Selection of low-variance expressed Malus x domestica (apple) genes for use as quantitative PCR reference genes (housekeepers)

To accurately measure gene expression using PCR-based approaches, there is the need for reference genes that have low variance in expression (housekeeping genes) to normalise the data for RNA quantity and quality. For non-model species such as Malus x domestica (apples), previously, the selection of reference genes relied on using homology to reference genes in model species. In this study, a genomics approach was used to identify apple genes with low variance in expression in 217 messenger RNA (mRNA)-seq data sets covering different tissues, during fruit development, and treated with a range of different stress conditions. Ten potential reference genes were chosen for validation by quantitative PCR (qPCR) over 29 different tissue types and treatments. From the combined mRNA-seq and qPCR results, three potential reference genes are proposed that can be used as good controls for PCR based expression studies. The three genes show homology to lipid transfer proteins, phytochrome protein phosphatase and the ubiquitination pathway. With the progression of research away from non-model species, this approach provides a robust method for selecting candidate genes for use as reference genes in qPCR.     

Integration of clinical and gene expression endpoints to explore furan-mediated hepatotoxicity

Molecular techniques, such as cDNA microarrays, are being used to aid in the elucidation of the mechanisms of toxicity of a variety of compounds. In this study, we evaluate the molecular effects of furan in the rat liver. Sprague-Dawley rats were exposed to 4 or 40 mg/kg furan for up to 14 days. Furan induced an initial degenerative and necrotic phenotype that was followed by inflammation and fibrosis, consistent with previous observations for this compound. RNA was harvested from each lobe of the liver at several time points to observe whether lobe-specific gene expression effects occurred. Similar gene expression changes were observed in all lobes, however the magnitude of gene expression change was more pronounced in the right lobe. Finally, to help determine the correlation between gene expression changes and liver pathology, we applied traditional microarray visualization tools to the assessment of clinical chemistry and pathology parameters.     

PEATmoss (Physcomitrella Expression Atlas Tool): a unified gene expression atlas for the model plant Physcomitrella patens

Physcomitrella patens is a bryophyte model plant that is often used to study plant evolution and development. Its resources are of great importance for comparative genomics and evo‐devo approaches. However, expression data from Physcomitrella patens were so far generated using different gene annotation versions and three different platforms: CombiMatrix and NimbleGen expression microarrays and RNA sequencing. The currently available P. patens expression data are distributed across three tools with different visualization methods to access the data. Here, we introduce an interactive expression atlas, Physcomitrella Expression Atlas Tool (PEATmoss), that unifies publicly available expression data for P. patens and provides multiple visualization methods to query the data in a single web‐based tool. Moreover, PEATmoss includes 35 expression experiments not previously available in any other expression atlas. To facilitate gene expression queries across different gene annotation versions, and to access P. patens annotations and related resources, a lookup database and web tool linked to PEATmoss was implemented. PEATmoss can be accessed at https://peatmoss.online.uni-marburg.de     

Use of Data-Biased Random Walks on Graphs for the Retrieval of Context-Specific Networks from Genomic Data

Extracting network-based functional relationships within genomic datasets is an important challenge in the computational analysis of large-scale data. Although many methods, both public and commercial, have been developed, the problem of identifying networks of interactions that are most relevant to the given input data still remains an open issue. Here, we have leveraged the method of random walks on graphs as a powerful platform for scoring network components based on simultaneous assessment of the experimental data as well as local network connectivity. Using this method, NetWalk, we can calculate distribution of Edge Flux values associated with each interaction in the network, which reflects the relevance of interactions based on the experimental data. We show that network-based analyses of genomic data are simpler and more accurate using NetWalk than with some of the currently employed methods. We also present NetWalk analysis of microarray gene expression data from MCF7 cells exposed to different doses of doxorubicin, which reveals a switch-like pattern in the p53 regulated network in cell cycle arrest and apoptosis. Our analyses demonstrate the use of NetWalk as a valuable tool in generating high-confidence hypotheses from high-content genomic data.     

