究了共显色效应对稀土元素光度分析的影响;探讨了在TRS-80型微处理机上用回归分析法处理实验数据问题。结果表明,在镧存在下用偶氮氯膦Ⅲ测定钇,可使测量误差从+26±15%降低至+3.6±3.0%。该方法简单、快速。     

Dual-detection approach for a charge variant analysis of monoclonal antibody combination products using imaged capillary isoelectric focusing

The clinical benefits of treatments with a combination of two or more therapeutic monoclonal antibodies (mAbs) have emerged in recent years. Imaged capillary isoelectric focusing is a frequently used technology in the biopharmaceutical industry for charge variant analysis of protein therapeutics. However, with the wide concentration ranges of combination products, one component may fall within the linear detection range, whereas the other does not. Here, we report a novel methodology to explore charge variants of mAb mixtures using multiple detection techniques simultaneously. We use ultraviolet absorbance to monitor the charge variants of the high-concentration component and native fluorescence (FL) to monitor the variants of the low-concentration one. Charge variants of mixtures that span 40-fold in ratio differences can be accurately quantified with this approach. In contrast to the conventional methods, it is not necessary to prepare and analyze two samples at different concentrations and combine the results for combination product testing. Additionally, the use of FL detection enables the charge variant analysis of highly potent/low abundant mAbs in a mixture. This methodology is more quality-control friendly and efficient for the charge variant analysis of combination products with wide ratios.     

ROMOS: A new computer code for nuclide identification and concentration calculation in NAA using the k0-concept

The program ROMOS and the assisting program RECAL are presented. These programs were designed to facilitate nuclide identification and concentration calculation in NAA using the k₀-concept. For nuclide identification classical tests (energy match, decay factor) are complemented with a test of the saturation factor including the sensitivity of neutron acctivation analysis for the candidate nuclide. For nuclides emitting more than one gamma-energy the intensity of the observed peaks in the spectrum are compared. Thereby full account is taken of relative detection efficiency and true coincidence effects. Practical experience in using the interactive code shows that it is a convenient and time-saving working aid for an experienced experimenter.     

Rapid detection of cyanobacterial toxins in precursor ion mode by liquid chromatography tandem mass spectrometry

We established an analytical method based on liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) in the precursor ion mode for simultaneous qualitative monitoring of various groups of cyanobacterial toxins. The toxin groups investigated were paralytic shellfish poisoning (PSP) toxins, anatoxins (ANAs), cylindrospermopsins (CYNs), microcystins (MCs), and nodularins (NODs), including rare and uncharacterized derivatives found in plankton and water matrices. Alternative analytical methods based on tandem mass spectrometry commonly operate in multiple reaction monitoring (MRM) mode and depend on prior knowledge of putative toxigenicity of the cyanobacterium species and strain, and the expected toxin variants. In contrast, the precursor ion mode yields diagnostic mass fragments for the detection of characteristic compounds of the different toxin classes and thus allows monitoring of a large set of unspecified cyanotoxins of various groups, even when the species composition is undetermined or uncertain. This rapid method enables screening for a wide spectrum of toxic cyanobacterial metabolites and degradation products in a single chromatographic separation with detection limits at nanogram levels. The precursor ion technique is a valuable adjunct to existing mass spectrometric methods for cyanotoxins, although it is not a complete replacement for detailed quantitative analysis requiring comprehensive sample cleanup.