A comparison of internal validation techniques for multifactor dimensionality reduction

Background  

It is hypothesized that common, complex diseases may be due to complex interactions between genetic and environmental factors, which are difficult to detect in high-dimensional data using traditional statistical approaches. Multifactor Dimensionality Reduction (MDR) is the most commonly used data-mining method to detect epistatic interactions. In all data-mining methods, it is important to consider internal validation procedures to obtain prediction estimates to prevent model over-fitting and reduce potential false positive findings. Currently, MDR utilizes cross-validation for internal validation. In this study, we incorporate the use of a three-way split (3WS) of the data in combination with a post-hoc pruning procedure as an alternative to cross-validation for internal model validation to reduce computation time without impairing performance. We compare the power to detect true disease causing loci using MDR with both 5- and 10-fold cross-validation to MDR with 3WS for a range of single-locus and epistatic disease models. Additionally, we analyze a dataset in HIV immunogenetics to demonstrate the results of the two strategies on real data.     

Characterisation of urokinase plasminogen activator receptor variants in human airway and peripheral cells

Background  

Expression of the urokinase plasminogen activator receptor (UPAR) has been shown to have clinical relevance in various cancers. We have recently identified UPAR as an asthma susceptibility gene and there is evidence to suggest that uPAR may be upregulated in lung diseases such as COPD and asthma. uPAR is a key receptor involved in the formation of the serine protease plasmin by interacting with uPA and has been implicated in many physiological processes including proliferation and migration. The current aim was to determine key regulatory regions and splice variants of UPAR and quantify its expression in primary human tissues and cells (including lung, bronchial epithelium (HBEC), airway smooth muscle (HASM) and peripheral cells).     

A major genetic component of BSE susceptibility

Background  

Coding variants of the prion protein gene (PRNP) have been shown to be major determinants for the susceptibility to transmitted prion diseases in humans, mice and sheep. However, to date, the effects of polymorphisms in the coding and regulatory regions of bovine PRNP on bovine spongiform encephalopathy (BSE) susceptibility have been considered marginal or non-existent. Here we analysed two insertion/deletion (indel) polymorphisms in the regulatory region of bovine PRNP in BSE affected animals and controls of four independent cattle populations from UK and Germany.     

Bias in error estimation when using cross-validation for model selection

Background  

Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers by choosing classifier parameter values that minimize the CV error estimate. We have evaluated the validity of using the CV error estimate of the optimized classifier as an estimate of the true error expected on independent data.     

Interleukin-1α enhances the aggressive behavior of pancreatic cancer cells by regulating the α6β1-integrin and urokinase plasminogen activator receptor expression

Background  

In human pancreatic cancer progression, the α₆β₁-integrin is expressed on cancer cell surface during invasion and metastasis formation. In this study, we investigated whether interleukin (IL)-1α induces the alterations of integrin subunits and urokinase plasminogen activator/urokinase plasminogen activator receptor (uPA/uPAR) expression in pancreatic cancer cells. We hypothesize that the alterations of integrin subunits and uPA/uPAR expression make an important role in signaling pathways responsible for biological behavior of pancreatic cancer cells.     

Recombinant production of <Emphasis Type="Italic">Streptococcus equisimilis</Emphasis> streptokinase by <Emphasis Type="Italic">Streptomyces lividans</Emphasis>

Background  

Streptokinase (SK) is a potent plasminogen activator with widespread clinical use as a thrombolytic agent. It is naturally secreted by several strains of beta-haemolytic streptococci. The low yields obtained in SK production, lack of developed gene transfer methodology and the pathogenesis of its natural host have been the principal reasons to search for a recombinant source for this important therapeutic protein. We report here the expression and secretion of SK by the Gram-positive bacterium Streptomyces lividans. The structural gene encoding SK was fused to the Streptomyces venezuelae CBS762.70 subtilisin inhibitor (vsi) signal sequence or to the Streptomyces lividans xylanase C (xlnC) signal sequence. The native Vsi protein is translocated via the Sec pathway while the native XlnC protein uses the twin-arginine translocation (Tat) pathway.     