Multiple tissue gene expression analyses in Japanese medaka (Oryzias latipes) exposed to hypoxia

Due in part to human population growth watersheds and coastal estuaries have been receiving increasing run-off of nutrients and genotoxins. As a consequence, the occurrences of nutrient-driven hypoxia in coastal waters appear to be increasing. Thus, understanding the molecular genetic response to hypoxia by model aquatic organisms is of interest both from environmental and physiological viewpoints. The major objectives of this study are to determine genome-wide gene expression profiles and to better understand how hypoxia influences global gene expression in medaka (Oryzias latipes), a well utilized aquatic model species. Herein we detail our development of a microarray containing 8046 medaka unigenes and describe our experimental results for measuring gene expression changes in the brain, gill, and liver of hypoxia exposed fish. Using conservative selection criteria, we determined that 501 genes in the brain, 442 in the gill, and 715 in the liver were differentially expressed in medaka exposed to hypoxia. These differentially expressed genes fell into a number of biological gene ontology groups related to general metabolism, catabolism, RNA and protein metabolism, etc. Two biological pathways, ubiquitin-proteasome and phosphatidylinositol signaling, were significantly dysregulated in medaka upon hypoxia exposure. Comparative genomics between medaka and human identified several human orthologies associated with known diseases.     

Integrating biological information into the statistical analysis and design of microarray experiments

Microarray technology is a powerful tool for animal functional genomics studies, with applications spanning from gene identification and mapping, to function and control of gene expression. Microarray assays, however, are complex and costly, and hence generally performed with relatively small number of animals. Nevertheless, they generate data sets of unprecedented complexity and dimensionality. Therefore, such trials require careful planning and experimental design, in addition to tailored statistical and computational tools for their appropriate data mining. In this review, we discuss experimental design and data analysis strategies, which incorporate prior genomic and biological knowledge, such as genotypes and gene function and pathway membership. We focus the discussion on the design of genetical genomics studies, and on significance testing for detection of differential expression. It is shown that the use of prior biological information can improve the efficiency of microarray experiments.     

Transfected cell microarrays: an efficient tool for high-throughput functional analysis

Transfected cell microarrays are considered to be a breakthrough methodology for high-throughput and high-content functional genomics. Here, recent advances in the cell microarray field are reviewed, along with its potential to increase the speed of determining gene function. These advances, combined with an increasing number and diversity of gene perturbing systems, such as RNAi and ectopic gene expression, provide tools for expanding our understanding of biology at the systems level.     

Transfected cell microarrays: an efficient tool for high-throughput functional analysis

Transfected cell microarrays are considered to be a breakthrough methodology for high-throughput and high-content functional genomics. Here, recent advances in the cell microarray field are reviewed, along with its potential to increase the speed of determining gene function. These advances, combined with an increasing number and diversity of gene perturbing systems, such as RNAi and ectopic gene expression, provide tools for expanding our understanding of biology at the systems level.     

Laser-Supported Dual Energy X-Ray Absorptiometry (DXL) Compared to Conventional Absorptiometry (DXA) and to FRAX as Tools for Fracture Risk Assessments

