Reference gene selection and RNA preservation protocol in the cat flea, Ctenocephalides felis, for gene expression studies

This work was supported by a Knowledge Transfer Network BBSRC Industrial Case (#414 BB/L502467/1) studentship in association Zoetis Inc.

Modified AHP for gene selection and cancer classification using type-2 fuzzy logic

This paper proposes a modification to the analytic hierarchy process (AHP) to select the most informative genes that serve as inputs to an interval type-2 fuzzy logic system (IT2FLS) for cancer classification. Unlike the conventional AHP, the modified AHP allows us to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test, and signal-to-noise ratio. The IT2FLS is introduced for the classification task due to its great ability for handling nonlinear, noisy, and outlier data, which are common problems in cancer microarray gene expression profiles. An unsupervised learning strategy using the fuzzy c-means clustering is employed to initialize parameters of the IT2FLS. Other classifiers such as multilayer perceptron network, support vector machine, and fuzzy ARTMAP are also implemented for comparisons. Experiments are carried out on three well-known microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, and prostate. Rather than the traditional cross validation, leave-one-out cross-validation strategy is applied for the experiments. Results demonstrate the performance dominance of the IT2FLS against the competing classifiers. More noticeably, the modified AHP improves the classification performance not only of the IT2FLS but of all other classifiers as well. Accordingly, the proposed combination between the modified AHP and IT2FLS is a powerful tool for cancer classification and can be implemented as a real clinical decision support system that is useful for medical practitioners.

Reference gene selection for gene expression studies in apple.

2011

Hidden Markov models for cancer classification using gene expression profiles

This paper introduces an approach to cancer classification through gene expression profiles by designing supervised learning hidden Markov models (HMMs). Gene expression of each tumor type is modelled by an HMM, which maximizes the likelihood of the data. Prominent discriminant genes are selected by a novel method based on a modification of the analytic hierarchy process (AHP). Unlike conventional AHP, the modified AHP allows to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test and signal to noise ratio. The modified AHP aggregates ranking results of individual gene selection methods to form stable and robust gene subsets. Experimental results demonstrate the performance dominance of the HMM approach against six comparable classifiers. Results also show that gene subsets generated by modified AHP lead to greater accuracy and stability compared to competing gene selection methods, i.e. information gain, symmetrical uncertainty, Bhattacharyya distance, and ReliefF. The modified AHP improves the classification performance not only of the HMM but also of all other classifiers. Accordingly, the proposed combination between the modified AHP and HMM is a powerful tool for cancer classification and useful as a real clinical decision support system for medical practitioners.

Loss of the maternal H19 gene induces changes in Igf2 methylation in both cis and trans

Recent investigations have shown that the maintenance of genomic imprinting of the murine insulin-like growth factor 2 (Igf2) gene involves at least two factors: the DNA (cytosine-5-)-methyltransferase activity, which is required to preserve the paternal specific expression of Igf2, and the H19 gene (lying 90 kb downstream of Igf2 gene), which upon inactivation leads to relaxation of the Igf2 imprint. It is not yet clear how these two factors are related to each other in the process of maintenance of Igf2 imprinting and, in particular, whether the latter is acting through cis elements or whether the H19 RNA itself is involved. By using Southern blots and the bisulfite genomic-sequencing technique, we have investigated the allelic methylation patterns (epigenotypes) of the Igf2 gene in two strains of mouse with distinct deletions of the H19 gene. The results show that maternal transmission of H19 gene deletions leads the maternal allele of Igf2 to adopt the epigenotype of the paternal allele and indicate that this phenomenon is influenced directly or indirectly by the H19 gene expression. More importantly, the bisulfite genomic-sequencing allowed us to show that the methylation pattern of the paternal allele of the Igf2 gene is affected in trans by deletions of the active maternal allele of the H19 gene. Selection during development for the appropriate expression of Igf2, dosage-dependent factors that bind to the Igf2 gene, or methylation transfer between the parental alleles could be involved in this trans effect.

Tissue-specific selection of reference genes is required for expression studies in the mouse model of maternal protein undernutrition

Suboptimal maternal nutrition during gestation results in the establishment of long-term phenotypic changes and an increased disease risk in the offspring. To elucidate how such environmental sensitivity results in physiological outcomes, the molecular characterisation of these offspring has become the focus of many studies. However, the likely modification of key cellular processes such as metabolism in response to maternal undernutrition raises the question of whether the genes typically used as reference constants in gene expression studies are suitable controls. Using a mouse model of maternal protein undernutrition, we have investigated the stability of seven commonly used reference genes (18s, Hprt1, Pgk1, Ppib, Sdha, Tbp and Tuba1) in a variety of offspring tissues including liver, kidney, heart, retro-peritoneal and inter-scapular fat, extra-embryonic placenta and yolk sac, as well as in the preimplantation blastocyst and blastocyst-derived embryonic stem cells. We find that although the selected reference genes are all highly stable within this system, they show tissue, treatment and sex-specific variation. Furthermore, software-based selection approaches rank reference genes differently and do not always identify genes which differ between conditions. Therefore, we recommend that reference gene selection for gene expression studies should be thoroughly validated for each tissue of interest. © 2011 Elsevier Inc.