Male preponderance in early diagnosed type 2 diabetes is associated with the ARE insertion/deletion polymorphism in the <Emphasis Type="Italic">PPP1R3A</Emphasis> locus

Background  

The ARE insertion/deletion polymorphism of PPP1R3A has been associated with variation in glycaemic parameters and prevalence of diabetes. We have investigated its role in age of diagnosis, body weight and glycaemic control in 1,950 individuals with type 2 diabetes in Tayside, Scotland, and compared the ARE2 allele frequencies with 1,014 local schoolchildren.     

Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction

Background  

Multifactor Dimensionality Reduction (MDR) has been introduced previously as a non-parametric statistical method for detecting gene-gene interactions. MDR performs a dimensional reduction by assigning multi-locus genotypes to either high- or low-risk groups and measuring the percentage of cases and controls incorrectly labelled by this classification – the classification error. The combination of variables that produces the lowest classification error is selected as the best or most fit model. The correctly and incorrectly labelled cases and controls can be expressed as a two-way contingency table. We sought to improve the ability of MDR to detect gene-gene interactions by replacing classification error with a different measure to score model quality.     

Molecular adaptation of a plant-bacterium outer membrane protease towards plague virulence factor Pla

Background  

Omptins are a family of outer membrane proteases that have spread by horizontal gene transfer in Gram-negative bacteria that infect vertebrates or plants. Despite structural similarity, the molecular functions of omptins differ in a manner that reflects the life style of their host bacteria. To simulate the molecular adaptation of omptins, we applied site-specific mutagenesis to make Epo of the plant pathogenic Erwinia pyrifoliae exhibit virulence-associated functions of its close homolog, the plasminogen activator Pla of Yersinia pestis. We addressed three virulence-associated functions exhibited by Pla, i.e., proteolytic activation of plasminogen, proteolytic degradation of serine protease inhibitors, and invasion into human cells.     

GASP: Gapped Ancestral Sequence Prediction for proteins

Background  

The prediction of ancestral protein sequences from multiple sequence alignments is useful for many bioinformatics analyses. Predicting ancestral sequences is not a simple procedure and relies on accurate alignments and phylogenies. Several algorithms exist based on Maximum Parsimony or Maximum Likelihood methods but many current implementations are unable to process residues with gaps, which may represent insertion/deletion (indel) events or sequence fragments.     

Quantitative prediction of the effect of genetic variation using hidden Markov models

Background

With the development of sequencing technologies, more and more sequence variants are available for investigation. Different classes of variants in the human genome have been identified, including single nucleotide substitutions, insertion and deletion, and large structural variations such as duplications and deletions. Insertion and deletion (indel) variants comprise a major proportion of human genetic variation. However, little is known about their effects on humans. The absence of understanding is largely due to the lack of both biological data and computational resources.

Results

This paper presents a new indel functional prediction method HMMvar based on HMM profiles, which capture the conservation information in sequences. The results demonstrate that a scoring strategy based on HMM profiles can achieve good performance in identifying deleterious or neutral variants for different data sets, and can predict the protein functional effects of both single and multiple mutations.

Conclusions

This paper proposed a quantitative prediction method, HMMvar, to predict the effect of genetic variation using hidden Markov models. The HMM based pipeline program implementing the method HMMvar is freely available at https://bioinformatics.cs.vt.edu/zhanglab/hmm.     

Learning genetic epistasis using Bayesian network scoring criteria

Background  

Gene-gene epistatic interactions likely play an important role in the genetic basis of many common diseases. Recently, machine-learning and data mining methods have been developed for learning epistatic relationships from data. A well-known combinatorial method that has been successfully applied for detecting epistasis is Multifactor Dimensionality Reduction (MDR). Jiang et al. created a combinatorial epistasis learning method called BNMBL to learn Bayesian network (BN) epistatic models. They compared BNMBL to MDR using simulated data sets. Each of these data sets was generated from a model that associates two SNPs with a disease and includes 18 unrelated SNPs. For each data set, BNMBL and MDR were used to score all 2-SNP models, and BNMBL learned significantly more correct models. In real data sets, we ordinarily do not know the number of SNPs that influence phenotype. BNMBL may not perform as well if we also scored models containing more than two SNPs. Furthermore, a number of other BN scoring criteria have been developed. They may detect epistatic interactions even better than BNMBL.     

