2D association and integrative omics analysis in rice provides systems biology view in trait analysis.

The interactions among genes and between genes and environment contribute significantly to the phenotypic variation of complex traits and may be possible explanations for missing heritability. However, to our knowledge no existing tool can address the two kinds of interactions. Here we propose a novel linear mixed model that considers not only the additive effects of biological markers but also the interaction effects of marker pairs. Interaction effect is demonstrated as a 2D association. Based on this linear mixed model, we developed a pipeline, namely PATOWAS. PATOWAS can be used to study transcriptome-wide and metabolome-wide associations in addition to genome-wide associations. Our case analysis with real rice recombinant inbred lines (RILs) at three omics levels demonstrates that 2D association mapping and integrative omics are able to provide a systems biology view into the analyzed traits, leading toward an answer about how genes, transcripts, proteins, and metabolites work together to produce an observable phenotype

Strain specific effects of low level lead exposure on associative learning and memory in rats.

Exposure to lead (Pb) remains a significant public health concern. Lead exposure in early life impairs the normal development of numerous cognitive and neurobehavioral processes. Previous work has shown that the effects of developmental Pb exposure on gene expression patterns in the brain are modulated by various factors including the developmental timing of the exposure, level of exposure, sex, and genetic background. Using gene microarray profiling, we previously reported a significant strain-specific effect of Pb exposure on the hippocampal transcriptome, with the greatest number of differentially expressed transcripts in Long Evans (LE) rats and the fewest in Sprague Dawley (SD) rats. The present study examined the extent to which this differential effect of Pb on hippocampal gene expression might influence behavior. Animals (males and females) were tested in a trace fear conditioning paradigm to evaluate effects of Pb exposures (perinatal (PERI; gestation to postnatal day 21) or early postnatal (EPN; postnatal day 1 to day 21)) on associative learning and memory. All animals (Pb-exposed and non-Pb-exposed controls) showed normal acquisition of the conditioned stimulus (tone)-unconditioned stimulus (footshock) association. Long Evans rats showed a significant deficit in short- and long-term recall, influenced by sex and the timing of Pb exposure (PERI or EPN). In contrast, Pb exposure had no significant effect on memory consolidation or recall in any SD rats. These results further demonstrate the important influence of genetic background to the functional outcomes from developmental Pb exposure

A novel neural network approach to cDNA microarray image segmentation

This is the post-print version of the Article. The official published version can be accessed from the link below. Copyright @ 2013 Elsevier.Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix(®) are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.This work was funded in part by the National Natural Science Foundation of China under Grants 61174136 and 61104041, the Natural Science Foundation of Jiangsu Province of China under Grant BK2011598, the International Science and Technology Cooperation Project of China under Grant No. 2011DFA12910, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

Internal standard-based analysis of microarray data. Part 1: analysis of differential gene expressions

Genome-scale microarray experiments for comparative analysis of gene expressions produce massive amounts of information. Traditional statistical approaches fail to achieve the required accuracy in sensitivity and specificity of the analysis. Since the problem can be resolved neither by increasing the number of replicates nor by manipulating thresholds, one needs a novel approach to the analysis. This article describes methods to improve the power of microarray analyses by defining internal standards to characterize features of the biological system being studied and the technological processes underlying the microarray experiments. Applying these methods, internal standards are identified and then the obtained parameters are used to define (i) genes that are distinct in their expression from background; (ii) genes that are differentially expressed; and finally (iii) genes that have similar dynamical behavio

Mammalian Brain As a Network of Networks

Acknowledgements AZ, SG and AL acknowledge support from the Russian Science Foundation (16-12-00077). Authors thank T. Kuznetsova for Fig. 6.Peer reviewedPublisher PD

A Comprehensive and Universal Method for Assessing the Performance of Differential Gene Expression Analyses

The number of methods for pre-processing and analysis of gene expression data continues to increase, often making it difficult to select the most appropriate approach. We present a simple procedure for comparative estimation of a variety of methods for microarray data pre-processing and analysis. Our approach is based on the use of real microarray data in which controlled fold changes are introduced into 20% of the data to provide a metric for comparison with the unmodified data. The data modifications can be easily applied to raw data measured with any technological platform and retains all the complex structures and statistical characteristics of the real-world data. The power of the method is illustrated by its application to the quantitative comparison of different methods of normalization and analysis of microarray data. Our results demonstrate that the method of controlled modifications of real experimental data provides a simple tool for assessing the performance of data preprocessing and analysis methods

Internal standard-based analysis of microarray data. Part 1: analysis of differential gene expressions

Genome-scale microarray experiments for comparative analysis of gene expressions produce massive amounts of information. Traditional statistical approaches fail to achieve the required accuracy in sensitivity and specificity of the analysis. Since the problem can be resolved neither by increasing the number of replicates nor by manipulating thresholds, one needs a novel approach to the analysis. This article describes methods to improve the power of microarray analyses by defining internal standards to characterize features of the biological system being studied and the technological processes underlying the microarray experiments. Applying these methods, internal standards are identified and then the obtained parameters are used to define (i) genes that are distinct in their expression from background; (ii) genes that are differentially expressed; and finally (iii) genes that have similar dynamical behavior

A multi-view approach to cDNA micro-array analysis

The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany

Down-regulatory mechanism of mammea E/BB from Mammea siamensis seed extract on Wilms' Tumor 1 expression in K562 cells.

BackgroundWilms' tumor 1 (WT1) is a biological marker for predicting leukemia progression. In this study, mammea E/BB, an active compound from Saraphi (Mammea siamensis) seed extract was examined for its effect on down-regulatory mechanism of WT1 gene expression, WT1 protein and mRNA stability, and cell proliferation in K562 cell line.MethodsM. siamensis seeds were obtained from the region of Chiang Mai (North of Thailand). Mammea E/BB was extracted from seeds of M. siamensis. WT1 protein expression and stability were evaluated by Western blot analysis. WT1 mRNA stability was assessed by qRT-PCR. WT1-DNA binding and WT1 promoter activity were assayed by ChIP assay and luciferase-reporter assay, respectively. Cell cycle arrest was studied by flow cytometry.ResultsTreatment with mammea E/BB led to down-regulation of WT1 expression. The suppression of WT1 expression did not involve protein and mRNA degradation. Rather, WT1 protein was down-regulated through disruption of transcriptional auto-regulation of the WT1 gene. Mammea E/BB inhibited WT1-DNA binding at the WT1 promoter and decreased luciferase activity. It also disrupted c-Fos/AP-1 binding to the WT1 promoter via ERK1/2 signaling pathway and induced S phase cell cycle arrest in K562 cells.ConclusionMammea E/BB had pleotropic effects on kinase signaling pathways, resulting in inhibition of leukemia cell proliferation