Dual X-ray and Laser (DXL) adds a measure of the external thickness of the heel, measured by laser, to a conventional measurement of bone mineral density (BMD) of the calcaneus, using Dual energy X-ray Absorptiometry (DXA). The addition of heel thickness aims at a better separation of fatty tissue from bone than the standard method of DXA, which may mistake fatty tissue for bone and vice versa. The primary aim of this study was to evaluate whether DXL of the calcaneus can be used to assess the 10-year risk of fractures. Secondary aims were to compare the predictive ability of DXL with the two most established methods, Dual energy X-ray Absorptiometry (DXA) of the hip and spine and the WHO fracture risk assessment tool, FRAX. In 1999 a cohort of 388 elderly Swedish women (mean age 73.2 years) was examined with all three methods. Prospective fracture data was collected in 2010 from health care registers. One SD decrease in BMD of the heel resulted in an age-adjusted Hazard Ratio (HR) of 1.47 for a hip fracture (95% CI 1.09–1.98). Harrell’s C is the Cox regression counterpart of the Area Under Curve (AUC) of the Receiver Operating Characteristic (ROC) as a measure of predictive accuracy. Harrell’s C for BMD of the calcaneus was 0.65 for prediction of hip fractures. These results were not significantly different from those for BMD of the femoral neck or for FRAX. The HR for a hip fracture, for one SD decrease in BMD at the femoral neck, was 1.72 (95% CI 1.21–2.44. Harrell’s C was 0.67 for BMD at the femoral neck and 0.59 for FRAX. We conclude that DXL of the calcaneus could be a useful tool for fracture risk assessments.     

Inferring mouse gene functions from genomic-scale data using a combined functional network/classification strategy

The complete set of mouse genes, as with the set of human genes, is still largely uncharacterized, with many pieces of experimental evidence accumulating regarding the activities and expression of the genes, but the majority of genes as yet still of unknown function. Within the context of the MouseFunc competition, we developed and applied two distinct large-scale data mining approaches to infer the functions (Gene Ontology annotations) of mouse genes from experimental observations from available functional genomics, proteomics, comparative genomics, and phenotypic data. The two strategies - the first using classifiers to map features to annotations, the second propagating annotations from characterized genes to uncharacterized genes along edges in a network constructed from the features - offer alternative and possibly complementary approaches to providing functional annotations. Here, we re-implement and evaluate these approaches and their combination for their ability to predict the proper functional annotations of genes in the MouseFunc data set. We show that, when controlling for the same set of input features, the network approach generally outperformed a naive Bayesian classifier approach, while their combination offers some improvement over either independently. We make our observations of predictive performance on the MouseFunc competition hold-out set, as well as on a ten-fold cross-validation of the MouseFunc data. Across all 1,339 annotated genes in the MouseFunc test set, the median predictive power was quite strong (median area under a receiver operating characteristic plot of 0.865 and average precision of 0.195), indicating that a mining-based strategy with existing data is a promising path towards discovering mammalian gene functions. As one product of this work, a high-confidence subset of the functional mouse gene network was produced - spanning >70% of mouse genes with >1.6 million associations - that is predictive of mouse (and therefore often human) gene function and functional associations. The network should be generally useful for mammalian gene functional analyses, such as for predicting interactions, inferring functional connections between genes and pathways, and prioritizing candidate genes. The network and all predictions are available on the worldwide web.     

Molecular genetics and comparative genomics reveal RNAi is not functional in malaria parasites

Techniques for targeted genetic disruption in Plasmodium, the causative agent of malaria, are currently intractable for those genes that are essential for blood stage development. The ability to use RNA interference (RNAi) to silence gene expression would provide a powerful means to gain valuable insight into the pathogenic blood stages but its functionality in Plasmodium remains controversial. Here we have used various RNA-based gene silencing approaches to test the utility of RNAi in malaria parasites and have undertaken an extensive comparative genomics search using profile hidden Markov models to clarify whether RNAi machinery exists in malaria. These investigative approaches revealed that Plasmodium lacks the enzymology required for RNAi-based ablation of gene expression and indeed no experimental evidence for RNAi was observed. In its absence, the most likely explanations for previously reported RNAi-mediated knockdown are either the general toxicity of introduced RNA (with global down-regulation of gene expression) or a specific antisense effect mechanistically distinct from RNAi, which will need systematic analysis if it is to be of use as a molecular genetic tool for malaria parasites.     