Vascular endothelial growth factor (VEGF) fails to improve blood flow and to promote collateralization in a diabetic mouse ischemic hindlimb model

Background

An insertion/deletion polymorphism in the α_2B-adrenoceptor (AR) has been associated with the risk for acute myocardial infarction (AMI) and sudden cardiac death. In this study we tested whether this polymorphism is associated with the risk for AMI among members of families with type 2 diabetes.

Methods

154 subjects with a history of AMI were matched for age and sex with one of their siblings who did not have a history of AMI. The prevalence of the genotypes of the α_2B-AR insertion/deletion polymorphism was compared between the siblings using McNemar's test. We also explored the data to see whether this genetic variation affects the risk for hypertension by using logistic regression models in the two subpopulations of subjects, with and without a history of AMI.

Results

Among all study subjects, 73 (24%) carried the α_2B-AR deletion/deletion genotype, 103 (33%) carried the insertion/insertion genotype, and 132 (43%) were heterozygous. The distribution of genotypes of the α_2B-AR insertion/deletion variation in the group of subjects with a history of AMI and their phenotype-discordant siblings did not statistically significantly differ from that expected by random distribution (p = 0.52): the deletion/deletion genotype was carried by 34 subjects with AMI (22%), and by 39 subjects without AMI (25%). Neither did we observe any significant difference in deletion allele frequencies of the α_2B-AR insertion/deletion polymorphism between patients with a history of AMI (0.44) and their sib-pair controls (0.46, p = 0.65). In an exploratory analysis, the α_2B-AR deletion/deletion genotype was associated with increased odds for hypertension compared with subjects carrying any of the other genotypes.

Conclusions

The deletion/deletion genotype of the α_2B-AR does not emerge in this study as a risk factor for AMI among members of families with type 2 diabetes; however, it might be involved in the development of hypertension.     

Aggregation and retention of human urokinase type plasminogen activator in the yeast endoplasmic reticulum

Background  

Secretion of recombinant proteins in yeast can be affected by their improper folding in the endoplasmic reticulum and subsequent elimination of the misfolded molecules via the endoplasmic reticulum associated protein degradation pathway. Recombinant proteins can also be degraded by the vacuolar protease complex. Human urokinase type plasminogen activator (uPA) is poorly secreted by yeast but the mechanisms interfering with its secretion are largely unknown.     

A new method for class prediction based on signed-rank algorithms applied to Affymetrix<Superscript>®</Superscript> microarray experiments

Background

The huge amount of data generated by DNA chips is a powerful basis to classify various pathologies. However, constant evolution of microarray technology makes it difficult to mix data from different chip types for class prediction of limited sample populations. Affymetrix^® technology provides both a quantitative fluorescence signal and a decision (detection call: absent or present) based on signed-rank algorithms applied to several hybridization repeats of each gene, with a per-chip normalization. We developed a new prediction method for class belonging based on the detection call only from recent Affymetrix chip type. Biological data were obtained by hybridization on U133A, U133B and U133Plus 2.0 microarrays of purified normal B cells and cells from three independent groups of multiple myeloma (MM) patients.

Results

After a call-based data reduction step to filter out non class-discriminative probe sets, the gene list obtained was reduced to a predictor with correction for multiple testing by iterative deletion of probe sets that sequentially improve inter-class comparisons and their significance. The error rate of the method was determined using leave-one-out and 5-fold cross-validation. It was successfully applied to (i) determine a sex predictor with the normal donor group classifying gender with no error in all patient groups except for male MM samples with a Y chromosome deletion, (ii) predict the immunoglobulin light and heavy chains expressed by the malignant myeloma clones of the validation group and (iii) predict sex, light and heavy chain nature for every new patient. Finally, this method was shown powerful when compared to the popular classification method Prediction Analysis of Microarray (PAM).