Lung cancer prediction from microarray data by gene expression programming

Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models. Prediction performance evaluations and comparisons between the authors' GEP models and three representative machine learning methods, support vector machine, multi-layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross-data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.

Two genes on A/J chromosome 18 are associated with susceptibility to Staphylococcus aureus infection by combined microarray and QTL analyses.

Although it has recently been shown that A/J mice are highly susceptible to Staphylococcus aureus sepsis as compared to C57BL/6J, the specific genes responsible for this differential phenotype are unknown. Using chromosome substitution strains (CSS), we found that loci on chromosomes 8, 11, and 18 influence susceptibility to S. aureus sepsis in A/J mice. We then used two candidate gene selection strategies to identify genes on these three chromosomes associated with S. aureus susceptibility, and targeted genes identified by both gene selection strategies. First, we used whole genome transcription profiling to identify 191 (56 on chr. 8, 100 on chr. 11, and 35 on chr. 18) genes on our three chromosomes of interest that are differentially expressed between S. aureus-infected A/J and C57BL/6J. Second, we identified two significant quantitative trait loci (QTL) for survival post-infection on chr. 18 using N(2) backcross mice (F(1) [C18A]xC57BL/6J). Ten genes on chr. 18 (March3, Cep120, Chmp1b, Dcp2, Dtwd2, Isoc1, Lman1, Spire1, Tnfaip8, and Seh1l) mapped to the two significant QTL regions and were also identified by the expression array selection strategy. Using real-time PCR, 6 of these 10 genes (Chmp1b, Dtwd2, Isoc1, Lman1, Tnfaip8, and Seh1l) showed significantly different expression levels between S. aureus-infected A/J and C57BL/6J. For two (Tnfaip8 and Seh1l) of these 6 genes, siRNA-mediated knockdown of gene expression in S. aureus-challenged RAW264.7 macrophages induced significant changes in the cytokine response (IL-1 beta and GM-CSF) compared to negative controls. These cytokine response changes were consistent with those seen in S. aureus-challenged peritoneal macrophages from CSS 18 mice (which contain A/J chromosome 18 but are otherwise C57BL/6J), but not C57BL/6J mice. These findings suggest that two genes, Tnfaip8 and Seh1l, may contribute to susceptibility to S. aureus in A/J mice, and represent promising candidates for human genetic susceptibility studies.

Association Study of 167 Candidate Genes for Schizophrenia Selected by a Multi-Domain Evidence-Based Prioritization Algorithm and Neurodevelopmental Hypothesis

Integrating evidence from multiple domains is useful in prioritizing disease candidate genes for subsequent testing. We ranked all known human genes (n = 3819) under linkage peaks in the Irish Study of High-Density Schizophrenia Families using three different evidence domains: 1) a meta-analysis of microarray gene expression results using the Stanley Brain collection, 2) a schizophrenia protein-protein interaction network, and 3) a systematic literature search. Each gene was assigned a domain-specific p-value and ranked after evaluating the evidence within each domain. For comparison to this
ranking process, a large-scale candidate gene hypothesis was also tested by including genes with Gene Ontology terms related to neurodevelopment. Subsequently, genotypes of 3725 SNPs in 167 genes from a custom Illumina iSelect array were used to evaluate the top ranked vs. hypothesis selected genes. Seventy-three genes were both highly ranked and involved in neurodevelopment (category 1) while 42 and 52 genes were exclusive to neurodevelopment (category 2) or highly ranked (category 3), respectively. The most significant associations were observed in genes PRKG1, PRKCE, and CNTN4 but no individual SNPs were significant after correction for multiple testing. Comparison of the approaches showed an excess of significant tests using the hypothesis-driven neurodevelopment category. Random selection of similar sized genes from two independent genome-wide association studies (GWAS) of schizophrenia showed the excess was unlikely by chance. In a further meta-analysis of three GWAS datasets, four candidate SNPs reached nominal significance. Although gene ranking using integrated sources of prior information did not enrich for significant results in the current experiment, gene selection using an a priori hypothesis (neurodevelopment) was superior to random selection. As such, further development of gene ranking strategies using more carefully selected sources of information is warranted.

