A hybrid SOM-SVM approach for the zebrafish gene expression analysis

Microarray technology can be employed to quantitatively measure the expression of thousands of genes in a single experiment. It has become one of the main tools for global gene expression analysis in molecular biology research in recent years. The large amount of expression data generated by this technology makes the study of certain complex biological problems possible, and machine learning methods are expected to play a crucial role in the analysis process. In this paper, we present our results from integrating the self-organizing map （SOM） and the support vector machine （SVM） for the analysis of the various functions of zebrafish genes based on their expression. The most distinctive characteristic of our zebrafish gene expression is that the number of samples of different classes is imbalanced. We discuss how SOM can be used as a data-filtering tool to improve the classification performance of the SVM on this data set.     

Comparisons of graph-structure clustering methods for gene expression data

Although many numerical clustering algorithms have been applied to gene expression dataanalysis,the essential step is still biological interpretation by manual inspection.The correlation betweengenetic co-regulation and affiliation to a common biological process is what biologists expect.Here,weintroduce some clustering algorithms that are based on graph structure constituted by biological knowledge.After applying a widely used dataset,we compared the result clusters of two of these algorithms in terms ofthe homogeneity of clusters and coherence of annotation and matching ratio.The results show that theclusters of knowledge-guided analysis are the kernel parts of the clusters of Gene Ontology (GO)-Clustersoftware,which contains the genes that are most expression correlative and most consistent with biologicalfunctions.Moreover,knowledge-guided analysis seems much more applicable than GO-Cluster in a largerdataset.     

Improve survival prediction using principal components of gene expression data

The purpose of many microarray studies is to find the association between gene expression and sample characteristics such as treatment type or sample phenotype. There has been a surge of efforts developing different methods for delineating the association. Aside from the high dimensionality of microarray data, one well recognized challenge is the fact that genes could be complicatedly inter-related, thus making many statistical methods inappropriate to use directly on the expression data. Multivariate methods such as principal component analysis （PCA） and clustering are often used as a part of the effort to capture the gene correlation, and the derived components or clusters are used to describe the association between gene expression and sample phenotype. We propose a method for patient population dichotomization using maximally selected test statistics in combination with the PCA method, which shows favorable results. The proposed method is compared with a currently well-recognized method.     

Validation of Zebrafish （Danio redo） Reference Genes for Quantitative Real-time RT-PCR Normalization

The normalization of quantitative real time RT-PCR （qRT-PCR） is important to obtain accurate gene expression data. The most common method for qRT-PCR normalization is to use reference, or housekeeping genes. However, there is emerging evidence that even reference genes can be regulated under different conditions, qRT-PCR has only recently been used in terms of zebrafish gene expression studies and there is no validated set of reference genes. This study characterizes the expression of nine possible reference genes during zebrafish embryonic development and in a zebrafish tissue panel. All nine reference genes exhibited variable expression. The fl-actin, EFlot and Rpll3ot genes comprise a validated reference gene panel for zebrafish developmental time course studies, and the EF1 or, Rpll3α and 18S rRNA genes are more suitable as a reference gene panel for zebrafish tissue analysis. Importantly, the zebrafish GAPDH gene appears unsuitable as reference gene for both types of studies.     

Recombination and heterologous expression of allophycocyanin gene in the chloroplast of Chlamydomonas reinhardtii

Heterogeneous expression of multiple genes in the nucleus of transgenic plants requires the introduction of an individual gene and the subsequent backcross to reconstitute multi-subunit proteins or metabolic pathways. In order to accomplish the expression of multiple genes in a single transformation event, we inserted both large and small subunits of allophycocyanin gene （apcA and apcB） into Chlamydomonas reinhardtii chloroplast expression vector, resulting in papc-S. The constructed vector was then introduced into the chloroplast of C. reinhardtii by micro-particle bombardment. Polymerase chain reaction and Southern blot analysis revealed that the two genes had integrated into the chloroplast genome. Western blot and enzyme-linked immunosorbent assay showed that the two genes from the prokaryotic cyanobacteria could be correctly expressed in the chloroplasts of C. reinhardtii. The expressed foreign protein in transformants accounted for about 2%-3% of total soluble proteins. These findings pave the way to the reconstitution of multi-subunit proteins or metabolic pathways in transgenic C. reinhardtii chloroplasts in a single transformation event.     

Alternative splicing and expression profile analysis of expressed sequence tags in domestic pig

Domestic pig （Sus scrofa domestica） is one of the most important mammals to humans. Alternative splicing is a cellular mechanism in eukaryotes that greatly increases the diversity of gene products. Expression sequence tags （ESTs） have been widely used for gene discovery, expression profile analysis, and alternative splicing detection. In this study, a total of 712,905 ESTs extracted from 101 different nonnormalized EST libraries of the domestic pig were analyzed. These EST libraries cover the nervous system, digestive system, immune system, and meat production related tissues from embryo, newborn, and adult pigs, making contributions to the analysis of alternative splicing variants as well as expression profiles in various stages of tissues. A modified approach was designed to cluster and assemble large EST datasets, aiming to detect alternative splicing together with EST abundance of each splicing variant. Much efforts were made to classify alternative splicing into different types and apply different filters to each type to get more reliable results. Finally, a total of 1,223 genes with average 2.8 splicing variants were detected among 16,540 unique genes. The overview of expression profiles would change when we take alternative splicing into account.     