Conclusion

This normalization-free method is routinely used for quality control and correction of collection errors in patient reports to clinicians. It can be easily extended to multiple class prediction suitable with clinical groups, and looks particularly promising through international cooperative projects like the "Microarray Quality Control project of US FDA" MAQC as a predictive classifier for diagnostic, prognostic and response to treatment. Finally, it can be used as a powerful tool to mine published data generated on Affymetrix systems and more generally classify samples with binary feature values.

     

Computational cluster validation for microarray data analysis: experimental assessment of Clest,Consensus Clustering,Figure of Merit,Gap Statistics and Model Explorer

Background  

Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, Data Analysis and Classification, in particular with regard to the prediction of the number of clusters in a dataset, usually established via internal validation measures. Despite the wealth of internal measures available in the literature, new ones have been recently proposed, some of them specifically for microarray data.     

The CD-loop of PAI-2 (SERPINB2) is redundant in the targeting, inhibition and clearance of cell surface uPA activity

Background  

Plasminogen activator inhibitor type-2 (PAI-2, SERPINB2) is an irreversible, specific inhibitor of the urokinase plasminogen activator (uPA). Since overexpression of uPA at the surface of cancer cells is linked to malignancy, targeting of uPA by exogenous recombinant PAI-2 has been proposed as the basis of potential cancer therapies. To this end, reproducible yields of high purity protein that maintains this targeting ability is required. Herein we validate the use in vitro of recombinant 6 × His-tagged-PAI-2 lacking the intrahelical loop between C and D alpha-helices (PAI-2 ΔCD-loop) for these purposes.     

Application of NIR Spectroscopy Coupled with PLS Regression for Quantification of Total Polyphenol Contents from the Fruit and Aerial Parts of Citrullus colocynthis

Introduction

Citrullus colocynthis (L.) Schrad is extensively used to treat diabetes, obesity, fever, cancer, amenorrhea, jaundice, leukemia, rheumatism, and respiratory diseases. Chemical studies have indicated the presence of several cucurbitacins, flavones, and other polyphenols in this plant. These phytochemical constituents are responsible for the interesting antioxidant and other biological activities of C. colocynthis.

Objective

In the present study, for the first time, near infrared (NIR) spectroscopy coupled with partial least square (PLS) regression analysis was used to quantify the polyphenolic phytochemicals of C. colocynthis.

Methodology

The fruit and aerial parts of the C. colocynthis were extracted individually in methanol followed by fractionation in n‐hexane, chloroform, ethyl acetate, n‐butanol, and water. Near infrared (NIR) spectra were obtained in absorption mode in the wavelength range 700–2500 nm. The PLS regression model was then built from the obtained spectral data to quantify the total polyphenol contents in the selected plant samples.

Results

The PLS regression model obtained had a R² value of 99% with a 0.98 correlationship value and a good prediction with a root mean square error of prediction (RMSEP) value of 1.89% and correlation of 0.98. These results were further confirmed through UV–vis spectroscopy and it is found that the ethyl acetate fraction has the maximum value for polyphenol contents (101.7 mg/100 g; NIR, 100.4 mg/100 g; UV–vis).

Conclusions

The polyphenolic phytochemicals of the fruit and aerial parts of C. colocynthis have been quantified successfully by using multivariate analysis in a non‐destructive, economical, precise, and highly sensitive method, which uses very simple sample preparation. Copyright © 2017 John Wiley & Sons, Ltd.     

A novel scheme to assess factors involved in the reproducibility of DNA-microarray data

Background  

In research laboratories using DNA-microarrays, usually a number of researchers perform experiments, each generating possible sources of error. There is a need for a quick and robust method to assess data quality and sources of errors in DNA-microarray experiments. To this end, a novel and cost-effective validation scheme was devised, implemented, and employed.