Selecting Relevant Genes with a Spectral Approach

Array technologies have made it possible to record simultaneously the expression pattern of thousands of genes. A fundamental problem in the analysis of gene expression data is the identification of highly relevant genes that either discriminate between phenotypic labels or are important with respect to the cellular process studied in the experiment: for example cell cycle or heat shock in yeast experiments, chemical or genetic perturbations of mammalian cell lines, and genes involved in class discovery for human tumors. In this paper we focus on the task of unsupervised gene selection. The problem of selecting a small subset of genes is particularly challenging as the datasets involved are typically characterized by a very small sample size ?? the order of few tens of tissue samples ??d by a very large feature space as the number of genes tend to be in the high thousands. We propose a model independent approach which scores candidate gene selections using spectral properties of the candidate affinity matrix. The algorithm is very straightforward to implement yet contains a number of remarkable properties which guarantee consistent sparse selections. To illustrate the value of our approach we applied our algorithm on five different datasets. The first consists of time course data from four well studied Hematopoietic cell lines (HL-60, Jurkat, NB4, and U937). The other four datasets include three well studied treatment outcomes (large cell lymphoma, childhood medulloblastomas, breast tumors) and one unpublished dataset (lymph status). We compared our approach both with other unsupervised methods (SOM,PCA,GS) and with supervised methods (SNR,RMB,RFE). The results clearly show that our approach considerably outperforms all the other unsupervised approaches in our study, is competitive with supervised methods and in some case even outperforms supervised approaches.

Baculovirus superinfection: a probable restriction factor on the surface display of proteins for library screening

In addition to the expression of recombinant proteins, baculoviruses have been developed as a platform for the display of complex eukaryotic proteins on the surface of virus particles or infected insect cells. Surface display has been used extensively for antigen presentation and targeted gene delivery but is also a candidate for the display of protein libraries for molecular screening. However, although baculovirus gene libraries can be efficiently expressed and displayed on the surface of insect cells, target gene selection is inefficient probably due to super-infection which gives rise to cells expressing more than one protein. In this report baculovirus superinfection of Sf9 cells has been investigated by the use of two recombinant multiple nucleopolyhedrovirus carrying green or red fluorescent proteins under the control of both early and late promoters (vAcBacGFP and vAcBacDsRed). The reporter gene expression was detected 8 hours after the infection of vAcBacGFP and cells in early and late phases of infection could be distinguished by the fluorescence intensity of the expressed protein. Simultaneous infection with vAcBacGFP and vAcBacDsRed viruses each at 0.5 MOI resulted in 80% of infected cells coexpressing the two fluorescent proteins at 48 hours post infection (hpi), and subsequent infection with the two viruses resulted in similar co-infection rate. Most Sf9 cells were re-infectable within the first several hours post infection, but the reinfection rate then decreased to a very low level by 16 hpi. Our data demonstrate that Sf9 cells were easily super-infectable during baculovirus infection, and super-infection could occur simultaneously at the time of the primary infection or subsequently during secondary infection by progeny viruses. The efficiency of super-infection may explain the difficulties of baculovirus display library screening but would benefit the production of complex proteins requiring co-expression of multiple polypeptides.

An agent-based hybrid system for microarray data analysis

This article reports our experience in agent-based hybrid construction for microarray data analysis. The contributions are twofold: We demonstrate that agent-based approaches are suitable for building hybrid systems in general, and that a genetic ensemble system is appropriate for microarray data analysis in particular. Created using an agent-based framework, this genetic ensemble system for microarray data analysis excels in both sample classification accuracy and gene selection reproducibility.

A new locus for autosomal dominant amelogenesis imperfecta on chromosome 8q24.3

Amelogenesis imperfecta (AI) is a collective term used to describe phenotypically diverse forms of defective tooth enamel development. AI has been reported to exhibit a variety of inheritance patterns, and several loci have been identified that are associated with AI. We have performed a genome-wide scan in a large Brazilian family segregating an autosomal dominant form of AI and mapped a novel locus to 8q24.3. A maximum multipoint LOD score of 7.5 was obtained at marker D8S2334 (146,101,309 bp). The disease locus lies in a 1.9 cM (2.1 Mb) region according to the Rutgers Combined Linkage-Physical map, between a VNTR marker (at 143,988,705 bp) and the telomere (146,274,826 bp). Ten candidate genes were identified based on gene ontology and microarray-facilitated gene selection using the expression of murine orthologues in dental tissue, and examined for the presence of a mutation. However, no causative mutation was identified.

RNA degradation differentially affects quantitative mRNA measurements of endogenous reference genes in human placenta

It has been highlighted that RNA quality and appropriate reference gene selection is crucial for the interpretation of RT-qPCR results in human placental samples. In this context we investigated the effect of RNA degradation on the mRNA abundance of seven frequently used reference genes in 119 human placental samples. Combining RNA integrity measurements, RT-qPCR analysis and mathematical modeling we found major differences regarding the effect of RNA degradation on the measured expression levels between the different reference genes. Furthermore, we demonstrated that a modified RNA extraction method significantly improved RNA quality and consequently increased transcript levels of all reference genes.