High throughput nano-liter RT-qPCR to classify soil contamination using a soil arthropod

Background  

To incorporate genomics data into environmental assessments a mechanistic perspective of interactions between chemicals and induced biological processes needs to be developed. Since chemical compounds with structural similarity often induce comparable biological responses in exposed animals, gene expression signatures can serve as a starting point for the assessment of chemicals and their toxicity, but only when relevant and stable gene panels are available. To design such a panel, we isolated differentially expressed gene fragments from the soil arthropod Folsomia candida, a species often used for ecotoxicological testing. Animals were exposed to two chemically distinct compounds, being a metal (cadmium) and a polycyclic aromatic hydrocarbon (phenanthrene). We investigated the affected molecular responses resulting from either treatment and developed and validated 44 qPCR assays for their responses using a high throughput nano-liter RT-qPCR platform for the analysis of the samples.     

Comparative genomics for reliable protein-function prediction from genomic data

Genomic data provide invaluable, yet unreliable information about protein function. However, if the overlap in information among various genomic datasets is taken into account, one observes an increase in the reliability of the protein-function predictions that can be made. Recently published approaches achieved this either by comparing the same type of data from multiple species (horizontal comparative genomics) or by using subtle, Bayesian methods to compare different types of genomic data from a single species (vertical comparative genomics). In this article, we discuss these methods, illustrating horizontal comparative genomics by comparing yeast two-hybrid (Y2H) data from Saccharomyces cerevisiae with Y2H data from Drosophila melanogaster, and illustrating vertical comparative genomics by comparing RNA expression data with proteomic data from Plasmodium falciparum.     

Gene expression profiles and genetic damage in benzo(a)pyrene diol epoxide-exposed TK6 cells

Microarray analysis is a powerful tool to identify the biological effects of drugs or chemicals on cellular gene expression. In this study, we compare the relationships between traditional measures of genetic toxicology and mutagen-induced alterations in gene expression profiles. TK6 cells were incubated with 0.01, 0.1, or 1.0 microM +/-anti-benzo(a)pyrene-trans-7,8-dihydrodiol-9,10-epoxide (BPDE) for 4 h and then cultured for an additional 20 h. Aliquots of the exposed cells were removed at 4 and 24 h in order to quantify DNA adduct levels by 32P post-labeling and measure cell viability by cloning efficiency and flow cytometry. Gene expression profiles were developed by extracting total RNA from the control and exposed cells at 4 and 24 h, labeling with Cy3 or Cy5 and hybridizing to a human 350 gene array. Mutant frequencies in the Thymidine Kinase and Hypoxanthine Phosphoribosyl Transferase genes were also determined. The 10alpha-(deoxyguanosin-N(2)-yl)-7alpha,8beta,9beta-trihydroxy-7,8,9,10-tetrahydrobenzo(a)pyrene (dG-N(2)-BPDE) adduct increased as a function of dose and was the only adduct identified. A dose-related decrease in cell viability was evident at 24 h, but not at 4 h. Cell death occurred by apoptosis. At 4 h, analysis of the gene expression profiles revealed that Glutathione Peroxidase and Gadd45 were consistently upregulated (greater than 1.5-fold and significantly (P < 0.001) greater than the control in two experiments) in response to 1.0 microM BPDE exposure. Fifteen genes were consistently down-regulated (less than 0.67-fold and significantly (P < 0.001) lower than the control in two experiments) at 4 h in cultures exposed to 1.0 microM BPDE. Genes with altered expression at 4 h included genes important in the progression of the cell-cycle and those that inhibit apoptosis. At 24 h post-exposure, 16 genes, involved in cell-cycle control, detoxification, and apoptosis were consistently upregulated; 10 genes were repressed in cultures exposed to the high dose of BPDE. Real-time quantitative PCR confirmed the differential expression of selected genes. These data suggest that changes in gene expression will help to identify effects of drugs and chemicals on molecular pathways in cells, and will provide useful information about the molecular responses associated with DNA damage. Of the endpoints evaluated, DNA adduct formation was the most sensitive indicator of DNA damage. DNA adduct formation was clearly evident at low doses, but the number of genes with significantly altered expression (P < 0.001) was minimal. Alterations in gene expression were more robust at doses associated with cellular toxicity and induction of mutations.