Clustering gene expression data based on predicted differential effects of GV interaction

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV （gene by variety） interaction using the adjusted unbiased prediction （AUP） method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.     

MiR-29a/b/c regulate human circadian gene hPER1 expression by targeting its 3/UTR

Several essential biological progresses in mammals are regulated by circadian rhythms. Though the molecular mechanisms of oscillating these circadian rhythms have been uncovered, the specific functions of the circadian genes are not very clear. It has been reported that knocking down circadian genes by microRNA is a useful strategy to explore the function of the circadian rhythms. In this study, through a forward bioinformatics screening ap- proach, we identified miR-29a/b/c as potent inhibitors for the human circadian gene hPER1. We further found that miR-29a/b/c could directly target hPER1 3/untranslated region （UTR） and down-regulate hPER1 at both mRNA and protein expression levels in human A549 cells. Thus, our findings suggested that the expression of hPER1 is regulated by miR-29a/b/c, which may also provide a new clue for the function ofhPER1.     

Deciphering Ascorbic Acid Regulatory Pathways in Ripening Tomato Fruit Using a Weighted Gene Correlation Network Analysis Approach

Genotype is generally determined by the co-expression of diverse genes and multiple regulatory pathways in plants. Gene co-expression analysis combining with physiological trait data provides very important information about the gene function and regulatory mechanism. L-Ascorbic acid （AsA）, which is an essential nutrient component for human health and plant metabolism, plays key roles in diverse biological processes such as cell cycle, cell expansion, stress resistance, hormone synthesis, and signaling. Here, we applied a weighted gene correlation network analysis approach based on gene expression values and AsA content data in ripening tomato （Solanum lycopersicum L.） fruit with different AsA content levels, which leads to identification of AsA relevant modules and vital genes in AsA regulatory pathways. Twenty- four modules were compartmentalized according to gene expression profiling. Among these modules, one negatively related module containing genes involved in redox processes and one positively related module enriched with genes involved in AsA biosynthetic and recycling pathways were further analyzed. The present work herein indicates that redox pathways as well as hormone-signal pathways are closely correlated with AsA accumulation in ripening tomato fruit, and allowed us to prioritize candidate genes for follow-up studies to dissect this interplay at the biochemical and molecular level.     

Validation of Zebrafish (Danio rerio) Reference Genes for Quantitative Real-time RT-PCR Normalization

The normalization of quantitative real time RT-PCR (qRT-PCR) is important to obtain accurate gene expression data. The most common method for qRT-PCR normalization is to use reference, or house- keeping genes. However, there is emerging evidence that even reference genes can be regulated under different conditions, qRT-PCR has only recently been used in terms of zebrafish gene expression studies and there is no validated set of reference genes. This study characterizes the expression of nine possible reference genes during zebrafish embryonic development and in a zebrafish tissue panel. All nine reference genes exhibited variable expression. The β-actin, EF1α and Rp113α genes comprise a validated reference gene panel for zebrafish developmental time course studies, and the EF1α, Rp113α and 18S rRNA genes are more suitable as a reference gene panel for zebrafish tissue analysis. Importantly, the zebrafish GAPDH gene appears unsuitable as reference gene for both types of studies.     

Response of Solanum tuberosum to Myzus persicae infestation at different stages of foliage maturity

Young leaves of the potato Solanum tuberosum L. cultivar Kardal contain resistance factors to the green peach aphid Myzuspersicae （Sulzer） （Hemiptera： Aphididae） and normal probing behavior is impeded. However, M. persicae can survive and reproduce on mature and senescent leaves of the cv. Kardal plant without problems. We compared the settling ofM. persicae on young and old leaves and analyzed the impact of aphids settling on the plant in terms of gene expression. Settling, as measured by aphid numbers staying on young or old leaves, showed that after 21 h significantly fewer aphids were found on the young leaves. At earlier time points there were no difference between young and old leaves, suggesting that the young leaf resistance factors are not located at the surface level but deeper in the tissue. Gene expression was measured in plants at 96 h postinfestation, which is at a late stage in the interaction and in compatible interactions this is long enough for host plant acceptance to occur. In old leaves of cv. Kardal （compatible interaction）, M. persicae infestation elicited a higher number of differentially regulated genes than in young leaves. The plant response to aphid infestation included a larger number of genes induced than repressed, and the proportion of induced versus repressed genes was larger in young than in old leaves. Several genes changing expression seem to be involved in changing the metabolic state of the leaf from source to sink.